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6,687	fsspec: support fsspec>=2023.12.0 glob changes	- adds support for the `fs.glob` changes introduced in `fsspec==2023.12.0` and unpins the current upper bound Should close #6644 Should close #6645 The `test_data_files` glob/pattern tests pass for me in: - `fsspec==2023.10.0` (the pinned max version in datasets `main`) - `fsspec==2023.12.0` (#6644) - `fsspec==2024.2.0` (#6645)	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6687", "html_url": "https://github.com/huggingface/datasets/pull/6687", "diff_url": "https://github.com/huggingface/datasets/pull/6687.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6687.patch", "merged_at": "2024-02-29T15:12:17" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6687). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Looking into the CI failure, this PR is incompatible with `huggingface-hub>=0.20.0`. It looks like there were several changes made to HfFileSystem in 0.20.0, @lhoestq any ideas on what the issue might be in particular?\r\n\r\na bisect indicates that it's related to https://github.com/huggingface/huggingface_hub/pull/1815", "It looks like huggingface-hub's `HfFileSystem.glob` is broken for exact string matches (that don't contain glob wildcards) when combining `huggingface-hub>=0.20.0` and `fsspec>=2023.12.0`.\r\n\r\nI did a quick test with huggingface-hub `main`, and adding this test case to `tests/test_hf_filesystem::HfFileSystemTests::test_glob` (https://github.com/huggingface/huggingface_hub/blob/main/tests/test_hf_file_system.py) passes with `fsspec==2023.10.0` and fails with `fsspec==2023.12.0`\r\n```python\r\n self.assertEqual(\r\n sorted(self.hffs.glob(self.hf_path + \"/.gitattributes\")),\r\n sorted([self.hf_path + \"/.gitattributes\"]),\r\n )\r\n\r\n```\r\n\r\nthe `hffs.glob()` call with a pattern that does not contain any wildcards returns an empty list:\r\n```\r\nE AssertionError: Lists differ: [] != ['datasets/__DUMMY_TRANSFORMERS_USER__/rep[35 chars]tes']\r\nE\r\nE Second list contains 1 additional elements.\r\nE First extra element 0:\r\nE 'datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes'\r\nE\r\nE - []\r\nE + ['datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes']\r\n```\r\n(and with the compatible/passing older fsspec versions the glob call returns the single exact file match as expected)\r\n\r\nSo it looks like the CI failure here isn't directly related to this PR. The failing patterns that don't contain any `*` wildcards are generated by `datasets` with or without this PR, but now that this PR installs the incompatible fsspec version, the underlying `HfFileSystem.glob()` call ends up failing.", "I just opened https://github.com/huggingface/huggingface_hub/pull/2056 to fix this.\r\n\r\nDo you mind if I continue this PR to run the CI against `huggingface_hub@main` until the fix is released ?\r\n\r\nEDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`", "I just added two additional patterns to cover cases like `test-data/xxx.csv` and `data-test/xxx.csv`", "<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) \| 0.005461 / 0.011353 (-0.005892) \| 0.003861 / 0.011008 (-0.007148) \| 0.063252 / 0.038508 (0.024744) \| 0.031474 / 0.023109 (0.008364) \| 0.250321 / 0.275898 (-0.025577) \| 0.275198 / 0.323480 (-0.048282) \| 0.003275 / 0.007986 (-0.004710) \| 0.002874 / 0.004328 (-0.001454) \| 0.049499 / 0.004250 (0.045248) \| 0.045334 / 0.037052 (0.008282) \| 0.266347 / 0.258489 (0.007858) \| 0.308974 / 0.293841 (0.015133) \| 0.027742 / 0.128546 (-0.100804) \| 0.010274 / 0.075646 (-0.065373) \| 0.207516 / 0.419271 (-0.211755) \| 0.036538 / 0.043533 (-0.006995) \| 0.247949 / 0.255139 (-0.007190) \| 0.268986 / 0.283200 (-0.014214) \| 0.019842 / 0.141683 (-0.121841) \| 1.117547 / 1.452155 (-0.334607) \| 1.175813 / 1.492716 (-0.316903) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103661 / 0.018006 (0.085655) \| 0.331023 / 0.000490 (0.330534) \| 0.000240 / 0.000200 (0.000040) \| 0.000041 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019767 / 0.037411 (-0.017645) \| 0.061500 / 0.014526 (0.046974) \| 0.075899 / 0.176557 (-0.100658) \| 0.122240 / 0.737135 (-0.614895) \| 0.074621 / 0.296338 (-0.221717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287501 / 0.215209 (0.072292) \| 2.794737 / 2.077655 (0.717082) \| 1.505362 / 1.504120 (0.001242) \| 1.379481 / 1.541195 (-0.161713) \| 1.394836 / 1.468490 (-0.073654) \| 0.545803 / 4.584777 (-4.038974) \| 2.364167 / 3.745712 (-1.381545) \| 2.800923 / 5.269862 (-2.468939) \| 1.723910 / 4.565676 (-2.841766) \| 0.061270 / 0.424275 (-0.363005) \| 0.005006 / 0.007607 (-0.002601) \| 0.334952 / 0.226044 (0.108908) \| 3.367122 / 2.268929 (1.098194) \| 1.839822 / 55.444624 (-53.604803) \| 1.553774 / 6.876477 (-5.322703) \| 1.583585 / 2.142072 (-0.558488) \| 0.624680 / 4.805227 (-4.180547) \| 0.116364 / 6.500664 (-6.384300) \| 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) \| 0.975207 / 1.841788 (-0.866580) \| 11.843126 / 8.074308 (3.768818) \| 9.418537 / 10.191392 (-0.772855) \| 0.130648 / 0.680424 (-0.549775) \| 0.013747 / 0.534201 (-0.520454) \| 0.288195 / 0.579283 (-0.291088) \| 0.269861 / 0.434364 (-0.164503) \| 0.326732 / 0.540337 (-0.213606) \| 0.441256 / 1.386936 (-0.945680) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005185 / 0.011353 (-0.006168) \| 0.003836 / 0.011008 (-0.007172) \| 0.050057 / 0.038508 (0.011549) \| 0.030929 / 0.023109 (0.007820) \| 0.263558 / 0.275898 (-0.012340) \| 0.284553 / 0.323480 (-0.038927) \| 0.004331 / 0.007986 (-0.003655) \| 0.002815 / 0.004328 (-0.001513) \| 0.050187 / 0.004250 (0.045936) \| 0.048431 / 0.037052 (0.011379) \| 0.271005 / 0.258489 (0.012515) \| 0.304749 / 0.293841 (0.010908) \| 0.029286 / 0.128546 (-0.099260) \| 0.010598 / 0.075646 (-0.065048) \| 0.058111 / 0.419271 (-0.361160) \| 0.053665 / 0.043533 (0.010132) \| 0.257574 / 0.255139 (0.002436) \| 0.285802 / 0.283200 (0.002602) \| 0.018917 / 0.141683 (-0.122766) \| 1.206517 / 1.452155 (-0.245638) \| 1.220572 / 1.492716 (-0.272144) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.122466 / 0.018006 (0.104460) \| 0.567887 / 0.000490 (0.567397) \| 0.000321 / 0.000200 (0.000121) \| 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) \| 0.022120 / 0.037411 (-0.015292) \| 0.075456 / 0.014526 (0.060931) \| 0.086385 / 0.176557 (-0.090171) \| 0.126045 / 0.737135 (-0.611091) \| 0.087502 / 0.296338 (-0.208837) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304847 / 0.215209 (0.089638) \| 3.008095 / 2.077655 (0.930441) \| 1.726178 / 1.504120 (0.222058) \| 1.592332 / 1.541195 (0.051138) \| 1.603714 / 1.468490 (0.135224) \| 0.576875 / 4.584777 (-4.007902) \| 2.450884 / 3.745712 (-1.294828) \| 2.719073 / 5.269862 (-2.550789) \| 1.775261 / 4.565676 (-2.790415) \| 0.063144 / 0.424275 (-0.361131) \| 0.005122 / 0.007607 (-0.002485) \| 0.350004 / 0.226044 (0.123960) \| 3.467146 / 2.268929 (1.198218) \| 2.062907 / 55.444624 (-53.381717) \| 1.798793 / 6.876477 (-5.077684) \| 1.921204 / 2.142072 (-0.220868) \| 0.651832 / 4.805227 (-4.153396) \| 0.122326 / 6.500664 (-6.378338) \| 0.041396 / 0.075469 (-0.034073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.024859 / 1.841788 (-0.816928) \| 12.569744 / 8.074308 (4.495436) \| 10.448487 / 10.191392 (0.257095) \| 0.131529 / 0.680424 (-0.548895) \| 0.014853 / 0.534201 (-0.519348) \| 0.287683 / 0.579283 (-0.291600) \| 0.289814 / 0.434364 (-0.144550) \| 0.323935 / 0.540337 (-0.216403) \| 0.425208 / 1.386936 (-0.961728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba71e92c59c9bd9d1ee6168691977f0c4728ed6e \"CML watermark\")\n", "> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`\r\n\r\nPlease note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:\r\n- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86\r\n- https://github.com/huggingface/lighteval/pull/84\r\n\r\nCC: @clefourrier \r\n" ]
6,686	Question: Is there any way for uploading a large image dataset?	I am uploading an image dataset like this: ``` dataset = load_dataset( "json", data_files={"train": "data/custom_dataset/train.json", "validation": "data/custom_dataset/val.json"}, ) dataset = dataset.cast_column("images", Sequence(Image())) dataset.push_to_hub("StanfordAIMI/custom_dataset", max_shard_size="1GB") ``` where it takes a long time in the `Map` process. Do you think I can use multi-processing to map all the image data to the memory first? For the `Map()` function, I can set `num_proc`. But for `push_to_hub` and `cast_column`, I can not find it. Thanks in advance! Best,	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "```\r\nimport pandas as pd\r\nfrom datasets import Dataset, Image\r\n\r\n# Read the CSV file\r\ndata = pd.read_csv(\"XXXX.csv\")\r\n\r\n# Create a Hugging Face Dataset\r\ndataset = Dataset.from_pandas(data)\r\ndataset = dataset.cast_column(\"file_name\", Image())\r\n\r\n# Upload to Hugging Face Hub (make sure authentication is set up)\r\ndataset.push_to_hub(\"XXXXX\"\")\r\n```\r\n\r\nstuck in \"Casting the dataset\r\n![截屏2024-05-02 11 44 50](https://github.com/huggingface/datasets/assets/48406770/dc012dc5-16f6-4fd5-9e02-1b705c552c5b)\r\n\"\r\n" ]
6,685	Updated Quickstart Notebook link	Fixed Quickstart Notebook Link in the [Overview notebook](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb)	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6685", "html_url": "https://github.com/huggingface/datasets/pull/6685", "diff_url": "https://github.com/huggingface/datasets/pull/6685.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6685.patch", "merged_at": "2024-02-25T18:48:08" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6685). 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) \| 0.005386 / 0.011353 (-0.005967) \| 0.003707 / 0.011008 (-0.007301) \| 0.062661 / 0.038508 (0.024153) \| 0.029058 / 0.023109 (0.005949) \| 0.249669 / 0.275898 (-0.026230) \| 0.280996 / 0.323480 (-0.042484) \| 0.004041 / 0.007986 (-0.003945) \| 0.002713 / 0.004328 (-0.001616) \| 0.047914 / 0.004250 (0.043664) \| 0.042014 / 0.037052 (0.004961) \| 0.265209 / 0.258489 (0.006720) \| 0.297320 / 0.293841 (0.003479) \| 0.028323 / 0.128546 (-0.100223) \| 0.010844 / 0.075646 (-0.064802) \| 0.205895 / 0.419271 (-0.213377) \| 0.035997 / 0.043533 (-0.007536) \| 0.245069 / 0.255139 (-0.010070) \| 0.266159 / 0.283200 (-0.017040) \| 0.017590 / 0.141683 (-0.124093) \| 1.132046 / 1.452155 (-0.320109) \| 1.177496 / 1.492716 (-0.315220) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.105441 / 0.018006 (0.087435) \| 0.301321 / 0.000490 (0.300831) \| 0.000211 / 0.000200 (0.000011) \| 0.000064 / 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) \| 0.018687 / 0.037411 (-0.018724) \| 0.061221 / 0.014526 (0.046695) \| 0.072556 / 0.176557 (-0.104001) \| 0.119641 / 0.737135 (-0.617495) \| 0.073781 / 0.296338 (-0.222557) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284564 / 0.215209 (0.069354) \| 2.795786 / 2.077655 (0.718131) \| 1.437059 / 1.504120 (-0.067061) \| 1.309319 / 1.541195 (-0.231876) \| 1.315849 / 1.468490 (-0.152641) \| 0.578571 / 4.584777 (-4.006206) \| 2.350754 / 3.745712 (-1.394958) \| 2.758499 / 5.269862 (-2.511362) \| 1.705545 / 4.565676 (-2.860131) \| 0.063660 / 0.424275 (-0.360615) \| 0.005506 / 0.007607 (-0.002101) \| 0.334915 / 0.226044 (0.108871) \| 3.295922 / 2.268929 (1.026994) \| 1.796513 / 55.444624 (-53.648111) \| 1.488113 / 6.876477 (-5.388364) \| 1.523042 / 2.142072 (-0.619031) \| 0.648169 / 4.805227 (-4.157058) \| 0.119321 / 6.500664 (-6.381343) \| 0.041932 / 0.075469 (-0.033537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982432 / 1.841788 (-0.859356) \| 11.344780 / 8.074308 (3.270472) \| 9.627219 / 10.191392 (-0.564173) \| 0.142590 / 0.680424 (-0.537834) \| 0.013899 / 0.534201 (-0.520302) \| 0.286335 / 0.579283 (-0.292948) \| 0.266552 / 0.434364 (-0.167812) \| 0.320361 / 0.540337 (-0.219977) \| 0.420303 / 1.386936 (-0.966633) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005251 / 0.011353 (-0.006102) \| 0.003515 / 0.011008 (-0.007494) \| 0.049344 / 0.038508 (0.010836) \| 0.032055 / 0.023109 (0.008945) \| 0.280653 / 0.275898 (0.004755) \| 0.303989 / 0.323480 (-0.019491) \| 0.004402 / 0.007986 (-0.003584) \| 0.002758 / 0.004328 (-0.001570) \| 0.050947 / 0.004250 (0.046697) \| 0.044405 / 0.037052 (0.007353) \| 0.292856 / 0.258489 (0.034367) \| 0.325307 / 0.293841 (0.031466) \| 0.047720 / 0.128546 (-0.080827) \| 0.010589 / 0.075646 (-0.065057) \| 0.057728 / 0.419271 (-0.361543) \| 0.033842 / 0.043533 (-0.009691) \| 0.285443 / 0.255139 (0.030304) \| 0.300013 / 0.283200 (0.016814) \| 0.017444 / 0.141683 (-0.124238) \| 1.152880 / 1.452155 (-0.299275) \| 1.200670 / 1.492716 (-0.292046) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092355 / 0.018006 (0.074349) \| 0.307907 / 0.000490 (0.307418) \| 0.000226 / 0.000200 (0.000026) \| 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) \| 0.021624 / 0.037411 (-0.015787) \| 0.075855 / 0.014526 (0.061329) \| 0.087109 / 0.176557 (-0.089447) \| 0.124859 / 0.737135 (-0.612276) \| 0.088933 / 0.296338 (-0.207406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294213 / 0.215209 (0.079004) \| 2.893146 / 2.077655 (0.815491) \| 1.595061 / 1.504120 (0.090942) \| 1.480959 / 1.541195 (-0.060236) \| 1.528277 / 1.468490 (0.059787) \| 0.570273 / 4.584777 (-4.014504) \| 2.412948 / 3.745712 (-1.332764) \| 2.675009 / 5.269862 (-2.594852) \| 1.724005 / 4.565676 (-2.841671) \| 0.063359 / 0.424275 (-0.360916) \| 0.005008 / 0.007607 (-0.002599) \| 0.346570 / 0.226044 (0.120526) \| 3.456566 / 2.268929 (1.187637) \| 1.973109 / 55.444624 (-53.471515) \| 1.657562 / 6.876477 (-5.218915) \| 1.790086 / 2.142072 (-0.351986) \| 0.655277 / 4.805227 (-4.149950) \| 0.117985 / 6.500664 (-6.382679) \| 0.041128 / 0.075469 (-0.034342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001428 / 1.841788 (-0.840360) \| 11.953458 / 8.074308 (3.879150) \| 10.188439 / 10.191392 (-0.002953) \| 0.140863 / 0.680424 (-0.539561) \| 0.015278 / 0.534201 (-0.518923) \| 0.288193 / 0.579283 (-0.291090) \| 0.281732 / 0.434364 (-0.152632) \| 0.328034 / 0.540337 (-0.212304) \| 0.414571 / 1.386936 (-0.972365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#531f35e688f81ec6b4c9044856a89a6b48142bd8 \"CML watermark\")\n" ]
6,684	Improve error message for gated datasets on load	Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6684", "html_url": "https://github.com/huggingface/datasets/pull/6684", "diff_url": "https://github.com/huggingface/datasets/pull/6684.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6684.patch", "merged_at": "2024-02-20T15:33:56" }	true	[ "Thank you ! Should we also add the link to the dataset page ?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6684). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thank you ! Should we also add the link to the dataset page ?\r\n\r\nGood idea! Done in https://github.com/huggingface/datasets/pull/6684/commits/4ab55210dca1815b6c2f23901598bfb29fc92a47", "Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n\r\nApparently your new message is also shown for datasets that don't exist, which is maybe not ideal", "Ah let me take a look!", "> Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n> \r\n> Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal\r\n\r\nFixed by reverting the error message root + added a small clarifying part", "<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) \| 0.005634 / 0.011353 (-0.005719) \| 0.003786 / 0.011008 (-0.007222) \| 0.064245 / 0.038508 (0.025737) \| 0.031228 / 0.023109 (0.008119) \| 0.248162 / 0.275898 (-0.027736) \| 0.273454 / 0.323480 (-0.050026) \| 0.003176 / 0.007986 (-0.004809) \| 0.002814 / 0.004328 (-0.001515) \| 0.049234 / 0.004250 (0.044984) \| 0.046075 / 0.037052 (0.009023) \| 0.262410 / 0.258489 (0.003921) \| 0.290597 / 0.293841 (-0.003244) \| 0.028545 / 0.128546 (-0.100001) \| 0.010881 / 0.075646 (-0.064766) \| 0.212098 / 0.419271 (-0.207173) \| 0.036406 / 0.043533 (-0.007127) \| 0.244571 / 0.255139 (-0.010568) \| 0.269537 / 0.283200 (-0.013663) \| 0.019574 / 0.141683 (-0.122109) \| 1.120369 / 1.452155 (-0.331785) \| 1.170188 / 1.492716 (-0.322529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.108088 / 0.018006 (0.090082) \| 0.299836 / 0.000490 (0.299346) \| 0.000204 / 0.000200 (0.000004) \| 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) \| 0.020881 / 0.037411 (-0.016531) \| 0.065290 / 0.014526 (0.050764) \| 0.074283 / 0.176557 (-0.102274) \| 0.122189 / 0.737135 (-0.614947) \| 0.077772 / 0.296338 (-0.218566) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278329 / 0.215209 (0.063120) \| 2.709885 / 2.077655 (0.632230) \| 1.428824 / 1.504120 (-0.075296) \| 1.314338 / 1.541195 (-0.226857) \| 1.349445 / 1.468490 (-0.119045) \| 0.571863 / 4.584777 (-4.012914) \| 2.358306 / 3.745712 (-1.387407) \| 2.873498 / 5.269862 (-2.396364) \| 1.779897 / 4.565676 (-2.785779) \| 0.062828 / 0.424275 (-0.361447) \| 0.005416 / 0.007607 (-0.002191) \| 0.337645 / 0.226044 (0.111601) \| 3.328868 / 2.268929 (1.059940) \| 1.793387 / 55.444624 (-53.651238) \| 1.539201 / 6.876477 (-5.337276) \| 1.589552 / 2.142072 (-0.552520) \| 0.645454 / 4.805227 (-4.159773) \| 0.116966 / 6.500664 (-6.383698) \| 0.043339 / 0.075469 (-0.032130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.995743 / 1.841788 (-0.846045) \| 12.096551 / 8.074308 (4.022243) \| 10.214299 / 10.191392 (0.022907) \| 0.133025 / 0.680424 (-0.547399) \| 0.014393 / 0.534201 (-0.519808) \| 0.289018 / 0.579283 (-0.290266) \| 0.267879 / 0.434364 (-0.166485) \| 0.324362 / 0.540337 (-0.215976) \| 0.425596 / 1.386936 (-0.961340) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005739 / 0.011353 (-0.005614) \| 0.003992 / 0.011008 (-0.007017) \| 0.051362 / 0.038508 (0.012854) \| 0.031707 / 0.023109 (0.008598) \| 0.274807 / 0.275898 (-0.001091) \| 0.298897 / 0.323480 (-0.024583) \| 0.004363 / 0.007986 (-0.003622) \| 0.002862 / 0.004328 (-0.001466) \| 0.050462 / 0.004250 (0.046212) \| 0.048158 / 0.037052 (0.011106) \| 0.282759 / 0.258489 (0.024270) \| 0.317766 / 0.293841 (0.023926) \| 0.060245 / 0.128546 (-0.068301) \| 0.011279 / 0.075646 (-0.064367) \| 0.061175 / 0.419271 (-0.358097) \| 0.035876 / 0.043533 (-0.007656) \| 0.273963 / 0.255139 (0.018824) \| 0.288788 / 0.283200 (0.005589) \| 0.019690 / 0.141683 (-0.121992) \| 1.167074 / 1.452155 (-0.285080) \| 1.206344 / 1.492716 (-0.286372) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091211 / 0.018006 (0.073205) \| 0.299295 / 0.000490 (0.298805) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.022718 / 0.037411 (-0.014693) \| 0.079483 / 0.014526 (0.064957) \| 0.087437 / 0.176557 (-0.089120) \| 0.126977 / 0.737135 (-0.610159) \| 0.089678 / 0.296338 (-0.206660) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294719 / 0.215209 (0.079510) \| 2.864505 / 2.077655 (0.786851) \| 1.583993 / 1.504120 (0.079873) \| 1.455079 / 1.541195 (-0.086115) \| 1.504080 / 1.468490 (0.035590) \| 0.569040 / 4.584777 (-4.015737) \| 2.423472 / 3.745712 (-1.322240) \| 2.742848 / 5.269862 (-2.527014) \| 1.785244 / 4.565676 (-2.780432) \| 0.062655 / 0.424275 (-0.361620) \| 0.005027 / 0.007607 (-0.002580) \| 0.343863 / 0.226044 (0.117818) \| 3.376286 / 2.268929 (1.107358) \| 1.933846 / 55.444624 (-53.510779) \| 1.667316 / 6.876477 (-5.209161) \| 1.815621 / 2.142072 (-0.326451) \| 0.639378 / 4.805227 (-4.165850) \| 0.116514 / 6.500664 (-6.384150) \| 0.042191 / 0.075469 (-0.033279) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007103 / 1.841788 (-0.834685) \| 12.791193 / 8.074308 (4.716885) \| 10.870575 / 10.191392 (0.679183) \| 0.131040 / 0.680424 (-0.549384) \| 0.016510 / 0.534201 (-0.517691) \| 0.288372 / 0.579283 (-0.290911) \| 0.275574 / 0.434364 (-0.158790) \| 0.327801 / 0.540337 (-0.212536) \| 0.415942 / 1.386936 (-0.970994) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b775390900132834e5edf487f5cbbf1299af1d88 \"CML watermark\")\n" ]
6,683	Fix imagefolder dataset url	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6683", "html_url": "https://github.com/huggingface/datasets/pull/6683", "diff_url": "https://github.com/huggingface/datasets/pull/6683.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6683.patch", "merged_at": "2024-02-19T17:18:10" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6683). 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) \| 0.005501 / 0.011353 (-0.005851) \| 0.003907 / 0.011008 (-0.007101) \| 0.063524 / 0.038508 (0.025016) \| 0.031773 / 0.023109 (0.008664) \| 0.244672 / 0.275898 (-0.031226) \| 0.293342 / 0.323480 (-0.030138) \| 0.004091 / 0.007986 (-0.003895) \| 0.002837 / 0.004328 (-0.001491) \| 0.049181 / 0.004250 (0.044930) \| 0.044515 / 0.037052 (0.007462) \| 0.263932 / 0.258489 (0.005443) \| 0.288412 / 0.293841 (-0.005429) \| 0.028338 / 0.128546 (-0.100208) \| 0.010865 / 0.075646 (-0.064781) \| 0.207979 / 0.419271 (-0.211293) \| 0.036149 / 0.043533 (-0.007384) \| 0.250674 / 0.255139 (-0.004465) \| 0.263232 / 0.283200 (-0.019968) \| 0.017919 / 0.141683 (-0.123763) \| 1.127794 / 1.452155 (-0.324360) \| 1.172071 / 1.492716 (-0.320645) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090435 / 0.018006 (0.072429) \| 0.300041 / 0.000490 (0.299552) \| 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) \| 0.018986 / 0.037411 (-0.018426) \| 0.064872 / 0.014526 (0.050346) \| 0.074738 / 0.176557 (-0.101818) \| 0.121577 / 0.737135 (-0.615558) \| 0.076416 / 0.296338 (-0.219923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279471 / 0.215209 (0.064262) \| 2.743066 / 2.077655 (0.665411) \| 1.429511 / 1.504120 (-0.074609) \| 1.315391 / 1.541195 (-0.225804) \| 1.371255 / 1.468490 (-0.097235) \| 0.570708 / 4.584777 (-4.014069) \| 2.373047 / 3.745712 (-1.372666) \| 2.813198 / 5.269862 (-2.456663) \| 1.768928 / 4.565676 (-2.796749) \| 0.066031 / 0.424275 (-0.358244) \| 0.005074 / 0.007607 (-0.002533) \| 0.333484 / 0.226044 (0.107440) \| 3.295002 / 2.268929 (1.026074) \| 1.796089 / 55.444624 (-53.648535) \| 1.521849 / 6.876477 (-5.354627) \| 1.604417 / 2.142072 (-0.537655) \| 0.645235 / 4.805227 (-4.159992) \| 0.119226 / 6.500664 (-6.381439) \| 0.043275 / 0.075469 (-0.032194) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986350 / 1.841788 (-0.855438) \| 11.921886 / 8.074308 (3.847578) \| 9.878841 / 10.191392 (-0.312551) \| 0.141072 / 0.680424 (-0.539352) \| 0.014514 / 0.534201 (-0.519687) \| 0.304060 / 0.579283 (-0.275223) \| 0.267844 / 0.434364 (-0.166520) \| 0.324881 / 0.540337 (-0.215457) \| 0.421426 / 1.386936 (-0.965510) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.006030) \| 0.003942 / 0.011008 (-0.007066) \| 0.050629 / 0.038508 (0.012121) \| 0.031176 / 0.023109 (0.008066) \| 0.279627 / 0.275898 (0.003729) \| 0.302667 / 0.323480 (-0.020813) \| 0.004281 / 0.007986 (-0.003705) \| 0.002900 / 0.004328 (-0.001428) \| 0.048168 / 0.004250 (0.043918) \| 0.046094 / 0.037052 (0.009042) \| 0.290714 / 0.258489 (0.032224) \| 0.321336 / 0.293841 (0.027496) \| 0.047934 / 0.128546 (-0.080612) \| 0.010773 / 0.075646 (-0.064873) \| 0.059439 / 0.419271 (-0.359832) \| 0.033644 / 0.043533 (-0.009889) \| 0.273710 / 0.255139 (0.018571) \| 0.295144 / 0.283200 (0.011944) \| 0.018115 / 0.141683 (-0.123568) \| 1.150302 / 1.452155 (-0.301853) \| 1.197304 / 1.492716 (-0.295412) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090262 / 0.018006 (0.072255) \| 0.300727 / 0.000490 (0.300238) \| 0.000228 / 0.000200 (0.000028) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022706 / 0.037411 (-0.014706) \| 0.077420 / 0.014526 (0.062894) \| 0.089119 / 0.176557 (-0.087437) \| 0.126760 / 0.737135 (-0.610375) \| 0.090702 / 0.296338 (-0.205637) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296558 / 0.215209 (0.081349) \| 2.865311 / 2.077655 (0.787656) \| 1.587355 / 1.504120 (0.083235) \| 1.491660 / 1.541195 (-0.049534) \| 1.513604 / 1.468490 (0.045114) \| 0.565209 / 4.584777 (-4.019568) \| 2.450648 / 3.745712 (-1.295064) \| 2.709941 / 5.269862 (-2.559921) \| 1.775032 / 4.565676 (-2.790645) \| 0.063767 / 0.424275 (-0.360508) \| 0.005047 / 0.007607 (-0.002560) \| 0.347406 / 0.226044 (0.121361) \| 3.416671 / 2.268929 (1.147743) \| 1.949653 / 55.444624 (-53.494971) \| 1.669885 / 6.876477 (-5.206592) \| 1.848125 / 2.142072 (-0.293947) \| 0.648179 / 4.805227 (-4.157048) \| 0.116374 / 6.500664 (-6.384290) \| 0.041816 / 0.075469 (-0.033653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.007009 / 1.841788 (-0.834779) \| 12.749964 / 8.074308 (4.675656) \| 10.765890 / 10.191392 (0.574498) \| 0.141743 / 0.680424 (-0.538681) \| 0.016077 / 0.534201 (-0.518124) \| 0.293275 / 0.579283 (-0.286008) \| 0.277064 / 0.434364 (-0.157300) \| 0.327039 / 0.540337 (-0.213299) \| 0.421784 / 1.386936 (-0.965152) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f807cd4c733a3616011a3f7f53a9fa56f7d5f685 \"CML watermark\")\n" ]
6,682	Update GitHub Actions to Node 20	Update GitHub Actions to Node 20. Fix #6679.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6682", "html_url": "https://github.com/huggingface/datasets/pull/6682", "diff_url": "https://github.com/huggingface/datasets/pull/6682.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6682.patch", "merged_at": "2024-02-28T06:56:34" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6682). 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) \| 0.005292 / 0.011353 (-0.006060) \| 0.003354 / 0.011008 (-0.007654) \| 0.063150 / 0.038508 (0.024642) \| 0.028616 / 0.023109 (0.005507) \| 0.242267 / 0.275898 (-0.033631) \| 0.267305 / 0.323480 (-0.056175) \| 0.003041 / 0.007986 (-0.004944) \| 0.003346 / 0.004328 (-0.000982) \| 0.048268 / 0.004250 (0.044018) \| 0.042070 / 0.037052 (0.005018) \| 0.256526 / 0.258489 (-0.001963) \| 0.279744 / 0.293841 (-0.014097) \| 0.027862 / 0.128546 (-0.100684) \| 0.010786 / 0.075646 (-0.064861) \| 0.206998 / 0.419271 (-0.212273) \| 0.035503 / 0.043533 (-0.008030) \| 0.248454 / 0.255139 (-0.006685) \| 0.265639 / 0.283200 (-0.017561) \| 0.019590 / 0.141683 (-0.122093) \| 1.134445 / 1.452155 (-0.317709) \| 1.194956 / 1.492716 (-0.297761) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090987 / 0.018006 (0.072981) \| 0.301907 / 0.000490 (0.301418) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018324 / 0.037411 (-0.019088) \| 0.061492 / 0.014526 (0.046966) \| 0.074166 / 0.176557 (-0.102391) \| 0.119990 / 0.737135 (-0.617145) \| 0.074554 / 0.296338 (-0.221785) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279646 / 0.215209 (0.064437) \| 2.773819 / 2.077655 (0.696164) \| 1.436460 / 1.504120 (-0.067660) \| 1.310303 / 1.541195 (-0.230892) \| 1.315328 / 1.468490 (-0.153162) \| 0.558328 / 4.584777 (-4.026449) \| 2.383819 / 3.745712 (-1.361893) \| 2.735034 / 5.269862 (-2.534827) \| 1.724413 / 4.565676 (-2.841263) \| 0.061476 / 0.424275 (-0.362799) \| 0.004899 / 0.007607 (-0.002708) \| 0.333195 / 0.226044 (0.107151) \| 3.228900 / 2.268929 (0.959971) \| 1.787315 / 55.444624 (-53.657309) \| 1.526949 / 6.876477 (-5.349527) \| 1.539816 / 2.142072 (-0.602257) \| 0.636926 / 4.805227 (-4.168302) \| 0.117533 / 6.500664 (-6.383131) \| 0.041859 / 0.075469 (-0.033610) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964637 / 1.841788 (-0.877151) \| 11.296021 / 8.074308 (3.221713) \| 9.375436 / 10.191392 (-0.815956) \| 0.140330 / 0.680424 (-0.540094) \| 0.013638 / 0.534201 (-0.520563) \| 0.287046 / 0.579283 (-0.292237) \| 0.265054 / 0.434364 (-0.169310) \| 0.331548 / 0.540337 (-0.208790) \| 0.438418 / 1.386936 (-0.948518) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.003853 / 0.011008 (-0.007155) \| 0.049301 / 0.038508 (0.010793) \| 0.030477 / 0.023109 (0.007368) \| 0.278507 / 0.275898 (0.002609) \| 0.298245 / 0.323480 (-0.025235) \| 0.004225 / 0.007986 (-0.003761) \| 0.002736 / 0.004328 (-0.001593) \| 0.049345 / 0.004250 (0.045094) \| 0.045141 / 0.037052 (0.008088) \| 0.290992 / 0.258489 (0.032503) \| 0.317430 / 0.293841 (0.023589) \| 0.029623 / 0.128546 (-0.098924) \| 0.010351 / 0.075646 (-0.065295) \| 0.058027 / 0.419271 (-0.361244) \| 0.051306 / 0.043533 (0.007773) \| 0.279947 / 0.255139 (0.024808) \| 0.296916 / 0.283200 (0.013717) \| 0.018859 / 0.141683 (-0.122823) \| 1.153484 / 1.452155 (-0.298670) \| 1.189141 / 1.492716 (-0.303575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091030 / 0.018006 (0.073024) \| 0.301305 / 0.000490 (0.300815) \| 0.000230 / 0.000200 (0.000030) \| 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) \| 0.021801 / 0.037411 (-0.015611) \| 0.075162 / 0.014526 (0.060636) \| 0.086455 / 0.176557 (-0.090102) \| 0.125431 / 0.737135 (-0.611705) \| 0.087797 / 0.296338 (-0.208542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295950 / 0.215209 (0.080741) \| 2.895839 / 2.077655 (0.818184) \| 1.603121 / 1.504120 (0.099001) \| 1.482162 / 1.541195 (-0.059033) \| 1.474231 / 1.468490 (0.005741) \| 0.571370 / 4.584777 (-4.013407) \| 2.466864 / 3.745712 (-1.278848) \| 2.607279 / 5.269862 (-2.662582) \| 1.723106 / 4.565676 (-2.842571) \| 0.062068 / 0.424275 (-0.362208) \| 0.004958 / 0.007607 (-0.002649) \| 0.345213 / 0.226044 (0.119168) \| 3.403916 / 2.268929 (1.134987) \| 1.935538 / 55.444624 (-53.509086) \| 1.658930 / 6.876477 (-5.217547) \| 1.767611 / 2.142072 (-0.374461) \| 0.645780 / 4.805227 (-4.159447) \| 0.116077 / 6.500664 (-6.384587) \| 0.040774 / 0.075469 (-0.034695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.025952 / 1.841788 (-0.815836) \| 11.935970 / 8.074308 (3.861662) \| 9.935799 / 10.191392 (-0.255593) \| 0.131081 / 0.680424 (-0.549343) \| 0.016010 / 0.534201 (-0.518191) \| 0.285476 / 0.579283 (-0.293807) \| 0.274928 / 0.434364 (-0.159435) \| 0.325788 / 0.540337 (-0.214550) \| 0.421666 / 1.386936 (-0.965270) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a79064b7a2255c0d6950dc998509ecefb893689 \"CML watermark\")\n" ]
6,681	Update release instructions	Update release instructions.	[ { "id": 4296013012, "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance", "name": "maintenance", "color": "d4c5f9", "default": false, "description": "Maintenance tasks" } ]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6681", "html_url": "https://github.com/huggingface/datasets/pull/6681", "diff_url": "https://github.com/huggingface/datasets/pull/6681.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6681.patch", "merged_at": "2024-02-28T07:17:22" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6681). 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) \| 0.005410 / 0.011353 (-0.005943) \| 0.003862 / 0.011008 (-0.007146) \| 0.063457 / 0.038508 (0.024949) \| 0.030081 / 0.023109 (0.006972) \| 0.250657 / 0.275898 (-0.025241) \| 0.275483 / 0.323480 (-0.047997) \| 0.004048 / 0.007986 (-0.003938) \| 0.002818 / 0.004328 (-0.001511) \| 0.048940 / 0.004250 (0.044689) \| 0.043397 / 0.037052 (0.006345) \| 0.262160 / 0.258489 (0.003671) \| 0.294154 / 0.293841 (0.000313) \| 0.030028 / 0.128546 (-0.098519) \| 0.010789 / 0.075646 (-0.064857) \| 0.209665 / 0.419271 (-0.209606) \| 0.035297 / 0.043533 (-0.008236) \| 0.253169 / 0.255139 (-0.001970) \| 0.271775 / 0.283200 (-0.011424) \| 0.018332 / 0.141683 (-0.123351) \| 1.152420 / 1.452155 (-0.299735) \| 1.262767 / 1.492716 (-0.229949) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089990 / 0.018006 (0.071984) \| 0.298552 / 0.000490 (0.298062) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.018414 / 0.037411 (-0.018997) \| 0.061566 / 0.014526 (0.047040) \| 0.075360 / 0.176557 (-0.101196) \| 0.123470 / 0.737135 (-0.613665) \| 0.075215 / 0.296338 (-0.221124) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279563 / 0.215209 (0.064354) \| 2.725212 / 2.077655 (0.647557) \| 1.446413 / 1.504120 (-0.057707) \| 1.321665 / 1.541195 (-0.219529) \| 1.352475 / 1.468490 (-0.116015) \| 0.568440 / 4.584777 (-4.016337) \| 2.393217 / 3.745712 (-1.352495) \| 2.793150 / 5.269862 (-2.476711) \| 1.764316 / 4.565676 (-2.801360) \| 0.063157 / 0.424275 (-0.361118) \| 0.005117 / 0.007607 (-0.002491) \| 0.333310 / 0.226044 (0.107265) \| 3.291000 / 2.268929 (1.022071) \| 1.824654 / 55.444624 (-53.619971) \| 1.558681 / 6.876477 (-5.317795) \| 1.580558 / 2.142072 (-0.561514) \| 0.649831 / 4.805227 (-4.155396) \| 0.118674 / 6.500664 (-6.381990) \| 0.042247 / 0.075469 (-0.033222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976552 / 1.841788 (-0.865236) \| 11.847361 / 8.074308 (3.773053) \| 9.490786 / 10.191392 (-0.700606) \| 0.141643 / 0.680424 (-0.538781) \| 0.013653 / 0.534201 (-0.520548) \| 0.284345 / 0.579283 (-0.294938) \| 0.268314 / 0.434364 (-0.166050) \| 0.339586 / 0.540337 (-0.200751) \| 0.445239 / 1.386936 (-0.941697) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005754 / 0.011353 (-0.005599) \| 0.004038 / 0.011008 (-0.006970) \| 0.050027 / 0.038508 (0.011519) \| 0.033598 / 0.023109 (0.010488) \| 0.286514 / 0.275898 (0.010616) \| 0.302493 / 0.323480 (-0.020986) \| 0.004254 / 0.007986 (-0.003731) \| 0.002827 / 0.004328 (-0.001502) \| 0.050433 / 0.004250 (0.046182) \| 0.046106 / 0.037052 (0.009054) \| 0.301522 / 0.258489 (0.043033) \| 0.325784 / 0.293841 (0.031943) \| 0.030014 / 0.128546 (-0.098532) \| 0.010891 / 0.075646 (-0.064756) \| 0.059899 / 0.419271 (-0.359373) \| 0.057252 / 0.043533 (0.013719) \| 0.280276 / 0.255139 (0.025137) \| 0.295632 / 0.283200 (0.012433) \| 0.019060 / 0.141683 (-0.122622) \| 1.141423 / 1.452155 (-0.310731) \| 1.226960 / 1.492716 (-0.265757) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091919 / 0.018006 (0.073913) \| 0.300769 / 0.000490 (0.300279) \| 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) \| 0.022467 / 0.037411 (-0.014945) \| 0.075342 / 0.014526 (0.060816) \| 0.087988 / 0.176557 (-0.088569) \| 0.128304 / 0.737135 (-0.608831) \| 0.089058 / 0.296338 (-0.207280) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294662 / 0.215209 (0.079453) \| 2.887743 / 2.077655 (0.810088) \| 1.591756 / 1.504120 (0.087636) \| 1.469249 / 1.541195 (-0.071945) \| 1.495639 / 1.468490 (0.027149) \| 0.575507 / 4.584777 (-4.009270) \| 2.449674 / 3.745712 (-1.296038) \| 2.737217 / 5.269862 (-2.532645) \| 1.783066 / 4.565676 (-2.782610) \| 0.063388 / 0.424275 (-0.360887) \| 0.005044 / 0.007607 (-0.002563) \| 0.344807 / 0.226044 (0.118763) \| 3.410845 / 2.268929 (1.141916) \| 1.967452 / 55.444624 (-53.477173) \| 1.699884 / 6.876477 (-5.176593) \| 1.862466 / 2.142072 (-0.279607) \| 0.663714 / 4.805227 (-4.141513) \| 0.118356 / 6.500664 (-6.382308) \| 0.041176 / 0.075469 (-0.034293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.013523 / 1.841788 (-0.828264) \| 12.498866 / 8.074308 (4.424558) \| 10.382595 / 10.191392 (0.191203) \| 0.141757 / 0.680424 (-0.538667) \| 0.015992 / 0.534201 (-0.518209) \| 0.295639 / 0.579283 (-0.283644) \| 0.278382 / 0.434364 (-0.155982) \| 0.330351 / 0.540337 (-0.209986) \| 0.431293 / 1.386936 (-0.955643) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf1aaa32eddd73076cf6600125661df4a32cb20a \"CML watermark\")\n" ]
6,680	Set dev version	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6680", "html_url": "https://github.com/huggingface/datasets/pull/6680", "diff_url": "https://github.com/huggingface/datasets/pull/6680.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6680.patch", "merged_at": "2024-02-19T10:00:40" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6680). 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) \| 0.004981 / 0.011353 (-0.006372) \| 0.003030 / 0.011008 (-0.007978) \| 0.059862 / 0.038508 (0.021354) \| 0.030595 / 0.023109 (0.007486) \| 0.262638 / 0.275898 (-0.013260) \| 0.276287 / 0.323480 (-0.047193) \| 0.003955 / 0.007986 (-0.004030) \| 0.002667 / 0.004328 (-0.001661) \| 0.047827 / 0.004250 (0.043576) \| 0.041170 / 0.037052 (0.004118) \| 0.252494 / 0.258489 (-0.005995) \| 0.277493 / 0.293841 (-0.016348) \| 0.027269 / 0.128546 (-0.101277) \| 0.010380 / 0.075646 (-0.065266) \| 0.204404 / 0.419271 (-0.214867) \| 0.035251 / 0.043533 (-0.008282) \| 0.244368 / 0.255139 (-0.010771) \| 0.258003 / 0.283200 (-0.025197) \| 0.016751 / 0.141683 (-0.124932) \| 1.134108 / 1.452155 (-0.318047) \| 1.159969 / 1.492716 (-0.332748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087011 / 0.018006 (0.069004) \| 0.295577 / 0.000490 (0.295087) \| 0.000213 / 0.000200 (0.000013) \| 0.000044 / 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) \| 0.017993 / 0.037411 (-0.019419) \| 0.061690 / 0.014526 (0.047164) \| 0.071791 / 0.176557 (-0.104765) \| 0.118282 / 0.737135 (-0.618853) \| 0.073453 / 0.296338 (-0.222885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284764 / 0.215209 (0.069555) \| 2.771791 / 2.077655 (0.694136) \| 1.469614 / 1.504120 (-0.034506) \| 1.334096 / 1.541195 (-0.207099) \| 1.339995 / 1.468490 (-0.128495) \| 0.562740 / 4.584777 (-4.022037) \| 2.390219 / 3.745712 (-1.355493) \| 2.679776 / 5.269862 (-2.590086) \| 1.684397 / 4.565676 (-2.881279) \| 0.062137 / 0.424275 (-0.362138) \| 0.004934 / 0.007607 (-0.002673) \| 0.336257 / 0.226044 (0.110212) \| 3.256330 / 2.268929 (0.987401) \| 1.801520 / 55.444624 (-53.643105) \| 1.520662 / 6.876477 (-5.355815) \| 1.537023 / 2.142072 (-0.605049) \| 0.644360 / 4.805227 (-4.160867) \| 0.115603 / 6.500664 (-6.385061) \| 0.040601 / 0.075469 (-0.034868) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982992 / 1.841788 (-0.858796) \| 11.002182 / 8.074308 (2.927873) \| 9.564671 / 10.191392 (-0.626721) \| 0.137682 / 0.680424 (-0.542742) \| 0.013936 / 0.534201 (-0.520265) \| 0.285898 / 0.579283 (-0.293385) \| 0.264426 / 0.434364 (-0.169938) \| 0.321615 / 0.540337 (-0.218723) \| 0.420216 / 1.386936 (-0.966720) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005239 / 0.011353 (-0.006114) \| 0.003165 / 0.011008 (-0.007844) \| 0.048176 / 0.038508 (0.009668) \| 0.030680 / 0.023109 (0.007571) \| 0.258176 / 0.275898 (-0.017722) \| 0.282342 / 0.323480 (-0.041138) \| 0.004218 / 0.007986 (-0.003767) \| 0.002616 / 0.004328 (-0.001713) \| 0.047253 / 0.004250 (0.043003) \| 0.044178 / 0.037052 (0.007126) \| 0.276942 / 0.258489 (0.018453) \| 0.312353 / 0.293841 (0.018512) \| 0.046714 / 0.128546 (-0.081832) \| 0.009892 / 0.075646 (-0.065755) \| 0.056123 / 0.419271 (-0.363149) \| 0.032691 / 0.043533 (-0.010842) \| 0.268781 / 0.255139 (0.013642) \| 0.285921 / 0.283200 (0.002722) \| 0.016050 / 0.141683 (-0.125633) \| 1.138058 / 1.452155 (-0.314096) \| 1.193405 / 1.492716 (-0.299311) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089280 / 0.018006 (0.071273) \| 0.288425 / 0.000490 (0.287935) \| 0.000201 / 0.000200 (0.000001) \| 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) \| 0.021536 / 0.037411 (-0.015875) \| 0.075157 / 0.014526 (0.060631) \| 0.088943 / 0.176557 (-0.087613) \| 0.125191 / 0.737135 (-0.611945) \| 0.087991 / 0.296338 (-0.208348) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285103 / 0.215209 (0.069894) \| 2.791798 / 2.077655 (0.714144) \| 1.518104 / 1.504120 (0.013984) \| 1.388690 / 1.541195 (-0.152505) \| 1.409896 / 1.468490 (-0.058594) \| 0.554077 / 4.584777 (-4.030700) \| 2.396994 / 3.745712 (-1.348718) \| 2.596801 / 5.269862 (-2.673060) \| 1.683761 / 4.565676 (-2.881915) \| 0.061209 / 0.424275 (-0.363066) \| 0.004735 / 0.007607 (-0.002873) \| 0.337566 / 0.226044 (0.111522) \| 3.258183 / 2.268929 (0.989254) \| 1.886185 / 55.444624 (-53.558439) \| 1.599148 / 6.876477 (-5.277329) \| 1.726867 / 2.142072 (-0.415206) \| 0.642784 / 4.805227 (-4.162444) \| 0.114947 / 6.500664 (-6.385717) \| 0.040450 / 0.075469 (-0.035019) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001316 / 1.841788 (-0.840472) \| 11.695367 / 8.074308 (3.621058) \| 9.854870 / 10.191392 (-0.336522) \| 0.136462 / 0.680424 (-0.543961) \| 0.016708 / 0.534201 (-0.517493) \| 0.286421 / 0.579283 (-0.292862) \| 0.270773 / 0.434364 (-0.163591) \| 0.322947 / 0.540337 (-0.217390) \| 0.416772 / 1.386936 (-0.970164) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ba542847314bd349301937e59c3de04ce13aa5e \"CML watermark\")\n" ]
6,679	Node.js 16 GitHub Actions are deprecated	`Node.js` 16 GitHub Actions are deprecated. See: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/ We should update them to Node 20. See warnings in our CI, e.g.: https://github.com/huggingface/datasets/actions/runs/7957295009?pr=6678 > Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20: actions/checkout@v3, actions/setup-python@v4. For more information see: https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/.	[ { "id": 4296013012, "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance", "name": "maintenance", "color": "d4c5f9", "default": false, "description": "Maintenance tasks" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,678	Release: 2.17.1	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6678", "html_url": "https://github.com/huggingface/datasets/pull/6678", "diff_url": "https://github.com/huggingface/datasets/pull/6678.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6678.patch", "merged_at": "2024-02-19T09:56:52" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6678). 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) \| 0.005070 / 0.011353 (-0.006283) \| 0.003685 / 0.011008 (-0.007323) \| 0.063191 / 0.038508 (0.024683) \| 0.030506 / 0.023109 (0.007397) \| 0.258033 / 0.275898 (-0.017865) \| 0.269790 / 0.323480 (-0.053690) \| 0.004180 / 0.007986 (-0.003805) \| 0.002811 / 0.004328 (-0.001517) \| 0.048718 / 0.004250 (0.044467) \| 0.043473 / 0.037052 (0.006421) \| 0.267306 / 0.258489 (0.008817) \| 0.290315 / 0.293841 (-0.003526) \| 0.027402 / 0.128546 (-0.101144) \| 0.010782 / 0.075646 (-0.064864) \| 0.207243 / 0.419271 (-0.212029) \| 0.035637 / 0.043533 (-0.007896) \| 0.264032 / 0.255139 (0.008893) \| 0.270450 / 0.283200 (-0.012749) \| 0.017407 / 0.141683 (-0.124276) \| 1.107481 / 1.452155 (-0.344674) \| 1.163187 / 1.492716 (-0.329529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095065 / 0.018006 (0.077059) \| 0.305169 / 0.000490 (0.304680) \| 0.000221 / 0.000200 (0.000021) \| 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) \| 0.017706 / 0.037411 (-0.019706) \| 0.061431 / 0.014526 (0.046905) \| 0.073541 / 0.176557 (-0.103016) \| 0.117326 / 0.737135 (-0.619809) \| 0.074368 / 0.296338 (-0.221971) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284533 / 0.215209 (0.069324) \| 2.775230 / 2.077655 (0.697575) \| 1.455196 / 1.504120 (-0.048924) \| 1.357651 / 1.541195 (-0.183544) \| 1.337477 / 1.468490 (-0.131013) \| 0.567439 / 4.584777 (-4.017338) \| 2.380612 / 3.745712 (-1.365100) \| 2.792305 / 5.269862 (-2.477556) \| 1.726501 / 4.565676 (-2.839176) \| 0.061729 / 0.424275 (-0.362546) \| 0.004928 / 0.007607 (-0.002679) \| 0.331989 / 0.226044 (0.105944) \| 3.301704 / 2.268929 (1.032776) \| 1.805107 / 55.444624 (-53.639518) \| 1.500434 / 6.876477 (-5.376043) \| 1.535548 / 2.142072 (-0.606524) \| 0.639490 / 4.805227 (-4.165737) \| 0.115876 / 6.500664 (-6.384788) \| 0.041895 / 0.075469 (-0.033574) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993584 / 1.841788 (-0.848203) \| 11.596680 / 8.074308 (3.522371) \| 9.631726 / 10.191392 (-0.559666) \| 0.141153 / 0.680424 (-0.539271) \| 0.014077 / 0.534201 (-0.520124) \| 0.288237 / 0.579283 (-0.291046) \| 0.261213 / 0.434364 (-0.173151) \| 0.323897 / 0.540337 (-0.216441) \| 0.420350 / 1.386936 (-0.966586) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005275 / 0.011353 (-0.006078) \| 0.003739 / 0.011008 (-0.007269) \| 0.049801 / 0.038508 (0.011293) \| 0.030544 / 0.023109 (0.007435) \| 0.264835 / 0.275898 (-0.011063) \| 0.297738 / 0.323480 (-0.025742) \| 0.004487 / 0.007986 (-0.003499) \| 0.002835 / 0.004328 (-0.001493) \| 0.048091 / 0.004250 (0.043841) \| 0.044375 / 0.037052 (0.007322) \| 0.286538 / 0.258489 (0.028049) \| 0.319561 / 0.293841 (0.025720) \| 0.047925 / 0.128546 (-0.080621) \| 0.010816 / 0.075646 (-0.064831) \| 0.057940 / 0.419271 (-0.361331) \| 0.033588 / 0.043533 (-0.009945) \| 0.270075 / 0.255139 (0.014936) \| 0.290441 / 0.283200 (0.007242) \| 0.017173 / 0.141683 (-0.124509) \| 1.164686 / 1.452155 (-0.287469) \| 1.213205 / 1.492716 (-0.279511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093408 / 0.018006 (0.075402) \| 0.305525 / 0.000490 (0.305036) \| 0.000235 / 0.000200 (0.000035) \| 0.000045 / 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) \| 0.021605 / 0.037411 (-0.015806) \| 0.075479 / 0.014526 (0.060953) \| 0.085990 / 0.176557 (-0.090567) \| 0.124783 / 0.737135 (-0.612352) \| 0.089108 / 0.296338 (-0.207230) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306222 / 0.215209 (0.091013) \| 2.987282 / 2.077655 (0.909627) \| 1.664714 / 1.504120 (0.160594) \| 1.523136 / 1.541195 (-0.018059) \| 1.534112 / 1.468490 (0.065622) \| 0.566347 / 4.584777 (-4.018430) \| 2.438641 / 3.745712 (-1.307071) \| 2.669048 / 5.269862 (-2.600814) \| 1.732935 / 4.565676 (-2.832741) \| 0.063460 / 0.424275 (-0.360815) \| 0.004973 / 0.007607 (-0.002634) \| 0.366233 / 0.226044 (0.140189) \| 3.553578 / 2.268929 (1.284649) \| 1.984343 / 55.444624 (-53.460281) \| 1.711038 / 6.876477 (-5.165439) \| 1.857346 / 2.142072 (-0.284726) \| 0.651077 / 4.805227 (-4.154150) \| 0.118670 / 6.500664 (-6.381994) \| 0.041839 / 0.075469 (-0.033631) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008230 / 1.841788 (-0.833558) \| 12.047403 / 8.074308 (3.973095) \| 10.039053 / 10.191392 (-0.152339) \| 0.141640 / 0.680424 (-0.538784) \| 0.014758 / 0.534201 (-0.519443) \| 0.285016 / 0.579283 (-0.294267) \| 0.275461 / 0.434364 (-0.158903) \| 0.325535 / 0.540337 (-0.214803) \| 0.415871 / 1.386936 (-0.971065) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d2268261bf0fb3eed8faae6bc1fa20a25b4382c \"CML watermark\")\n" ]
6,677	Pass through information about location of cache directory.	If cache directory is set, information is not passed through. Pass download config in as an arg too.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6677", "html_url": "https://github.com/huggingface/datasets/pull/6677", "diff_url": "https://github.com/huggingface/datasets/pull/6677.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6677.patch", "merged_at": "2024-02-28T18:51:15" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6677). 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) \| 0.007162 / 0.011353 (-0.004191) \| 0.004125 / 0.011008 (-0.006883) \| 0.064011 / 0.038508 (0.025503) \| 0.031795 / 0.023109 (0.008686) \| 0.248761 / 0.275898 (-0.027137) \| 0.275130 / 0.323480 (-0.048350) \| 0.003138 / 0.007986 (-0.004847) \| 0.002736 / 0.004328 (-0.001592) \| 0.050515 / 0.004250 (0.046264) \| 0.044787 / 0.037052 (0.007735) \| 0.261997 / 0.258489 (0.003507) \| 0.292170 / 0.293841 (-0.001671) \| 0.028122 / 0.128546 (-0.100424) \| 0.010780 / 0.075646 (-0.064866) \| 0.208805 / 0.419271 (-0.210467) \| 0.036362 / 0.043533 (-0.007171) \| 0.251599 / 0.255139 (-0.003540) \| 0.271200 / 0.283200 (-0.012000) \| 0.020215 / 0.141683 (-0.121468) \| 1.133352 / 1.452155 (-0.318803) \| 1.185240 / 1.492716 (-0.307477) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089990 / 0.018006 (0.071984) \| 0.298099 / 0.000490 (0.297609) \| 0.000221 / 0.000200 (0.000021) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018432 / 0.037411 (-0.018980) \| 0.062641 / 0.014526 (0.048115) \| 0.075210 / 0.176557 (-0.101346) \| 0.122239 / 0.737135 (-0.614897) \| 0.078914 / 0.296338 (-0.217424) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287682 / 0.215209 (0.072473) \| 2.815030 / 2.077655 (0.737375) \| 1.499512 / 1.504120 (-0.004607) \| 1.370210 / 1.541195 (-0.170985) \| 1.381944 / 1.468490 (-0.086546) \| 0.571645 / 4.584777 (-4.013132) \| 2.377773 / 3.745712 (-1.367939) \| 2.757206 / 5.269862 (-2.512655) \| 1.717159 / 4.565676 (-2.848518) \| 0.063038 / 0.424275 (-0.361237) \| 0.004913 / 0.007607 (-0.002694) \| 0.340854 / 0.226044 (0.114810) \| 3.348087 / 2.268929 (1.079159) \| 1.843123 / 55.444624 (-53.601502) \| 1.569714 / 6.876477 (-5.306763) \| 1.593791 / 2.142072 (-0.548281) \| 0.642865 / 4.805227 (-4.162362) \| 0.116933 / 6.500664 (-6.383731) \| 0.041891 / 0.075469 (-0.033578) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976453 / 1.841788 (-0.865334) \| 12.229986 / 8.074308 (4.155678) \| 9.617912 / 10.191392 (-0.573480) \| 0.141292 / 0.680424 (-0.539132) \| 0.013732 / 0.534201 (-0.520469) \| 0.291424 / 0.579283 (-0.287859) \| 0.264748 / 0.434364 (-0.169616) \| 0.345262 / 0.540337 (-0.195075) \| 0.445126 / 1.386936 (-0.941810) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005286 / 0.011353 (-0.006067) \| 0.003749 / 0.011008 (-0.007259) \| 0.049070 / 0.038508 (0.010562) \| 0.031779 / 0.023109 (0.008670) \| 0.275636 / 0.275898 (-0.000262) \| 0.296956 / 0.323480 (-0.026524) \| 0.004278 / 0.007986 (-0.003708) \| 0.002702 / 0.004328 (-0.001626) \| 0.049658 / 0.004250 (0.045408) \| 0.046025 / 0.037052 (0.008973) \| 0.293238 / 0.258489 (0.034749) \| 0.316676 / 0.293841 (0.022835) \| 0.029277 / 0.128546 (-0.099269) \| 0.010096 / 0.075646 (-0.065550) \| 0.059861 / 0.419271 (-0.359411) \| 0.054310 / 0.043533 (0.010778) \| 0.275025 / 0.255139 (0.019886) \| 0.292995 / 0.283200 (0.009796) \| 0.018448 / 0.141683 (-0.123235) \| 1.150805 / 1.452155 (-0.301350) \| 1.178310 / 1.492716 (-0.314406) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092644 / 0.018006 (0.074638) \| 0.297979 / 0.000490 (0.297489) \| 0.000207 / 0.000200 (0.000007) \| 0.000042 / 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) \| 0.021758 / 0.037411 (-0.015654) \| 0.076734 / 0.014526 (0.062208) \| 0.088522 / 0.176557 (-0.088034) \| 0.126190 / 0.737135 (-0.610945) \| 0.090466 / 0.296338 (-0.205873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305355 / 0.215209 (0.090146) \| 2.978927 / 2.077655 (0.901272) \| 1.612312 / 1.504120 (0.108192) \| 1.485829 / 1.541195 (-0.055366) \| 1.513303 / 1.468490 (0.044813) \| 0.592368 / 4.584777 (-3.992409) \| 2.448529 / 3.745712 (-1.297183) \| 2.713460 / 5.269862 (-2.556402) \| 1.803859 / 4.565676 (-2.761817) \| 0.065630 / 0.424275 (-0.358645) \| 0.005072 / 0.007607 (-0.002535) \| 0.358340 / 0.226044 (0.132295) \| 3.528516 / 2.268929 (1.259588) \| 1.977901 / 55.444624 (-53.466723) \| 1.692526 / 6.876477 (-5.183950) \| 1.858405 / 2.142072 (-0.283668) \| 0.676169 / 4.805227 (-4.129059) \| 0.121136 / 6.500664 (-6.379528) \| 0.041384 / 0.075469 (-0.034085) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011801 / 1.841788 (-0.829987) \| 12.496459 / 8.074308 (4.422151) \| 10.465659 / 10.191392 (0.274267) \| 0.154121 / 0.680424 (-0.526302) \| 0.016796 / 0.534201 (-0.517405) \| 0.288908 / 0.579283 (-0.290376) \| 0.274328 / 0.434364 (-0.160036) \| 0.322366 / 0.540337 (-0.217971) \| 0.423498 / 1.386936 (-0.963438) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52b9273b5ddbcadfdb512a693bc813b21e863b1b \"CML watermark\")\n" ]
6,676	Can't Read List of JSON Files Properly	### Describe the bug Trying to read a bunch of JSON files into Dataset class but default approach doesn't work. I don't get why it works when I read it one by one but not when I pass as a list :man_shrugging: The code fails with ``` ArrowInvalid: JSON parse error: Invalid value. in row 0 UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug This doesn't work ``` from datasets import Dataset # dir contains 100 json files. Dataset.from_json("/PUT SOME PATH HERE/*") ``` This works: ``` from datasets import concatenate_datasets ls_ds = [] for file in list_of_json_files: ls_ds.append(Dataset.from_json(file)) ds = concatenate_datasets(ls_ds) ``` ### Expected behavior I expect this to read json files properly as error is not clear ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Found the issue, if there are other files in the directory, it gets caught into this `` so essentially it should be `.json`. Could we possibly to check for list of files to make sure the pattern matches json files and raise error if not?", "I don't think we should filter for `.json` as this might silently remove desired files for many users. And this could be a major breaking change for many organizations.\r\n\r\nYou could do the globbing yourself which would keep the code clean.\r\n\r\n```python\r\nfrom glob import glob\r\n\r\nDataset.from_json(glob('folder/.json'))\r\n```", "I think it should still be fine to log a warning message in case the folder contains different files? I also don't get why would this be breaking as in the end using `from_FILE_TYPE` should be able to read a specific file type only. Maybe some other use case I am not aware of but since globbing or this case not mentioned anywhere in the doc, I spent quite a bit of time trying to figure out where the issue was. Just making sure it's clear for users." ]
6,675	Allow image model (color conversion) to be specified as part of datasets Image() decode	### Feature request Typical torchvision / torch Datasets in image applications apply color conversion in the Dataset portion of the code as part of image decode, separately from the image transform stack. This is true for PIL.Image where convert is usually called in dataset, for native torchvision https://pytorch.org/vision/main/generated/torchvision.io.decode_jpeg.html, and similarly in tensorflow.data pipelines decode_jpeg or https://www.tensorflow.org/api_docs/python/tf/io/decode_and_crop_jpeg have a channels arg that allows controlling the image mode in the decode step. datasets currently requires this pattern (from [examples](https://huggingface.co/docs/datasets/main/en/image_process)): ``` from torchvision.transforms import Compose, ColorJitter, ToTensor jitter = Compose( [ ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.7), ToTensor(), ] ) def transforms(examples): examples["pixel_values"] = [jitter(image.convert("RGB")) for image in examples["image"]] return examples ``` ### Motivation It would be nice to be able to handle `image.convert("RGB")` (or other modes) in the decode step, before applying torchvision transforms, this would reduce differences in code when handling pipelines that can handle torchvision, webdatset, or hf datasets with fewer code differences and without needing to handle image mode argument passing in two different stages of the pipelines... ### Your contribution Can do a PR with guidance on how mode should be passed / set on the dataset.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "It would be a great addition indeed :)\r\n\r\nThis can be implemented the same way we have `sampling_rate` for Audio(): we just add a new parameter to the Image() type and take this parameter into account in `Image.decode_example`\r\n\r\nEDIT: adding an example of how it can be used:\r\n\r\n```python\r\nds = ds.cast_column(\"image\", Image(mode=...))\r\n```" ]
6,674	Depprcated Overview.ipynb Link to new Quickstart Notebook invalid	### Describe the bug For the dreprecated notebook found [here](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb). The link to the new notebook is broken. ### Steps to reproduce the bug Click the [Quickstart notebook](https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb) link in the notebook. ### Expected behavior I believe is it suposed to link [here](https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb). That is mentioned in the readme. ### Environment info Colab	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Good catch! Feel free to open a PR to fix the link." ]
6,673	IterableDataset `set_epoch` is ignored when DataLoader `persistent_workers=True`	### Describe the bug When persistent workers are enabled, the epoch that's set via the IterableDataset instance held by the training process is ignored by the workers as they are disconnected across processes. PyTorch samplers for non-iterable datasets have a mechanism to sync this, datasets.IterableDataset does not. In my own use of IterableDatasets I usually track the epoch count which crosses process boundaries in a multiprocessing.Value ### Steps to reproduce the bug Use a streaming dataset (Iterable) w/ the recommended pattern below and `persistent_workers=True` in the torch DataLoader. ``` for epoch in range(epochs): shuffled_dataset.set_epoch(epoch) for example in shuffled_dataset: ... ``` ### Expected behavior When the canonical bit of code above is used with `num_workers > 0` and `persistent_workers=True`, the epoch set via `set_epoch()` is propagated to the IterableDataset instances in the worker processes ### Environment info N/A	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" }, { "id": 3287858981, "node_id": "MDU6TGFiZWwzMjg3ODU4OTgx", "url": "https://api.github.com/repos/huggingface/datasets/labels/streaming", "name": "streaming", "color": "fef2c0", "default": false, "description": "" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,672	Remove deprecated verbose parameter from CSV builder	Remove deprecated `verbose` parameter from CSV builder. Note that the `verbose` parameter is deprecated since pandas 2.2.0. See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450 Fix #6671.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6672", "html_url": "https://github.com/huggingface/datasets/pull/6672", "diff_url": "https://github.com/huggingface/datasets/pull/6672.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6672.patch", "merged_at": "2024-02-19T09:20:22" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I am merging this PR (so that it is included in the next patch release) to remove the deprecation warning raised by the CSV builder from pandas 2.2.0.", "<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) \| 0.005374 / 0.011353 (-0.005979) \| 0.003833 / 0.011008 (-0.007175) \| 0.063465 / 0.038508 (0.024957) \| 0.029564 / 0.023109 (0.006455) \| 0.252759 / 0.275898 (-0.023139) \| 0.274726 / 0.323480 (-0.048754) \| 0.004014 / 0.007986 (-0.003971) \| 0.002754 / 0.004328 (-0.001574) \| 0.049351 / 0.004250 (0.045101) \| 0.041858 / 0.037052 (0.004806) \| 0.269023 / 0.258489 (0.010534) \| 0.290670 / 0.293841 (-0.003171) \| 0.028435 / 0.128546 (-0.100111) \| 0.010988 / 0.075646 (-0.064658) \| 0.207447 / 0.419271 (-0.211824) \| 0.035945 / 0.043533 (-0.007588) \| 0.257336 / 0.255139 (0.002197) \| 0.267310 / 0.283200 (-0.015890) \| 0.018575 / 0.141683 (-0.123108) \| 1.144515 / 1.452155 (-0.307640) \| 1.214614 / 1.492716 (-0.278102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103527 / 0.018006 (0.085521) \| 0.310607 / 0.000490 (0.310117) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.018597 / 0.037411 (-0.018814) \| 0.063176 / 0.014526 (0.048650) \| 0.073553 / 0.176557 (-0.103003) \| 0.120648 / 0.737135 (-0.616487) \| 0.075625 / 0.296338 (-0.220713) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289148 / 0.215209 (0.073939) \| 2.798351 / 2.077655 (0.720696) \| 1.487909 / 1.504120 (-0.016211) \| 1.369945 / 1.541195 (-0.171250) \| 1.378889 / 1.468490 (-0.089602) \| 0.569825 / 4.584777 (-4.014952) \| 2.413309 / 3.745712 (-1.332403) \| 2.795668 / 5.269862 (-2.474193) \| 1.757748 / 4.565676 (-2.807929) \| 0.064686 / 0.424275 (-0.359589) \| 0.005027 / 0.007607 (-0.002580) \| 0.341835 / 0.226044 (0.115791) \| 3.349915 / 2.268929 (1.080987) \| 1.864253 / 55.444624 (-53.580371) \| 1.595788 / 6.876477 (-5.280688) \| 1.666127 / 2.142072 (-0.475945) \| 0.665239 / 4.805227 (-4.139989) \| 0.120563 / 6.500664 (-6.380101) \| 0.043649 / 0.075469 (-0.031820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988543 / 1.841788 (-0.853244) \| 11.973275 / 8.074308 (3.898967) \| 9.685401 / 10.191392 (-0.505991) \| 0.141416 / 0.680424 (-0.539008) \| 0.014328 / 0.534201 (-0.519873) \| 0.287063 / 0.579283 (-0.292220) \| 0.266284 / 0.434364 (-0.168080) \| 0.324643 / 0.540337 (-0.215694) \| 0.423845 / 1.386936 (-0.963091) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005430 / 0.011353 (-0.005923) \| 0.003770 / 0.011008 (-0.007239) \| 0.050879 / 0.038508 (0.012371) \| 0.031929 / 0.023109 (0.008819) \| 0.297739 / 0.275898 (0.021841) \| 0.319380 / 0.323480 (-0.004100) \| 0.004348 / 0.007986 (-0.003637) \| 0.002783 / 0.004328 (-0.001545) \| 0.050024 / 0.004250 (0.045774) \| 0.045209 / 0.037052 (0.008157) \| 0.307608 / 0.258489 (0.049119) \| 0.338168 / 0.293841 (0.044327) \| 0.051712 / 0.128546 (-0.076834) \| 0.011092 / 0.075646 (-0.064554) \| 0.059830 / 0.419271 (-0.359441) \| 0.033894 / 0.043533 (-0.009638) \| 0.295278 / 0.255139 (0.040139) \| 0.310749 / 0.283200 (0.027550) \| 0.018676 / 0.141683 (-0.123007) \| 1.201086 / 1.452155 (-0.251069) \| 1.258214 / 1.492716 (-0.234502) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094079 / 0.018006 (0.076073) \| 0.304657 / 0.000490 (0.304168) \| 0.000225 / 0.000200 (0.000026) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021969 / 0.037411 (-0.015442) \| 0.075749 / 0.014526 (0.061223) \| 0.087878 / 0.176557 (-0.088679) \| 0.126022 / 0.737135 (-0.611114) \| 0.089466 / 0.296338 (-0.206873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286415 / 0.215209 (0.071206) \| 2.831867 / 2.077655 (0.754212) \| 1.584119 / 1.504120 (0.079999) \| 1.468454 / 1.541195 (-0.072740) \| 1.495831 / 1.468490 (0.027341) \| 0.579569 / 4.584777 (-4.005208) \| 2.477248 / 3.745712 (-1.268464) \| 2.830536 / 5.269862 (-2.439325) \| 1.820188 / 4.565676 (-2.745488) \| 0.064408 / 0.424275 (-0.359867) \| 0.005156 / 0.007607 (-0.002451) \| 0.342391 / 0.226044 (0.116347) \| 3.424380 / 2.268929 (1.155452) \| 1.993110 / 55.444624 (-53.451514) \| 1.702971 / 6.876477 (-5.173506) \| 1.844281 / 2.142072 (-0.297792) \| 0.668208 / 4.805227 (-4.137020) \| 0.120306 / 6.500664 (-6.380358) \| 0.042127 / 0.075469 (-0.033342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.019118 / 1.841788 (-0.822670) \| 12.418330 / 8.074308 (4.344022) \| 10.474226 / 10.191392 (0.282834) \| 0.148510 / 0.680424 (-0.531914) \| 0.015107 / 0.534201 (-0.519094) \| 0.289488 / 0.579283 (-0.289795) \| 0.278149 / 0.434364 (-0.156215) \| 0.334655 / 0.540337 (-0.205682) \| 0.419127 / 1.386936 (-0.967809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58733d2824192fc748cc8730cf77c33be5ded2ea \"CML watermark\")\n" ]
6,671	CSV builder raises deprecation warning on verbose parameter	CSV builder raises a deprecation warning on `verbose` parameter: ``` FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version. ``` See: - https://github.com/pandas-dev/pandas/pull/56556 - https://github.com/pandas-dev/pandas/pull/57450	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,670	ValueError	### Describe the bug ValueError Traceback (most recent call last) [<ipython-input-11-9b99bc80ec23>](https://localhost:8080/#) in <cell line: 11>() 9 import numpy as np 10 import matplotlib.pyplot as plt ---> 11 from datasets import DatasetDict, Dataset 12 from transformers import AutoTokenizer, AutoModelForSequenceClassification 13 from transformers import Trainer, TrainingArguments 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 16 __version__ = "2.17.0" 17 ---> 18 from .arrow_dataset import Dataset 19 from .arrow_reader import ReadInstruction 20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 65 66 from . import config ---> 67 from .arrow_reader import ArrowReader 68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 69 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 27 28 import pyarrow as pa ---> 29 import pyarrow.parquet as pq 30 from tqdm.contrib.concurrent import thread_map 31 [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module> 18 # flake8: noqa 19 ---> 20 from .core import * [/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module> 34 import pyarrow as pa 35 import pyarrow.lib as lib ---> 36 import pyarrow._parquet as _parquet 37 38 from pyarrow._parquet import (ParquetReader, Statistics, # noqa /usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet() ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Expected behavior Resolve the binary incompatibility ### Environment info Google Colab Note book	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi @prashanth19bolukonda,\r\n\r\nYou have to restart the notebook runtime session after the installation of `datasets`.\r\n\r\nDuplicate of:\r\n- #5923", "Thank you soo much\r\n\r\nOn Fri, Feb 16, 2024 at 8:14 PM Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> Closed #6670 <https://github.com/huggingface/datasets/issues/6670> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6670#event-11829788289>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YDQOBUFUWMR4C5O3QTYT5WDJAVCNFSM6AAAAABDL24S5SVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHAZDSNZYHAZDQOI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> @.*>\r\n>\r\n" ]
6,669	attribute error when writing trainer.train()	### Describe the bug AttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, {self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, {self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecationsAttributeError Traceback (most recent call last) Cell In[39], line 2 1 # Start the training process ----> 2 trainer.train() File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, kwargs) 1537 hf_hub_utils.enable_progress_bars() 1538 else: -> 1539 return inner_training_loop( 1540 args=args, 1541 resume_from_checkpoint=resume_from_checkpoint, 1542 trial=trial, 1543 ignore_keys_for_eval=ignore_keys_for_eval, 1544 ) File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1833 rng_to_sync = True 1835 step = -1 -> 1836 for step, inputs in enumerate(epoch_iterator): 1837 total_batched_samples += 1 1839 if self.args.include_num_input_tokens_seen: File /opt/conda/lib/python3.10/site-packages/accelerate/data_loader.py:451, in DataLoaderShard.__iter__(self) 449 # We iterate one batch ahead to check when we are at the end 450 try: --> 451 current_batch = next(dataloader_iter) 452 except StopIteration: 453 yield File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self) 627 if self._sampler_iter is None: 628 # TODO([https://github.com/pytorch/pytorch/issues/76750)](https://github.com/pytorch/pytorch/issues/76750)%3C/span%3E) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ 633 self._IterableDataset_len_called is not None and \ 634 self._num_yielded > self._IterableDataset_len_called: File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674, in _SingleProcessDataLoaderIter._next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in _MapDatasetFetcher.fetch(self, possibly_batched_index) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51, in <listcomp>(.0) 49 data = self.dataset.__getitems__(possibly_batched_index) 50 else: ---> 51 data = [self.dataset[idx] for idx in possibly_batched_index] 52 else: 53 data = self.dataset[possibly_batched_index] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:540, in format_table(table, key, formatter, format_columns, output_all_columns) 538 else: 539 pa_table_to_format = pa_table.drop(col for col in pa_table.column_names if col not in format_columns) --> 540 formatted_output = formatter(pa_table_to_format, query_type=query_type) 541 if output_all_columns: 542 if isinstance(formatted_output, MutableMapping): File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:57, in TorchFormatter.format_row(self, pa_table) 56 def format_row(self, pa_table: pa.Table) -> dict: ---> 57 row = self.numpy_arrow_extractor().extract_row(pa_table) 58 return self.recursive_tensorize(row) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:154, in NumpyArrowExtractor.extract_row(self, pa_table) 153 def extract_row(self, pa_table: pa.Table) -> dict: --> 154 return _unnest(self.extract_batch(pa_table)) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in NumpyArrowExtractor.extract_batch(self, pa_table) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:160, in <dictcomp>(.0) 159 def extract_batch(self, pa_table: pa.Table) -> dict: --> 160 return {col: self._arrow_array_to_numpy(pa_table[col]) for col in pa_table.column_names} File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:196, in NumpyArrowExtractor._arrow_array_to_numpy(self, pa_array) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: --> 196 if any( 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, {self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:197, in <genexpr>(.0) 194 array: List = pa_array.to_numpy(zero_copy_only=zero_copy_only).tolist() 195 if len(array) > 0: 196 if any( --> 197 (isinstance(x, np.ndarray) and (x.dtype == np.object or x.shape != array[0].shape)) 198 or (isinstance(x, float) and np.isnan(x)) 199 for x in array 200 ): 201 return np.array(array, copy=False, {self.np_array_kwargs, "dtype": np.object}) 202 return np.array(array, copy=False, self.np_array_kwargs) File /opt/conda/lib/python3.10/site-packages/numpy/__init__.py:324, in __getattr__(attr) 319 warnings.warn( 320 f"In the future `np.{attr}` will be defined as the " 321 "corresponding NumPy scalar.", FutureWarning, stacklevel=2) 323 if attr in __former_attrs__: --> 324 raise AttributeError(__former_attrs__[attr]) 326 if attr == 'testing': 327 import numpy.testing as testing AttributeError: module 'numpy' has no attribute 'object'. `np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations Please help me to resolve the above error ### Steps to reproduce the bug Please resolve the issue of deprecated function np.object to object in the numpy ### Expected behavior np.object should be written as object only ### Environment info kaggle notebook	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Kaggle notebooks use an outdated version of `datasets`, so you should update the `datasets` installation (with `!pip install -U datasets`) to avoid the error.", "Thank you for your response\r\n\r\nOn Thu, Feb 29, 2024 at 10:55 PM Mario Šaško *@.>\r\nwrote:\r\n\r\n> Closed #6669 <https://github.com/huggingface/datasets/issues/6669> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6669#event-11969246964>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YG2RRVMYONNKPLBVE3YV5SAPAVCNFSM6AAAAABDLZ3BTSVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHE3DSMRUGY4TMNA>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n" ]
6,668	Chapter 6 - Issue Loading `cnn_dailymail` dataset	### Describe the bug So I am getting this bug when I try to run cell 4 of the Chapter 6 notebook code: `dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0")` Error Message: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[4], line 4 1 #hide_output 2 from datasets import load_dataset ----> 4 dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0") 7 # dataset = load_dataset("ccdv/cnn_dailymail", version="3.0.0", trust_remote_code=True) 8 print(f"Features: {dataset['train'].column_names}") File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\load.py:2587, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, config_kwargs) 2583 # Build dataset for splits 2584 keep_in_memory = ( 2585 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2586 ) -> 2587 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2588 # Rename and cast features to match task schema 2589 if task is not None: 2590 # To avoid issuing the same warning twice File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1244, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1241 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1243 # Create a dataset for each of the given splits -> 1244 datasets = map_nested( 1245 partial( 1246 self._build_single_dataset, 1247 run_post_process=run_post_process, 1248 verification_mode=verification_mode, 1249 in_memory=in_memory, 1250 ), 1251 split, 1252 map_tuple=True, 1253 disable_tqdm=True, 1254 ) 1255 if isinstance(datasets, dict): 1256 datasets = DatasetDict(datasets) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:477, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: --> 477 mapped = [ 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:478, in <listcomp>(.0) 466 mapped = [ 467 map_nested( 468 function=function, (...) 474 for obj in iterable 475 ] 476 elif num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length: 477 mapped = [ --> 478 _single_map_nested((function, obj, types, None, True, None)) 479 for obj in hf_tqdm(iterable, disable=disable_tqdm, desc=desc) 480 ] 481 else: 482 with warnings.catch_warnings(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\utils\py_utils.py:370, in _single_map_nested(args) 368 # Singleton first to spare some computation 369 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 370 return function(data_struct) 372 # Reduce logging to keep things readable in multiprocessing with tqdm 373 if rank is not None and logging.get_verbosity() < logging.WARNING: File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1274, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory) 1271 split = Split(split) 1273 # Build base dataset -> 1274 ds = self._as_dataset( 1275 split=split, 1276 in_memory=in_memory, 1277 ) 1278 if run_post_process: 1279 for resource_file_name in self._post_processing_resources(split).values(): File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\builder.py:1348, in DatasetBuilder._as_dataset(self, split, in_memory) 1346 if self._check_legacy_cache(): 1347 dataset_name = self.name -> 1348 dataset_kwargs = ArrowReader(cache_dir, self.info).read( 1349 name=dataset_name, 1350 instructions=split, 1351 split_infos=self.info.splits.values(), 1352 in_memory=in_memory, 1353 ) 1354 fingerprint = self._get_dataset_fingerprint(split) 1355 return Dataset(fingerprint=fingerprint, dataset_kwargs) File ~\anaconda3\envs\nlp-transformers\lib\site-packages\datasets\arrow_reader.py:254, in BaseReader.read(self, name, instructions, split_infos, in_memory) 252 if not files: 253 msg = f'Instruction "{instructions}" corresponds to no data!' --> 254 raise ValueError(msg) 255 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) ValueError: Instruction "validation" corresponds to no data! ```` Looks like the data is not being loaded. Any advice would be appreciated. Thanks! ### Steps to reproduce the bug Run all cells of Chapter 6 notebook. ### Expected behavior Data should load correctly without any errors. ### Environment info - `datasets` version: 2.17.0 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.18 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,667	Default config for squad is incorrect	### Describe the bug If you download Squad, it will download the plain_text version, but the config still specifies "default", so if you set the offline mode the cache will try to look it up according to the config_id which is "default" and this will say; ValueError: Couldn't find cache for squad for config 'default' Available configs in the cache: ['plain_text'] ### Steps to reproduce the bug 1. export HF_DATASETS_OFFLINE=0 2. load_dataset("squad") 3. export HF_DATASETS_OFFLINE=1 4. load_dataset("squad") ### Expected behavior We should change the config_name I guess? ### Environment info linux, latest version of datasets	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "you can try: pip install datasets==2.16.1" ]
6,665	Allow SplitDict setitem to replace existing SplitInfo	Fix this code provided by @clefourrier ```python import datasets import os token = os.getenv("TOKEN") results = datasets.load_dataset("gaia-benchmark/results_public", "2023", token=token, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD) results["test"] = datasets.Dataset.from_list([row for row in results["test"] if row["model"] != "StateFlow"]) results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") ``` ``` ValueError Traceback (most recent call last) Cell In[43], line 1 ----> 1 results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test") File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/arrow_dataset.py:5498, in Dataset.push_to_hub(self, repo_id, config_name, split, private, token, branch, max_shard_size, num_shards, embed_external_files) 5496 repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes 5497 repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size -> 5498 repo_info.splits[split] = SplitInfo( 5499 split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name 5500 ) 5501 info_to_dump = repo_info 5502 # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/splits.py:541, in SplitDict.__setitem__(self, key, value) 539 raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')") 540 if key in self: --> 541 raise ValueError(f"Split {key} already present") 542 super().__setitem__(key, value) ValueError: Split test already present ```	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6665", "html_url": "https://github.com/huggingface/datasets/pull/6665", "diff_url": "https://github.com/huggingface/datasets/pull/6665.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6665.patch", "merged_at": "2024-03-01T15:56:38" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6665). 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) \| 0.004968 / 0.011353 (-0.006385) \| 0.003732 / 0.011008 (-0.007276) \| 0.063672 / 0.038508 (0.025164) \| 0.027066 / 0.023109 (0.003957) \| 0.253306 / 0.275898 (-0.022592) \| 0.283382 / 0.323480 (-0.040098) \| 0.004217 / 0.007986 (-0.003768) \| 0.002865 / 0.004328 (-0.001464) \| 0.048672 / 0.004250 (0.044421) \| 0.040740 / 0.037052 (0.003688) \| 0.271848 / 0.258489 (0.013359) \| 0.293162 / 0.293841 (-0.000679) \| 0.027410 / 0.128546 (-0.101136) \| 0.010605 / 0.075646 (-0.065042) \| 0.210545 / 0.419271 (-0.208726) \| 0.036085 / 0.043533 (-0.007447) \| 0.259807 / 0.255139 (0.004668) \| 0.274056 / 0.283200 (-0.009144) \| 0.018812 / 0.141683 (-0.122871) \| 1.116687 / 1.452155 (-0.335468) \| 1.164276 / 1.492716 (-0.328440) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092874 / 0.018006 (0.074868) \| 0.355897 / 0.000490 (0.355407) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.018461 / 0.037411 (-0.018950) \| 0.062061 / 0.014526 (0.047535) \| 0.072353 / 0.176557 (-0.104203) \| 0.119162 / 0.737135 (-0.617974) \| 0.082974 / 0.296338 (-0.213364) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291631 / 0.215209 (0.076422) \| 2.861495 / 2.077655 (0.783841) \| 1.496753 / 1.504120 (-0.007367) \| 1.371164 / 1.541195 (-0.170031) \| 1.415473 / 1.468490 (-0.053018) \| 0.566778 / 4.584777 (-4.017999) \| 2.376209 / 3.745712 (-1.369503) \| 2.812326 / 5.269862 (-2.457535) \| 1.765640 / 4.565676 (-2.800037) \| 0.063274 / 0.424275 (-0.361001) \| 0.004933 / 0.007607 (-0.002674) \| 0.342345 / 0.226044 (0.116301) \| 3.407487 / 2.268929 (1.138558) \| 1.856646 / 55.444624 (-53.587978) \| 1.590284 / 6.876477 (-5.286193) \| 1.610068 / 2.142072 (-0.532004) \| 0.656007 / 4.805227 (-4.149220) \| 0.118310 / 6.500664 (-6.382354) \| 0.042596 / 0.075469 (-0.032873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.991392 / 1.841788 (-0.850395) \| 11.612397 / 8.074308 (3.538089) \| 9.627836 / 10.191392 (-0.563556) \| 0.130575 / 0.680424 (-0.549848) \| 0.014152 / 0.534201 (-0.520049) \| 0.289736 / 0.579283 (-0.289548) \| 0.260041 / 0.434364 (-0.174323) \| 0.339730 / 0.540337 (-0.200608) \| 0.447529 / 1.386936 (-0.939407) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005315 / 0.011353 (-0.006038) \| 0.003955 / 0.011008 (-0.007053) \| 0.049618 / 0.038508 (0.011110) \| 0.030404 / 0.023109 (0.007295) \| 0.258727 / 0.275898 (-0.017171) \| 0.282020 / 0.323480 (-0.041460) \| 0.004356 / 0.007986 (-0.003629) \| 0.002866 / 0.004328 (-0.001462) \| 0.049122 / 0.004250 (0.044872) \| 0.045534 / 0.037052 (0.008482) \| 0.269560 / 0.258489 (0.011071) \| 0.301225 / 0.293841 (0.007384) \| 0.029786 / 0.128546 (-0.098761) \| 0.010433 / 0.075646 (-0.065213) \| 0.058222 / 0.419271 (-0.361049) \| 0.052968 / 0.043533 (0.009435) \| 0.256605 / 0.255139 (0.001467) \| 0.279899 / 0.283200 (-0.003300) \| 0.018233 / 0.141683 (-0.123450) \| 1.164060 / 1.452155 (-0.288095) \| 1.218049 / 1.492716 (-0.274667) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093646 / 0.018006 (0.075639) \| 0.288804 / 0.000490 (0.288314) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.022193 / 0.037411 (-0.015219) \| 0.075507 / 0.014526 (0.060981) \| 0.086091 / 0.176557 (-0.090465) \| 0.127433 / 0.737135 (-0.609703) \| 0.087064 / 0.296338 (-0.209274) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292459 / 0.215209 (0.077250) \| 2.842430 / 2.077655 (0.764776) \| 1.505824 / 1.504120 (0.001704) \| 1.377052 / 1.541195 (-0.164143) \| 1.408757 / 1.468490 (-0.059733) \| 0.571705 / 4.584777 (-4.013072) \| 2.459798 / 3.745712 (-1.285914) \| 2.714826 / 5.269862 (-2.555035) \| 1.782064 / 4.565676 (-2.783613) \| 0.063113 / 0.424275 (-0.361162) \| 0.005099 / 0.007607 (-0.002509) \| 0.343624 / 0.226044 (0.117579) \| 3.415806 / 2.268929 (1.146878) \| 1.853253 / 55.444624 (-53.591371) \| 1.584392 / 6.876477 (-5.292084) \| 1.720384 / 2.142072 (-0.421689) \| 0.646637 / 4.805227 (-4.158590) \| 0.118072 / 6.500664 (-6.382593) \| 0.041362 / 0.075469 (-0.034107) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.020086 / 1.841788 (-0.821701) \| 12.303980 / 8.074308 (4.229672) \| 10.322869 / 10.191392 (0.131477) \| 0.140959 / 0.680424 (-0.539465) \| 0.015372 / 0.534201 (-0.518829) \| 0.288552 / 0.579283 (-0.290731) \| 0.278243 / 0.434364 (-0.156121) \| 0.328399 / 0.540337 (-0.211939) \| 0.433618 / 1.386936 (-0.953318) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9469092d88ff7bb4d3f7fe6c2de0109ca458b5da \"CML watermark\")\n" ]
6,664	Revert the changes in `arrow_writer.py` from #6636	#6636 broke `write_examples_on_file` and `write_batch` from the class `ArrowWriter`. I'm undoing these changes. See #6663. Note the current implementation doesn't keep the order of the columns and the schema, thus setting a wrong schema for each column.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6664", "html_url": "https://github.com/huggingface/datasets/pull/6664", "diff_url": "https://github.com/huggingface/datasets/pull/6664.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6664.patch", "merged_at": "2024-02-16T02:31:11" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6664). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Hi! We can't revert this as the \"reverted\" implementation has quadratic time complexity. Instead, let's fix it:\r\n\r\nI agree, but it's the implementation we have had so far. Why don't we:\r\n1. Release a hotfix ASAP (since would be doing a revert, we know it works as before) so people can continue using this library fine since AFAIU right now mostly writing examples for people is broken.\r\n2. Then, focus on still applying the performance improvement and release again", "The fix is straightforward, so one patch release (after this PR is merged) is enough.\r\n\r\nBtw, let's also add a test to `tests/test_arrow_writer.py` to avoid this issue in the future.", "> Btw, let's also add a test to tests/test_arrow_writer.py to avoid this issue in the future.\r\n\r\nWould you mind adding such test, as you're more familiar with the codebase?", "<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) \| 0.005083 / 0.011353 (-0.006270) \| 0.003697 / 0.011008 (-0.007311) \| 0.063302 / 0.038508 (0.024794) \| 0.028866 / 0.023109 (0.005757) \| 0.249987 / 0.275898 (-0.025911) \| 0.270803 / 0.323480 (-0.052677) \| 0.004096 / 0.007986 (-0.003890) \| 0.002752 / 0.004328 (-0.001577) \| 0.049156 / 0.004250 (0.044906) \| 0.042936 / 0.037052 (0.005884) \| 0.266907 / 0.258489 (0.008418) \| 0.291462 / 0.293841 (-0.002379) \| 0.027703 / 0.128546 (-0.100844) \| 0.011006 / 0.075646 (-0.064641) \| 0.206238 / 0.419271 (-0.213033) \| 0.035446 / 0.043533 (-0.008087) \| 0.248923 / 0.255139 (-0.006216) \| 0.264141 / 0.283200 (-0.019058) \| 0.017545 / 0.141683 (-0.124138) \| 1.157145 / 1.452155 (-0.295009) \| 1.199007 / 1.492716 (-0.293710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092741 / 0.018006 (0.074734) \| 0.299057 / 0.000490 (0.298567) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.017936 / 0.037411 (-0.019475) \| 0.061552 / 0.014526 (0.047026) \| 0.072938 / 0.176557 (-0.103618) \| 0.118192 / 0.737135 (-0.618944) \| 0.074589 / 0.296338 (-0.221750) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287186 / 0.215209 (0.071977) \| 2.795694 / 2.077655 (0.718039) \| 1.474386 / 1.504120 (-0.029734) \| 1.359065 / 1.541195 (-0.182130) \| 1.375295 / 1.468490 (-0.093196) \| 0.569448 / 4.584777 (-4.015329) \| 2.374428 / 3.745712 (-1.371284) \| 2.770198 / 5.269862 (-2.499663) \| 1.716346 / 4.565676 (-2.849330) \| 0.063173 / 0.424275 (-0.361102) \| 0.005031 / 0.007607 (-0.002576) \| 0.333197 / 0.226044 (0.107153) \| 3.271739 / 2.268929 (1.002811) \| 1.826406 / 55.444624 (-53.618218) \| 1.554537 / 6.876477 (-5.321939) \| 1.565927 / 2.142072 (-0.576146) \| 0.649796 / 4.805227 (-4.155431) \| 0.118371 / 6.500664 (-6.382293) \| 0.042536 / 0.075469 (-0.032933) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969882 / 1.841788 (-0.871906) \| 11.638201 / 8.074308 (3.563893) \| 9.759370 / 10.191392 (-0.432022) \| 0.128069 / 0.680424 (-0.552355) \| 0.013493 / 0.534201 (-0.520708) \| 0.287324 / 0.579283 (-0.291959) \| 0.267542 / 0.434364 (-0.166821) \| 0.320072 / 0.540337 (-0.220265) \| 0.421132 / 1.386936 (-0.965804) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005679 / 0.011353 (-0.005674) \| 0.003746 / 0.011008 (-0.007262) \| 0.050149 / 0.038508 (0.011641) \| 0.034382 / 0.023109 (0.011273) \| 0.289802 / 0.275898 (0.013904) \| 0.314993 / 0.323480 (-0.008487) \| 0.004488 / 0.007986 (-0.003498) \| 0.002786 / 0.004328 (-0.001542) \| 0.047987 / 0.004250 (0.043737) \| 0.046589 / 0.037052 (0.009537) \| 0.301420 / 0.258489 (0.042931) \| 0.335384 / 0.293841 (0.041543) \| 0.050701 / 0.128546 (-0.077845) \| 0.010987 / 0.075646 (-0.064660) \| 0.058292 / 0.419271 (-0.360979) \| 0.033973 / 0.043533 (-0.009560) \| 0.288923 / 0.255139 (0.033784) \| 0.306263 / 0.283200 (0.023064) \| 0.018856 / 0.141683 (-0.122827) \| 1.160721 / 1.452155 (-0.291433) \| 1.208151 / 1.492716 (-0.284565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092633 / 0.018006 (0.074626) \| 0.300353 / 0.000490 (0.299864) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.022257 / 0.037411 (-0.015154) \| 0.075417 / 0.014526 (0.060892) \| 0.087289 / 0.176557 (-0.089268) \| 0.125416 / 0.737135 (-0.611720) \| 0.088751 / 0.296338 (-0.207588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286477 / 0.215209 (0.071268) \| 2.801931 / 2.077655 (0.724277) \| 1.553034 / 1.504120 (0.048914) \| 1.426152 / 1.541195 (-0.115043) \| 1.443824 / 1.468490 (-0.024666) \| 0.563298 / 4.584777 (-4.021479) \| 2.428968 / 3.745712 (-1.316744) \| 2.685964 / 5.269862 (-2.583897) \| 1.752304 / 4.565676 (-2.813372) \| 0.064174 / 0.424275 (-0.360101) \| 0.005079 / 0.007607 (-0.002528) \| 0.344899 / 0.226044 (0.118855) \| 3.372528 / 2.268929 (1.103600) \| 1.900723 / 55.444624 (-53.543901) \| 1.623721 / 6.876477 (-5.252756) \| 1.781009 / 2.142072 (-0.361064) \| 0.655229 / 4.805227 (-4.149998) \| 0.116050 / 6.500664 (-6.384614) \| 0.040374 / 0.075469 (-0.035095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004714 / 1.841788 (-0.837074) \| 12.108179 / 8.074308 (4.033871) \| 10.233447 / 10.191392 (0.042055) \| 0.141438 / 0.680424 (-0.538986) \| 0.015387 / 0.534201 (-0.518814) \| 0.288068 / 0.579283 (-0.291216) \| 0.277025 / 0.434364 (-0.157339) \| 0.331714 / 0.540337 (-0.208623) \| 0.424209 / 1.386936 (-0.962727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bdebf1922663c30744efb8869c86b28f102b84dd \"CML watermark\")\n" ]
6,663	`write_examples_on_file` and `write_batch` are broken in `ArrowWriter`	### Describe the bug `write_examples_on_file` and `write_batch` are broken in `ArrowWriter` since #6636. The order between the columns and the schema is not preserved anymore. So these functions don't work anymore unless the order happens to align well. ### Steps to reproduce the bug Try to do `write_batch` with anything that has many columns, and it's likely to break. ### Expected behavior I expect these functions to work, instead of it trying to cast a column to its incorrect type. ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-5.15.0-1040-aws-x86_64-with-glibc2.35 - Python version: 3.10.13 - `huggingface_hub` version: 0.19.4 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.", "> Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.\r\n\r\nI feel that'd be good, but it'd be great to release a hotfix ASAP (a revert is a fast thing to do) so people can continue using this library and then focus on still applying the improvement.", "Fixed by #6664 " ]
6,662	fix: show correct package name to install biopython	When you try to download a dataset that uses [biopython](https://github.com/biopython/biopython), like `load_dataset("InstaDeepAI/multi_species_genomes")`, you get the error: ``` >>> from datasets import load_dataset >>> dataset = load_dataset("InstaDeepAI/multi_species_genomes") /home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py:1454: FutureWarning: The repository for InstaDeepAI/multi_species_genomes contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/InstaDeepAI/multi_species_genomes You can avoid this message in future by passing the argument `trust_remote_code=True`. Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. warnings.warn( Downloading builder script: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7.51k/7.51k [00:00<00:00, 7.67MB/s] Downloading readme: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 17.2k/17.2k [00:00<00:00, 11.0MB/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2548, in load_dataset builder_instance = load_dataset_builder( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 2220, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1871, in dataset_module_factory raise e1 from None File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1844, in dataset_module_factory ).get_module() File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 1466, in get_module local_imports = _download_additional_modules( File "/home/j.vangoey/.pyenv/versions/multi_species_genomes/lib/python3.10/site-packages/datasets/load.py", line 346, in _download_additional_modules raise ImportError( ImportError: To be able to use InstaDeepAI/multi_species_genomes, you need to install the following dependency: Bio. Please install it using 'pip install Bio' for instance. >>> ``` `Bio` comes from the `biopython` package that can be installed with `pip install biopython`, not with `pip install Bio` as suggested. This PR adds special logic to show the correct package name in the error message of ` _download_additional_modules`, similarly as is done for `sklearn` / `scikit-learn` already. There are more packages where importable module name differs from the PyPI package name, so this could be made more generic, like: ``` # Mapping of importable module names to their PyPI package names package_map = { "sklearn": "scikit-learn", "Bio": "biopython", "PIL": "Pillow", "bs4": "beautifulsoup4" } for module_name, pypi_name in package_map.items(): if module_name in needs_to_be_installed.keys(): needs_to_be_installed[module_name] = pypi_name ```	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6662", "html_url": "https://github.com/huggingface/datasets/pull/6662", "diff_url": "https://github.com/huggingface/datasets/pull/6662.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6662.patch", "merged_at": "2024-03-01T17:43:39" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6662). 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) \| 0.005135 / 0.011353 (-0.006218) \| 0.003666 / 0.011008 (-0.007342) \| 0.062660 / 0.038508 (0.024152) \| 0.028656 / 0.023109 (0.005546) \| 0.249601 / 0.275898 (-0.026297) \| 0.265745 / 0.323480 (-0.057735) \| 0.002935 / 0.007986 (-0.005051) \| 0.002606 / 0.004328 (-0.001723) \| 0.048774 / 0.004250 (0.044523) \| 0.043643 / 0.037052 (0.006591) \| 0.263114 / 0.258489 (0.004625) \| 0.284596 / 0.293841 (-0.009245) \| 0.027818 / 0.128546 (-0.100728) \| 0.010726 / 0.075646 (-0.064921) \| 0.205900 / 0.419271 (-0.213371) \| 0.035646 / 0.043533 (-0.007887) \| 0.245599 / 0.255139 (-0.009540) \| 0.267706 / 0.283200 (-0.015493) \| 0.018441 / 0.141683 (-0.123242) \| 1.143365 / 1.452155 (-0.308790) \| 1.191823 / 1.492716 (-0.300893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089703 / 0.018006 (0.071696) \| 0.298073 / 0.000490 (0.297583) \| 0.000209 / 0.000200 (0.000009) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018068 / 0.037411 (-0.019343) \| 0.061416 / 0.014526 (0.046890) \| 0.075989 / 0.176557 (-0.100567) \| 0.120765 / 0.737135 (-0.616370) \| 0.075476 / 0.296338 (-0.220863) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284043 / 0.215209 (0.068834) \| 2.770282 / 2.077655 (0.692627) \| 1.473040 / 1.504120 (-0.031080) \| 1.349064 / 1.541195 (-0.192131) \| 1.362783 / 1.468490 (-0.105708) \| 0.560765 / 4.584777 (-4.024012) \| 2.357731 / 3.745712 (-1.387981) \| 2.745771 / 5.269862 (-2.524090) \| 1.726764 / 4.565676 (-2.838913) \| 0.061212 / 0.424275 (-0.363063) \| 0.004902 / 0.007607 (-0.002705) \| 0.336963 / 0.226044 (0.110919) \| 3.324519 / 2.268929 (1.055591) \| 1.825826 / 55.444624 (-53.618798) \| 1.548811 / 6.876477 (-5.327666) \| 1.570618 / 2.142072 (-0.571454) \| 0.642411 / 4.805227 (-4.162816) \| 0.116068 / 6.500664 (-6.384596) \| 0.042433 / 0.075469 (-0.033036) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988402 / 1.841788 (-0.853386) \| 11.509601 / 8.074308 (3.435293) \| 9.555338 / 10.191392 (-0.636054) \| 0.138728 / 0.680424 (-0.541696) \| 0.014107 / 0.534201 (-0.520094) \| 0.285465 / 0.579283 (-0.293818) \| 0.263086 / 0.434364 (-0.171278) \| 0.327469 / 0.540337 (-0.212869) \| 0.444799 / 1.386936 (-0.942137) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005359 / 0.011353 (-0.005993) \| 0.003605 / 0.011008 (-0.007403) \| 0.049734 / 0.038508 (0.011226) \| 0.029792 / 0.023109 (0.006683) \| 0.276384 / 0.275898 (0.000486) \| 0.297915 / 0.323480 (-0.025564) \| 0.004949 / 0.007986 (-0.003036) \| 0.002713 / 0.004328 (-0.001616) \| 0.049499 / 0.004250 (0.045249) \| 0.044969 / 0.037052 (0.007917) \| 0.284558 / 0.258489 (0.026069) \| 0.315170 / 0.293841 (0.021329) \| 0.029457 / 0.128546 (-0.099089) \| 0.010573 / 0.075646 (-0.065073) \| 0.058191 / 0.419271 (-0.361080) \| 0.051461 / 0.043533 (0.007928) \| 0.270744 / 0.255139 (0.015605) \| 0.291664 / 0.283200 (0.008465) \| 0.018607 / 0.141683 (-0.123076) \| 1.158799 / 1.452155 (-0.293355) \| 1.210509 / 1.492716 (-0.282208) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090277 / 0.018006 (0.072270) \| 0.298748 / 0.000490 (0.298258) \| 0.000228 / 0.000200 (0.000028) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021850 / 0.037411 (-0.015561) \| 0.075433 / 0.014526 (0.060907) \| 0.087171 / 0.176557 (-0.089386) \| 0.125828 / 0.737135 (-0.611308) \| 0.090343 / 0.296338 (-0.205996) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297267 / 0.215209 (0.082058) \| 2.865234 / 2.077655 (0.787579) \| 1.595024 / 1.504120 (0.090904) \| 1.476100 / 1.541195 (-0.065094) \| 1.494896 / 1.468490 (0.026406) \| 0.569086 / 4.584777 (-4.015691) \| 2.401976 / 3.745712 (-1.343736) \| 2.676091 / 5.269862 (-2.593771) \| 1.742087 / 4.565676 (-2.823590) \| 0.065161 / 0.424275 (-0.359114) \| 0.005006 / 0.007607 (-0.002602) \| 0.342302 / 0.226044 (0.116257) \| 3.450571 / 2.268929 (1.181643) \| 1.928754 / 55.444624 (-53.515871) \| 1.672823 / 6.876477 (-5.203653) \| 1.798830 / 2.142072 (-0.343243) \| 0.648730 / 4.805227 (-4.156498) \| 0.116433 / 6.500664 (-6.384231) \| 0.040683 / 0.075469 (-0.034786) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.006158 / 1.841788 (-0.835630) \| 12.200093 / 8.074308 (4.125785) \| 10.180691 / 10.191392 (-0.010701) \| 0.146620 / 0.680424 (-0.533804) \| 0.015621 / 0.534201 (-0.518580) \| 0.287956 / 0.579283 (-0.291327) \| 0.277231 / 0.434364 (-0.157133) \| 0.323815 / 0.540337 (-0.216522) \| 0.429655 / 1.386936 (-0.957281) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273e16f9a21d6eaba1fd40fbdf0c05e66642c5a7 \"CML watermark\")\n" ]
6,661	Import error on Google Colab	### Describe the bug Cannot be imported on Google Colab, the import throws the following error: ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject ### Steps to reproduce the bug 1. `! pip install -U datasets` 2. `import datasets` ### Expected behavior Should be possible to use the library ### Environment info - `datasets` version: 2.17.0 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! This can happen if an incompatible `pyarrow` version (`pyarrow<12.0.0`) has been imported before the `datasets` installation and the Colab session hasn't been restarted afterward. To avoid the error, go to \"Runtime -> Restart session\" after `!pip install -U datasets` and before `import datasets`, or insert the `import os; os.kill(os.getpid(), 9)` cell between `!pip install -U datasets` and `import datasets` to do the same programmatically.", "One possible cause might be the one pointed out by @mariosasko above, and you get the following warning on Colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\n\r\nOn the other hand, if the old version of `pyarrow` is not previously imported (before the installation of `datasets`), the reported issue here is not reproducible: `datasets` can be installed, imported and used on Colab.", "Duplicate of:\r\n- #5923", "Google Colab now pre-installs PyArrow 14.0.2, making this issue unlikely to happen. So, I'm unpinning it." ]
6,660	Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes	This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example: ```python from datasets import Dataset, Sequence, Value, Features def gen(): for i in range(100): yield {'seq': list(range(i, i + 20))} ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)})) ds.set_format('torch') print(ds[0]) ``` This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format: ``` TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool. ``` This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6660", "html_url": "https://github.com/huggingface/datasets/pull/6660", "diff_url": "https://github.com/huggingface/datasets/pull/6660.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6660.patch", "merged_at": "2024-03-01T18:52:37" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6660). 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) \| 0.004995 / 0.011353 (-0.006357) \| 0.003230 / 0.011008 (-0.007779) \| 0.062836 / 0.038508 (0.024328) \| 0.026684 / 0.023109 (0.003575) \| 0.249286 / 0.275898 (-0.026612) \| 0.272936 / 0.323480 (-0.050544) \| 0.003952 / 0.007986 (-0.004033) \| 0.002708 / 0.004328 (-0.001620) \| 0.055346 / 0.004250 (0.051095) \| 0.040023 / 0.037052 (0.002971) \| 0.263350 / 0.258489 (0.004860) \| 0.294727 / 0.293841 (0.000886) \| 0.027280 / 0.128546 (-0.101266) \| 0.010273 / 0.075646 (-0.065373) \| 0.206035 / 0.419271 (-0.213236) \| 0.035715 / 0.043533 (-0.007818) \| 0.255474 / 0.255139 (0.000335) \| 0.273960 / 0.283200 (-0.009240) \| 0.018563 / 0.141683 (-0.123120) \| 1.140013 / 1.452155 (-0.312142) \| 1.188655 / 1.492716 (-0.304062) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091895 / 0.018006 (0.073888) \| 0.284621 / 0.000490 (0.284131) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018610 / 0.037411 (-0.018801) \| 0.061554 / 0.014526 (0.047028) \| 0.072454 / 0.176557 (-0.104103) \| 0.120283 / 0.737135 (-0.616853) \| 0.073744 / 0.296338 (-0.222595) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288850 / 0.215209 (0.073641) \| 2.836761 / 2.077655 (0.759107) \| 1.533407 / 1.504120 (0.029287) \| 1.409394 / 1.541195 (-0.131801) \| 1.421667 / 1.468490 (-0.046823) \| 0.566183 / 4.584777 (-4.018594) \| 2.390670 / 3.745712 (-1.355043) \| 2.732031 / 5.269862 (-2.537831) \| 1.730886 / 4.565676 (-2.834791) \| 0.064280 / 0.424275 (-0.359995) \| 0.004959 / 0.007607 (-0.002648) \| 0.342664 / 0.226044 (0.116619) \| 3.398969 / 2.268929 (1.130040) \| 1.887354 / 55.444624 (-53.557270) \| 1.572955 / 6.876477 (-5.303522) \| 1.596179 / 2.142072 (-0.545894) \| 0.645844 / 4.805227 (-4.159383) \| 0.118050 / 6.500664 (-6.382614) \| 0.042158 / 0.075469 (-0.033311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959170 / 1.841788 (-0.882617) \| 11.276491 / 8.074308 (3.202183) \| 9.471198 / 10.191392 (-0.720194) \| 0.128346 / 0.680424 (-0.552078) \| 0.013851 / 0.534201 (-0.520350) \| 0.286125 / 0.579283 (-0.293158) \| 0.266915 / 0.434364 (-0.167449) \| 0.332811 / 0.540337 (-0.207526) \| 0.444780 / 1.386936 (-0.942156) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005665 / 0.011353 (-0.005687) \| 0.003267 / 0.011008 (-0.007741) \| 0.050238 / 0.038508 (0.011730) \| 0.032882 / 0.023109 (0.009773) \| 0.269320 / 0.275898 (-0.006578) \| 0.293140 / 0.323480 (-0.030340) \| 0.004127 / 0.007986 (-0.003858) \| 0.002728 / 0.004328 (-0.001601) \| 0.049360 / 0.004250 (0.045109) \| 0.043764 / 0.037052 (0.006712) \| 0.291211 / 0.258489 (0.032722) \| 0.319745 / 0.293841 (0.025904) \| 0.029371 / 0.128546 (-0.099175) \| 0.010212 / 0.075646 (-0.065434) \| 0.059064 / 0.419271 (-0.360207) \| 0.051148 / 0.043533 (0.007615) \| 0.276698 / 0.255139 (0.021559) \| 0.292329 / 0.283200 (0.009129) \| 0.018349 / 0.141683 (-0.123334) \| 1.150816 / 1.452155 (-0.301338) \| 1.184292 / 1.492716 (-0.308425) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091646 / 0.018006 (0.073640) \| 0.301737 / 0.000490 (0.301247) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.021529 / 0.037411 (-0.015883) \| 0.075596 / 0.014526 (0.061070) \| 0.087912 / 0.176557 (-0.088645) \| 0.125240 / 0.737135 (-0.611895) \| 0.088035 / 0.296338 (-0.208303) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305097 / 0.215209 (0.089888) \| 2.979612 / 2.077655 (0.901957) \| 1.647009 / 1.504120 (0.142889) \| 1.520251 / 1.541195 (-0.020944) \| 1.513361 / 1.468490 (0.044870) \| 0.571733 / 4.584777 (-4.013044) \| 2.415587 / 3.745712 (-1.330125) \| 2.615983 / 5.269862 (-2.653879) \| 1.732637 / 4.565676 (-2.833039) \| 0.062830 / 0.424275 (-0.361445) \| 0.004972 / 0.007607 (-0.002635) \| 0.348559 / 0.226044 (0.122515) \| 3.450567 / 2.268929 (1.181639) \| 1.970743 / 55.444624 (-53.473882) \| 1.702232 / 6.876477 (-5.174245) \| 1.799592 / 2.142072 (-0.342480) \| 0.649477 / 4.805227 (-4.155751) \| 0.115940 / 6.500664 (-6.384724) \| 0.040364 / 0.075469 (-0.035105) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.000014 / 1.841788 (-0.841773) \| 11.937886 / 8.074308 (3.863578) \| 10.169478 / 10.191392 (-0.021914) \| 0.153359 / 0.680424 (-0.527064) \| 0.015205 / 0.534201 (-0.518996) \| 0.287812 / 0.579283 (-0.291471) \| 0.278688 / 0.434364 (-0.155676) \| 0.322831 / 0.540337 (-0.217507) \| 0.425631 / 1.386936 (-0.961305) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e176efbed29374e7c2cd33da64aeeae3c11ca0f \"CML watermark\")\n" ]
6,659	Change default compression argument for JsonDatasetWriter	Change default compression type from `None` to "infer", to align with pandas' defaults. Documentation asks the user to supply `to_json_kwargs` with arguments suitable for pandas' `to_json` method. At the same time, while pandas' by default uses ["infer"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) for compression, datasets enforce `None` as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing. Moreover, while it is probably outside of the scope of this pull request, `compression` argument needs to be capable of taking `dict` as input (along with `str`), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with `NotImplementedError`, as it expects either `None` or `str` specifying compression algo.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6659", "html_url": "https://github.com/huggingface/datasets/pull/6659", "diff_url": "https://github.com/huggingface/datasets/pull/6659.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6659.patch", "merged_at": "2024-03-01T17:44:55" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Can someone check this out?", "<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) \| 0.005008 / 0.011353 (-0.006345) \| 0.003267 / 0.011008 (-0.007741) \| 0.064140 / 0.038508 (0.025632) \| 0.027419 / 0.023109 (0.004309) \| 0.246692 / 0.275898 (-0.029206) \| 0.271303 / 0.323480 (-0.052177) \| 0.004127 / 0.007986 (-0.003859) \| 0.002698 / 0.004328 (-0.001631) \| 0.050415 / 0.004250 (0.046165) \| 0.040323 / 0.037052 (0.003271) \| 0.265738 / 0.258489 (0.007249) \| 0.291556 / 0.293841 (-0.002285) \| 0.027924 / 0.128546 (-0.100622) \| 0.010206 / 0.075646 (-0.065441) \| 0.207106 / 0.419271 (-0.212165) \| 0.036087 / 0.043533 (-0.007446) \| 0.250412 / 0.255139 (-0.004727) \| 0.269014 / 0.283200 (-0.014186) \| 0.018102 / 0.141683 (-0.123581) \| 1.135137 / 1.452155 (-0.317018) \| 1.177718 / 1.492716 (-0.314998) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095557 / 0.018006 (0.077550) \| 0.306235 / 0.000490 (0.305745) \| 0.000214 / 0.000200 (0.000014) \| 0.000044 / 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) \| 0.018217 / 0.037411 (-0.019194) \| 0.060993 / 0.014526 (0.046467) \| 0.072748 / 0.176557 (-0.103808) \| 0.119357 / 0.737135 (-0.617778) \| 0.073719 / 0.296338 (-0.222619) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295924 / 0.215209 (0.080715) \| 2.901071 / 2.077655 (0.823417) \| 1.497316 / 1.504120 (-0.006804) \| 1.371232 / 1.541195 (-0.169962) \| 1.395643 / 1.468490 (-0.072847) \| 0.577548 / 4.584777 (-4.007229) \| 2.383813 / 3.745712 (-1.361899) \| 2.764451 / 5.269862 (-2.505411) \| 1.733074 / 4.565676 (-2.832602) \| 0.063730 / 0.424275 (-0.360545) \| 0.004933 / 0.007607 (-0.002674) \| 0.347135 / 0.226044 (0.121090) \| 3.390814 / 2.268929 (1.121885) \| 1.849454 / 55.444624 (-53.595170) \| 1.561801 / 6.876477 (-5.314675) \| 1.587818 / 2.142072 (-0.554254) \| 0.652061 / 4.805227 (-4.153166) \| 0.117195 / 6.500664 (-6.383469) \| 0.041922 / 0.075469 (-0.033548) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949050 / 1.841788 (-0.892738) \| 11.353664 / 8.074308 (3.279355) \| 9.261581 / 10.191392 (-0.929811) \| 0.140374 / 0.680424 (-0.540050) \| 0.014254 / 0.534201 (-0.519946) \| 0.288124 / 0.579283 (-0.291159) \| 0.262888 / 0.434364 (-0.171476) \| 0.330774 / 0.540337 (-0.209564) \| 0.444777 / 1.386936 (-0.942159) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005162 / 0.011353 (-0.006191) \| 0.003418 / 0.011008 (-0.007591) \| 0.049764 / 0.038508 (0.011256) \| 0.029336 / 0.023109 (0.006226) \| 0.278570 / 0.275898 (0.002672) \| 0.300676 / 0.323480 (-0.022804) \| 0.004292 / 0.007986 (-0.003694) \| 0.002745 / 0.004328 (-0.001584) \| 0.049194 / 0.004250 (0.044943) \| 0.044036 / 0.037052 (0.006984) \| 0.299258 / 0.258489 (0.040769) \| 0.324451 / 0.293841 (0.030610) \| 0.029777 / 0.128546 (-0.098769) \| 0.010426 / 0.075646 (-0.065221) \| 0.057267 / 0.419271 (-0.362004) \| 0.051276 / 0.043533 (0.007743) \| 0.278012 / 0.255139 (0.022873) \| 0.297099 / 0.283200 (0.013899) \| 0.018340 / 0.141683 (-0.123343) \| 1.179255 / 1.452155 (-0.272899) \| 1.231536 / 1.492716 (-0.261180) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092546 / 0.018006 (0.074540) \| 0.299959 / 0.000490 (0.299469) \| 0.000220 / 0.000200 (0.000020) \| 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) \| 0.021657 / 0.037411 (-0.015755) \| 0.075440 / 0.014526 (0.060914) \| 0.086246 / 0.176557 (-0.090310) \| 0.126511 / 0.737135 (-0.610624) \| 0.091303 / 0.296338 (-0.205036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294775 / 0.215209 (0.079566) \| 2.868973 / 2.077655 (0.791319) \| 1.666971 / 1.504120 (0.162851) \| 1.545680 / 1.541195 (0.004486) \| 1.559983 / 1.468490 (0.091493) \| 0.572191 / 4.584777 (-4.012586) \| 2.429317 / 3.745712 (-1.316395) \| 2.673334 / 5.269862 (-2.596527) \| 1.758114 / 4.565676 (-2.807563) \| 0.063766 / 0.424275 (-0.360509) \| 0.005070 / 0.007607 (-0.002537) \| 0.345488 / 0.226044 (0.119443) \| 3.464525 / 2.268929 (1.195596) \| 1.975717 / 55.444624 (-53.468908) \| 1.686671 / 6.876477 (-5.189806) \| 1.825434 / 2.142072 (-0.316638) \| 0.655853 / 4.805227 (-4.149374) \| 0.116372 / 6.500664 (-6.384292) \| 0.040647 / 0.075469 (-0.034822) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.014080 / 1.841788 (-0.827707) \| 12.038496 / 8.074308 (3.964188) \| 10.354536 / 10.191392 (0.163144) \| 0.130285 / 0.680424 (-0.550139) \| 0.015514 / 0.534201 (-0.518687) \| 0.284743 / 0.579283 (-0.294540) \| 0.280275 / 0.434364 (-0.154088) \| 0.321175 / 0.540337 (-0.219162) \| 0.425840 / 1.386936 (-0.961096) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb6c6d46a171c4fa1b65167cb81998e2f863892 \"CML watermark\")\n" ]
6,658	[Resumable IterableDataset] Add IterableDataset state_dict	A simple implementation of a mechanism to resume an IterableDataset. It works by restarting at the latest shard and skip samples. It provides fast resuming (though not instantaneous). Example: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({"a": range(5)}).to_iterable_dataset(num_shards=3) ds = concatenate_datasets([ds] * 2) print(f"{ds.state_dict()=}") for i, example in enumerate(ds): print(example) if i == 6: state_dict = ds.state_dict() print("checkpoint") ds.load_state_dict(state_dict) print(f"resuming from checkpoint {ds.state_dict()=}") for example in ds: print(example) ``` returns ``` ds.state_dict()={'ex_iterable_idx': 0, 'ex_iterables': [{'shard_idx': 0, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 0}]} {'a': 0} {'a': 1} {'a': 2} {'a': 3} {'a': 4} {'a': 0} {'a': 1} checkpoint {'a': 2} {'a': 3} {'a': 4} resuming from checkpoint ds.state_dict()={'ex_iterable_idx': 1, 'ex_iterables': [{'shard_idx': 3, 'shard_example_idx': 0}, {'shard_idx': 0, 'shard_example_idx': 2}]} {'a': 2} {'a': 3} {'a': 4} ```	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6658", "html_url": "https://github.com/huggingface/datasets/pull/6658", "diff_url": "https://github.com/huggingface/datasets/pull/6658.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6658.patch", "merged_at": "2024-06-03T19:15:39" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6658). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "would be nice to have this feature in the new dataset release!", "Before finalising this this I'd like to make sure this philosophy makes sense for other libs like `accelerate` for example.\r\n\r\ncc @muellerzr I'd love your feedback on this one\r\ncc @LysandreJik also if you think other people should take a look", "> One design question though: what's the logic behind self._state_dict rather than having it all be state_dict?\r\n\r\nThe `_state_dict` is the internal object that is updated in-place while you iterate on the dataset.\r\n\r\nWe need to copy it every time the user accesses it.\r\n\r\nOtherwise we would get\r\n```python\r\nstate_dict = ds.state_dict()\r\nfor x in ds:\r\n assert ds.state_dict() == state_dict # and actually `assert ds.state_dict() is state_dict`\r\n```\r\n\r\nThe state is updated in-place since it's made of dictionaries that are shared with the steps in the IterableDataset pipeline.", "What do you think of making it a full property with a docstring explicitly stating users shouldn’t call/modify it directly?\r\n\r\nI can imagine some exploratory users getting curious", "I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)", "Sure, I can agree with that!", "Just a small note mentioning returns a copy of the state dict should be enough imo", "looking forward as well for this PR to be merge", "> I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)\r\n\r\nHi, I'm experimenting with LLM pretraining using your code. I found that the time of resuming an iterable dataset can be reduced to 5% (my streaming process includes tokenization), but I'm not sure if I'm using it correctly. Could you help me check it? Thanks.\r\n\r\n```\r\nclass CustomTrainer(Trainer):\r\n def _save_rng_state(self, output_dir):\r\n super()._save_rng_state(output_dir)\r\n if self.args.should_save:\r\n with open(os.path.join(output_dir, f'iterable_data_state_dict.json'), 'w', encoding='utf-8') as fo:\r\n json.dump(self.train_dataset.state_dict(), fo, ensure_ascii=False)\r\n```\r\n\r\n```\r\n dataset = <A IterableDataset constructed by (interleave, map(tokenization))>\r\n lask_ckpt_iterable_data_state_dict_file_path = os.path.join(training_args.resume_from_checkpoint, f'iterable_data_state_dict.json')\r\n if os.path.exists(lask_ckpt_iterable_data_state_dict_file_path) and finetuning_args.load_iteratable_state_dict:\r\n if not training_args.ignore_data_skip:\r\n raise ValueError(f'Found `iterable_data_state_dict_file_path`: `{lask_ckpt_iterable_data_state_dict_file_path}`. Please set `ignore_data_skip`=True to skip tokenization.')\r\n with open(lask_ckpt_iterable_data_state_dict_file_path) as f:\r\n lask_ckpt_iterable_data_state_dict = json.load(f)\r\n dataset.load_state_dict(lask_ckpt_iterable_data_state_dict)\r\n logger.info(f'Loading `iterable_data_state_dict` from {lask_ckpt_iterable_data_state_dict_file_path}')\r\n```\r\n", "it sounds good to me :)", "@lhoestq Hi, if I set `prefetch`, does this dataset work well?", "It does work well if you prefetch and then resume from a state, but you might lose the samples that were in the prefetch buffer of the DataLoader (which could be acceptable in some circumstances).\r\n\r\nFortunately we're about to ship an integration with the new StatefulDataLoader from torchdata which can help on this matter :)", "yeah, what I meant is that prefetch might drop a few data entries. really looking forward to the new StatefulDataLoader. ：)", "<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) \| 0.005788 / 0.011353 (-0.005564) \| 0.004036 / 0.011008 (-0.006972) \| 0.064720 / 0.038508 (0.026212) \| 0.034990 / 0.023109 (0.011881) \| 0.245488 / 0.275898 (-0.030410) \| 0.272596 / 0.323480 (-0.050884) \| 0.003170 / 0.007986 (-0.004815) \| 0.002867 / 0.004328 (-0.001461) \| 0.049961 / 0.004250 (0.045711) \| 0.050951 / 0.037052 (0.013899) \| 0.257757 / 0.258489 (-0.000732) \| 0.292957 / 0.293841 (-0.000884) \| 0.027739 / 0.128546 (-0.100807) \| 0.010942 / 0.075646 (-0.064705) \| 0.205153 / 0.419271 (-0.214118) \| 0.037892 / 0.043533 (-0.005641) \| 0.247536 / 0.255139 (-0.007603) \| 0.267239 / 0.283200 (-0.015960) \| 0.021490 / 0.141683 (-0.120193) \| 1.107306 / 1.452155 (-0.344848) \| 1.144675 / 1.492716 (-0.348041) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103212 / 0.018006 (0.085205) \| 0.315174 / 0.000490 (0.314684) \| 0.000229 / 0.000200 (0.000029) \| 0.000044 / 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) \| 0.019771 / 0.037411 (-0.017641) \| 0.064033 / 0.014526 (0.049507) \| 0.076751 / 0.176557 (-0.099805) \| 0.122615 / 0.737135 (-0.614521) \| 0.078490 / 0.296338 (-0.217848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286236 / 0.215209 (0.071027) \| 2.841469 / 2.077655 (0.763814) \| 1.514079 / 1.504120 (0.009959) \| 1.393792 / 1.541195 (-0.147403) \| 1.432741 / 1.468490 (-0.035749) \| 0.571003 / 4.584777 (-4.013774) \| 2.369031 / 3.745712 (-1.376681) \| 2.825246 / 5.269862 (-2.444616) \| 1.858524 / 4.565676 (-2.707153) \| 0.065366 / 0.424275 (-0.358909) \| 0.005107 / 0.007607 (-0.002500) \| 0.341010 / 0.226044 (0.114965) \| 3.443894 / 2.268929 (1.174966) \| 1.879192 / 55.444624 (-53.565433) \| 1.603046 / 6.876477 (-5.273431) \| 1.807639 / 2.142072 (-0.334433) \| 0.646726 / 4.805227 (-4.158502) \| 0.119409 / 6.500664 (-6.381255) \| 0.044564 / 0.075469 (-0.030905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971026 / 1.841788 (-0.870762) \| 12.593884 / 8.074308 (4.519576) \| 10.305243 / 10.191392 (0.113851) \| 0.132018 / 0.680424 (-0.548406) \| 0.014387 / 0.534201 (-0.519814) \| 0.288597 / 0.579283 (-0.290686) \| 0.267373 / 0.434364 (-0.166991) \| 0.325626 / 0.540337 (-0.214711) \| 0.488808 / 1.386936 (-0.898128) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005991 / 0.011353 (-0.005362) \| 0.004028 / 0.011008 (-0.006980) \| 0.051951 / 0.038508 (0.013443) \| 0.036870 / 0.023109 (0.013761) \| 0.263777 / 0.275898 (-0.012122) \| 0.290914 / 0.323480 (-0.032566) \| 0.004594 / 0.007986 (-0.003392) \| 0.002971 / 0.004328 (-0.001357) \| 0.049699 / 0.004250 (0.045449) \| 0.044939 / 0.037052 (0.007887) \| 0.275055 / 0.258489 (0.016566) \| 0.316244 / 0.293841 (0.022403) \| 0.030501 / 0.128546 (-0.098045) \| 0.011197 / 0.075646 (-0.064449) \| 0.058718 / 0.419271 (-0.360554) \| 0.034926 / 0.043533 (-0.008607) \| 0.259172 / 0.255139 (0.004033) \| 0.280127 / 0.283200 (-0.003072) \| 0.019775 / 0.141683 (-0.121908) \| 1.169468 / 1.452155 (-0.282687) \| 1.178098 / 1.492716 (-0.314619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101633 / 0.018006 (0.083626) \| 0.314684 / 0.000490 (0.314194) \| 0.000224 / 0.000200 (0.000024) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024071 / 0.037411 (-0.013341) \| 0.079894 / 0.014526 (0.065368) \| 0.090915 / 0.176557 (-0.085642) \| 0.132397 / 0.737135 (-0.604738) \| 0.091919 / 0.296338 (-0.204419) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296237 / 0.215209 (0.081028) \| 2.891752 / 2.077655 (0.814097) \| 1.551937 / 1.504120 (0.047817) \| 1.414179 / 1.541195 (-0.127016) \| 1.450192 / 1.468490 (-0.018298) \| 0.556272 / 4.584777 (-4.028504) \| 0.952374 / 3.745712 (-2.793339) \| 2.709450 / 5.269862 (-2.560411) \| 1.771251 / 4.565676 (-2.794426) \| 0.061873 / 0.424275 (-0.362402) \| 0.005058 / 0.007607 (-0.002549) \| 0.344790 / 0.226044 (0.118746) \| 3.398982 / 2.268929 (1.130053) \| 1.905832 / 55.444624 (-53.538792) \| 1.632357 / 6.876477 (-5.244120) \| 1.822913 / 2.142072 (-0.319160) \| 0.643426 / 4.805227 (-4.161802) \| 0.117321 / 6.500664 (-6.383343) \| 0.042107 / 0.075469 (-0.033363) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974921 / 1.841788 (-0.866867) \| 12.497801 / 8.074308 (4.423493) \| 11.216174 / 10.191392 (1.024782) \| 0.135288 / 0.680424 (-0.545136) \| 0.016731 / 0.534201 (-0.517470) \| 0.287987 / 0.579283 (-0.291296) \| 0.130246 / 0.434364 (-0.304117) \| 0.323282 / 0.540337 (-0.217055) \| 0.414595 / 1.386936 (-0.972341) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#43fd659eacab37b50abfe7f1b4efe2564055990c \"CML watermark\")\n", "@lhoestq Hello, I'm wondering if there are any solutions to work with shuffle now. I've noticed the caveats in docs, \r\n> examples from shuffle buffers are lost when resuming and the buffers are refilled with new data ", "Hi ! I haven't experimented with implementing state_dict for the shuffle buffer. Not sure if this is a good idea to add this, given a shuffle buffer can be quite big and poses serialization challenges.\r\n\r\nIt shouldn't be difficult to experiment with a simple implementation in `BufferShuffledExamplesIterable` though", "@lhoestq thank you for your quick response! I'll try it :}", "@lhoestq Hi, just revise the `BufferShuffledExamplesIterable` and it works\r\n```py\r\n\r\nclass BufferShuffledExamplesIterable(datasets.iterable_dataset.BufferShuffledExamplesIterable):\r\n\r\n def __init__(self, args, kwargs):\r\n super().__init__(args, kwargs)\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = self.ex_iterable._init_state_dict()\r\n self._state_dict['mem_buffer'] = ([],)\r\n self._state_dict['gloabl_example_index'] = 0\r\n return self._state_dict\r\n\r\n def __iter__(self):\r\n buffer_size = self.buffer_size\r\n rng = deepcopy(self.generator)\r\n indices_iterator = self._iter_random_indices(rng, buffer_size)\r\n # this is the shuffle buffer that we keep in memory\r\n mem_buffer = self._state_dict['mem_buffer'][0]\r\n gloabl_example_index_start = self._state_dict[\"gloabl_example_index\"] if self._state_dict else 0\r\n # skip already consumed ones\r\n for i in range(gloabl_example_index_start):\r\n _ = next(indices_iterator)\r\n for x in self.ex_iterable:\r\n if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n i = next(indices_iterator)\r\n if self._state_dict:\r\n self._state_dict['gloabl_example_index'] += 1\r\n yield mem_buffer[i]\r\n mem_buffer[i] = x # replace the picked example by a new one\r\n else: # otherwise, keep filling the buffer\r\n mem_buffer.append(x)\r\n # when we run out of examples, we shuffle the remaining examples in the buffer and yield them\r\n rng.shuffle(mem_buffer)\r\n yield from mem_buffer\r\n\r\n def shuffle_data_sources(self, generator: np.random.Generator) -> BufferShuffledExamplesIterable:\r\n \"\"\"Shuffle the wrapped examples iterable as well as the shuffling buffer.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shuffle_data_sources(generator), buffer_size=self.buffer_size, generator=generator\r\n )\r\n\r\n def shard_data_sources(self, worker_id: int, num_workers: int) -> BufferShuffledExamplesIterable:\r\n \"\"\"Keep only the requested shard.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shard_data_sources(worker_id, num_workers),\r\n buffer_size=self.buffer_size,\r\n generator=self.generator,\r\n )\r\n\r\n def load_state_dict(self, state_dict: dict) -> dict:\r\n def _inner_load_state_dict(state, new_state):\r\n if new_state is not None and isinstance(state, dict):\r\n for key in state:\r\n state[key] = _inner_load_state_dict(state[key], new_state[key])\r\n return state\r\n elif new_state is not None and isinstance(state, list):\r\n for i in range(len(state)):\r\n state[i] = _inner_load_state_dict(state[i], new_state[i])\r\n return state\r\n return new_state\r\n\r\n return _inner_load_state_dict(self._state_dict, state_dict)\r\n```\r\n\r\nI've noticed that it uses significantly more RAM than the original version and experiences a considerable decrease in GPU utilization. Could you offer some suggestions to address this issue?\r\n\r\nor is it prohibited** to maintain sth except for simple indices that small enough for each worker 😢 \r\n\r\n", "Some ExamplesIterable copy and store old versions of the state_dict of parent ExamplesIterable. It is the case for example for batched `map()` (state_dict of beginning of the batch) or `interleave_dataset()` (state_dict of the previous step since it buffers one example to know if the iterable is exhausted).\r\n\r\nCopying a shuffle buffer takes some RAM and some time, which can slow down the data loading pipeline.\r\nMaybe the examples in the shuffle buffer shouldn't not be copied (only do a shallow copy of the list), this would surely help." ]
6,657	Release not pushed to conda channel	### Describe the bug The github actions step to publish the release 2.17.0 to conda channel has failed due to expired token. Can some one please update the anaconda token rerun the failed action? @albertvillanova ? ![image](https://github.com/huggingface/datasets/assets/7138162/1b56ad3d-7643-4778-9cce-4bf531717700) ### Steps to reproduce the bug Please see this actions [link](https://github.com/huggingface/datasets/actions/runs/7842473662) ### Expected behavior The action runs successfully and the latest release is pushed to HuggingFace conda channel ### Environment info Not applicable.	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Thanks for reporting, @atulsaurav.\r\n\r\nWe are investigating the issue. ", "I can't fix this issue because I do not appear as a team member of the huggingface datasets project: https://anaconda.org/huggingface/datasets\r\n\r\n@lhoestq could you please add `datasets` team members to the corresponding Anaconda project?\r\n\r\nOnce this done, I could recreate and update the Anaconda token, as mentioned above it seems the current one has expired.", "I think @LysandreJik has access ?", "FYI it failed for 2.18.0 too: https://github.com/huggingface/datasets/actions/runs/8117132330/job/22188677936", "We updated the token and I re-ran the conda releases :)" ]
6,656	Error when loading a big local json file	### Describe the bug When trying to load big json files from a local directory, `load_dataset` throws the following error ``` Traceback (most recent call last): File "/miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/builder.py", line 1989, in _prepare_split_single writer.write_table(table) File "miniconda3/envs/conda-env/lib/python3.10/site-packages/datasets/arrow_writer.py", line 573, in write_table pa_table = pa_table.combine_chunks() File "pyarrow/table.pxi", line 3638, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays ``` ### Steps to reproduce the bug 1. Download a big file, e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-train.json.gz` 2. Load it like `data = load_dataset("json", data_files=["nq-train.json"], split="train")` ```python from datasets import load_dataset data = load_dataset("json", data_files=["nq-train.json"], split="train") ``` A similarly formatted but smaller file, e.g. e.g. `https://dl.fbaipublicfiles.com/dpr/data/retriever/biencoder-nq-dev.json.gz` is loaded without issues ```python from datasets import load_dataset data = load_dataset("json", data_files=["nq-dev.json"], split="train") ``` ### Expected behavior It should load normally ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "I get similar when dealing with a large jsonl file (6k lines), \r\n\r\n> TypeError: Couldn't cast array of type timestamp[us] to null\r\n\r\nYet when I split it into 1k lines, files, load_dataset works fine!\r\n\r\nhttps://github.com/huggingface/course/issues/692\r\n\r\n" ]
6,655	Cannot load the dataset go_emotions	### Describe the bug When I run the following code I get an exception; `go_emotions = load_dataset("go_emotions")` > AttributeError Traceback (most recent call last) Cell In[6], [line 1](vscode-notebook-cell:?execution_count=6&line=1) ----> [1](vscode-notebook-cell:?execution_count=6&line=1) go_emotions = load_dataset("go_emotions") [2](vscode-notebook-cell:?execution_count=6&line=2) data = go_emotions.data File [c:\Users\hijik\anaconda3\Lib\site-packages\datasets\load.py:2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs) [2518](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2518) verification_mode = VerificationMode( [2519](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2519) (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS [2520](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2520) ) [2522](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2522) # Create a dataset builder -> [2523](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2523) builder_instance = load_dataset_builder( [2524](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2524) path=path, [2525](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2525) name=name, [2526](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2526) data_dir=data_dir, [2527](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2527) data_files=data_files, [2528](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2528) cache_dir=cache_dir, [2529](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2529) features=features, [2530](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2530) download_config=download_config, [2531](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2531) download_mode=download_mode, [2532](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2532) revision=revision, [2533](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2533) token=token, [2534](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2534) storage_options=storage_options, [2535](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2535) trust_remote_code=trust_remote_code, [2536](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/load.py:2536) _require_default_config_name=name is None, ... ---> [63](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:63) if issubclass(obj_type, transformers.PreTrainedTokenizerBase): [64](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:64) pklregister(obj_type)(_save_transformersPreTrainedTokenizerBase) [66](file:///C:/Users/hijik/anaconda3/Lib/site-packages/datasets/utils/_dill.py:66) # Unwrap `torch.compile`-ed functions AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase' Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?10bc0728-6947-456e-9a3e-f056872b04c6) or open in a [text editor](command:workbench.action.openLargeOutput?10bc0728-6947-456e-9a3e-f056872b04c6). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)... ### Steps to reproduce the bug ``` from datasets import load_dataset go_emotions = load_dataset("go_emotions") ``` ### Expected behavior Should simply load the variable with the data from the file ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.16.1 - Platform: Windows-10-10.0.22631-SP0 - Python version: 3.11.4 - `huggingface_hub` version: 0.20.3 - PyArrow version: 11.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Thanks for reporting, @arame.\r\n\r\nI guess you have an old version of `transformers` (that submodule is present in `transformers` since version 3.0.1, since nearly 4 years ago). If you update it, the error should disappear:\r\n```shell\r\npip install -U transformers\r\n```\r\n\r\nOn the other hand, I am wondering: does it make sense to use `transformers` in this case, even if we don't need it to load the `go_emotions` dataset (already converted to Parquet files)?\r\n- Maybe @mariosasko can give some insight, as he included these code lines:\r\n - #6454\r\n\r\nhttps://github.com/huggingface/datasets/blob/9751fb14594d354e952f0ebdfaf31cb203b011e7/src/datasets/utils/_dill.py#L60-L63\r\n", "The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n\r\nHowever, the logic does not account for `transformers<3`, so we should add a version check to fix that.", "> The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n> \r\n> However, the logic does not account for `transformers<3`, so we should add a version check to fix that.\r\n\r\nThank you for that Mario. Would this fix solve the problem and do you have any idea when it will be done? \r\nI tried the pip install suggested by Albert and it made no difference.", "I tried running the code today and the problem appears to be fixed." ]
6,654	Batched dataset map throws exception that cannot cast fixed length array to Sequence	### Describe the bug I encountered a TypeError when batch processing a dataset with Sequence features in datasets package version 2.16.1. The error arises from a mismatch in handling fixed-size list arrays during the map function execution. Debugging pinpoints the issue to an if-statement in datasets/table.py, line 2093, failing to correctly process sequence lengths. ### Steps to reproduce the bug Create virtual environment and activate ``` virtualenv venv source venv/bin/activate ``` Then install the datasets package (I'm using the latest version) ``` pip install datasets==2.16.1 ``` Then run ```python # bug.py from datasets import Dataset from datasets.features import Features, Sequence, Value data = { "num": [[1, 2], [3, 4]], } features = Features({'num': Sequence(feature=Value(dtype='int32'), length=2)}) dataset = Dataset.from_dict(data, features=features) dataset.map(lambda x: x, batched=True, batch_size=1) ``` ### Expected behavior I get the following stack trace ``` Map: 50%\|█████ \| 1/2 [00:00<00:00, 423.92 examples/s] Traceback (most recent call last): File "/PATH/TO/BUG_PORT/bug.py", line 9, in <module> dataset.map(lambda x: x, batched=True, batch_size=1) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single writer.write_batch(batch) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 551, in write_batch array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper return pa.chunked_array([func(chunk, args, kwargs) for chunk in array.chunks]) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in <listcomp> return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) File "/PATH/TO/BUG_PORT/venv/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type fixed_size_list<item: int32>[2] to Sequence(feature=Value(dtype='int32', id=None), length=2, id=None) ``` After some debugging, I found that the if-statement that is actually failing is line 2093 in `datasets/table.py` ```python # datasets/table.py ... 2093 if feature.length len(array) == len(array_values): 2094 return pa.FixedSizeListArray.from_arrays(_c(array_values, feature.feature), feature.length) ... ``` ### Environment info Platform: MacOS Datasets version: datasets==2.16.1 Python version: 3.9.6	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi ! This issue has been fixed by https://github.com/huggingface/datasets/pull/6283\r\n\r\nCan you try again with the new release 2.17.0 ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\n", "Amazing! It's indeed fixed now. Thanks!" ]
6,653	Set dev version	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6653", "html_url": "https://github.com/huggingface/datasets/pull/6653", "diff_url": "https://github.com/huggingface/datasets/pull/6653.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6653.patch", "merged_at": "2024-02-09T10:12:12" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6653). 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) \| 0.005076 / 0.011353 (-0.006277) \| 0.003424 / 0.011008 (-0.007584) \| 0.064195 / 0.038508 (0.025687) \| 0.031742 / 0.023109 (0.008633) \| 0.244774 / 0.275898 (-0.031124) \| 0.268529 / 0.323480 (-0.054951) \| 0.003970 / 0.007986 (-0.004016) \| 0.002657 / 0.004328 (-0.001672) \| 0.048847 / 0.004250 (0.044597) \| 0.042196 / 0.037052 (0.005144) \| 0.266044 / 0.258489 (0.007555) \| 0.282400 / 0.293841 (-0.011441) \| 0.027617 / 0.128546 (-0.100929) \| 0.010400 / 0.075646 (-0.065246) \| 0.205910 / 0.419271 (-0.213362) \| 0.035820 / 0.043533 (-0.007713) \| 0.247750 / 0.255139 (-0.007389) \| 0.267318 / 0.283200 (-0.015882) \| 0.017980 / 0.141683 (-0.123703) \| 1.107263 / 1.452155 (-0.344892) \| 1.173208 / 1.492716 (-0.319509) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095830 / 0.018006 (0.077824) \| 0.293891 / 0.000490 (0.293401) \| 0.000257 / 0.000200 (0.000057) \| 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) \| 0.018138 / 0.037411 (-0.019273) \| 0.061631 / 0.014526 (0.047105) \| 0.073038 / 0.176557 (-0.103519) \| 0.118317 / 0.737135 (-0.618818) \| 0.074190 / 0.296338 (-0.222148) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287026 / 0.215209 (0.071817) \| 2.786137 / 2.077655 (0.708482) \| 1.472575 / 1.504120 (-0.031544) \| 1.346919 / 1.541195 (-0.194276) \| 1.388535 / 1.468490 (-0.079955) \| 0.565731 / 4.584777 (-4.019046) \| 2.382573 / 3.745712 (-1.363139) \| 2.736926 / 5.269862 (-2.532935) \| 1.716517 / 4.565676 (-2.849159) \| 0.062168 / 0.424275 (-0.362108) \| 0.004924 / 0.007607 (-0.002683) \| 0.341897 / 0.226044 (0.115853) \| 3.355715 / 2.268929 (1.086787) \| 1.837014 / 55.444624 (-53.607611) \| 1.532063 / 6.876477 (-5.344414) \| 1.548193 / 2.142072 (-0.593880) \| 0.634995 / 4.805227 (-4.170232) \| 0.115622 / 6.500664 (-6.385042) \| 0.042252 / 0.075469 (-0.033217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970713 / 1.841788 (-0.871075) \| 11.727576 / 8.074308 (3.653268) \| 9.806524 / 10.191392 (-0.384868) \| 0.127622 / 0.680424 (-0.552802) \| 0.014140 / 0.534201 (-0.520061) \| 0.286832 / 0.579283 (-0.292451) \| 0.266556 / 0.434364 (-0.167808) \| 0.325940 / 0.540337 (-0.214398) \| 0.421839 / 1.386936 (-0.965097) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005495 / 0.011353 (-0.005858) \| 0.003676 / 0.011008 (-0.007332) \| 0.054361 / 0.038508 (0.015853) \| 0.030743 / 0.023109 (0.007633) \| 0.277200 / 0.275898 (0.001302) \| 0.313459 / 0.323480 (-0.010021) \| 0.004316 / 0.007986 (-0.003670) \| 0.002750 / 0.004328 (-0.001578) \| 0.049491 / 0.004250 (0.045241) \| 0.044268 / 0.037052 (0.007215) \| 0.292529 / 0.258489 (0.034039) \| 0.326524 / 0.293841 (0.032683) \| 0.048040 / 0.128546 (-0.080507) \| 0.010390 / 0.075646 (-0.065256) \| 0.058459 / 0.419271 (-0.360813) \| 0.033765 / 0.043533 (-0.009768) \| 0.276003 / 0.255139 (0.020864) \| 0.297299 / 0.283200 (0.014099) \| 0.018532 / 0.141683 (-0.123151) \| 1.157639 / 1.452155 (-0.294515) \| 1.220492 / 1.492716 (-0.272225) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093903 / 0.018006 (0.075897) \| 0.303005 / 0.000490 (0.302515) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.021580 / 0.037411 (-0.015831) \| 0.076176 / 0.014526 (0.061650) \| 0.086998 / 0.176557 (-0.089558) \| 0.124148 / 0.737135 (-0.612987) \| 0.088613 / 0.296338 (-0.207725) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300623 / 0.215209 (0.085414) \| 2.911876 / 2.077655 (0.834221) \| 1.588398 / 1.504120 (0.084278) \| 1.471251 / 1.541195 (-0.069944) \| 1.505528 / 1.468490 (0.037038) \| 0.570635 / 4.584777 (-4.014142) \| 2.485769 / 3.745712 (-1.259943) \| 2.785355 / 5.269862 (-2.484507) \| 1.752944 / 4.565676 (-2.812732) \| 0.063146 / 0.424275 (-0.361129) \| 0.004980 / 0.007607 (-0.002627) \| 0.354577 / 0.226044 (0.128532) \| 3.477181 / 2.268929 (1.208253) \| 1.951906 / 55.444624 (-53.492718) \| 1.677169 / 6.876477 (-5.199307) \| 1.686338 / 2.142072 (-0.455735) \| 0.637156 / 4.805227 (-4.168071) \| 0.117732 / 6.500664 (-6.382932) \| 0.041091 / 0.075469 (-0.034378) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010071 / 1.841788 (-0.831717) \| 12.172242 / 8.074308 (4.097934) \| 10.422811 / 10.191392 (0.231419) \| 0.137185 / 0.680424 (-0.543239) \| 0.014643 / 0.534201 (-0.519558) \| 0.287248 / 0.579283 (-0.292035) \| 0.272779 / 0.434364 (-0.161585) \| 0.331761 / 0.540337 (-0.208576) \| 0.417266 / 1.386936 (-0.969670) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9751fb14594d354e952f0ebdfaf31cb203b011e7 \"CML watermark\")\n" ]
6,652	Release: 2.17.0	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6652", "html_url": "https://github.com/huggingface/datasets/pull/6652", "diff_url": "https://github.com/huggingface/datasets/pull/6652.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6652.patch", "merged_at": "2024-02-09T10:05:35" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6652). 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) \| 0.005207 / 0.011353 (-0.006145) \| 0.003785 / 0.011008 (-0.007223) \| 0.064221 / 0.038508 (0.025713) \| 0.028981 / 0.023109 (0.005872) \| 0.246215 / 0.275898 (-0.029683) \| 0.268058 / 0.323480 (-0.055422) \| 0.004028 / 0.007986 (-0.003958) \| 0.002804 / 0.004328 (-0.001525) \| 0.048878 / 0.004250 (0.044627) \| 0.042641 / 0.037052 (0.005589) \| 0.255590 / 0.258489 (-0.002899) \| 0.287377 / 0.293841 (-0.006464) \| 0.027772 / 0.128546 (-0.100774) \| 0.010637 / 0.075646 (-0.065009) \| 0.211526 / 0.419271 (-0.207746) \| 0.035789 / 0.043533 (-0.007744) \| 0.243042 / 0.255139 (-0.012097) \| 0.268369 / 0.283200 (-0.014830) \| 0.017907 / 0.141683 (-0.123776) \| 1.138829 / 1.452155 (-0.313326) \| 1.175732 / 1.492716 (-0.316984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094205 / 0.018006 (0.076199) \| 0.304317 / 0.000490 (0.303827) \| 0.000206 / 0.000200 (0.000006) \| 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) \| 0.018424 / 0.037411 (-0.018987) \| 0.061719 / 0.014526 (0.047193) \| 0.073471 / 0.176557 (-0.103085) \| 0.121577 / 0.737135 (-0.615558) \| 0.075134 / 0.296338 (-0.221204) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275178 / 0.215209 (0.059969) \| 2.689222 / 2.077655 (0.611568) \| 1.396680 / 1.504120 (-0.107439) \| 1.278782 / 1.541195 (-0.262413) \| 1.326632 / 1.468490 (-0.141858) \| 0.566915 / 4.584777 (-4.017862) \| 2.365928 / 3.745712 (-1.379784) \| 2.785435 / 5.269862 (-2.484427) \| 1.745131 / 4.565676 (-2.820546) \| 0.062798 / 0.424275 (-0.361477) \| 0.005107 / 0.007607 (-0.002500) \| 0.330441 / 0.226044 (0.104396) \| 3.266265 / 2.268929 (0.997337) \| 1.792588 / 55.444624 (-53.652036) \| 1.516021 / 6.876477 (-5.360455) \| 1.562750 / 2.142072 (-0.579323) \| 0.652964 / 4.805227 (-4.152264) \| 0.117813 / 6.500664 (-6.382852) \| 0.042372 / 0.075469 (-0.033097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.010107 / 1.841788 (-0.831680) \| 11.819910 / 8.074308 (3.745602) \| 9.701673 / 10.191392 (-0.489719) \| 0.178165 / 0.680424 (-0.502259) \| 0.014438 / 0.534201 (-0.519763) \| 0.297733 / 0.579283 (-0.281550) \| 0.264914 / 0.434364 (-0.169450) \| 0.324531 / 0.540337 (-0.215806) \| 0.430207 / 1.386936 (-0.956729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005848 / 0.011353 (-0.005505) \| 0.003870 / 0.011008 (-0.007138) \| 0.050379 / 0.038508 (0.011871) \| 0.031238 / 0.023109 (0.008129) \| 0.276839 / 0.275898 (0.000941) \| 0.299488 / 0.323480 (-0.023992) \| 0.005143 / 0.007986 (-0.002842) \| 0.002725 / 0.004328 (-0.001604) \| 0.048184 / 0.004250 (0.043934) \| 0.046232 / 0.037052 (0.009180) \| 0.287058 / 0.258489 (0.028569) \| 0.322659 / 0.293841 (0.028818) \| 0.047598 / 0.128546 (-0.080949) \| 0.011116 / 0.075646 (-0.064530) \| 0.058252 / 0.419271 (-0.361019) \| 0.033404 / 0.043533 (-0.010128) \| 0.277650 / 0.255139 (0.022511) \| 0.295610 / 0.283200 (0.012410) \| 0.018124 / 0.141683 (-0.123559) \| 1.135052 / 1.452155 (-0.317103) \| 1.194261 / 1.492716 (-0.298456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095595 / 0.018006 (0.077588) \| 0.306408 / 0.000490 (0.305918) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.022027 / 0.037411 (-0.015385) \| 0.076224 / 0.014526 (0.061698) \| 0.087441 / 0.176557 (-0.089116) \| 0.126636 / 0.737135 (-0.610499) \| 0.089442 / 0.296338 (-0.206896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291315 / 0.215209 (0.076106) \| 2.835304 / 2.077655 (0.757650) \| 1.581102 / 1.504120 (0.076982) \| 1.463046 / 1.541195 (-0.078149) \| 1.481982 / 1.468490 (0.013492) \| 0.559989 / 4.584777 (-4.024788) \| 2.385262 / 3.745712 (-1.360450) \| 2.773478 / 5.269862 (-2.496383) \| 1.744427 / 4.565676 (-2.821249) \| 0.062687 / 0.424275 (-0.361589) \| 0.005149 / 0.007607 (-0.002458) \| 0.374600 / 0.226044 (0.148555) \| 3.376507 / 2.268929 (1.107579) \| 1.935290 / 55.444624 (-53.509334) \| 1.663227 / 6.876477 (-5.213250) \| 1.678987 / 2.142072 (-0.463085) \| 0.638970 / 4.805227 (-4.166258) \| 0.120000 / 6.500664 (-6.380664) \| 0.040862 / 0.075469 (-0.034608) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008795 / 1.841788 (-0.832993) \| 12.275084 / 8.074308 (4.200776) \| 10.340088 / 10.191392 (0.148696) \| 0.136454 / 0.680424 (-0.543970) \| 0.014404 / 0.534201 (-0.519797) \| 0.289478 / 0.579283 (-0.289805) \| 0.279243 / 0.434364 (-0.155121) \| 0.330992 / 0.540337 (-0.209346) \| 0.422043 / 1.386936 (-0.964893) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70633576ecf1f3f5e5cdfd8c9189246b3604f4b6 \"CML watermark\")\n" ]
6,651	Slice splits support for datasets.load_from_disk	### Feature request Support for slice splits in `datasets.load_from_disk`, similar to how it's already supported for `datasets.load_dataset`. ### Motivation Slice splits are convienient in a numer of cases - adding support to `datasets.load_from_disk` would make working with local datasets easier and homogenize the APIs of load_from_disk and load_dataset. ### Your contribution Sure, if the devs think the feature request is sensible.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,650	AttributeError: 'InMemoryTable' object has no attribute '_batches'	### Describe the bug ``` Traceback (most recent call last): File "finetune.py", line 103, in <module> main(args) File "finetune.py", line 45, in main data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer, File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 868, in map { File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/dataset_dict.py", line 869, in <dictcomp> k: dataset.map( File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3432, in _map_single arrow_formatted_shard = shard.with_format("arrow") File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2667, in with_format dataset = copy.deepcopy(self) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 270, in _reconstruct state = deepcopy(state, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/opt/conda/envs/ptca/lib/python3.8/copy.py", line 153, in deepcopy y = copier(memo) File "/opt/conda/envs/ptca/lib/python3.8/site-packages/datasets/table.py", line 176, in __deepcopy__ memo[id(self._batches)] = list(self._batches) AttributeError: 'InMemoryTable' object has no attribute '_batches' ``` ### Steps to reproduce the bug I'm running an MLOps flow using AzureML. The error appears when I run the following function in my training script: ```python data_tokenized = data.map(partial(funcs.tokenize_function, tokenizer, seq_length), batched=True, batch_size=batch_size, remove_columns=['col1', 'col2']) ``` ```python def tokenize_function(tok, seq_length, example) # Pad so that each batch has the same sequence length inp = tok(example['col1'], padding=True, truncation=True) outp = tok(example['col2'], padding="max_length", max_length=seq_length) res = { 'input_ids': inp['input_ids'], 'attention_mask': inp['attention_mask'], 'decoder_input_ids': outp['input_ids'], 'labels': outp['input_ids'], 'decoder_attention_mask': outp['attention_mask'] } return res ``` ### Expected behavior Processing proceeds without errors. I ran this same workflow 2 weeks ago without a problem. I recreated the environment since then but it doesn't appear that datasets versions have changed since Dec. '23. ### Environment info datasets 2.16.1 transformers 4.35.2 pyarrow 15.0.0 pyarrow-hotfix 0.6 torch 2.0.1 I'm not using the latest transformers version because there was an error due to a conflict with Azure mlflow when I tried the last time.	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Does running the following code also return the same error on your machine? \r\n\r\n```python\r\nimport copy\r\nimport pyarrow as pa\r\nfrom datasets.table import InMemoryTable\r\n\r\ncopy.deepcopy(InMemoryTable(pa.table({\"a\": [1, 2, 3], \"b\": [\"foo\", \"bar\", \"foobar\"]})))\r\n```", "No, it doesn't, it runs fine. But what's really strange is that the error just went away after I reran the data prep script for conversion from csv to a datasets object. I realize that's not very helpful since the problem isn't reproducible. ", "Feel free to close the issue then :)." ]
6,649	Minor multi gpu doc improvement	just added torch.no_grad and eval()	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6649", "html_url": "https://github.com/huggingface/datasets/pull/6649", "diff_url": "https://github.com/huggingface/datasets/pull/6649.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6649.patch", "merged_at": "2024-02-08T11:17:35" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6649). 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) \| 0.005197 / 0.011353 (-0.006156) \| 0.003469 / 0.011008 (-0.007539) \| 0.062306 / 0.038508 (0.023798) \| 0.028417 / 0.023109 (0.005308) \| 0.241147 / 0.275898 (-0.034751) \| 0.270910 / 0.323480 (-0.052569) \| 0.003053 / 0.007986 (-0.004933) \| 0.003343 / 0.004328 (-0.000985) \| 0.048044 / 0.004250 (0.043794) \| 0.043738 / 0.037052 (0.006686) \| 0.259274 / 0.258489 (0.000785) \| 0.282522 / 0.293841 (-0.011319) \| 0.027807 / 0.128546 (-0.100739) \| 0.010413 / 0.075646 (-0.065234) \| 0.206322 / 0.419271 (-0.212950) \| 0.035770 / 0.043533 (-0.007763) \| 0.243465 / 0.255139 (-0.011674) \| 0.261596 / 0.283200 (-0.021604) \| 0.018613 / 0.141683 (-0.123070) \| 1.115509 / 1.452155 (-0.336645) \| 1.189403 / 1.492716 (-0.303314) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.086075 / 0.018006 (0.068069) \| 0.296140 / 0.000490 (0.295650) \| 0.000198 / 0.000200 (-0.000002) \| 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) \| 0.018238 / 0.037411 (-0.019173) \| 0.061783 / 0.014526 (0.047257) \| 0.072014 / 0.176557 (-0.104543) \| 0.118746 / 0.737135 (-0.618389) \| 0.073279 / 0.296338 (-0.223060) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278281 / 0.215209 (0.063072) \| 2.772209 / 2.077655 (0.694555) \| 1.404503 / 1.504120 (-0.099617) \| 1.274753 / 1.541195 (-0.266441) \| 1.304394 / 1.468490 (-0.164096) \| 0.556903 / 4.584777 (-4.027874) \| 2.335428 / 3.745712 (-1.410284) \| 2.712255 / 5.269862 (-2.557606) \| 1.722252 / 4.565676 (-2.843425) \| 0.061268 / 0.424275 (-0.363007) \| 0.005029 / 0.007607 (-0.002578) \| 0.326112 / 0.226044 (0.100067) \| 3.207917 / 2.268929 (0.938988) \| 1.743513 / 55.444624 (-53.701111) \| 1.476418 / 6.876477 (-5.400059) \| 1.489776 / 2.142072 (-0.652297) \| 0.628181 / 4.805227 (-4.177046) \| 0.115959 / 6.500664 (-6.384706) \| 0.041854 / 0.075469 (-0.033615) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969039 / 1.841788 (-0.872749) \| 11.178646 / 8.074308 (3.104338) \| 9.639716 / 10.191392 (-0.551676) \| 0.139750 / 0.680424 (-0.540674) \| 0.014230 / 0.534201 (-0.519971) \| 0.285318 / 0.579283 (-0.293965) \| 0.260788 / 0.434364 (-0.173576) \| 0.324183 / 0.540337 (-0.216154) \| 0.416326 / 1.386936 (-0.970610) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005149 / 0.011353 (-0.006204) \| 0.003469 / 0.011008 (-0.007539) \| 0.049761 / 0.038508 (0.011253) \| 0.030723 / 0.023109 (0.007614) \| 0.271562 / 0.275898 (-0.004336) \| 0.297843 / 0.323480 (-0.025637) \| 0.004296 / 0.007986 (-0.003690) \| 0.002704 / 0.004328 (-0.001624) \| 0.048890 / 0.004250 (0.044640) \| 0.044776 / 0.037052 (0.007723) \| 0.285490 / 0.258489 (0.027001) \| 0.312888 / 0.293841 (0.019047) \| 0.046239 / 0.128546 (-0.082307) \| 0.010238 / 0.075646 (-0.065408) \| 0.057968 / 0.419271 (-0.361304) \| 0.033295 / 0.043533 (-0.010238) \| 0.274320 / 0.255139 (0.019181) \| 0.296199 / 0.283200 (0.012999) \| 0.017856 / 0.141683 (-0.123827) \| 1.147532 / 1.452155 (-0.304622) \| 1.211647 / 1.492716 (-0.281070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089655 / 0.018006 (0.071649) \| 0.297275 / 0.000490 (0.296785) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.021739 / 0.037411 (-0.015672) \| 0.075041 / 0.014526 (0.060515) \| 0.085754 / 0.176557 (-0.090802) \| 0.124512 / 0.737135 (-0.612623) \| 0.086926 / 0.296338 (-0.209412) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290306 / 0.215209 (0.075097) \| 2.847404 / 2.077655 (0.769749) \| 1.606175 / 1.504120 (0.102055) \| 1.483220 / 1.541195 (-0.057974) \| 1.514551 / 1.468490 (0.046061) \| 0.559332 / 4.584777 (-4.025445) \| 2.403089 / 3.745712 (-1.342624) \| 2.715179 / 5.269862 (-2.554683) \| 1.688340 / 4.565676 (-2.877337) \| 0.062057 / 0.424275 (-0.362218) \| 0.004955 / 0.007607 (-0.002652) \| 0.338909 / 0.226044 (0.112865) \| 3.356882 / 2.268929 (1.087954) \| 1.942259 / 55.444624 (-53.502366) \| 1.675195 / 6.876477 (-5.201282) \| 1.688158 / 2.142072 (-0.453914) \| 0.637270 / 4.805227 (-4.167957) \| 0.114314 / 6.500664 (-6.386350) \| 0.040677 / 0.075469 (-0.034792) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.022126 / 1.841788 (-0.819661) \| 11.783359 / 8.074308 (3.709051) \| 10.247652 / 10.191392 (0.056260) \| 0.138188 / 0.680424 (-0.542236) \| 0.014850 / 0.534201 (-0.519351) \| 0.287414 / 0.579283 (-0.291869) \| 0.274393 / 0.434364 (-0.159971) \| 0.327255 / 0.540337 (-0.213082) \| 0.416355 / 1.386936 (-0.970581) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#727a952367966a98b759d54f333b1e2c28cfd4d4 \"CML watermark\")\n" ]
6,648	Document usage of hfh cli instead of git	(basically the same content as the hfh upload docs, but adapted for datasets)	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6648", "html_url": "https://github.com/huggingface/datasets/pull/6648", "diff_url": "https://github.com/huggingface/datasets/pull/6648.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6648.patch", "merged_at": "2024-02-08T13:51:39" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6648). 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) \| 0.004951 / 0.011353 (-0.006402) \| 0.003187 / 0.011008 (-0.007821) \| 0.062959 / 0.038508 (0.024451) \| 0.028037 / 0.023109 (0.004928) \| 0.241374 / 0.275898 (-0.034524) \| 0.262792 / 0.323480 (-0.060688) \| 0.004132 / 0.007986 (-0.003854) \| 0.002766 / 0.004328 (-0.001563) \| 0.051416 / 0.004250 (0.047165) \| 0.040957 / 0.037052 (0.003904) \| 0.260760 / 0.258489 (0.002271) \| 0.282018 / 0.293841 (-0.011823) \| 0.027689 / 0.128546 (-0.100857) \| 0.010433 / 0.075646 (-0.065214) \| 0.211598 / 0.419271 (-0.207674) \| 0.035447 / 0.043533 (-0.008086) \| 0.244333 / 0.255139 (-0.010806) \| 0.263192 / 0.283200 (-0.020008) \| 0.016816 / 0.141683 (-0.124867) \| 1.103188 / 1.452155 (-0.348967) \| 1.179093 / 1.492716 (-0.313623) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092412 / 0.018006 (0.074406) \| 0.301226 / 0.000490 (0.300736) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.018146 / 0.037411 (-0.019265) \| 0.061447 / 0.014526 (0.046921) \| 0.072162 / 0.176557 (-0.104394) \| 0.118965 / 0.737135 (-0.618170) \| 0.073756 / 0.296338 (-0.222583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285361 / 0.215209 (0.070152) \| 2.776928 / 2.077655 (0.699273) \| 1.506859 / 1.504120 (0.002739) \| 1.379119 / 1.541195 (-0.162075) \| 1.401798 / 1.468490 (-0.066692) \| 0.572512 / 4.584777 (-4.012265) \| 2.403793 / 3.745712 (-1.341919) \| 2.740496 / 5.269862 (-2.529366) \| 1.714611 / 4.565676 (-2.851065) \| 0.063496 / 0.424275 (-0.360780) \| 0.005009 / 0.007607 (-0.002598) \| 0.342438 / 0.226044 (0.116393) \| 3.368129 / 2.268929 (1.099200) \| 1.831200 / 55.444624 (-53.613424) \| 1.553611 / 6.876477 (-5.322866) \| 1.578116 / 2.142072 (-0.563956) \| 0.653034 / 4.805227 (-4.152193) \| 0.117724 / 6.500664 (-6.382940) \| 0.041188 / 0.075469 (-0.034282) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972520 / 1.841788 (-0.869268) \| 11.186297 / 8.074308 (3.111989) \| 9.485829 / 10.191392 (-0.705563) \| 0.139715 / 0.680424 (-0.540708) \| 0.013705 / 0.534201 (-0.520496) \| 0.287384 / 0.579283 (-0.291899) \| 0.266784 / 0.434364 (-0.167580) \| 0.320789 / 0.540337 (-0.219548) \| 0.417484 / 1.386936 (-0.969452) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005570 / 0.011353 (-0.005783) \| 0.003416 / 0.011008 (-0.007592) \| 0.051160 / 0.038508 (0.012652) \| 0.031082 / 0.023109 (0.007973) \| 0.279336 / 0.275898 (0.003438) \| 0.300529 / 0.323480 (-0.022951) \| 0.004320 / 0.007986 (-0.003666) \| 0.002781 / 0.004328 (-0.001548) \| 0.049642 / 0.004250 (0.045391) \| 0.044379 / 0.037052 (0.007327) \| 0.293797 / 0.258489 (0.035308) \| 0.317844 / 0.293841 (0.024003) \| 0.049697 / 0.128546 (-0.078849) \| 0.010624 / 0.075646 (-0.065023) \| 0.058834 / 0.419271 (-0.360437) \| 0.033869 / 0.043533 (-0.009664) \| 0.280547 / 0.255139 (0.025408) \| 0.300685 / 0.283200 (0.017486) \| 0.017010 / 0.141683 (-0.124673) \| 1.172277 / 1.452155 (-0.279878) \| 1.205359 / 1.492716 (-0.287358) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074907) \| 0.303561 / 0.000490 (0.303071) \| 0.000219 / 0.000200 (0.000019) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022379 / 0.037411 (-0.015032) \| 0.075460 / 0.014526 (0.060934) \| 0.085795 / 0.176557 (-0.090762) \| 0.124776 / 0.737135 (-0.612360) \| 0.088260 / 0.296338 (-0.208079) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302873 / 0.215209 (0.087664) \| 2.936173 / 2.077655 (0.858519) \| 1.589251 / 1.504120 (0.085131) \| 1.477552 / 1.541195 (-0.063643) \| 1.479322 / 1.468490 (0.010832) \| 0.570481 / 4.584777 (-4.014296) \| 2.434137 / 3.745712 (-1.311575) \| 2.774012 / 5.269862 (-2.495849) \| 1.718103 / 4.565676 (-2.847574) \| 0.061951 / 0.424275 (-0.362324) \| 0.004992 / 0.007607 (-0.002615) \| 0.352250 / 0.226044 (0.126205) \| 3.457417 / 2.268929 (1.188488) \| 1.934587 / 55.444624 (-53.510037) \| 1.646904 / 6.876477 (-5.229573) \| 1.669429 / 2.142072 (-0.472643) \| 0.649665 / 4.805227 (-4.155562) \| 0.116630 / 6.500664 (-6.384034) \| 0.040669 / 0.075469 (-0.034800) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011488 / 1.841788 (-0.830300) \| 11.866394 / 8.074308 (3.792086) \| 10.144588 / 10.191392 (-0.046804) \| 0.129931 / 0.680424 (-0.550493) \| 0.014885 / 0.534201 (-0.519316) \| 0.287463 / 0.579283 (-0.291821) \| 0.280754 / 0.434364 (-0.153610) \| 0.330139 / 0.540337 (-0.210199) \| 0.414653 / 1.386936 (-0.972283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#585275b8deaebd1bdcbd3725fa63172395791c73 \"CML watermark\")\n" ]
6,647	Update loading.mdx to include "jsonl" file loading.	* A small update to the documentation, noting the ability to load jsonl files.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6647", "html_url": "https://github.com/huggingface/datasets/pull/6647", "diff_url": "https://github.com/huggingface/datasets/pull/6647.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6647.patch", "merged_at": null }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6647). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thanks for adding the explicit loading command.\r\n> \r\n> However, I would move it just below, where we present the JSON-Lines example.\r\n> \r\n> * Maybe adding that this format is called JSON-Lines\r\n> * Add the example after the JSON-Lines data example\r\n> \r\n> https://github.com/huggingface/datasets/blob/14d9afbb7ae1b787c450261ca0ff374551993031/docs/source/loading.mdx#L135-L138\r\n\r\nThank you @albertvillanova for the feedback! I moved the jsonl file loading example to a more appropriate location. " ]
6,646	Better multi-gpu example	Use Qwen1.5-0.5B-Chat as an easy example for multi-GPU the previous example was using a model for translation and the way it was setup was not really the right way to use the model.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6646", "html_url": "https://github.com/huggingface/datasets/pull/6646", "diff_url": "https://github.com/huggingface/datasets/pull/6646.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6646.patch", "merged_at": "2024-02-07T14:59:11" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6646). 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) \| 0.005598 / 0.011353 (-0.005755) \| 0.003640 / 0.011008 (-0.007369) \| 0.064557 / 0.038508 (0.026049) \| 0.029645 / 0.023109 (0.006536) \| 0.243695 / 0.275898 (-0.032203) \| 0.261252 / 0.323480 (-0.062228) \| 0.004067 / 0.007986 (-0.003919) \| 0.002883 / 0.004328 (-0.001446) \| 0.049192 / 0.004250 (0.044942) \| 0.045299 / 0.037052 (0.008246) \| 0.273207 / 0.258489 (0.014718) \| 0.288668 / 0.293841 (-0.005173) \| 0.028114 / 0.128546 (-0.100432) \| 0.010597 / 0.075646 (-0.065049) \| 0.215345 / 0.419271 (-0.203927) \| 0.036119 / 0.043533 (-0.007414) \| 0.243718 / 0.255139 (-0.011421) \| 0.266657 / 0.283200 (-0.016543) \| 0.018176 / 0.141683 (-0.123507) \| 1.127926 / 1.452155 (-0.324229) \| 1.168066 / 1.492716 (-0.324650) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096001 / 0.018006 (0.077994) \| 0.304317 / 0.000490 (0.303828) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018241 / 0.037411 (-0.019170) \| 0.061505 / 0.014526 (0.046979) \| 0.072456 / 0.176557 (-0.104101) \| 0.118315 / 0.737135 (-0.618821) \| 0.075154 / 0.296338 (-0.221184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278748 / 0.215209 (0.063538) \| 2.729923 / 2.077655 (0.652268) \| 1.416835 / 1.504120 (-0.087285) \| 1.294016 / 1.541195 (-0.247179) \| 1.323249 / 1.468490 (-0.145241) \| 0.575389 / 4.584777 (-4.009388) \| 2.404923 / 3.745712 (-1.340789) \| 2.769233 / 5.269862 (-2.500629) \| 1.742340 / 4.565676 (-2.823336) \| 0.062664 / 0.424275 (-0.361611) \| 0.004951 / 0.007607 (-0.002656) \| 0.335024 / 0.226044 (0.108979) \| 3.291446 / 2.268929 (1.022518) \| 1.797095 / 55.444624 (-53.647530) \| 1.532963 / 6.876477 (-5.343513) \| 1.529315 / 2.142072 (-0.612758) \| 0.654922 / 4.805227 (-4.150305) \| 0.118772 / 6.500664 (-6.381892) \| 0.042034 / 0.075469 (-0.033435) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983646 / 1.841788 (-0.858141) \| 11.518625 / 8.074308 (3.444317) \| 9.538781 / 10.191392 (-0.652611) \| 0.140300 / 0.680424 (-0.540124) \| 0.013966 / 0.534201 (-0.520235) \| 0.287071 / 0.579283 (-0.292212) \| 0.270201 / 0.434364 (-0.164163) \| 0.323294 / 0.540337 (-0.217044) \| 0.418130 / 1.386936 (-0.968806) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005508 / 0.011353 (-0.005844) \| 0.003714 / 0.011008 (-0.007294) \| 0.050031 / 0.038508 (0.011523) \| 0.031866 / 0.023109 (0.008756) \| 0.272248 / 0.275898 (-0.003650) \| 0.295105 / 0.323480 (-0.028375) \| 0.005179 / 0.007986 (-0.002807) \| 0.002820 / 0.004328 (-0.001508) \| 0.048896 / 0.004250 (0.044646) \| 0.045975 / 0.037052 (0.008922) \| 0.287662 / 0.258489 (0.029173) \| 0.321139 / 0.293841 (0.027298) \| 0.049242 / 0.128546 (-0.079304) \| 0.010732 / 0.075646 (-0.064914) \| 0.057943 / 0.419271 (-0.361328) \| 0.033527 / 0.043533 (-0.010006) \| 0.271746 / 0.255139 (0.016607) \| 0.291404 / 0.283200 (0.008204) \| 0.019351 / 0.141683 (-0.122332) \| 1.157221 / 1.452155 (-0.294934) \| 1.215757 / 1.492716 (-0.276959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096950 / 0.018006 (0.078944) \| 0.312002 / 0.000490 (0.311512) \| 0.000223 / 0.000200 (0.000023) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022288 / 0.037411 (-0.015123) \| 0.075282 / 0.014526 (0.060756) \| 0.087445 / 0.176557 (-0.089112) \| 0.125617 / 0.737135 (-0.611519) \| 0.088878 / 0.296338 (-0.207460) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291961 / 0.215209 (0.076752) \| 2.881445 / 2.077655 (0.803790) \| 1.586128 / 1.504120 (0.082008) \| 1.458636 / 1.541195 (-0.082558) \| 1.487001 / 1.468490 (0.018511) \| 0.575466 / 4.584777 (-4.009311) \| 2.454941 / 3.745712 (-1.290771) \| 2.878077 / 5.269862 (-2.391785) \| 1.787215 / 4.565676 (-2.778462) \| 0.064010 / 0.424275 (-0.360265) \| 0.005092 / 0.007607 (-0.002516) \| 0.360500 / 0.226044 (0.134455) \| 3.465574 / 2.268929 (1.196646) \| 1.957516 / 55.444624 (-53.487108) \| 1.666282 / 6.876477 (-5.210195) \| 1.690070 / 2.142072 (-0.452002) \| 0.661323 / 4.805227 (-4.143905) \| 0.117824 / 6.500664 (-6.382840) \| 0.042286 / 0.075469 (-0.033183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.026517 / 1.841788 (-0.815270) \| 12.083347 / 8.074308 (4.009039) \| 10.269319 / 10.191392 (0.077927) \| 0.139253 / 0.680424 (-0.541171) \| 0.016258 / 0.534201 (-0.517943) \| 0.290583 / 0.579283 (-0.288700) \| 0.284338 / 0.434364 (-0.150026) \| 0.335865 / 0.540337 (-0.204473) \| 0.416600 / 1.386936 (-0.970336) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba3cfad91e9366cda0ba203700fc745d8bcd1f17 \"CML watermark\")\n", "Thanks, I was needing this example today <3 " ]
6,645	Support fsspec 2024.2	Support fsspec 2024.2. First, we should address: - #6644	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "I'd be very grateful. This upper bound banished me straight into dependency hell today. :(" ]
6,644	Support fsspec 2023.12	Support fsspec 2023.12 by handling previous and new glob behavior.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "The pinned fsspec version range dependency conflict has been affecting several of our users in https://github.com/iterative/dvc. I've opened an initial PR that I think should resolve the glob behavior changes with using datasets + the latest fsspec release.\r\n\r\nPlease let us know if there's any other fsspec related behavior in datasets that needs to be updated to get 2024.2 supported, we'd like to get this conflict resolved as quickly as possible and we're willing to contribute any additional work that's required here.\r\n\r\ncc @dberenbaum" ]
6,643	Faiss GPU index cannot be serialised when passed to trainer	### Describe the bug I am working on a retrieval project and encountering I have encountered two issues in the hugging face faiss integration: 1. I am trying to pass in a dataset with a faiss index to the Huggingface trainer. The code works for a cpu faiss index, but doesn't for a gpu one, getting error: ``` File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1543, in train return inner_training_loop( File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 1555, in _inner_training_loop train_dataloader = self.get_train_dataloader() File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 831, in get_train_dataloader train_dataset = self._remove_unused_columns(train_dataset, description="training") File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/transformers/trainer.py", line 725, in _remove_unused_columns return dataset.remove_columns(ignored_columns) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/fingerprint.py", line 481, in wrapper out = func(dataset, args, *kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2146, in remove_columns dataset = copy.deepcopy(self) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, rv) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 271, in _reconstruct state = deepcopy(state, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 146, in deepcopy y = copier(x, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 231, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/copy.py", line 161, in deepcopy rv = reductor(4) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 556, in index_getstate return {"this": serialize_index(self).tobytes()} File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/__init__.py", line 1607, in serialize_index write_index(index, writer) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/faiss/swigfaiss.py", line 9843, in write_index return _swigfaiss.write_index(args) RuntimeError: Error in void faiss::write_index(const faiss::Index, faiss::IOWriter) at /project/faiss/faiss/impl/index_write.cpp:590: don't know how to serialize this type of index ``` The index was created with the add_faiss_index method ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) ``` 2. Athough faiss is written to be compatible on the gpu for searching [https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU) I am getting error when trying to use the hugggingface code to do the search on gpu. This seems to be caused by this line https://github.com/huggingface/datasets/blob/f9975f636542df7f95c27065ea93147440d690b7/src/datasets/search.py#L376 producing error ``` total_scores, total_examples = self.dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 773, in get_nearest_examples_batch total_scores, total_indices = self.search_batch(index_name, queries, k, kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 727, in search_batch return self._indexes[index_name].search_batch(queries, k, kwargs) File "/users/rubman/.conda/envs/protein_npt_env/lib/python3.10/site-packages/datasets/search.py", line 376, in search_batch if not queries.flags.c_contiguous: AttributeError: 'Tensor' object has no attribute 'flags' ``` ### Steps to reproduce the bug ``` train_dataset.add_faiss_index( column='embeddings', index_name='embeddings', string_factory=faiss_index_string, train_size=config.faiss_train_size, device=0, # Use -1 for CPU, or specify GPU device ID faiss_verbose=True ) Trainer( model=model, args=args, train_dataset=train_dataset, eval_dataset=eval_dataset, data_collator=data_collator, tokenizer=tokenizer ) train_dataset.get_nearest_examples_batch('embeddings', embeddings, k=self.k) ``` ### Expected behavior I would expect the faiss database code to be gpu compatible ### Environment info huggingface Version: 2.16.1	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi ! make sure your query embeddings are numpy arrays, not torch tensors ;)", "Hi Quentin, not sure how that solves the problem number 1. I am trying to pass on a dataset with a faiss gpu for training to the standard trainer but getting this serialisation error. What is a workaround this? I do not want to remove the faiss index, as I would want to use it to create batches of retrieved samples from the dataset. \r\nThanks in advance for your help!", "Issue number one seems to be an issue with FAISS indexes not being compatible with copy.deepcopy.\r\n\r\nMaybe you try to not remove the columns, e.g. by passing `remove_unused_columns=False`" ]
6,642	Differently dataset object saved than it is loaded.	### Describe the bug Differently sized object is saved than it is loaded. ### Steps to reproduce the bug Hi, I save dataset in a following way: ``` dataset = load_dataset("json", data_files={ "train": os.path.join(input_folder, f"{task_meta_type}_{task_type}_train.jsonl"), "test": os.path.join(input_folder, f"{task_meta_type}_{task_type}_test.jsonl")}) print(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") dataset.save_to_disk(os.path.join(output_folder, f"{task_meta_type}_{task_type}")) ``` this yields output ``` .data/hf_dataset/propaganda_zanr Length of train dataset: 7642 Length of test dataset: 1000 ``` Everything looks fine. Then I load the dataset ```python from datasets import load_dataset dataset_path = ".data/hf_dataset/propaganda_zanr" dataset = load_dataset(dataset_path) print(f"Length of train dataset: {len(dataset['train'])}") print(f"Length of test dataset: {len(dataset['test'])}") ``` this prints ``` Generating train split: 1 examples [00:00, 72.10 examples/s] Generating test split: 1 examples [00:00, 100.69 examples/s] Length of train dataset: 1 Length of test dataset: 1 ``` I dont' understand :( ### Expected behavior same object is loaded ### Environment info datasets==2.16.1	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "I see now, that I have to use `load_from_disk`, in order to load dataset properly, not `load_dataset`. Why is this behavior split? Why do we need both, `load_dataset` and `load_from_disk`?\r\n\r\nUnless answered, I believe this might be helpful for other hf datasets newbies.\r\n\r\nAnyway, made a `load_dataset` compatible dataset in a following way. I created a directory, and just copied jsonl there as `train.jsonl/test.jsonl`.\r\n```python\r\noutput_folder = os.path.join(args.output_folder, f\"{task_meta_type}_{task_type}\")\r\nos.makedirs(output_folder, exist_ok=True)\r\nfile = f\"{task_meta_type}_{task_type}_train.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"train.jsonl\"))\r\n# now test\r\nfile = f\"{task_meta_type}_{task_type}_test.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"test.jsonl\"))\r\n```\r\n", "Hi @MFajcik, \r\n\r\nYou can find information about save_to_disk/load_from_disk in our docs:\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/process#save\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.save_to_disk\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.load_from_disk" ]
6,641	unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte	### Describe the bug unicodedecodeerror: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte ### Steps to reproduce the bug ``` import sys sys.getdefaultencoding() 'utf-8' from datasets import load_dataset print(f"Train dataset size: {len(dataset['train'])}") print(f"Test dataset size: {len(dataset['test'])}") Resolving data files: 100% 159/159 [00:00<00:00, 9909.28it/s] Using custom data configuration samsum-0b1209637541c9e6 Downloading and preparing dataset json/samsum to C:/Users/Administrator/.cache/huggingface/datasets/json/samsum-0b1209637541c9e6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51... Downloading data files: 100% 3/3 [00:00<00:00, 119.99it/s] Extracting data files: 100% 3/3 [00:00<00:00, 9.54it/s] Generating train split: 88392/0 [00:15<00:00, 86848.17 examples/s] Generating test split: 0/0 [00:00<?, ? examples/s] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:132, in Json._generate_tables(self, files) 131 try: --> 132 pa_table = paj.read_json( 133 io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) 134 ) 135 break File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\_json.pyx:290, in pyarrow._json.read_json() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pyarrow\error.pxi:100, in pyarrow.lib.check_status() ArrowInvalid: JSON parse error: Invalid value. in row 0 During handling of the above exception, another exception occurred: UnicodeDecodeError Traceback (most recent call last) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1819, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1818 _time = time.time() -> 1819 for _, table in generator: 1820 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\packaged_modules\json\json.py:153, in Json._generate_tables(self, files) 152 with open(file, encoding="utf-8") as f: --> 153 dataset = json.load(f) 154 except json.JSONDecodeError: File ~\AppData\Local\Programs\Python\Python310\lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, kw) 276 """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing 277 a JSON document) to a Python object. 278 (...) 291 kwarg; otherwise ``JSONDecoder`` is used. 292 """ --> 293 return loads(fp.read(), 294 cls=cls, object_hook=object_hook, 295 parse_float=parse_float, parse_int=parse_int, 296 parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, kw) File ~\AppData\Local\Programs\Python\Python310\lib\codecs.py:322, in BufferedIncrementalDecoder.decode(self, input, final) 321 data = self.buffer + input --> 322 (result, consumed) = self._buffer_decode(data, self.errors, final) 323 # keep undecoded input until the next call UnicodeDecodeError: 'utf-8' codec can't decode byte 0xac in position 25: invalid start byte The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[81], line 5 1 from datasets import load_dataset 3 # Load dataset from the hub 4 #dataset = load_dataset("json",data_files="C:/Users/Administrator/Desktop/samsum/samsum/data/corpus/train.json",field="data") ----> 5 dataset = load_dataset('json',"samsum") 6 #dataset = load_dataset("samsum") 7 print(f"Train dataset size: {len(dataset['train'])}") File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\load.py:1758, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, config_kwargs) 1755 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1757 # Download and prepare data -> 1758 builder_instance.download_and_prepare( 1759 download_config=download_config, 1760 download_mode=download_mode, 1761 ignore_verifications=ignore_verifications, 1762 try_from_hf_gcs=try_from_hf_gcs, 1763 num_proc=num_proc, 1764 ) 1766 # Build dataset for splits 1767 keep_in_memory = ( 1768 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1769 ) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:860, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 858 if num_proc is not None: 859 prepare_split_kwargs["num_proc"] = num_proc --> 860 self._download_and_prepare( 861 dl_manager=dl_manager, 862 verify_infos=verify_infos, 863 prepare_split_kwargs, 864 download_and_prepare_kwargs, 865 ) 866 # Sync info 867 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:953, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 949 split_dict.add(split_generator.split_info) 951 try: 952 # Prepare split will record examples associated to the split --> 953 self._prepare_split(split_generator, prepare_split_kwargs) 954 except OSError as e: 955 raise OSError( 956 "Cannot find data file. " 957 + (self.manual_download_instructions or "") 958 + "\nOriginal error:\n" 959 + str(e) 960 ) from None File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1708, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1706 gen_kwargs = split_generator.gen_kwargs 1707 job_id = 0 -> 1708 for job_id, done, content in self._prepare_split_single( 1709 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1710 ): 1711 if done: 1712 result = content File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\datasets\builder.py:1851, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1849 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1850 e = e.__context__ -> 1851 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1853 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior can't load dataset ### Environment info dataset:samsum system :win10 gpu:m40 24G	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi @Hughhuh. \r\n\r\nI have formatted the issue because it was not easily readable. Additionally, the environment info is incomplete: it seems you did not run the proposed CLI command `datasets-cli env` and essential information is missing: version of `datasets`, version of `pyarrow`,...\r\n\r\nWith the information you provided, it seems an issue with the specific \"samsum\" dataset. I'm transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/samsum/discussions/5" ]
6,640	Sign Language Support	### Feature request Currently, there are only several Sign Language labels, I would like to propose adding all the Signed Languages as new labels which are described in this ISO standard: https://www.evertype.com/standards/iso639/sign-language.html ### Motivation Datasets currently only have labels for several signed languages. There are more signed languages in the world. Furthermore, some signed languages that have a lot of online data cannot be found because of this reason (for instance, German Sign Language, and there is no German Sign Language label on huggingface datasets even though there are a lot of readily available sign language datasets exist for German Sign Language, which are used very frequently in Sign Language Processing papers, and models.) ### Your contribution I can submit a PR for this as well, adding the ISO codes and languages to the labels in datasets.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,639	Run download_and_prepare if missing splits	A first step towards https://github.com/huggingface/datasets/issues/6529	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6639", "html_url": "https://github.com/huggingface/datasets/pull/6639", "diff_url": "https://github.com/huggingface/datasets/pull/6639.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6639.patch", "merged_at": null }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6639). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
6,638	Cannot download wmt16 dataset	### Describe the bug As of this morning (PST) 2/1/2024, seeing the wmt16 dataset is missing from opus , could you suggest an alternative? ``` Downloading data files: 0%\| \| 0/4 [00:00<?, ?it/s]Traceback (most recent call last): File "test.py", line 2, in <module> raw_datasets = load_dataset("wmt16","ro-en",split="train") File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1717, in _download_and_prepare super()._download_and_prepare( File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1027, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/746749a11d25c02058042da7502d973ff410e73457f3d305fc1177dc0e8c4227/wmt_utils.py", line 754, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 565, in download_and_extract return self.extract(self.download(url_or_urls)) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 428, in download downloaded_path_or_paths = map_nested( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 464, in map_nested mapped = [ File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 465, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 384, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py", line 367, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py", line 454, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 182, in cached_path output_path = get_from_cache( File "/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py", line 596, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz ``` ### Steps to reproduce the bug ``` from datasets import load_dataset raw_datasets = load_dataset("wmt16","ro-en",split="train") ``` ### Expected behavior Expect the dataset to be downloaded/ at least a clean exit with error explaining dataset is missing and a suggestion for next steps ### Environment info - `datasets` version: 2.14.7 - Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.17.3 - PyArrow version: 15.0.0 - Pandas version: 2.0.1	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Looks like it works with latest datasets repository\r\n```\r\n- `datasets` version: 2.16.2.dev0\r\n- Platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 2.0.1\r\n- `fsspec` version: 2023.10.0\r\n```\r\n\r\nCould you explain which is the minimum version that fixes this?\r\nEdit: Looks like that's 2.16.0, will close out issue" ]
6,637	'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets	### Describe the bug If you: 1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset 2. Set the output format to torch tensors with .with_format('torch') Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting. ### Steps to reproduce the bug ```python import datasets import torch from tqdm import tqdm rand_a = torch.randn(3,224,224) rand_b = torch.randn(3,224,224) a = torch.stack([rand_a] * 1000) b = torch.stack([rand_b] * 1000) features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")}) ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset() ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset() # Iterating through either dataset with torch formatting is really fast (2000it/s on my machine) for example in tqdm(ds_a.with_format('torch')): pass # Iterating through either dataset shuffled is also pretty fast (100it/s on my machine) for example in tqdm(ds_a.shuffle()): pass # Iterating through this interleaved dataset is pretty fast (200it/s on my machine) ds_fast = datasets.interleave_datasets([ds_a, ds_b]) for example in tqdm(ds_fast): pass # Iterating through either dataset with torch formatting after shuffling is really slow... (<2it/s on my machine) for example in tqdm(ds_a.shuffle().with_format('torch')): pass # Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)... ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch') for example in tqdm(ds_slow): pass # Even doing this is way faster!! (70it/s on my machine) for example in tqdm(ds_fast): test = torch.tensor(example['tensor']) ``` ### Expected behavior Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch'). ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38 - Python version: 3.11.6 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "The \"torch\" formatting is usually fast because we do zero-copy conversion from the Arrow data on your disk to Torch tensors. However IterableDataset shuffling seems to do data copies that slow down the pipeline, and it shuffles python objects instead of Arrow data.\r\n\r\nTo fix this we need to implement `BufferShuffledExamplesIterable.iter_arrow()` (same as regular `BufferShuffledExamplesIterable.__iter__()` but yields Arrow tables)\r\n\r\nhttps://github.com/huggingface/datasets/blob/b7d854b7fd3e9a330e21b76ee8421d4a7ebb4a7a/src/datasets/iterable_dataset.py#L968-L974\r\n" ]
6,636	Faster column validation and reordering	I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6636", "html_url": "https://github.com/huggingface/datasets/pull/6636", "diff_url": "https://github.com/huggingface/datasets/pull/6636.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6636.patch", "merged_at": "2024-02-06T23:03:38" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I still saw that it took ~3.5 minutes per batch on 6000 features when using `dataset.map(lambda x: x, batched=True)`. From the profile, the culprits were mainly with `ArrowWriter.write_batch` and `ArrowWriter._build_writer`. The slow down from `_build_writer` is due to updating existing features with the inferred ones. I don't think this can be optimized any further, but fortunately, I can avoid this by setting the `features` in `map`. On the other hand, `write_batch` selects cols based on intersection and difference between schema names and example keys using two for loops. The same exists in `ArrowWriter.write_examples_on_file`. Optimizing the column selection using set operations effectively brings it from 3.5 minutes per batch down to 6 seconds per batch. Can we add these changes along with this PR?\r\n\r\nEdit: Ah just realized you can avoid the issue with inferring features altogether when you set the format to arrow (or pandas).", "<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) \| 0.004990 / 0.011353 (-0.006363) \| 0.003138 / 0.011008 (-0.007870) \| 0.062368 / 0.038508 (0.023860) \| 0.028634 / 0.023109 (0.005524) \| 0.241297 / 0.275898 (-0.034601) \| 0.264433 / 0.323480 (-0.059047) \| 0.003133 / 0.007986 (-0.004852) \| 0.003444 / 0.004328 (-0.000885) \| 0.048522 / 0.004250 (0.044271) \| 0.043700 / 0.037052 (0.006648) \| 0.257054 / 0.258489 (-0.001435) \| 0.277551 / 0.293841 (-0.016290) \| 0.027132 / 0.128546 (-0.101414) \| 0.010395 / 0.075646 (-0.065251) \| 0.208003 / 0.419271 (-0.211269) \| 0.035814 / 0.043533 (-0.007719) \| 0.250098 / 0.255139 (-0.005041) \| 0.266726 / 0.283200 (-0.016474) \| 0.018424 / 0.141683 (-0.123259) \| 1.129242 / 1.452155 (-0.322912) \| 1.167674 / 1.492716 (-0.325042) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091808 / 0.018006 (0.073802) \| 0.298726 / 0.000490 (0.298236) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.019119 / 0.037411 (-0.018292) \| 0.061969 / 0.014526 (0.047443) \| 0.073392 / 0.176557 (-0.103165) \| 0.119460 / 0.737135 (-0.617675) \| 0.074072 / 0.296338 (-0.222266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281435 / 0.215209 (0.066226) \| 2.702094 / 2.077655 (0.624439) \| 1.411541 / 1.504120 (-0.092579) \| 1.284084 / 1.541195 (-0.257111) \| 1.302638 / 1.468490 (-0.165852) \| 0.562420 / 4.584777 (-4.022357) \| 2.364890 / 3.745712 (-1.380822) \| 2.744033 / 5.269862 (-2.525828) \| 1.699000 / 4.565676 (-2.866677) \| 0.062315 / 0.424275 (-0.361961) \| 0.004982 / 0.007607 (-0.002625) \| 0.334385 / 0.226044 (0.108341) \| 3.203268 / 2.268929 (0.934339) \| 1.766998 / 55.444624 (-53.677627) \| 1.497164 / 6.876477 (-5.379313) \| 1.509996 / 2.142072 (-0.632077) \| 0.633014 / 4.805227 (-4.172213) \| 0.115317 / 6.500664 (-6.385347) \| 0.041120 / 0.075469 (-0.034349) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965877 / 1.841788 (-0.875911) \| 11.219909 / 8.074308 (3.145601) \| 9.333822 / 10.191392 (-0.857570) \| 0.136482 / 0.680424 (-0.543941) \| 0.013632 / 0.534201 (-0.520569) \| 0.287251 / 0.579283 (-0.292032) \| 0.262786 / 0.434364 (-0.171578) \| 0.322893 / 0.540337 (-0.217444) \| 0.418180 / 1.386936 (-0.968756) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005444 / 0.011353 (-0.005909) \| 0.003147 / 0.011008 (-0.007862) \| 0.049242 / 0.038508 (0.010734) \| 0.030944 / 0.023109 (0.007834) \| 0.281901 / 0.275898 (0.006003) \| 0.303820 / 0.323480 (-0.019660) \| 0.004326 / 0.007986 (-0.003659) \| 0.002696 / 0.004328 (-0.001632) \| 0.048306 / 0.004250 (0.044055) \| 0.044145 / 0.037052 (0.007093) \| 0.297253 / 0.258489 (0.038764) \| 0.324062 / 0.293841 (0.030221) \| 0.046724 / 0.128546 (-0.081823) \| 0.010079 / 0.075646 (-0.065567) \| 0.057635 / 0.419271 (-0.361636) \| 0.033621 / 0.043533 (-0.009912) \| 0.282303 / 0.255139 (0.027164) \| 0.300761 / 0.283200 (0.017561) \| 0.017116 / 0.141683 (-0.124567) \| 1.156519 / 1.452155 (-0.295636) \| 1.216087 / 1.492716 (-0.276630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093011 / 0.018006 (0.075005) \| 0.301310 / 0.000490 (0.300820) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.023112 / 0.037411 (-0.014299) \| 0.075192 / 0.014526 (0.060666) \| 0.086213 / 0.176557 (-0.090343) \| 0.125853 / 0.737135 (-0.611282) \| 0.087754 / 0.296338 (-0.208585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301095 / 0.215209 (0.085886) \| 2.911769 / 2.077655 (0.834114) \| 1.614708 / 1.504120 (0.110588) \| 1.494497 / 1.541195 (-0.046698) \| 1.506978 / 1.468490 (0.038488) \| 0.572743 / 4.584777 (-4.012034) \| 2.417142 / 3.745712 (-1.328570) \| 2.755338 / 5.269862 (-2.514523) \| 1.711026 / 4.565676 (-2.854650) \| 0.062732 / 0.424275 (-0.361543) \| 0.005031 / 0.007607 (-0.002576) \| 0.352343 / 0.226044 (0.126298) \| 3.465183 / 2.268929 (1.196255) \| 1.958795 / 55.444624 (-53.485829) \| 1.682239 / 6.876477 (-5.194238) \| 1.688897 / 2.142072 (-0.453176) \| 0.643311 / 4.805227 (-4.161916) \| 0.115426 / 6.500664 (-6.385238) \| 0.040338 / 0.075469 (-0.035131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005322 / 1.841788 (-0.836466) \| 11.779380 / 8.074308 (3.705072) \| 10.041574 / 10.191392 (-0.149818) \| 0.127617 / 0.680424 (-0.552807) \| 0.015840 / 0.534201 (-0.518361) \| 0.286905 / 0.579283 (-0.292378) \| 0.275180 / 0.434364 (-0.159183) \| 0.332498 / 0.540337 (-0.207840) \| 0.410719 / 1.386936 (-0.976217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32b206d47f582380f9c64578dcfa6c48252db3b8 \"CML watermark\")\n" ]
6,635	Fix missing info when loading some datasets from Parquet export	Fix getting the info for script-based datasets with Parquet export with a single config not named "default". E.g. ```python from datasets import load_dataset_builder b = load_dataset_builder("bookcorpus") print(b.info.features) # should print {'text': Value(dtype='string', id=None)} ``` I fixed this by setting the default config name when there is only one config.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6635", "html_url": "https://github.com/huggingface/datasets/pull/6635", "diff_url": "https://github.com/huggingface/datasets/pull/6635.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6635.patch", "merged_at": "2024-02-07T16:41:04" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6635). 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) \| 0.005577 / 0.011353 (-0.005776) \| 0.004452 / 0.011008 (-0.006556) \| 0.067849 / 0.038508 (0.029341) \| 0.032328 / 0.023109 (0.009219) \| 0.256924 / 0.275898 (-0.018974) \| 0.273410 / 0.323480 (-0.050070) \| 0.004359 / 0.007986 (-0.003626) \| 0.003484 / 0.004328 (-0.000845) \| 0.053880 / 0.004250 (0.049630) \| 0.058142 / 0.037052 (0.021089) \| 0.268863 / 0.258489 (0.010374) \| 0.307977 / 0.293841 (0.014136) \| 0.028840 / 0.128546 (-0.099707) \| 0.011808 / 0.075646 (-0.063839) \| 0.216277 / 0.419271 (-0.202995) \| 0.039245 / 0.043533 (-0.004288) \| 0.250420 / 0.255139 (-0.004719) \| 0.273642 / 0.283200 (-0.009557) \| 0.019340 / 0.141683 (-0.122342) \| 1.176734 / 1.452155 (-0.275421) \| 1.250643 / 1.492716 (-0.242074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181210 / 0.018006 (0.163204) \| 1.070750 / 0.000490 (1.070261) \| 0.000315 / 0.000200 (0.000115) \| 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) \| 0.022905 / 0.037411 (-0.014507) \| 0.064549 / 0.014526 (0.050023) \| 0.077113 / 0.176557 (-0.099443) \| 0.131976 / 0.737135 (-0.605159) \| 0.081266 / 0.296338 (-0.215072) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291363 / 0.215209 (0.076154) \| 2.851691 / 2.077655 (0.774036) \| 1.592815 / 1.504120 (0.088695) \| 1.494550 / 1.541195 (-0.046645) \| 1.516464 / 1.468490 (0.047974) \| 0.583244 / 4.584777 (-4.001532) \| 2.504907 / 3.745712 (-1.240805) \| 3.183490 / 5.269862 (-2.086371) \| 1.932854 / 4.565676 (-2.632823) \| 0.067564 / 0.424275 (-0.356711) \| 0.006587 / 0.007607 (-0.001020) \| 0.346368 / 0.226044 (0.120324) \| 3.428256 / 2.268929 (1.159327) \| 1.994176 / 55.444624 (-53.450448) \| 1.688116 / 6.876477 (-5.188360) \| 1.767653 / 2.142072 (-0.374420) \| 0.673867 / 4.805227 (-4.131360) \| 0.125582 / 6.500664 (-6.375082) \| 0.047198 / 0.075469 (-0.028271) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.002895 / 1.841788 (-0.838893) \| 16.332893 / 8.074308 (8.258585) \| 10.781993 / 10.191392 (0.590601) \| 0.153919 / 0.680424 (-0.526505) \| 0.015528 / 0.534201 (-0.518673) \| 0.306182 / 0.579283 (-0.273101) \| 0.296380 / 0.434364 (-0.137984) \| 0.341432 / 0.540337 (-0.198905) \| 0.455900 / 1.386936 (-0.931036) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006442 / 0.011353 (-0.004911) \| 0.004433 / 0.011008 (-0.006576) \| 0.053327 / 0.038508 (0.014819) \| 0.035966 / 0.023109 (0.012856) \| 0.280913 / 0.275898 (0.005015) \| 0.308419 / 0.323480 (-0.015061) \| 0.005842 / 0.007986 (-0.002144) \| 0.003789 / 0.004328 (-0.000539) \| 0.053983 / 0.004250 (0.049732) \| 0.069052 / 0.037052 (0.032000) \| 0.299225 / 0.258489 (0.040736) \| 0.336470 / 0.293841 (0.042629) \| 0.068170 / 0.128546 (-0.060377) \| 0.012259 / 0.075646 (-0.063388) \| 0.064166 / 0.419271 (-0.355106) \| 0.037291 / 0.043533 (-0.006241) \| 0.281318 / 0.255139 (0.026179) \| 0.297093 / 0.283200 (0.013893) \| 0.021358 / 0.141683 (-0.120324) \| 1.189584 / 1.452155 (-0.262571) \| 1.256985 / 1.492716 (-0.235731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216726 / 0.018006 (0.198720) \| 2.496957 / 0.000490 (2.496467) \| 0.000336 / 0.000200 (0.000136) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026604 / 0.037411 (-0.010807) \| 0.080398 / 0.014526 (0.065873) \| 0.094475 / 0.176557 (-0.082082) \| 0.136263 / 0.737135 (-0.600873) \| 0.097898 / 0.296338 (-0.198440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295171 / 0.215209 (0.079962) \| 2.947530 / 2.077655 (0.869875) \| 1.607531 / 1.504120 (0.103411) \| 1.485045 / 1.541195 (-0.056150) \| 1.524899 / 1.468490 (0.056409) \| 0.572934 / 4.584777 (-4.011843) \| 2.544320 / 3.745712 (-1.201393) \| 3.292630 / 5.269862 (-1.977232) \| 1.927138 / 4.565676 (-2.638539) \| 0.068560 / 0.424275 (-0.355715) \| 0.005982 / 0.007607 (-0.001625) \| 0.345833 / 0.226044 (0.119789) \| 3.424253 / 2.268929 (1.155324) \| 2.195017 / 55.444624 (-53.249608) \| 1.712037 / 6.876477 (-5.164440) \| 1.763899 / 2.142072 (-0.378174) \| 0.653776 / 4.805227 (-4.151451) \| 0.123056 / 6.500664 (-6.377609) \| 0.044572 / 0.075469 (-0.030897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.033400 / 1.841788 (-0.808388) \| 15.409887 / 8.074308 (7.335579) \| 11.220990 / 10.191392 (1.029597) \| 0.153603 / 0.680424 (-0.526821) \| 0.016866 / 0.534201 (-0.517335) \| 0.311945 / 0.579283 (-0.267338) \| 0.307048 / 0.434364 (-0.127316) \| 0.350422 / 0.540337 (-0.189915) \| 0.447308 / 1.386936 (-0.939628) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14d9afbb7ae1b787c450261ca0ff374551993031 \"CML watermark\")\n" ]
6,634	Support data_dir parameter in push_to_hub	Support `data_dir` parameter in `push_to_hub`. This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en".	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6634", "html_url": "https://github.com/huggingface/datasets/pull/6634", "diff_url": "https://github.com/huggingface/datasets/pull/6634.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6634.patch", "merged_at": "2024-02-05T10:26:40" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.005125 / 0.011353 (-0.006228) \| 0.003772 / 0.011008 (-0.007236) \| 0.063258 / 0.038508 (0.024750) \| 0.029479 / 0.023109 (0.006370) \| 0.245554 / 0.275898 (-0.030344) \| 0.266395 / 0.323480 (-0.057085) \| 0.003063 / 0.007986 (-0.004922) \| 0.003298 / 0.004328 (-0.001031) \| 0.049242 / 0.004250 (0.044991) \| 0.042390 / 0.037052 (0.005338) \| 0.258176 / 0.258489 (-0.000313) \| 0.279935 / 0.293841 (-0.013906) \| 0.027910 / 0.128546 (-0.100637) \| 0.011033 / 0.075646 (-0.064613) \| 0.207763 / 0.419271 (-0.211509) \| 0.036127 / 0.043533 (-0.007405) \| 0.247363 / 0.255139 (-0.007776) \| 0.261309 / 0.283200 (-0.021890) \| 0.020259 / 0.141683 (-0.121424) \| 1.152760 / 1.452155 (-0.299395) \| 1.194853 / 1.492716 (-0.297863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088936 / 0.018006 (0.070930) \| 0.298396 / 0.000490 (0.297906) \| 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) \| 0.018434 / 0.037411 (-0.018977) \| 0.061991 / 0.014526 (0.047466) \| 0.072786 / 0.176557 (-0.103771) \| 0.120437 / 0.737135 (-0.616698) \| 0.078375 / 0.296338 (-0.217964) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275821 / 0.215209 (0.060612) \| 2.703358 / 2.077655 (0.625703) \| 1.446783 / 1.504120 (-0.057337) \| 1.333556 / 1.541195 (-0.207639) \| 1.325753 / 1.468490 (-0.142737) \| 0.565196 / 4.584777 (-4.019581) \| 2.411193 / 3.745712 (-1.334520) \| 2.702764 / 5.269862 (-2.567098) \| 1.727425 / 4.565676 (-2.838252) \| 0.062966 / 0.424275 (-0.361309) \| 0.004985 / 0.007607 (-0.002622) \| 0.333473 / 0.226044 (0.107428) \| 3.270615 / 2.268929 (1.001687) \| 1.822213 / 55.444624 (-53.622411) \| 1.546572 / 6.876477 (-5.329905) \| 1.568767 / 2.142072 (-0.573305) \| 0.655907 / 4.805227 (-4.149321) \| 0.117173 / 6.500664 (-6.383491) \| 0.042415 / 0.075469 (-0.033054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987966 / 1.841788 (-0.853822) \| 11.851206 / 8.074308 (3.776898) \| 10.327751 / 10.191392 (0.136359) \| 0.127929 / 0.680424 (-0.552494) \| 0.013781 / 0.534201 (-0.520420) \| 0.286910 / 0.579283 (-0.292373) \| 0.273615 / 0.434364 (-0.160749) \| 0.323373 / 0.540337 (-0.216965) \| 0.426407 / 1.386936 (-0.960529) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005412 / 0.011353 (-0.005941) \| 0.003619 / 0.011008 (-0.007389) \| 0.049603 / 0.038508 (0.011095) \| 0.031246 / 0.023109 (0.008136) \| 0.279723 / 0.275898 (0.003825) \| 0.298557 / 0.323480 (-0.024923) \| 0.004253 / 0.007986 (-0.003733) \| 0.002758 / 0.004328 (-0.001570) \| 0.048931 / 0.004250 (0.044680) \| 0.044245 / 0.037052 (0.007193) \| 0.295876 / 0.258489 (0.037387) \| 0.322720 / 0.293841 (0.028879) \| 0.046746 / 0.128546 (-0.081800) \| 0.010841 / 0.075646 (-0.064805) \| 0.058528 / 0.419271 (-0.360744) \| 0.034224 / 0.043533 (-0.009308) \| 0.279192 / 0.255139 (0.024053) \| 0.299775 / 0.283200 (0.016576) \| 0.017862 / 0.141683 (-0.123820) \| 1.154478 / 1.452155 (-0.297677) \| 1.190483 / 1.492716 (-0.302234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088717 / 0.018006 (0.070710) \| 0.297905 / 0.000490 (0.297415) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.021458 / 0.037411 (-0.015953) \| 0.075616 / 0.014526 (0.061090) \| 0.087080 / 0.176557 (-0.089476) \| 0.125315 / 0.737135 (-0.611821) \| 0.088958 / 0.296338 (-0.207381) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287085 / 0.215209 (0.071876) \| 2.807798 / 2.077655 (0.730143) \| 1.552201 / 1.504120 (0.048081) \| 1.422374 / 1.541195 (-0.118820) \| 1.437908 / 1.468490 (-0.030582) \| 0.569738 / 4.584777 (-4.015039) \| 2.493921 / 3.745712 (-1.251791) \| 2.648376 / 5.269862 (-2.621486) \| 1.741721 / 4.565676 (-2.823955) \| 0.063023 / 0.424275 (-0.361253) \| 0.005166 / 0.007607 (-0.002441) \| 0.336927 / 0.226044 (0.110882) \| 3.384517 / 2.268929 (1.115588) \| 1.909888 / 55.444624 (-53.534736) \| 1.641879 / 6.876477 (-5.234597) \| 1.727734 / 2.142072 (-0.414338) \| 0.647127 / 4.805227 (-4.158100) \| 0.115831 / 6.500664 (-6.384833) \| 0.041161 / 0.075469 (-0.034309) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.016310 / 1.841788 (-0.825477) \| 12.088500 / 8.074308 (4.014192) \| 10.799730 / 10.191392 (0.608338) \| 0.129049 / 0.680424 (-0.551375) \| 0.015379 / 0.534201 (-0.518822) \| 0.291352 / 0.579283 (-0.287931) \| 0.284579 / 0.434364 (-0.149785) \| 0.331214 / 0.540337 (-0.209124) \| 0.422902 / 1.386936 (-0.964034) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#991169ed4901d129d0e0ab8d7fccd6a0728da4b8 \"CML watermark\")\n" ]
6,633	dataset viewer requires no-script	null	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6633", "html_url": "https://github.com/huggingface/datasets/pull/6633", "diff_url": "https://github.com/huggingface/datasets/pull/6633.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6633.patch", "merged_at": "2024-01-31T13:59:01" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6633). 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) \| 0.005172 / 0.011353 (-0.006181) \| 0.003694 / 0.011008 (-0.007314) \| 0.063098 / 0.038508 (0.024590) \| 0.028161 / 0.023109 (0.005052) \| 0.262288 / 0.275898 (-0.013610) \| 0.281867 / 0.323480 (-0.041613) \| 0.004088 / 0.007986 (-0.003898) \| 0.002745 / 0.004328 (-0.001583) \| 0.049071 / 0.004250 (0.044820) \| 0.040629 / 0.037052 (0.003577) \| 0.282766 / 0.258489 (0.024277) \| 0.297998 / 0.293841 (0.004157) \| 0.028057 / 0.128546 (-0.100489) \| 0.010878 / 0.075646 (-0.064768) \| 0.207410 / 0.419271 (-0.211861) \| 0.035600 / 0.043533 (-0.007933) \| 0.260157 / 0.255139 (0.005018) \| 0.273252 / 0.283200 (-0.009948) \| 0.017403 / 0.141683 (-0.124280) \| 1.150798 / 1.452155 (-0.301356) \| 1.200485 / 1.492716 (-0.292231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093783 / 0.018006 (0.075777) \| 0.302112 / 0.000490 (0.301622) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.018254 / 0.037411 (-0.019158) \| 0.061083 / 0.014526 (0.046557) \| 0.074899 / 0.176557 (-0.101657) \| 0.119616 / 0.737135 (-0.617520) \| 0.075269 / 0.296338 (-0.221069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275878 / 0.215209 (0.060669) \| 2.694778 / 2.077655 (0.617123) \| 1.423810 / 1.504120 (-0.080310) \| 1.309444 / 1.541195 (-0.231750) \| 1.327898 / 1.468490 (-0.140592) \| 0.568621 / 4.584777 (-4.016155) \| 2.345849 / 3.745712 (-1.399863) \| 2.901281 / 5.269862 (-2.368580) \| 1.777959 / 4.565676 (-2.787717) \| 0.063539 / 0.424275 (-0.360736) \| 0.005011 / 0.007607 (-0.002596) \| 0.331212 / 0.226044 (0.105168) \| 3.200379 / 2.268929 (0.931451) \| 1.780766 / 55.444624 (-53.663859) \| 1.517178 / 6.876477 (-5.359299) \| 1.587307 / 2.142072 (-0.554765) \| 0.651939 / 4.805227 (-4.153288) \| 0.116646 / 6.500664 (-6.384018) \| 0.043325 / 0.075469 (-0.032144) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996894 / 1.841788 (-0.844894) \| 11.495397 / 8.074308 (3.421089) \| 10.255784 / 10.191392 (0.064392) \| 0.129006 / 0.680424 (-0.551418) \| 0.013967 / 0.534201 (-0.520234) \| 0.284847 / 0.579283 (-0.294436) \| 0.265610 / 0.434364 (-0.168754) \| 0.320176 / 0.540337 (-0.220162) \| 0.429526 / 1.386936 (-0.957410) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005582 / 0.011353 (-0.005771) \| 0.003867 / 0.011008 (-0.007142) \| 0.050416 / 0.038508 (0.011908) \| 0.030996 / 0.023109 (0.007887) \| 0.275987 / 0.275898 (0.000089) \| 0.289487 / 0.323480 (-0.033993) \| 0.005149 / 0.007986 (-0.002837) \| 0.002806 / 0.004328 (-0.001522) \| 0.049617 / 0.004250 (0.045366) \| 0.046949 / 0.037052 (0.009897) \| 0.281596 / 0.258489 (0.023107) \| 0.330948 / 0.293841 (0.037108) \| 0.049645 / 0.128546 (-0.078901) \| 0.010953 / 0.075646 (-0.064693) \| 0.058546 / 0.419271 (-0.360725) \| 0.034010 / 0.043533 (-0.009523) \| 0.270525 / 0.255139 (0.015386) \| 0.289749 / 0.283200 (0.006550) \| 0.018755 / 0.141683 (-0.122927) \| 1.163072 / 1.452155 (-0.289082) \| 1.213400 / 1.492716 (-0.279316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092397 / 0.018006 (0.074390) \| 0.299376 / 0.000490 (0.298886) \| 0.000211 / 0.000200 (0.000011) \| 0.000044 / 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) \| 0.022496 / 0.037411 (-0.014916) \| 0.076886 / 0.014526 (0.062361) \| 0.087186 / 0.176557 (-0.089371) \| 0.126092 / 0.737135 (-0.611044) \| 0.088832 / 0.296338 (-0.207507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288885 / 0.215209 (0.073676) \| 2.839851 / 2.077655 (0.762196) \| 1.587556 / 1.504120 (0.083436) \| 1.470249 / 1.541195 (-0.070945) \| 1.518080 / 1.468490 (0.049590) \| 0.569646 / 4.584777 (-4.015131) \| 2.417574 / 3.745712 (-1.328138) \| 2.737368 / 5.269862 (-2.532494) \| 1.784419 / 4.565676 (-2.781257) \| 0.064104 / 0.424275 (-0.360171) \| 0.005138 / 0.007607 (-0.002469) \| 0.346214 / 0.226044 (0.120169) \| 3.439541 / 2.268929 (1.170612) \| 1.944792 / 55.444624 (-53.499832) \| 1.675762 / 6.876477 (-5.200714) \| 1.851871 / 2.142072 (-0.290201) \| 0.652932 / 4.805227 (-4.152295) \| 0.118953 / 6.500664 (-6.381711) \| 0.041011 / 0.075469 (-0.034459) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017690 / 1.841788 (-0.824098) \| 12.610531 / 8.074308 (4.536223) \| 11.223165 / 10.191392 (1.031773) \| 0.131637 / 0.680424 (-0.548786) \| 0.016733 / 0.534201 (-0.517468) \| 0.288491 / 0.579283 (-0.290792) \| 0.275899 / 0.434364 (-0.158465) \| 0.331837 / 0.540337 (-0.208500) \| 0.421695 / 1.386936 (-0.965241) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d9dfa9a8c077c783729a279623926faa9e2f3f1 \"CML watermark\")\n" ]
6,632	Fix reload cache with data dir	The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`) I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly close https://github.com/huggingface/datasets/issues/6609	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6632", "html_url": "https://github.com/huggingface/datasets/pull/6632", "diff_url": "https://github.com/huggingface/datasets/pull/6632.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6632.patch", "merged_at": "2024-02-06T17:21:24" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6632). 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) \| 0.004913 / 0.011353 (-0.006440) \| 0.003595 / 0.011008 (-0.007413) \| 0.068385 / 0.038508 (0.029876) \| 0.028612 / 0.023109 (0.005503) \| 0.236590 / 0.275898 (-0.039308) \| 0.261890 / 0.323480 (-0.061590) \| 0.003027 / 0.007986 (-0.004958) \| 0.002674 / 0.004328 (-0.001654) \| 0.049255 / 0.004250 (0.045004) \| 0.040500 / 0.037052 (0.003447) \| 0.248759 / 0.258489 (-0.009730) \| 0.280299 / 0.293841 (-0.013542) \| 0.027300 / 0.128546 (-0.101247) \| 0.010475 / 0.075646 (-0.065171) \| 0.208744 / 0.419271 (-0.210527) \| 0.035214 / 0.043533 (-0.008319) \| 0.251922 / 0.255139 (-0.003217) \| 0.263582 / 0.283200 (-0.019618) \| 0.018738 / 0.141683 (-0.122945) \| 1.150940 / 1.452155 (-0.301215) \| 1.187240 / 1.492716 (-0.305476) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093505 / 0.018006 (0.075499) \| 0.301101 / 0.000490 (0.300611) \| 0.000232 / 0.000200 (0.000032) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017779 / 0.037411 (-0.019632) \| 0.061412 / 0.014526 (0.046886) \| 0.074353 / 0.176557 (-0.102203) \| 0.118717 / 0.737135 (-0.618418) \| 0.074214 / 0.296338 (-0.222125) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281722 / 0.215209 (0.066513) \| 2.716867 / 2.077655 (0.639212) \| 1.423379 / 1.504120 (-0.080741) \| 1.315379 / 1.541195 (-0.225816) \| 1.294638 / 1.468490 (-0.173852) \| 0.549658 / 4.584777 (-4.035119) \| 2.349889 / 3.745712 (-1.395823) \| 2.722354 / 5.269862 (-2.547507) \| 1.700271 / 4.565676 (-2.865406) \| 0.061099 / 0.424275 (-0.363176) \| 0.004931 / 0.007607 (-0.002677) \| 0.339181 / 0.226044 (0.113136) \| 3.242467 / 2.268929 (0.973538) \| 1.777929 / 55.444624 (-53.666696) \| 1.498380 / 6.876477 (-5.378097) \| 1.511482 / 2.142072 (-0.630590) \| 0.627076 / 4.805227 (-4.178151) \| 0.115936 / 6.500664 (-6.384729) \| 0.041791 / 0.075469 (-0.033678) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983132 / 1.841788 (-0.858656) \| 11.431810 / 8.074308 (3.357502) \| 10.298918 / 10.191392 (0.107526) \| 0.139754 / 0.680424 (-0.540670) \| 0.013984 / 0.534201 (-0.520217) \| 0.283627 / 0.579283 (-0.295656) \| 0.264970 / 0.434364 (-0.169393) \| 0.323896 / 0.540337 (-0.216441) \| 0.420132 / 1.386936 (-0.966804) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005323 / 0.011353 (-0.006030) \| 0.003725 / 0.011008 (-0.007283) \| 0.050191 / 0.038508 (0.011683) \| 0.032196 / 0.023109 (0.009087) \| 0.265037 / 0.275898 (-0.010861) \| 0.289573 / 0.323480 (-0.033907) \| 0.004345 / 0.007986 (-0.003640) \| 0.002794 / 0.004328 (-0.001534) \| 0.048955 / 0.004250 (0.044705) \| 0.045421 / 0.037052 (0.008369) \| 0.279792 / 0.258489 (0.021303) \| 0.307374 / 0.293841 (0.013533) \| 0.046997 / 0.128546 (-0.081549) \| 0.010531 / 0.075646 (-0.065115) \| 0.058921 / 0.419271 (-0.360351) \| 0.033620 / 0.043533 (-0.009912) \| 0.268138 / 0.255139 (0.012999) \| 0.285941 / 0.283200 (0.002742) \| 0.018396 / 0.141683 (-0.123287) \| 1.151089 / 1.452155 (-0.301066) \| 1.209351 / 1.492716 (-0.283365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074252) \| 0.300893 / 0.000490 (0.300403) \| 0.000212 / 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) \| 0.022233 / 0.037411 (-0.015178) \| 0.075220 / 0.014526 (0.060694) \| 0.085901 / 0.176557 (-0.090656) \| 0.125080 / 0.737135 (-0.612056) \| 0.086978 / 0.296338 (-0.209361) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292877 / 0.215209 (0.077667) \| 2.841005 / 2.077655 (0.763350) \| 1.555168 / 1.504120 (0.051048) \| 1.420801 / 1.541195 (-0.120394) \| 1.431475 / 1.468490 (-0.037015) \| 0.569803 / 4.584777 (-4.014974) \| 2.451731 / 3.745712 (-1.293981) \| 2.662825 / 5.269862 (-2.607036) \| 1.732260 / 4.565676 (-2.833416) \| 0.063030 / 0.424275 (-0.361245) \| 0.004971 / 0.007607 (-0.002637) \| 0.345250 / 0.226044 (0.119206) \| 3.390909 / 2.268929 (1.121980) \| 1.908666 / 55.444624 (-53.535959) \| 1.628976 / 6.876477 (-5.247501) \| 1.719270 / 2.142072 (-0.422803) \| 0.653712 / 4.805227 (-4.151515) \| 0.116423 / 6.500664 (-6.384241) \| 0.040835 / 0.075469 (-0.034634) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005538 / 1.841788 (-0.836250) \| 12.105381 / 8.074308 (4.031073) \| 10.656295 / 10.191392 (0.464903) \| 0.131850 / 0.680424 (-0.548574) \| 0.016297 / 0.534201 (-0.517904) \| 0.285566 / 0.579283 (-0.293717) \| 0.276086 / 0.434364 (-0.158278) \| 0.326663 / 0.540337 (-0.213675) \| 0.410639 / 1.386936 (-0.976297) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1dc3f04586ee65c890b74649afc42316121af689 \"CML watermark\")\n" ]
6,631	Fix filelock: use current umask for filelock >= 3.10	reported in https://github.com/huggingface/evaluate/issues/542 cc @stas00 @williamberrios close https://github.com/huggingface/datasets/issues/6589	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6631", "html_url": "https://github.com/huggingface/datasets/pull/6631", "diff_url": "https://github.com/huggingface/datasets/pull/6631.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6631.patch", "merged_at": "2024-01-30T15:28:37" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6631). 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) \| 0.005076 / 0.011353 (-0.006277) \| 0.003665 / 0.011008 (-0.007343) \| 0.063602 / 0.038508 (0.025094) \| 0.029103 / 0.023109 (0.005993) \| 0.233133 / 0.275898 (-0.042765) \| 0.257000 / 0.323480 (-0.066480) \| 0.003059 / 0.007986 (-0.004926) \| 0.004007 / 0.004328 (-0.000321) \| 0.049804 / 0.004250 (0.045553) \| 0.039946 / 0.037052 (0.002893) \| 0.248003 / 0.258489 (-0.010486) \| 0.272729 / 0.293841 (-0.021112) \| 0.027542 / 0.128546 (-0.101004) \| 0.010745 / 0.075646 (-0.064901) \| 0.207686 / 0.419271 (-0.211586) \| 0.035438 / 0.043533 (-0.008095) \| 0.236864 / 0.255139 (-0.018275) \| 0.258610 / 0.283200 (-0.024590) \| 0.017225 / 0.141683 (-0.124458) \| 1.130894 / 1.452155 (-0.321261) \| 1.171266 / 1.492716 (-0.321450) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092532 / 0.018006 (0.074525) \| 0.301650 / 0.000490 (0.301161) \| 0.000216 / 0.000200 (0.000016) \| 0.000045 / 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) \| 0.018175 / 0.037411 (-0.019237) \| 0.061538 / 0.014526 (0.047012) \| 0.073673 / 0.176557 (-0.102884) \| 0.120676 / 0.737135 (-0.616460) \| 0.074753 / 0.296338 (-0.221586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283625 / 0.215209 (0.068416) \| 2.794903 / 2.077655 (0.717248) \| 1.485149 / 1.504120 (-0.018970) \| 1.361154 / 1.541195 (-0.180041) \| 1.371436 / 1.468490 (-0.097054) \| 0.580401 / 4.584777 (-4.004376) \| 2.457068 / 3.745712 (-1.288644) \| 2.760878 / 5.269862 (-2.508984) \| 1.725507 / 4.565676 (-2.840169) \| 0.063632 / 0.424275 (-0.360644) \| 0.005036 / 0.007607 (-0.002572) \| 0.337167 / 0.226044 (0.111122) \| 3.314508 / 2.268929 (1.045579) \| 1.863412 / 55.444624 (-53.581213) \| 1.621966 / 6.876477 (-5.254511) \| 1.600422 / 2.142072 (-0.541651) \| 0.647753 / 4.805227 (-4.157475) \| 0.117169 / 6.500664 (-6.383495) \| 0.042338 / 0.075469 (-0.033131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981818 / 1.841788 (-0.859969) \| 12.044657 / 8.074308 (3.970349) \| 10.654091 / 10.191392 (0.462699) \| 0.130693 / 0.680424 (-0.549731) \| 0.014733 / 0.534201 (-0.519468) \| 0.317432 / 0.579283 (-0.261851) \| 0.267196 / 0.434364 (-0.167168) \| 0.329310 / 0.540337 (-0.211028) \| 0.433379 / 1.386936 (-0.953557) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005502 / 0.011353 (-0.005851) \| 0.003951 / 0.011008 (-0.007057) \| 0.050651 / 0.038508 (0.012143) \| 0.031802 / 0.023109 (0.008693) \| 0.281384 / 0.275898 (0.005485) \| 0.303900 / 0.323480 (-0.019580) \| 0.004451 / 0.007986 (-0.003534) \| 0.002801 / 0.004328 (-0.001527) \| 0.048688 / 0.004250 (0.044438) \| 0.044717 / 0.037052 (0.007664) \| 0.295017 / 0.258489 (0.036528) \| 0.328003 / 0.293841 (0.034162) \| 0.048421 / 0.128546 (-0.080125) \| 0.011254 / 0.075646 (-0.064392) \| 0.058223 / 0.419271 (-0.361048) \| 0.033915 / 0.043533 (-0.009618) \| 0.279893 / 0.255139 (0.024754) \| 0.297605 / 0.283200 (0.014405) \| 0.017115 / 0.141683 (-0.124568) \| 1.146966 / 1.452155 (-0.305189) \| 1.191650 / 1.492716 (-0.301066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092524 / 0.018006 (0.074518) \| 0.309332 / 0.000490 (0.308842) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 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) \| 0.022265 / 0.037411 (-0.015146) \| 0.075732 / 0.014526 (0.061206) \| 0.087340 / 0.176557 (-0.089217) \| 0.126079 / 0.737135 (-0.611056) \| 0.090349 / 0.296338 (-0.205990) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288882 / 0.215209 (0.073673) \| 2.833046 / 2.077655 (0.755392) \| 1.602905 / 1.504120 (0.098785) \| 1.473110 / 1.541195 (-0.068085) \| 1.491300 / 1.468490 (0.022810) \| 0.557799 / 4.584777 (-4.026978) \| 2.439526 / 3.745712 (-1.306186) \| 2.669336 / 5.269862 (-2.600526) \| 1.719472 / 4.565676 (-2.846204) \| 0.062456 / 0.424275 (-0.361819) \| 0.005058 / 0.007607 (-0.002549) \| 0.343706 / 0.226044 (0.117662) \| 3.422397 / 2.268929 (1.153469) \| 1.983679 / 55.444624 (-53.460946) \| 1.673784 / 6.876477 (-5.202693) \| 1.785144 / 2.142072 (-0.356928) \| 0.643127 / 4.805227 (-4.162100) \| 0.115254 / 6.500664 (-6.385410) \| 0.041235 / 0.075469 (-0.034235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005448 / 1.841788 (-0.836340) \| 12.240100 / 8.074308 (4.165792) \| 11.051965 / 10.191392 (0.860573) \| 0.130438 / 0.680424 (-0.549986) \| 0.015918 / 0.534201 (-0.518283) \| 0.287468 / 0.579283 (-0.291815) \| 0.287699 / 0.434364 (-0.146665) \| 0.324561 / 0.540337 (-0.215777) \| 0.418820 / 1.386936 (-0.968116) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#237a2a688155e23cfbcdfadd2d491ce1667fa494 \"CML watermark\")\n" ]
6,630	Bump max range of dill to 0.3.8	Release on Jan 27, 2024: https://pypi.org/project/dill/0.3.8/#history	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6630", "html_url": "https://github.com/huggingface/datasets/pull/6630", "diff_url": "https://github.com/huggingface/datasets/pull/6630.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6630.patch", "merged_at": "2024-01-30T15:12:25" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6630). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hmm these errors look pretty weird... can they be retried?", "Hi, thanks for working on this! To fix the errors, you also need to update [this file](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/_dill.py) (by adding `version.parse(\"0.3.8\").release` to the lists)", "<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) \| 0.005068 / 0.011353 (-0.006285) \| 0.003657 / 0.011008 (-0.007351) \| 0.062914 / 0.038508 (0.024406) \| 0.027965 / 0.023109 (0.004855) \| 0.241804 / 0.275898 (-0.034094) \| 0.268069 / 0.323480 (-0.055411) \| 0.004066 / 0.007986 (-0.003920) \| 0.002704 / 0.004328 (-0.001624) \| 0.048745 / 0.004250 (0.044495) \| 0.042158 / 0.037052 (0.005106) \| 0.257670 / 0.258489 (-0.000819) \| 0.279419 / 0.293841 (-0.014422) \| 0.027193 / 0.128546 (-0.101353) \| 0.010379 / 0.075646 (-0.065267) \| 0.207009 / 0.419271 (-0.212262) \| 0.035494 / 0.043533 (-0.008039) \| 0.246025 / 0.255139 (-0.009114) \| 0.265906 / 0.283200 (-0.017294) \| 0.017335 / 0.141683 (-0.124348) \| 1.134052 / 1.452155 (-0.318103) \| 1.184668 / 1.492716 (-0.308049) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093137 / 0.018006 (0.075130) \| 0.302279 / 0.000490 (0.301789) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.018190 / 0.037411 (-0.019221) \| 0.061436 / 0.014526 (0.046910) \| 0.073102 / 0.176557 (-0.103454) \| 0.119782 / 0.737135 (-0.617354) \| 0.074292 / 0.296338 (-0.222046) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285905 / 0.215209 (0.070696) \| 2.809051 / 2.077655 (0.731397) \| 1.470305 / 1.504120 (-0.033814) \| 1.350457 / 1.541195 (-0.190738) \| 1.349111 / 1.468490 (-0.119379) \| 0.568277 / 4.584777 (-4.016500) \| 2.353046 / 3.745712 (-1.392666) \| 2.805862 / 5.269862 (-2.463999) \| 1.750275 / 4.565676 (-2.815401) \| 0.062370 / 0.424275 (-0.361905) \| 0.004954 / 0.007607 (-0.002653) \| 0.335609 / 0.226044 (0.109564) \| 3.367200 / 2.268929 (1.098271) \| 1.829431 / 55.444624 (-53.615193) \| 1.545093 / 6.876477 (-5.331384) \| 1.571107 / 2.142072 (-0.570966) \| 0.640279 / 4.805227 (-4.164949) \| 0.116209 / 6.500664 (-6.384455) \| 0.042308 / 0.075469 (-0.033161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982972 / 1.841788 (-0.858816) \| 11.424370 / 8.074308 (3.350062) \| 10.427111 / 10.191392 (0.235719) \| 0.129477 / 0.680424 (-0.550946) \| 0.014166 / 0.534201 (-0.520035) \| 0.287597 / 0.579283 (-0.291686) \| 0.265588 / 0.434364 (-0.168776) \| 0.324007 / 0.540337 (-0.216330) \| 0.430766 / 1.386936 (-0.956170) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.006005) \| 0.003733 / 0.011008 (-0.007275) \| 0.049520 / 0.038508 (0.011011) \| 0.031177 / 0.023109 (0.008068) \| 0.281854 / 0.275898 (0.005956) \| 0.300937 / 0.323480 (-0.022543) \| 0.004385 / 0.007986 (-0.003601) \| 0.002841 / 0.004328 (-0.001488) \| 0.048661 / 0.004250 (0.044411) \| 0.044258 / 0.037052 (0.007205) \| 0.295651 / 0.258489 (0.037162) \| 0.322872 / 0.293841 (0.029031) \| 0.048924 / 0.128546 (-0.079622) \| 0.010742 / 0.075646 (-0.064905) \| 0.059327 / 0.419271 (-0.359944) \| 0.033938 / 0.043533 (-0.009595) \| 0.282235 / 0.255139 (0.027096) \| 0.297432 / 0.283200 (0.014233) \| 0.018295 / 0.141683 (-0.123388) \| 1.164459 / 1.452155 (-0.287696) \| 1.214511 / 1.492716 (-0.278205) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091441 / 0.018006 (0.073435) \| 0.303023 / 0.000490 (0.302533) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.022024 / 0.037411 (-0.015388) \| 0.075570 / 0.014526 (0.061044) \| 0.086761 / 0.176557 (-0.089796) \| 0.126437 / 0.737135 (-0.610698) \| 0.088354 / 0.296338 (-0.207984) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289360 / 0.215209 (0.074151) \| 2.816433 / 2.077655 (0.738779) \| 1.561442 / 1.504120 (0.057322) \| 1.438168 / 1.541195 (-0.103027) \| 1.453398 / 1.468490 (-0.015092) \| 0.579474 / 4.584777 (-4.005303) \| 2.458640 / 3.745712 (-1.287072) \| 2.638572 / 5.269862 (-2.631290) \| 1.725218 / 4.565676 (-2.840458) \| 0.063550 / 0.424275 (-0.360725) \| 0.005220 / 0.007607 (-0.002387) \| 0.338883 / 0.226044 (0.112838) \| 3.353585 / 2.268929 (1.084656) \| 1.913186 / 55.444624 (-53.531438) \| 1.667445 / 6.876477 (-5.209032) \| 1.740085 / 2.142072 (-0.401987) \| 0.646369 / 4.805227 (-4.158859) \| 0.116737 / 6.500664 (-6.383927) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023180 / 1.841788 (-0.818608) \| 12.078398 / 8.074308 (4.004090) \| 10.952012 / 10.191392 (0.760620) \| 0.131335 / 0.680424 (-0.549089) \| 0.015701 / 0.534201 (-0.518499) \| 0.289709 / 0.579283 (-0.289574) \| 0.270495 / 0.434364 (-0.163869) \| 0.331773 / 0.540337 (-0.208565) \| 0.417660 / 1.386936 (-0.969276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b21d74f5c0ab8a85838af04de8ad85e71b0ac4f \"CML watermark\")\n" ]
6,629	Support push_to_hub without org/user to default to logged-in user	This behavior is aligned with: - the behavior of `datasets` before merging #6519 - the behavior described in the corresponding docstring - the behavior of `huggingface_hub.create_repo` Revert "Support push_to_hub canonical datasets (#6519)" - This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541. Fix #6597.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6629", "html_url": "https://github.com/huggingface/datasets/pull/6629", "diff_url": "https://github.com/huggingface/datasets/pull/6629.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6629.patch", "merged_at": "2024-02-05T12:29:36" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6629). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.005222 / 0.011353 (-0.006131) \| 0.003621 / 0.011008 (-0.007387) \| 0.063091 / 0.038508 (0.024583) \| 0.029395 / 0.023109 (0.006285) \| 0.231445 / 0.275898 (-0.044453) \| 0.256716 / 0.323480 (-0.066764) \| 0.004905 / 0.007986 (-0.003081) \| 0.002703 / 0.004328 (-0.001625) \| 0.048526 / 0.004250 (0.044276) \| 0.041382 / 0.037052 (0.004330) \| 0.247468 / 0.258489 (-0.011021) \| 0.270670 / 0.293841 (-0.023171) \| 0.028088 / 0.128546 (-0.100458) \| 0.010661 / 0.075646 (-0.064985) \| 0.205812 / 0.419271 (-0.213459) \| 0.035880 / 0.043533 (-0.007653) \| 0.237310 / 0.255139 (-0.017829) \| 0.255440 / 0.283200 (-0.027760) \| 0.018334 / 0.141683 (-0.123349) \| 1.128815 / 1.452155 (-0.323340) \| 1.204771 / 1.492716 (-0.287945) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089175 / 0.018006 (0.071169) \| 0.298584 / 0.000490 (0.298095) \| 0.000206 / 0.000200 (0.000006) \| 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) \| 0.018532 / 0.037411 (-0.018880) \| 0.061158 / 0.014526 (0.046632) \| 0.074177 / 0.176557 (-0.102380) \| 0.119408 / 0.737135 (-0.617728) \| 0.073821 / 0.296338 (-0.222518) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277630 / 0.215209 (0.062420) \| 2.735038 / 2.077655 (0.657383) \| 1.437251 / 1.504120 (-0.066868) \| 1.304596 / 1.541195 (-0.236598) \| 1.316830 / 1.468490 (-0.151661) \| 0.551057 / 4.584777 (-4.033720) \| 2.337247 / 3.745712 (-1.408465) \| 2.761501 / 5.269862 (-2.508361) \| 1.729000 / 4.565676 (-2.836677) \| 0.069398 / 0.424275 (-0.354877) \| 0.005059 / 0.007607 (-0.002548) \| 0.359594 / 0.226044 (0.133550) \| 3.283325 / 2.268929 (1.014397) \| 1.777410 / 55.444624 (-53.667214) \| 1.518522 / 6.876477 (-5.357954) \| 1.546712 / 2.142072 (-0.595361) \| 0.627047 / 4.805227 (-4.178180) \| 0.117058 / 6.500664 (-6.383606) \| 0.043437 / 0.075469 (-0.032032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.056303 / 1.841788 (-0.785484) \| 11.552295 / 8.074308 (3.477987) \| 10.184582 / 10.191392 (-0.006810) \| 0.129061 / 0.680424 (-0.551363) \| 0.014093 / 0.534201 (-0.520108) \| 0.292268 / 0.579283 (-0.287015) \| 0.264750 / 0.434364 (-0.169614) \| 0.334770 / 0.540337 (-0.205567) \| 0.436749 / 1.386936 (-0.950187) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005408 / 0.011353 (-0.005945) \| 0.003650 / 0.011008 (-0.007358) \| 0.054263 / 0.038508 (0.015755) \| 0.031112 / 0.023109 (0.008003) \| 0.270582 / 0.275898 (-0.005316) \| 0.303506 / 0.323480 (-0.019974) \| 0.004351 / 0.007986 (-0.003635) \| 0.002654 / 0.004328 (-0.001674) \| 0.049631 / 0.004250 (0.045381) \| 0.045209 / 0.037052 (0.008156) \| 0.284992 / 0.258489 (0.026503) \| 0.316653 / 0.293841 (0.022812) \| 0.049526 / 0.128546 (-0.079020) \| 0.010696 / 0.075646 (-0.064951) \| 0.057859 / 0.419271 (-0.361413) \| 0.034227 / 0.043533 (-0.009306) \| 0.269656 / 0.255139 (0.014517) \| 0.288766 / 0.283200 (0.005567) \| 0.017892 / 0.141683 (-0.123791) \| 1.167492 / 1.452155 (-0.284662) \| 1.217263 / 1.492716 (-0.275454) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089306 / 0.018006 (0.071299) \| 0.300774 / 0.000490 (0.300284) \| 0.000198 / 0.000200 (-0.000002) \| 0.000042 / 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) \| 0.022050 / 0.037411 (-0.015361) \| 0.076781 / 0.014526 (0.062255) \| 0.086597 / 0.176557 (-0.089959) \| 0.125094 / 0.737135 (-0.612042) \| 0.089412 / 0.296338 (-0.206927) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287444 / 0.215209 (0.072235) \| 2.830047 / 2.077655 (0.752392) \| 1.567492 / 1.504120 (0.063372) \| 1.439875 / 1.541195 (-0.101320) \| 1.461699 / 1.468490 (-0.006791) \| 0.569595 / 4.584777 (-4.015182) \| 2.454391 / 3.745712 (-1.291322) \| 2.655829 / 5.269862 (-2.614032) \| 1.756122 / 4.565676 (-2.809554) \| 0.063333 / 0.424275 (-0.360942) \| 0.005086 / 0.007607 (-0.002521) \| 0.351210 / 0.226044 (0.125166) \| 3.375545 / 2.268929 (1.106617) \| 1.945367 / 55.444624 (-53.499258) \| 1.662635 / 6.876477 (-5.213841) \| 1.762859 / 2.142072 (-0.379213) \| 0.651889 / 4.805227 (-4.153339) \| 0.118341 / 6.500664 (-6.382323) \| 0.040897 / 0.075469 (-0.034572) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005270 / 1.841788 (-0.836518) \| 12.247847 / 8.074308 (4.173539) \| 10.828131 / 10.191392 (0.636739) \| 0.129741 / 0.680424 (-0.550683) \| 0.015184 / 0.534201 (-0.519017) \| 0.295440 / 0.579283 (-0.283843) \| 0.276759 / 0.434364 (-0.157605) \| 0.329046 / 0.540337 (-0.211291) \| 0.421750 / 1.386936 (-0.965186) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea261ddc295527d0c1cd9f90fb61668f14135608 \"CML watermark\")\n" ]
6,628	Make CLI test support multi-processing	Support passing `--num_proc` to CLI test. This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6628", "html_url": "https://github.com/huggingface/datasets/pull/6628", "diff_url": "https://github.com/huggingface/datasets/pull/6628.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6628.patch", "merged_at": "2024-02-05T10:23:13" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6628). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<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) \| 0.004907 / 0.011353 (-0.006446) \| 0.003200 / 0.011008 (-0.007808) \| 0.062601 / 0.038508 (0.024093) \| 0.028607 / 0.023109 (0.005498) \| 0.242688 / 0.275898 (-0.033210) \| 0.263754 / 0.323480 (-0.059726) \| 0.003084 / 0.007986 (-0.004901) \| 0.002744 / 0.004328 (-0.001585) \| 0.048686 / 0.004250 (0.044436) \| 0.040734 / 0.037052 (0.003682) \| 0.262585 / 0.258489 (0.004096) \| 0.282822 / 0.293841 (-0.011019) \| 0.027470 / 0.128546 (-0.101076) \| 0.010356 / 0.075646 (-0.065290) \| 0.206397 / 0.419271 (-0.212874) \| 0.035440 / 0.043533 (-0.008093) \| 0.248599 / 0.255139 (-0.006540) \| 0.268869 / 0.283200 (-0.014331) \| 0.018542 / 0.141683 (-0.123141) \| 1.128139 / 1.452155 (-0.324016) \| 1.172115 / 1.492716 (-0.320602) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107939 / 0.018006 (0.089933) \| 0.301801 / 0.000490 (0.301311) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.018505 / 0.037411 (-0.018906) \| 0.061350 / 0.014526 (0.046824) \| 0.072645 / 0.176557 (-0.103912) \| 0.119459 / 0.737135 (-0.617676) \| 0.074711 / 0.296338 (-0.221628) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275132 / 0.215209 (0.059922) \| 2.714936 / 2.077655 (0.637281) \| 1.434204 / 1.504120 (-0.069916) \| 1.328358 / 1.541195 (-0.212837) \| 1.320706 / 1.468490 (-0.147784) \| 0.555723 / 4.584777 (-4.029054) \| 2.401335 / 3.745712 (-1.344378) \| 2.765609 / 5.269862 (-2.504253) \| 1.715207 / 4.565676 (-2.850470) \| 0.074990 / 0.424275 (-0.349285) \| 0.004999 / 0.007607 (-0.002608) \| 0.328435 / 0.226044 (0.102390) \| 3.254945 / 2.268929 (0.986017) \| 1.781105 / 55.444624 (-53.663519) \| 1.509491 / 6.876477 (-5.366985) \| 1.520670 / 2.142072 (-0.621402) \| 0.636411 / 4.805227 (-4.168817) \| 0.115616 / 6.500664 (-6.385048) \| 0.041633 / 0.075469 (-0.033836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975462 / 1.841788 (-0.866326) \| 11.480359 / 8.074308 (3.406051) \| 10.528665 / 10.191392 (0.337273) \| 0.141323 / 0.680424 (-0.539100) \| 0.013510 / 0.534201 (-0.520691) \| 0.293570 / 0.579283 (-0.285713) \| 0.259956 / 0.434364 (-0.174408) \| 0.331440 / 0.540337 (-0.208898) \| 0.453487 / 1.386936 (-0.933449) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005278 / 0.011353 (-0.006075) \| 0.003400 / 0.011008 (-0.007608) \| 0.049442 / 0.038508 (0.010934) \| 0.031738 / 0.023109 (0.008628) \| 0.292334 / 0.275898 (0.016436) \| 0.308931 / 0.323480 (-0.014549) \| 0.004290 / 0.007986 (-0.003696) \| 0.002738 / 0.004328 (-0.001591) \| 0.048944 / 0.004250 (0.044694) \| 0.044273 / 0.037052 (0.007221) \| 0.301434 / 0.258489 (0.042945) \| 0.333067 / 0.293841 (0.039226) \| 0.048741 / 0.128546 (-0.079805) \| 0.010357 / 0.075646 (-0.065289) \| 0.057777 / 0.419271 (-0.361495) \| 0.033892 / 0.043533 (-0.009641) \| 0.286921 / 0.255139 (0.031782) \| 0.306204 / 0.283200 (0.023005) \| 0.018764 / 0.141683 (-0.122919) \| 1.142000 / 1.452155 (-0.310155) \| 1.206728 / 1.492716 (-0.285988) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094233 / 0.018006 (0.076227) \| 0.302553 / 0.000490 (0.302063) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.021814 / 0.037411 (-0.015598) \| 0.075143 / 0.014526 (0.060617) \| 0.087717 / 0.176557 (-0.088840) \| 0.126079 / 0.737135 (-0.611056) \| 0.089083 / 0.296338 (-0.207255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293844 / 0.215209 (0.078635) \| 2.859481 / 2.077655 (0.781827) \| 1.580366 / 1.504120 (0.076246) \| 1.462633 / 1.541195 (-0.078562) \| 1.471052 / 1.468490 (0.002562) \| 0.574755 / 4.584777 (-4.010022) \| 2.408925 / 3.745712 (-1.336787) \| 2.673618 / 5.269862 (-2.596243) \| 1.746218 / 4.565676 (-2.819459) \| 0.063435 / 0.424275 (-0.360840) \| 0.005023 / 0.007607 (-0.002584) \| 0.341990 / 0.226044 (0.115946) \| 3.430862 / 2.268929 (1.161933) \| 1.953869 / 55.444624 (-53.490755) \| 1.661276 / 6.876477 (-5.215201) \| 1.761575 / 2.142072 (-0.380498) \| 0.656388 / 4.805227 (-4.148839) \| 0.117774 / 6.500664 (-6.382890) \| 0.040290 / 0.075469 (-0.035179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004315 / 1.841788 (-0.837473) \| 12.249719 / 8.074308 (4.175411) \| 10.942703 / 10.191392 (0.751311) \| 0.128552 / 0.680424 (-0.551872) \| 0.015958 / 0.534201 (-0.518242) \| 0.287330 / 0.579283 (-0.291953) \| 0.274336 / 0.434364 (-0.160028) \| 0.326233 / 0.540337 (-0.214104) \| 0.414548 / 1.386936 (-0.972388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db47d6d95c5346368710d3c852f20ffc1b0f1c1c \"CML watermark\")\n" ]
6,627	Disable `tqdm` bars in non-interactive environments	Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default). For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6627", "html_url": "https://github.com/huggingface/datasets/pull/6627", "diff_url": "https://github.com/huggingface/datasets/pull/6627.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6627.patch", "merged_at": "2024-01-29T15:41:32" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6627). 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) \| 0.004944 / 0.011353 (-0.006409) \| 0.003279 / 0.011008 (-0.007729) \| 0.063041 / 0.038508 (0.024533) \| 0.029888 / 0.023109 (0.006779) \| 0.259138 / 0.275898 (-0.016760) \| 0.276907 / 0.323480 (-0.046573) \| 0.004015 / 0.007986 (-0.003970) \| 0.002647 / 0.004328 (-0.001682) \| 0.048944 / 0.004250 (0.044693) \| 0.039412 / 0.037052 (0.002360) \| 0.278069 / 0.258489 (0.019580) \| 0.299139 / 0.293841 (0.005298) \| 0.027272 / 0.128546 (-0.101274) \| 0.010445 / 0.075646 (-0.065202) \| 0.206925 / 0.419271 (-0.212347) \| 0.035589 / 0.043533 (-0.007944) \| 0.256805 / 0.255139 (0.001666) \| 0.275128 / 0.283200 (-0.008072) \| 0.017888 / 0.141683 (-0.123795) \| 1.136983 / 1.452155 (-0.315172) \| 1.167495 / 1.492716 (-0.325222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088167 / 0.018006 (0.070161) \| 0.297360 / 0.000490 (0.296871) \| 0.000231 / 0.000200 (0.000031) \| 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) \| 0.018114 / 0.037411 (-0.019297) \| 0.061217 / 0.014526 (0.046691) \| 0.072269 / 0.176557 (-0.104288) \| 0.120607 / 0.737135 (-0.616528) \| 0.073517 / 0.296338 (-0.222822) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282580 / 0.215209 (0.067371) \| 2.758650 / 2.077655 (0.680995) \| 1.425125 / 1.504120 (-0.078995) \| 1.303182 / 1.541195 (-0.238013) \| 1.341035 / 1.468490 (-0.127455) \| 0.549485 / 4.584777 (-4.035292) \| 2.346297 / 3.745712 (-1.399415) \| 2.686457 / 5.269862 (-2.583405) \| 1.684789 / 4.565676 (-2.880888) \| 0.061279 / 0.424275 (-0.362996) \| 0.004902 / 0.007607 (-0.002705) \| 0.333089 / 0.226044 (0.107044) \| 3.297016 / 2.268929 (1.028087) \| 1.765614 / 55.444624 (-53.679010) \| 1.499314 / 6.876477 (-5.377162) \| 1.501275 / 2.142072 (-0.640797) \| 0.619039 / 4.805227 (-4.186189) \| 0.114284 / 6.500664 (-6.386380) \| 0.041481 / 0.075469 (-0.033988) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973924 / 1.841788 (-0.867863) \| 11.268266 / 8.074308 (3.193958) \| 10.304738 / 10.191392 (0.113346) \| 0.129297 / 0.680424 (-0.551127) \| 0.014894 / 0.534201 (-0.519307) \| 0.287658 / 0.579283 (-0.291626) \| 0.266476 / 0.434364 (-0.167888) \| 0.322199 / 0.540337 (-0.218138) \| 0.419568 / 1.386936 (-0.967368) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005220 / 0.011353 (-0.006133) \| 0.003310 / 0.011008 (-0.007698) \| 0.049707 / 0.038508 (0.011199) \| 0.031148 / 0.023109 (0.008039) \| 0.284644 / 0.275898 (0.008746) \| 0.302767 / 0.323480 (-0.020712) \| 0.004245 / 0.007986 (-0.003740) \| 0.002677 / 0.004328 (-0.001651) \| 0.049870 / 0.004250 (0.045620) \| 0.043922 / 0.037052 (0.006870) \| 0.294955 / 0.258489 (0.036466) \| 0.322144 / 0.293841 (0.028303) \| 0.047211 / 0.128546 (-0.081336) \| 0.010492 / 0.075646 (-0.065155) \| 0.058152 / 0.419271 (-0.361120) \| 0.033508 / 0.043533 (-0.010025) \| 0.281266 / 0.255139 (0.026127) \| 0.300010 / 0.283200 (0.016810) \| 0.017616 / 0.141683 (-0.124067) \| 1.124658 / 1.452155 (-0.327496) \| 1.167222 / 1.492716 (-0.325495) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089085 / 0.018006 (0.071079) \| 0.297912 / 0.000490 (0.297423) \| 0.000211 / 0.000200 (0.000011) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021669 / 0.037411 (-0.015742) \| 0.075648 / 0.014526 (0.061123) \| 0.086054 / 0.176557 (-0.090503) \| 0.125236 / 0.737135 (-0.611899) \| 0.088146 / 0.296338 (-0.208192) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295238 / 0.215209 (0.080029) \| 2.870002 / 2.077655 (0.792347) \| 1.582534 / 1.504120 (0.078414) \| 1.466710 / 1.541195 (-0.074485) \| 1.475352 / 1.468490 (0.006861) \| 0.554745 / 4.584777 (-4.030032) \| 2.412533 / 3.745712 (-1.333179) \| 2.583863 / 5.269862 (-2.685999) \| 1.689124 / 4.565676 (-2.876552) \| 0.061353 / 0.424275 (-0.362922) \| 0.005015 / 0.007607 (-0.002592) \| 0.338733 / 0.226044 (0.112688) \| 3.356710 / 2.268929 (1.087781) \| 1.932143 / 55.444624 (-53.512481) \| 1.660081 / 6.876477 (-5.216396) \| 1.764961 / 2.142072 (-0.377111) \| 0.640002 / 4.805227 (-4.165225) \| 0.115251 / 6.500664 (-6.385413) \| 0.040627 / 0.075469 (-0.034842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992296 / 1.841788 (-0.849492) \| 11.821259 / 8.074308 (3.746951) \| 10.715570 / 10.191392 (0.524178) \| 0.142934 / 0.680424 (-0.537489) \| 0.015680 / 0.534201 (-0.518521) \| 0.287435 / 0.579283 (-0.291848) \| 0.276817 / 0.434364 (-0.157547) \| 0.327823 / 0.540337 (-0.212515) \| 0.413404 / 1.386936 (-0.973532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#82c78b614d34ee42180d35a882875a28d6281db0 \"CML watermark\")\n" ]
6,626	Raise error on bad split name	e.g. dashes '-' are not allowed in split names This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test cc @AndreaFrancis	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6626", "html_url": "https://github.com/huggingface/datasets/pull/6626", "diff_url": "https://github.com/huggingface/datasets/pull/6626.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6626.patch", "merged_at": "2024-01-29T15:12:18" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6626). 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) \| 0.005085 / 0.011353 (-0.006268) \| 0.003592 / 0.011008 (-0.007417) \| 0.062591 / 0.038508 (0.024083) \| 0.031063 / 0.023109 (0.007954) \| 0.247029 / 0.275898 (-0.028869) \| 0.273706 / 0.323480 (-0.049774) \| 0.004034 / 0.007986 (-0.003951) \| 0.002672 / 0.004328 (-0.001657) \| 0.048407 / 0.004250 (0.044156) \| 0.049229 / 0.037052 (0.012177) \| 0.264316 / 0.258489 (0.005827) \| 0.284953 / 0.293841 (-0.008888) \| 0.027712 / 0.128546 (-0.100834) \| 0.010619 / 0.075646 (-0.065027) \| 0.210017 / 0.419271 (-0.209254) \| 0.035636 / 0.043533 (-0.007897) \| 0.252830 / 0.255139 (-0.002309) \| 0.278772 / 0.283200 (-0.004428) \| 0.017356 / 0.141683 (-0.124326) \| 1.140202 / 1.452155 (-0.311953) \| 1.204807 / 1.492716 (-0.287909) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089130 / 0.018006 (0.071123) \| 0.300115 / 0.000490 (0.299626) \| 0.000213 / 0.000200 (0.000013) \| 0.000042 / 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) \| 0.018352 / 0.037411 (-0.019059) \| 0.061431 / 0.014526 (0.046905) \| 0.073911 / 0.176557 (-0.102646) \| 0.121230 / 0.737135 (-0.615906) \| 0.074867 / 0.296338 (-0.221471) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282272 / 0.215209 (0.067063) \| 2.737413 / 2.077655 (0.659759) \| 1.446651 / 1.504120 (-0.057469) \| 1.319686 / 1.541195 (-0.221508) \| 1.327479 / 1.468490 (-0.141011) \| 0.558003 / 4.584777 (-4.026774) \| 2.361623 / 3.745712 (-1.384089) \| 2.770436 / 5.269862 (-2.499425) \| 1.703450 / 4.565676 (-2.862227) \| 0.062034 / 0.424275 (-0.362241) \| 0.005070 / 0.007607 (-0.002537) \| 0.337265 / 0.226044 (0.111221) \| 3.299438 / 2.268929 (1.030509) \| 1.781273 / 55.444624 (-53.663351) \| 1.512743 / 6.876477 (-5.363734) \| 1.530995 / 2.142072 (-0.611077) \| 0.630210 / 4.805227 (-4.175017) \| 0.116219 / 6.500664 (-6.384445) \| 0.042220 / 0.075469 (-0.033249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946341 / 1.841788 (-0.895446) \| 11.462179 / 8.074308 (3.387871) \| 10.603314 / 10.191392 (0.411922) \| 0.128826 / 0.680424 (-0.551598) \| 0.013994 / 0.534201 (-0.520207) \| 0.288142 / 0.579283 (-0.291141) \| 0.266941 / 0.434364 (-0.167422) \| 0.329392 / 0.540337 (-0.210946) \| 0.431720 / 1.386936 (-0.955216) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003587 / 0.011008 (-0.007422) \| 0.049437 / 0.038508 (0.010929) \| 0.031940 / 0.023109 (0.008831) \| 0.276651 / 0.275898 (0.000752) \| 0.297240 / 0.323480 (-0.026240) \| 0.004202 / 0.007986 (-0.003784) \| 0.002709 / 0.004328 (-0.001619) \| 0.048647 / 0.004250 (0.044397) \| 0.044147 / 0.037052 (0.007095) \| 0.291171 / 0.258489 (0.032682) \| 0.319297 / 0.293841 (0.025456) \| 0.048167 / 0.128546 (-0.080379) \| 0.010630 / 0.075646 (-0.065016) \| 0.058402 / 0.419271 (-0.360869) \| 0.033817 / 0.043533 (-0.009716) \| 0.300546 / 0.255139 (0.045407) \| 0.319396 / 0.283200 (0.036197) \| 0.017736 / 0.141683 (-0.123946) \| 1.159590 / 1.452155 (-0.292565) \| 1.191778 / 1.492716 (-0.300939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088971 / 0.018006 (0.070965) \| 0.299721 / 0.000490 (0.299231) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021895 / 0.037411 (-0.015516) \| 0.075388 / 0.014526 (0.060862) \| 0.087446 / 0.176557 (-0.089111) \| 0.126339 / 0.737135 (-0.610796) \| 0.089329 / 0.296338 (-0.207010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296642 / 0.215209 (0.081433) \| 2.916023 / 2.077655 (0.838368) \| 1.593180 / 1.504120 (0.089060) \| 1.470491 / 1.541195 (-0.070704) \| 1.485713 / 1.468490 (0.017223) \| 0.577204 / 4.584777 (-4.007573) \| 2.436463 / 3.745712 (-1.309249) \| 2.651004 / 5.269862 (-2.618858) \| 1.754026 / 4.565676 (-2.811651) \| 0.064943 / 0.424275 (-0.359332) \| 0.005115 / 0.007607 (-0.002492) \| 0.362082 / 0.226044 (0.136038) \| 3.498198 / 2.268929 (1.229270) \| 1.951936 / 55.444624 (-53.492688) \| 1.682027 / 6.876477 (-5.194450) \| 1.751768 / 2.142072 (-0.390304) \| 0.668479 / 4.805227 (-4.136748) \| 0.119934 / 6.500664 (-6.380730) \| 0.041419 / 0.075469 (-0.034050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978145 / 1.841788 (-0.863643) \| 11.984984 / 8.074308 (3.910676) \| 10.732377 / 10.191392 (0.540985) \| 0.141868 / 0.680424 (-0.538555) \| 0.015256 / 0.534201 (-0.518945) \| 0.288488 / 0.579283 (-0.290795) \| 0.276091 / 0.434364 (-0.158273) \| 0.330429 / 0.540337 (-0.209908) \| 0.423964 / 1.386936 (-0.962972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb8497b9dec2a3807c887b8184f902d1d8d7c25a \"CML watermark\")\n" ]
6,624	How to download the laion-coco dataset	The laion coco dataset is not available now. How to download it https://huggingface.co/datasets/laion/laion-coco	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi, this dataset has been disabled by the authors, so unfortunately it's no longer possible to download it." ]
6,623	streaming datasets doesn't work properly with multi-node	### Feature request Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it. Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)? But in this case I noticed that the: First iteraton: first GPU will get → [1, 2] first GPU will get → [3, 4] Second iteraton: first GPU will get → [5] first GPU will get → Nothing which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync. So my questions are: 1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues? 2. Do we need to use `DistributedSampler`? If yes, how? 3. in the docstrings of `split_dataset_by_node`, this is mentioned: "If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples." Can you explain the last part here? 4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing? ### Motivation Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it. ### Your contribution Yes, I can help in submitting the PR once we get mutual understanding on how it should behave.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "@mariosasko, @lhoestq, @albertvillanova\r\nhey guys! can anyone help? or can you guys suggest who can help with this?", "Hi ! \r\n\r\n1. When the dataset is running of of examples, the last batches received by the GPU can be incomplete or empty/missing. We haven't implemented yet a way to ignore the last batch. It might require the datasets to provide the number of examples per shard though, so that we can know when to stop.\r\n2. Samplers are not compatible with IterableDatasets in pytorch\r\n3. if `dataset.n_shards % world_size != 0` then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of `world_size` so that each example goes to one exactly one GPU.\r\n4. no, sharding should be down up-front and can take some time depending on the dataset size and format", "> if dataset.n_shards % world_size != 0 then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of world_size so that each example goes to one exactly one GPU.\r\n\r\nconsidering there's just 1 shard and 2 worker nodes, do you mean each worker node will load the whole dataset but still receive half of that shard while streaming?", "Yes both nodes will stream from the 1 shard, but each node will skip half of the examples. This way in total each example is seen once and exactly once during you distributed training.\r\n\r\nThough it terms of I/O, the dataset is effectively read/streamed twice.", "what if the number of samples in that shard % num_nodes != 0? it will break/get stuck? or is the data repeated in that case for gradient sync?", "In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n\r\nIn the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.", "> In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n> \r\n> In the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.\r\n\r\nIs there any method to modify one dataset's n_shard? modify the number of files is ok? one file == one shard?", "> modify the number of files is ok? one file == one shard?\r\n\r\nYep, one file == one shard :)" ]
6,622	multi-GPU map does not work	### Describe the bug Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here): https://youtu.be/RNbdPkSppc4 ### Steps to reproduce the bug - ### Expected behavior - ### Environment info x2 RTX A4000	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)" ]
6,621	deleted	...	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,620	wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}	### Describe the bug I'm trying to run a rag example, and the dataset is wiki_dpr. wiki_dpr download and extracting have been completed successfully. However, at the generating train split stage, an error from wiki_dpr.py keeps popping up. Especially in "_generate_examples" : 1. The following error occurs in the line id, text, title = line.strip().split("\t") ValueError: not enough values to unpack (expected 3, got 2) -> This part handles exceptions so that even if an error occurs, it passes. 2. ID mismatch between lines {id} and vector {vec_id} This error seems to occur at the line " assert int(id) == int(vec_id),". After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id. Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!! ### Steps to reproduce the bug Occurs in the generating train split step when running the rag example in the transformers repository. Specifically, it is an error in wiki_dpr.py. ### Expected behavior . ### Environment info python 3.8	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Thanks for reporting, @kiehls90.\r\n\r\nAs this seems an issue with the specific \"wiki_dpr\" dataset, I am transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/wiki_dpr/discussions/13" ]
6,619	Migrate from `setup.cfg` to `pyproject.toml`	Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6619", "html_url": "https://github.com/huggingface/datasets/pull/6619", "diff_url": "https://github.com/huggingface/datasets/pull/6619.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6619.patch", "merged_at": "2024-01-26T15:47:32" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). 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) \| 0.005066 / 0.011353 (-0.006287) \| 0.003678 / 0.011008 (-0.007330) \| 0.063057 / 0.038508 (0.024549) \| 0.031250 / 0.023109 (0.008140) \| 0.248856 / 0.275898 (-0.027042) \| 0.266932 / 0.323480 (-0.056548) \| 0.003814 / 0.007986 (-0.004172) \| 0.002843 / 0.004328 (-0.001485) \| 0.049210 / 0.004250 (0.044959) \| 0.041514 / 0.037052 (0.004462) \| 0.264874 / 0.258489 (0.006385) \| 0.288834 / 0.293841 (-0.005007) \| 0.027457 / 0.128546 (-0.101089) \| 0.011071 / 0.075646 (-0.064575) \| 0.206433 / 0.419271 (-0.212839) \| 0.035381 / 0.043533 (-0.008152) \| 0.246829 / 0.255139 (-0.008310) \| 0.271094 / 0.283200 (-0.012106) \| 0.017790 / 0.141683 (-0.123893) \| 1.134618 / 1.452155 (-0.317536) \| 1.182600 / 1.492716 (-0.310116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094970 / 0.018006 (0.076964) \| 0.306438 / 0.000490 (0.305949) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.017786 / 0.037411 (-0.019625) \| 0.060652 / 0.014526 (0.046127) \| 0.072619 / 0.176557 (-0.103937) \| 0.119460 / 0.737135 (-0.617676) \| 0.073580 / 0.296338 (-0.222759) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279304 / 0.215209 (0.064095) \| 2.747179 / 2.077655 (0.669524) \| 1.438291 / 1.504120 (-0.065829) \| 1.313405 / 1.541195 (-0.227789) \| 1.354569 / 1.468490 (-0.113921) \| 0.578375 / 4.584777 (-4.006402) \| 2.424576 / 3.745712 (-1.321136) \| 2.831513 / 5.269862 (-2.438348) \| 1.756062 / 4.565676 (-2.809614) \| 0.064460 / 0.424275 (-0.359815) \| 0.005065 / 0.007607 (-0.002542) \| 0.335003 / 0.226044 (0.108958) \| 3.310500 / 2.268929 (1.041571) \| 1.778017 / 55.444624 (-53.666607) \| 1.504743 / 6.876477 (-5.371734) \| 1.532843 / 2.142072 (-0.609229) \| 0.662110 / 4.805227 (-4.143118) \| 0.118239 / 6.500664 (-6.382425) \| 0.042135 / 0.075469 (-0.033335) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945650 / 1.841788 (-0.896137) \| 11.623179 / 8.074308 (3.548871) \| 10.927315 / 10.191392 (0.735923) \| 0.131050 / 0.680424 (-0.549374) \| 0.014725 / 0.534201 (-0.519476) \| 0.290716 / 0.579283 (-0.288567) \| 0.272357 / 0.434364 (-0.162007) \| 0.323274 / 0.540337 (-0.217064) \| 0.426692 / 1.386936 (-0.960244) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005478 / 0.011353 (-0.005875) \| 0.003618 / 0.011008 (-0.007390) \| 0.049599 / 0.038508 (0.011091) \| 0.030814 / 0.023109 (0.007705) \| 0.273663 / 0.275898 (-0.002235) \| 0.292099 / 0.323480 (-0.031381) \| 0.004196 / 0.007986 (-0.003790) \| 0.002779 / 0.004328 (-0.001550) \| 0.047812 / 0.004250 (0.043562) \| 0.045095 / 0.037052 (0.008043) \| 0.286288 / 0.258489 (0.027799) \| 0.314125 / 0.293841 (0.020284) \| 0.047940 / 0.128546 (-0.080606) \| 0.010714 / 0.075646 (-0.064932) \| 0.057453 / 0.419271 (-0.361819) \| 0.033482 / 0.043533 (-0.010051) \| 0.273391 / 0.255139 (0.018252) \| 0.284936 / 0.283200 (0.001736) \| 0.017805 / 0.141683 (-0.123878) \| 1.148303 / 1.452155 (-0.303852) \| 1.185268 / 1.492716 (-0.307448) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092442 / 0.018006 (0.074436) \| 0.309908 / 0.000490 (0.309418) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.022874 / 0.037411 (-0.014537) \| 0.078238 / 0.014526 (0.063712) \| 0.088844 / 0.176557 (-0.087713) \| 0.127054 / 0.737135 (-0.610081) \| 0.089809 / 0.296338 (-0.206530) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292360 / 0.215209 (0.077151) \| 2.842700 / 2.077655 (0.765045) \| 1.571071 / 1.504120 (0.066951) \| 1.450773 / 1.541195 (-0.090422) \| 1.467090 / 1.468490 (-0.001400) \| 0.583529 / 4.584777 (-4.001248) \| 2.469284 / 3.745712 (-1.276428) \| 2.844426 / 5.269862 (-2.425435) \| 1.773336 / 4.565676 (-2.792341) \| 0.064585 / 0.424275 (-0.359690) \| 0.005098 / 0.007607 (-0.002509) \| 0.342816 / 0.226044 (0.116771) \| 3.363309 / 2.268929 (1.094381) \| 1.922834 / 55.444624 (-53.521790) \| 1.649702 / 6.876477 (-5.226774) \| 1.672727 / 2.142072 (-0.469345) \| 0.665015 / 4.805227 (-4.140212) \| 0.124764 / 6.500664 (-6.375900) \| 0.041564 / 0.075469 (-0.033905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988970 / 1.841788 (-0.852818) \| 12.148983 / 8.074308 (4.074675) \| 11.132697 / 10.191392 (0.941305) \| 0.131596 / 0.680424 (-0.548828) \| 0.015700 / 0.534201 (-0.518501) \| 0.288819 / 0.579283 (-0.290464) \| 0.276692 / 0.434364 (-0.157672) \| 0.330260 / 0.540337 (-0.210078) \| 0.421612 / 1.386936 (-0.965324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d627fb8357f39d78d79e704712609c7b34bdeba4 \"CML watermark\")\n" ]
6,618	While importing load_dataset from datasets	### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Can you please share the error's stack trace so we can see where it comes from?", "We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...\r\n\r\nI am closing the issue. Feel free to reopen it (with additional information) if the problem persists.", "Yeah 👍\r\n\r\nOn Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> We cannot reproduce the issue and we do not have enough information:\r\n> environment info (need to run datasets-cli env), stack trace,...\r\n>\r\n> I am closing the issue. Feel free to reopen it (with additional\r\n> information) if the problem persists.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n", "Please downgrade the version of urllib3 if you have the same issue:\r\n\r\n!pip install urllib3==1.25.11", "> Please downgrade the version of urllib3 if you have the same issue:\r\n> \r\n> !pip install urllib3==1.25.11\r\n\r\nThis worked for me. Thanks.\r\n\r\nI use python 3.11 and datasets==2.20.0. Downgrading urllib3 to 1.25.11 worked in my case." ]
6,617	Fix CI: pyarrow 15, pandas 2.2 and sqlachemy	this should fix the CI failures on `main` close https://github.com/huggingface/datasets/issues/5477	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6617", "html_url": "https://github.com/huggingface/datasets/pull/6617", "diff_url": "https://github.com/huggingface/datasets/pull/6617.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6617.patch", "merged_at": "2024-01-26T14:50:44" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6617). 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) \| 0.004774 / 0.011353 (-0.006579) \| 0.003397 / 0.011008 (-0.007611) \| 0.063862 / 0.038508 (0.025354) \| 0.029353 / 0.023109 (0.006244) \| 0.245921 / 0.275898 (-0.029977) \| 0.268414 / 0.323480 (-0.055066) \| 0.002834 / 0.007986 (-0.005152) \| 0.002606 / 0.004328 (-0.001723) \| 0.049690 / 0.004250 (0.045439) \| 0.041637 / 0.037052 (0.004585) \| 0.262526 / 0.258489 (0.004037) \| 0.288200 / 0.293841 (-0.005641) \| 0.027233 / 0.128546 (-0.101313) \| 0.010322 / 0.075646 (-0.065324) \| 0.213860 / 0.419271 (-0.205411) \| 0.034930 / 0.043533 (-0.008602) \| 0.249256 / 0.255139 (-0.005883) \| 0.270016 / 0.283200 (-0.013184) \| 0.019413 / 0.141683 (-0.122270) \| 1.124801 / 1.452155 (-0.327354) \| 1.166224 / 1.492716 (-0.326492) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091641 / 0.018006 (0.073635) \| 0.299679 / 0.000490 (0.299189) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018084 / 0.037411 (-0.019327) \| 0.060143 / 0.014526 (0.045617) \| 0.072556 / 0.176557 (-0.104001) \| 0.118555 / 0.737135 (-0.618580) \| 0.073786 / 0.296338 (-0.222553) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278193 / 0.215209 (0.062984) \| 2.707954 / 2.077655 (0.630300) \| 1.483575 / 1.504120 (-0.020545) \| 1.371939 / 1.541195 (-0.169256) \| 1.395009 / 1.468490 (-0.073481) \| 0.559949 / 4.584777 (-4.024828) \| 2.372529 / 3.745712 (-1.373183) \| 2.823641 / 5.269862 (-2.446221) \| 1.722999 / 4.565676 (-2.842678) \| 0.062535 / 0.424275 (-0.361741) \| 0.004970 / 0.007607 (-0.002637) \| 0.338625 / 0.226044 (0.112580) \| 3.317576 / 2.268929 (1.048648) \| 1.854552 / 55.444624 (-53.590073) \| 1.589323 / 6.876477 (-5.287154) \| 1.624630 / 2.142072 (-0.517442) \| 0.638388 / 4.805227 (-4.166839) \| 0.116675 / 6.500664 (-6.383989) \| 0.041850 / 0.075469 (-0.033619) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938025 / 1.841788 (-0.903763) \| 11.450072 / 8.074308 (3.375764) \| 10.414943 / 10.191392 (0.223551) \| 0.128416 / 0.680424 (-0.552007) \| 0.013798 / 0.534201 (-0.520403) \| 0.287997 / 0.579283 (-0.291286) \| 0.259976 / 0.434364 (-0.174387) \| 0.320737 / 0.540337 (-0.219601) \| 0.424292 / 1.386936 (-0.962644) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005107 / 0.011353 (-0.006246) \| 0.003374 / 0.011008 (-0.007634) \| 0.050067 / 0.038508 (0.011559) \| 0.031419 / 0.023109 (0.008310) \| 0.275303 / 0.275898 (-0.000595) \| 0.286736 / 0.323480 (-0.036744) \| 0.004177 / 0.007986 (-0.003808) \| 0.002742 / 0.004328 (-0.001586) \| 0.049011 / 0.004250 (0.044761) \| 0.044373 / 0.037052 (0.007321) \| 0.289189 / 0.258489 (0.030700) \| 0.320117 / 0.293841 (0.026276) \| 0.050154 / 0.128546 (-0.078392) \| 0.010541 / 0.075646 (-0.065106) \| 0.058318 / 0.419271 (-0.360954) \| 0.033090 / 0.043533 (-0.010443) \| 0.276820 / 0.255139 (0.021681) \| 0.290854 / 0.283200 (0.007654) \| 0.017268 / 0.141683 (-0.124415) \| 1.159345 / 1.452155 (-0.292809) \| 1.224829 / 1.492716 (-0.267887) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092468 / 0.018006 (0.074462) \| 0.301176 / 0.000490 (0.300686) \| 0.000216 / 0.000200 (0.000017) \| 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) \| 0.021858 / 0.037411 (-0.015553) \| 0.074873 / 0.014526 (0.060347) \| 0.086238 / 0.176557 (-0.090318) \| 0.125555 / 0.737135 (-0.611580) \| 0.087791 / 0.296338 (-0.208547) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292283 / 0.215209 (0.077073) \| 2.847306 / 2.077655 (0.769651) \| 1.600833 / 1.504120 (0.096713) \| 1.474253 / 1.541195 (-0.066942) \| 1.474871 / 1.468490 (0.006381) \| 0.576427 / 4.584777 (-4.008350) \| 2.380116 / 3.745712 (-1.365596) \| 2.782059 / 5.269862 (-2.487803) \| 1.730642 / 4.565676 (-2.835035) \| 0.063860 / 0.424275 (-0.360415) \| 0.005019 / 0.007607 (-0.002588) \| 0.343247 / 0.226044 (0.117202) \| 3.393427 / 2.268929 (1.124498) \| 1.935346 / 55.444624 (-53.509278) \| 1.680124 / 6.876477 (-5.196353) \| 1.665788 / 2.142072 (-0.476285) \| 0.648767 / 4.805227 (-4.156460) \| 0.121962 / 6.500664 (-6.378702) \| 0.040669 / 0.075469 (-0.034800) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996535 / 1.841788 (-0.845252) \| 12.074553 / 8.074308 (4.000245) \| 10.812740 / 10.191392 (0.621348) \| 0.142690 / 0.680424 (-0.537734) \| 0.014977 / 0.534201 (-0.519224) \| 0.285619 / 0.579283 (-0.293664) \| 0.269401 / 0.434364 (-0.164963) \| 0.329882 / 0.540337 (-0.210456) \| 0.416169 / 1.386936 (-0.970767) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#129b9e0565e7a2ceaca64b99dcbf39504661cfa9 \"CML watermark\")\n" ]
6,616	Use schema metadata only if it matches features	e.g. if we use `map` in arrow format and transform the table, the returned table might have new columns but the metadata might be wrong	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6616", "html_url": "https://github.com/huggingface/datasets/pull/6616", "diff_url": "https://github.com/huggingface/datasets/pull/6616.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6616.patch", "merged_at": "2024-01-26T16:19:12" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6616). 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) \| 0.005382 / 0.011353 (-0.005970) \| 0.003853 / 0.011008 (-0.007155) \| 0.062629 / 0.038508 (0.024121) \| 0.030344 / 0.023109 (0.007234) \| 0.245394 / 0.275898 (-0.030505) \| 0.266004 / 0.323480 (-0.057476) \| 0.003183 / 0.007986 (-0.004802) \| 0.002795 / 0.004328 (-0.001533) \| 0.048357 / 0.004250 (0.044107) \| 0.043834 / 0.037052 (0.006782) \| 0.255979 / 0.258489 (-0.002510) \| 0.280803 / 0.293841 (-0.013038) \| 0.028200 / 0.128546 (-0.100347) \| 0.010856 / 0.075646 (-0.064791) \| 0.207076 / 0.419271 (-0.212195) \| 0.036286 / 0.043533 (-0.007247) \| 0.246492 / 0.255139 (-0.008647) \| 0.265861 / 0.283200 (-0.017338) \| 0.018309 / 0.141683 (-0.123374) \| 1.155136 / 1.452155 (-0.297018) \| 1.214342 / 1.492716 (-0.278375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092530 / 0.018006 (0.074524) \| 0.344951 / 0.000490 (0.344461) \| 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) \| 0.018324 / 0.037411 (-0.019087) \| 0.063137 / 0.014526 (0.048611) \| 0.074683 / 0.176557 (-0.101874) \| 0.120224 / 0.737135 (-0.616912) \| 0.083107 / 0.296338 (-0.213232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288631 / 0.215209 (0.073422) \| 2.817992 / 2.077655 (0.740337) \| 1.473609 / 1.504120 (-0.030511) \| 1.336610 / 1.541195 (-0.204585) \| 1.354807 / 1.468490 (-0.113683) \| 0.568776 / 4.584777 (-4.016001) \| 2.412607 / 3.745712 (-1.333105) \| 2.832816 / 5.269862 (-2.437045) \| 1.789899 / 4.565676 (-2.775778) \| 0.063602 / 0.424275 (-0.360673) \| 0.004993 / 0.007607 (-0.002615) \| 0.338830 / 0.226044 (0.112786) \| 3.302550 / 2.268929 (1.033621) \| 1.827907 / 55.444624 (-53.616717) \| 1.589857 / 6.876477 (-5.286620) \| 1.647746 / 2.142072 (-0.494326) \| 0.658461 / 4.805227 (-4.146766) \| 0.120360 / 6.500664 (-6.380304) \| 0.042989 / 0.075469 (-0.032480) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945487 / 1.841788 (-0.896301) \| 11.846335 / 8.074308 (3.772027) \| 10.483199 / 10.191392 (0.291807) \| 0.131853 / 0.680424 (-0.548570) \| 0.014230 / 0.534201 (-0.519971) \| 0.288700 / 0.579283 (-0.290584) \| 0.276086 / 0.434364 (-0.158278) \| 0.326225 / 0.540337 (-0.214112) \| 0.422874 / 1.386936 (-0.964062) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006234 / 0.011353 (-0.005118) \| 0.004104 / 0.011008 (-0.006904) \| 0.049967 / 0.038508 (0.011459) \| 0.037157 / 0.023109 (0.014048) \| 0.261892 / 0.275898 (-0.014006) \| 0.284304 / 0.323480 (-0.039176) \| 0.004482 / 0.007986 (-0.003504) \| 0.002920 / 0.004328 (-0.001409) \| 0.048827 / 0.004250 (0.044577) \| 0.052258 / 0.037052 (0.015206) \| 0.277121 / 0.258489 (0.018632) \| 0.304177 / 0.293841 (0.010336) \| 0.053537 / 0.128546 (-0.075009) \| 0.011137 / 0.075646 (-0.064509) \| 0.058188 / 0.419271 (-0.361083) \| 0.034283 / 0.043533 (-0.009250) \| 0.261912 / 0.255139 (0.006773) \| 0.273851 / 0.283200 (-0.009348) \| 0.017824 / 0.141683 (-0.123859) \| 1.130454 / 1.452155 (-0.321701) \| 1.176834 / 1.492716 (-0.315882) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102104 / 0.018006 (0.084098) \| 0.302873 / 0.000490 (0.302383) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.022470 / 0.037411 (-0.014941) \| 0.076776 / 0.014526 (0.062250) \| 0.088220 / 0.176557 (-0.088337) \| 0.130030 / 0.737135 (-0.607105) \| 0.089955 / 0.296338 (-0.206383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284070 / 0.215209 (0.068861) \| 2.769130 / 2.077655 (0.691475) \| 1.546379 / 1.504120 (0.042259) \| 1.435849 / 1.541195 (-0.105346) \| 1.478616 / 1.468490 (0.010126) \| 0.569185 / 4.584777 (-4.015592) \| 2.504721 / 3.745712 (-1.240992) \| 2.778267 / 5.269862 (-2.491595) \| 1.860360 / 4.565676 (-2.705316) \| 0.073465 / 0.424275 (-0.350810) \| 0.005108 / 0.007607 (-0.002499) \| 0.335185 / 0.226044 (0.109140) \| 3.314799 / 2.268929 (1.045870) \| 1.934824 / 55.444624 (-53.509801) \| 1.656247 / 6.876477 (-5.220229) \| 1.785422 / 2.142072 (-0.356650) \| 0.673677 / 4.805227 (-4.131551) \| 0.117692 / 6.500664 (-6.382972) \| 0.041648 / 0.075469 (-0.033821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972143 / 1.841788 (-0.869645) \| 12.980353 / 8.074308 (4.906045) \| 11.056189 / 10.191392 (0.864797) \| 0.134592 / 0.680424 (-0.545832) \| 0.015972 / 0.534201 (-0.518229) \| 0.301691 / 0.579283 (-0.277593) \| 0.286332 / 0.434364 (-0.148032) \| 0.329025 / 0.540337 (-0.211312) \| 0.422585 / 1.386936 (-0.964351) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6eb492c7072f21cb417801957c087888f252d2d1 \"CML watermark\")\n" ]
6,615	...	...	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]
6,614	`datasets/downloads` cleanup tool	### Feature request Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do: ``` sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+ sudo find /data/huggingface/datasets/downloads -type d -empty -delete ``` could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO. Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not. Thank you @Wauplin (requested to be tagged)	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,612	cnn_dailymail repeats itself	### Describe the bug When I try to load `cnn_dailymail` dataset, it takes longer than usual and when I checked the dataset it's 3x bigger than it's supposed to be. Check https://huggingface.co/datasets/cnn_dailymail: it says 287k rows for train. But when I check length of train split it says 861339. Also I checked data: ``` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][287113] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][574226] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ." ``` The datasets seems to be updated 6 days ago to convert it to Parquet. Probably, there is some issue with backward compatability. ### Steps to reproduce the bug 1. ``` from datasets import load_dataset ds = load_dataset('cnn_dailymail', '3.0.0') len(ds['train']) ``` ### Expected behavior It should not repeat itself. ### Environment info datasets==2.13.2 Python==3.7.13	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi ! We recently updated `cnn_dailymail` and now `datasets>=2.14` is needed to load it.\r\n\r\nYou can update `datasets` with\r\n\r\n```\r\npip install -U datasets\r\n```" ]
6,611	`load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError`	### Describe the bug When loading a large dataset (>1000GB) from S3 I run into the following error: ``` Traceback (most recent call last): File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper return await func(args, kwargs) File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call raise error_class(parsed_response, operation_name) botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module> dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download return self.get(rpath, lpath, recursive=recursive, kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, args, *kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get return await _run_coros_in_chunks( File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks await asyncio.gather(chunk, return_exceptions=return_exceptions), File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for return await fut File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file body, content_length = await _open_file(range=0) File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file resp = await self._call_s3( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3 return await _error_wrapper( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper raise err PermissionError: The difference between the request time and the current time is too large. ``` The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here: - https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la - https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path. ### Steps to reproduce the bug 1. Create large dataset 2. Try loading it from s3 using: ``` dataset = load_from_disk("s3://...", storage_options=storage_options) ``` ### Expected behavior Load dataset without running into this error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.3 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,610	cast_column to Sequence(subfeatures_dict) has err	### Describe the bug I am working with the following demo code: ``` from datasets import load_dataset from datasets.features import Sequence, Value, ClassLabel, Features ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/") ais_dataset = ais_dataset["train"] def add_class(example): example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"} return example ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32) ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence( { "bbox": Sequence(Value(dtype="int64")), "label": ClassLabel(names=["cat", "dog"]) })) print(ais_dataset[0]) ``` However, executing this code results in an error: ``` File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type int64 to Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None) ``` Upon examining the source code in datasets/table.py at line 2035: ``` if isinstance(feature, Sequence) and isinstance(feature.feature, dict): feature = { name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items() } ``` I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error. ### Steps to reproduce the bug run my demo code ### Expected behavior no exception ### Environment info python 3.9 datasets: 2.16.1	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```", "> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks" ]
6,609	Wrong path for cache directory in offline mode	### Describe the bug Dear huggingfacers, I'm trying to use a subset of the-stack dataset. When I run the command the first time ``` dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' ) ``` It downloads the files and caches them normally. Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path: `Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` when the right path is: `'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran` Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error: ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled. Your help with this issue is greatly appreciated. Thanks a lot for the great work. ### Steps to reproduce the bug 1: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` 2: `HF_DATASETS_OFFLINE=1` 3: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` ### Expected behavior being able to use the cached data ### Environment info several different systems	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "+1", "same error in 2.16.1", "@kongjiellx any luck with the issue?", "I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`", "Thanks @lhoestq !" ]
6,608	Add `with_rank` param to `Dataset.filter`	Fix #6564	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6608", "html_url": "https://github.com/huggingface/datasets/pull/6608", "diff_url": "https://github.com/huggingface/datasets/pull/6608.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6608.patch", "merged_at": "2024-01-29T16:36:53" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6608). 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) \| 0.005376 / 0.011353 (-0.005977) \| 0.004691 / 0.011008 (-0.006317) \| 0.064061 / 0.038508 (0.025553) \| 0.030397 / 0.023109 (0.007288) \| 0.242656 / 0.275898 (-0.033242) \| 0.275586 / 0.323480 (-0.047894) \| 0.003460 / 0.007986 (-0.004526) \| 0.003125 / 0.004328 (-0.001203) \| 0.050496 / 0.004250 (0.046246) \| 0.045833 / 0.037052 (0.008781) \| 0.255222 / 0.258489 (-0.003267) \| 0.287303 / 0.293841 (-0.006538) \| 0.027755 / 0.128546 (-0.100791) \| 0.011251 / 0.075646 (-0.064396) \| 0.208456 / 0.419271 (-0.210816) \| 0.037219 / 0.043533 (-0.006314) \| 0.249592 / 0.255139 (-0.005547) \| 0.261243 / 0.283200 (-0.021957) \| 0.020735 / 0.141683 (-0.120948) \| 1.130017 / 1.452155 (-0.322137) \| 1.208558 / 1.492716 (-0.284158) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098891 / 0.018006 (0.080885) \| 0.439042 / 0.000490 (0.438552) \| 0.000333 / 0.000200 (0.000133) \| 0.000045 / 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) \| 0.018356 / 0.037411 (-0.019055) \| 0.062416 / 0.014526 (0.047891) \| 0.075613 / 0.176557 (-0.100944) \| 0.122009 / 0.737135 (-0.615126) \| 0.078195 / 0.296338 (-0.218144) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273804 / 0.215209 (0.058595) \| 2.706480 / 2.077655 (0.628826) \| 1.456196 / 1.504120 (-0.047924) \| 1.353301 / 1.541195 (-0.187893) \| 1.378913 / 1.468490 (-0.089577) \| 0.556885 / 4.584777 (-4.027892) \| 2.358961 / 3.745712 (-1.386752) \| 2.871830 / 5.269862 (-2.398031) \| 1.765212 / 4.565676 (-2.800464) \| 0.062172 / 0.424275 (-0.362103) \| 0.004974 / 0.007607 (-0.002633) \| 0.330375 / 0.226044 (0.104331) \| 3.264550 / 2.268929 (0.995621) \| 1.824444 / 55.444624 (-53.620181) \| 1.561189 / 6.876477 (-5.315287) \| 1.671020 / 2.142072 (-0.471052) \| 0.633408 / 4.805227 (-4.171819) \| 0.116080 / 6.500664 (-6.384584) \| 0.044606 / 0.075469 (-0.030863) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980757 / 1.841788 (-0.861031) \| 12.553534 / 8.074308 (4.479225) \| 10.517668 / 10.191392 (0.326276) \| 0.130528 / 0.680424 (-0.549896) \| 0.013960 / 0.534201 (-0.520241) \| 0.289615 / 0.579283 (-0.289668) \| 0.267277 / 0.434364 (-0.167087) \| 0.324139 / 0.540337 (-0.216198) \| 0.440325 / 1.386936 (-0.946611) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005388 / 0.011353 (-0.005965) \| 0.004043 / 0.011008 (-0.006966) \| 0.050514 / 0.038508 (0.012005) \| 0.031413 / 0.023109 (0.008303) \| 0.275122 / 0.275898 (-0.000776) \| 0.307518 / 0.323480 (-0.015962) \| 0.004440 / 0.007986 (-0.003546) \| 0.003301 / 0.004328 (-0.001027) \| 0.049200 / 0.004250 (0.044949) \| 0.045704 / 0.037052 (0.008651) \| 0.285265 / 0.258489 (0.026776) \| 0.318942 / 0.293841 (0.025101) \| 0.053893 / 0.128546 (-0.074653) \| 0.011855 / 0.075646 (-0.063791) \| 0.060951 / 0.419271 (-0.358321) \| 0.034397 / 0.043533 (-0.009136) \| 0.276108 / 0.255139 (0.020969) \| 0.290981 / 0.283200 (0.007781) \| 0.019986 / 0.141683 (-0.121697) \| 1.205695 / 1.452155 (-0.246460) \| 1.255942 / 1.492716 (-0.236774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101910 / 0.018006 (0.083904) \| 0.320551 / 0.000490 (0.320061) \| 0.000299 / 0.000200 (0.000099) \| 0.000058 / 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) \| 0.022387 / 0.037411 (-0.015024) \| 0.076380 / 0.014526 (0.061854) \| 0.090404 / 0.176557 (-0.086153) \| 0.127106 / 0.737135 (-0.610030) \| 0.089873 / 0.296338 (-0.206465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288433 / 0.215209 (0.073223) \| 2.827005 / 2.077655 (0.749350) \| 1.548760 / 1.504120 (0.044640) \| 1.419545 / 1.541195 (-0.121650) \| 1.456531 / 1.468490 (-0.011959) \| 0.570254 / 4.584777 (-4.014523) \| 2.441318 / 3.745712 (-1.304394) \| 2.778647 / 5.269862 (-2.491215) \| 1.755255 / 4.565676 (-2.810422) \| 0.062581 / 0.424275 (-0.361694) \| 0.005205 / 0.007607 (-0.002402) \| 0.342189 / 0.226044 (0.116145) \| 3.401208 / 2.268929 (1.132279) \| 1.941447 / 55.444624 (-53.503178) \| 1.652578 / 6.876477 (-5.223899) \| 1.768558 / 2.142072 (-0.373514) \| 0.656537 / 4.805227 (-4.148690) \| 0.116901 / 6.500664 (-6.383763) \| 0.041408 / 0.075469 (-0.034061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001715 / 1.841788 (-0.840073) \| 12.533073 / 8.074308 (4.458765) \| 11.086084 / 10.191392 (0.894692) \| 0.134368 / 0.680424 (-0.546055) \| 0.015255 / 0.534201 (-0.518946) \| 0.291769 / 0.579283 (-0.287514) \| 0.283311 / 0.434364 (-0.151053) \| 0.327857 / 0.540337 (-0.212481) \| 0.413854 / 1.386936 (-0.973083) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46931085bd8a3fdbc63b68b5ee4b8f62029c7557 \"CML watermark\")\n" ]
6,607	Update features.py to avoid bfloat16 unsupported error	Fixes https://github.com/huggingface/datasets/issues/6566 Let me know if there's any tests I need to clear.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6607", "html_url": "https://github.com/huggingface/datasets/pull/6607", "diff_url": "https://github.com/huggingface/datasets/pull/6607.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6607.patch", "merged_at": "2024-05-17T09:40:13" }	true	[ "I think not all torch tensors should be converted to float, what if it's a tensor of integers for example ?\r\nMaybe you can check for the tensor dtype before converting", "@lhoestq Please could this be merged? 🙏", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005552 / 0.011353 (-0.005801) \| 0.003707 / 0.011008 (-0.007301) \| 0.063794 / 0.038508 (0.025286) \| 0.031897 / 0.023109 (0.008788) \| 0.263086 / 0.275898 (-0.012812) \| 0.281184 / 0.323480 (-0.042296) \| 0.003183 / 0.007986 (-0.004802) \| 0.002681 / 0.004328 (-0.001648) \| 0.050259 / 0.004250 (0.046009) \| 0.048395 / 0.037052 (0.011342) \| 0.266925 / 0.258489 (0.008436) \| 0.298146 / 0.293841 (0.004305) \| 0.027995 / 0.128546 (-0.100551) \| 0.010689 / 0.075646 (-0.064957) \| 0.204956 / 0.419271 (-0.214316) \| 0.036453 / 0.043533 (-0.007080) \| 0.255406 / 0.255139 (0.000267) \| 0.271388 / 0.283200 (-0.011811) \| 0.019748 / 0.141683 (-0.121935) \| 1.103926 / 1.452155 (-0.348228) \| 1.167250 / 1.492716 (-0.325466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100483 / 0.018006 (0.082477) \| 0.307331 / 0.000490 (0.306841) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.018918 / 0.037411 (-0.018493) \| 0.062569 / 0.014526 (0.048044) \| 0.074935 / 0.176557 (-0.101621) \| 0.122590 / 0.737135 (-0.614545) \| 0.076475 / 0.296338 (-0.219864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279001 / 0.215209 (0.063792) \| 2.771630 / 2.077655 (0.693975) \| 1.439666 / 1.504120 (-0.064454) \| 1.303422 / 1.541195 (-0.237773) \| 1.355670 / 1.468490 (-0.112820) \| 0.576264 / 4.584777 (-4.008513) \| 2.394868 / 3.745712 (-1.350844) \| 2.941487 / 5.269862 (-2.328375) \| 1.808733 / 4.565676 (-2.756943) \| 0.063691 / 0.424275 (-0.360584) \| 0.005399 / 0.007607 (-0.002208) \| 0.335610 / 0.226044 (0.109566) \| 3.295903 / 2.268929 (1.026974) \| 1.771836 / 55.444624 (-53.672788) \| 1.511246 / 6.876477 (-5.365231) \| 1.535926 / 2.142072 (-0.606147) \| 0.649020 / 4.805227 (-4.156207) \| 0.119754 / 6.500664 (-6.380910) \| 0.043319 / 0.075469 (-0.032150) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967275 / 1.841788 (-0.874513) \| 12.358482 / 8.074308 (4.284174) \| 9.933324 / 10.191392 (-0.258068) \| 0.133565 / 0.680424 (-0.546859) \| 0.015650 / 0.534201 (-0.518551) \| 0.286978 / 0.579283 (-0.292305) \| 0.262912 / 0.434364 (-0.171451) \| 0.330335 / 0.540337 (-0.210002) \| 0.427671 / 1.386936 (-0.959265) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005660 / 0.011353 (-0.005693) \| 0.003908 / 0.011008 (-0.007101) \| 0.051874 / 0.038508 (0.013366) \| 0.033141 / 0.023109 (0.010032) \| 0.270512 / 0.275898 (-0.005386) \| 0.296790 / 0.323480 (-0.026690) \| 0.004335 / 0.007986 (-0.003651) \| 0.002842 / 0.004328 (-0.001487) \| 0.078264 / 0.004250 (0.074014) \| 0.044436 / 0.037052 (0.007384) \| 0.283230 / 0.258489 (0.024741) \| 0.318026 / 0.293841 (0.024185) \| 0.031459 / 0.128546 (-0.097087) \| 0.010710 / 0.075646 (-0.064937) \| 0.058152 / 0.419271 (-0.361119) \| 0.034021 / 0.043533 (-0.009512) \| 0.269956 / 0.255139 (0.014817) \| 0.288783 / 0.283200 (0.005583) \| 0.019246 / 0.141683 (-0.122436) \| 1.127264 / 1.452155 (-0.324891) \| 1.169777 / 1.492716 (-0.322939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101523 / 0.018006 (0.083516) \| 0.315120 / 0.000490 (0.314630) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.023078 / 0.037411 (-0.014333) \| 0.080021 / 0.014526 (0.065495) \| 0.089574 / 0.176557 (-0.086982) \| 0.131258 / 0.737135 (-0.605877) \| 0.090604 / 0.296338 (-0.205734) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302197 / 0.215209 (0.086988) \| 2.980071 / 2.077655 (0.902416) \| 1.585480 / 1.504120 (0.081360) \| 1.462904 / 1.541195 (-0.078291) \| 1.501102 / 1.468490 (0.032612) \| 0.580342 / 4.584777 (-4.004435) \| 0.972118 / 3.745712 (-2.773594) \| 2.930530 / 5.269862 (-2.339331) \| 1.824132 / 4.565676 (-2.741545) \| 0.064711 / 0.424275 (-0.359564) \| 0.005084 / 0.007607 (-0.002523) \| 0.352693 / 0.226044 (0.126649) \| 3.522775 / 2.268929 (1.253847) \| 1.965063 / 55.444624 (-53.479561) \| 1.679250 / 6.876477 (-5.197226) \| 1.711691 / 2.142072 (-0.430382) \| 0.663719 / 4.805227 (-4.141509) \| 0.119858 / 6.500664 (-6.380806) \| 0.041744 / 0.075469 (-0.033725) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017970 / 1.841788 (-0.823817) \| 12.898917 / 8.074308 (4.824609) \| 10.244728 / 10.191392 (0.053336) \| 0.133860 / 0.680424 (-0.546564) \| 0.016044 / 0.534201 (-0.518157) \| 0.287543 / 0.579283 (-0.291740) \| 0.126418 / 0.434364 (-0.307946) \| 0.394970 / 0.540337 (-0.145368) \| 0.420455 / 1.386936 (-0.966481) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d71ffeb10bc129f6f923cfadb5ccd9383b8033 \"CML watermark\")\n" ]
6,606	Dedicated RNG object for fingerprinting	Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6606", "html_url": "https://github.com/huggingface/datasets/pull/6606", "diff_url": "https://github.com/huggingface/datasets/pull/6606.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6606.patch", "merged_at": "2024-01-26T15:05:34" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). 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) \| 0.005625 / 0.011353 (-0.005728) \| 0.003313 / 0.011008 (-0.007695) \| 0.063997 / 0.038508 (0.025489) \| 0.028949 / 0.023109 (0.005839) \| 0.250069 / 0.275898 (-0.025829) \| 0.271412 / 0.323480 (-0.052068) \| 0.003837 / 0.007986 (-0.004148) \| 0.002632 / 0.004328 (-0.001697) \| 0.048351 / 0.004250 (0.044100) \| 0.040664 / 0.037052 (0.003612) \| 0.267540 / 0.258489 (0.009051) \| 0.285237 / 0.293841 (-0.008604) \| 0.026962 / 0.128546 (-0.101584) \| 0.010417 / 0.075646 (-0.065229) \| 0.211430 / 0.419271 (-0.207842) \| 0.035411 / 0.043533 (-0.008122) \| 0.258867 / 0.255139 (0.003728) \| 0.278562 / 0.283200 (-0.004638) \| 0.017690 / 0.141683 (-0.123993) \| 1.128813 / 1.452155 (-0.323342) \| 1.169384 / 1.492716 (-0.323333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091322 / 0.018006 (0.073316) \| 0.303272 / 0.000490 (0.302782) \| 0.000202 / 0.000200 (0.000002) \| 0.000044 / 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) \| 0.017551 / 0.037411 (-0.019861) \| 0.060027 / 0.014526 (0.045502) \| 0.073431 / 0.176557 (-0.103125) \| 0.120550 / 0.737135 (-0.616585) \| 0.073107 / 0.296338 (-0.223231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283064 / 0.215209 (0.067855) \| 2.754593 / 2.077655 (0.676938) \| 1.477303 / 1.504120 (-0.026817) \| 1.341072 / 1.541195 (-0.200123) \| 1.366625 / 1.468490 (-0.101865) \| 0.573467 / 4.584777 (-4.011310) \| 2.395225 / 3.745712 (-1.350487) \| 2.777021 / 5.269862 (-2.492841) \| 1.720733 / 4.565676 (-2.844944) \| 0.063339 / 0.424275 (-0.360936) \| 0.004954 / 0.007607 (-0.002653) \| 0.350359 / 0.226044 (0.124315) \| 3.376221 / 2.268929 (1.107293) \| 1.835539 / 55.444624 (-53.609086) \| 1.558064 / 6.876477 (-5.318413) \| 1.582778 / 2.142072 (-0.559294) \| 0.649918 / 4.805227 (-4.155309) \| 0.117761 / 6.500664 (-6.382903) \| 0.041771 / 0.075469 (-0.033698) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950202 / 1.841788 (-0.891586) \| 11.476160 / 8.074308 (3.401852) \| 10.290618 / 10.191392 (0.099226) \| 0.140659 / 0.680424 (-0.539765) \| 0.014525 / 0.534201 (-0.519676) \| 0.287253 / 0.579283 (-0.292030) \| 0.266204 / 0.434364 (-0.168160) \| 0.327818 / 0.540337 (-0.212519) \| 0.431680 / 1.386936 (-0.955256) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005096 / 0.011353 (-0.006257) \| 0.003460 / 0.011008 (-0.007548) \| 0.049474 / 0.038508 (0.010966) \| 0.031063 / 0.023109 (0.007954) \| 0.272899 / 0.275898 (-0.002999) \| 0.291859 / 0.323480 (-0.031621) \| 0.004858 / 0.007986 (-0.003128) \| 0.002598 / 0.004328 (-0.001731) \| 0.049074 / 0.004250 (0.044824) \| 0.044722 / 0.037052 (0.007669) \| 0.285262 / 0.258489 (0.026772) \| 0.314168 / 0.293841 (0.020327) \| 0.046346 / 0.128546 (-0.082200) \| 0.010384 / 0.075646 (-0.065262) \| 0.058331 / 0.419271 (-0.360940) \| 0.033728 / 0.043533 (-0.009805) \| 0.276217 / 0.255139 (0.021078) \| 0.295465 / 0.283200 (0.012265) \| 0.018215 / 0.141683 (-0.123467) \| 1.163847 / 1.452155 (-0.288308) \| 1.213901 / 1.492716 (-0.278816) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091953 / 0.018006 (0.073947) \| 0.299977 / 0.000490 (0.299487) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 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) \| 0.022031 / 0.037411 (-0.015381) \| 0.075067 / 0.014526 (0.060541) \| 0.087305 / 0.176557 (-0.089251) \| 0.125530 / 0.737135 (-0.611605) \| 0.088761 / 0.296338 (-0.207578) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302682 / 0.215209 (0.087473) \| 2.941509 / 2.077655 (0.863854) \| 1.643399 / 1.504120 (0.139280) \| 1.530148 / 1.541195 (-0.011046) \| 1.542067 / 1.468490 (0.073577) \| 0.575883 / 4.584777 (-4.008894) \| 2.434320 / 3.745712 (-1.311392) \| 2.761683 / 5.269862 (-2.508179) \| 1.732068 / 4.565676 (-2.833609) \| 0.063543 / 0.424275 (-0.360732) \| 0.005089 / 0.007607 (-0.002518) \| 0.351314 / 0.226044 (0.125269) \| 3.494572 / 2.268929 (1.225643) \| 2.032503 / 55.444624 (-53.412121) \| 1.697949 / 6.876477 (-5.178528) \| 1.700392 / 2.142072 (-0.441680) \| 0.650757 / 4.805227 (-4.154471) \| 0.116719 / 6.500664 (-6.383945) \| 0.040559 / 0.075469 (-0.034910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978218 / 1.841788 (-0.863570) \| 11.972379 / 8.074308 (3.898071) \| 10.725735 / 10.191392 (0.534343) \| 0.130564 / 0.680424 (-0.549860) \| 0.015396 / 0.534201 (-0.518805) \| 0.286900 / 0.579283 (-0.292383) \| 0.279633 / 0.434364 (-0.154730) \| 0.327483 / 0.540337 (-0.212854) \| 0.417848 / 1.386936 (-0.969088) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adfe8f8fa37b9f220c152f5b8b2473ba2cef0307 \"CML watermark\")\n" ]
6,605	ELI5 no longer available, but referenced in example code	Here, an example code is given: https://huggingface.co/docs/transformers/tasks/language_modeling This code + article references the ELI5 dataset. ELI5 is no longer available, as the ELI5 dataset page states: https://huggingface.co/datasets/eli5 "Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data. Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable. " Please change the example code to use a different dataset.	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Addressed in https://github.com/huggingface/transformers/pull/28715." ]
6,604	Transform fingerprint collisions due to setting fixed random seed	### Describe the bug The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45. This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets. ### Steps to reproduce the bug n/a ### Expected behavior Use `uuid` v4 instead of `random.getrandbits()` ### Environment info `datasets` main branch	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "I've opened a PR with a fix.", "I don't think the PR fixes the root cause, since it still relies on the `random` library which will often have its seed fixed. I think the builtin `uuid.uuid4()` is a better choice: https://docs.python.org/3/library/uuid.html" ]
6,603	datasets map `cache_file_name` does not work	### Describe the bug In the documentation `datasets.Dataset.map` arg `cache_file_name` is said to be a string, but it doesn't work. ### Steps to reproduce the bug 1. pick a dataset 2. write a map function 3. do `ds.map(..., cache_file_name='some_filename')` 4. it crashes ### Expected behavior It will tell you the filename you specified does not exist or it will generate a new file and tell you the filename does not exist. ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Unfortunately, I'm unable to reproduce this error. Can you share the reproducer?", "```\r\nds = datasets.Dataset.from_dict(dict(a=[i for i in range(100)]))\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-fn\") # this worked\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/filename\") # this failed\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/\") # this failed\r\n\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/whatever-folder/tmp1_izxvoo'\r\n```\r\n\r\nIt will fail if the filename parents do not exists. If we have `os.makedirs(\"/tmp/whatever-folder\")`, then it worked.\r\n\r\nMaybe add the `mkdir -p` into the map function?" ]
6,602	Index error when data is large	### Describe the bug At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is `total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)` The fix is setting a larger `max_shard_size` ### Steps to reproduce the bug 1. create a dataset with large dense tensors per row 2. set a small `max_shard_size` say 1MB 3. `save_to_disk` ### Expected behavior ``` raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 10 out of range for dataset of size 10. ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,601	add safety checks when using only part of dataset	Added some checks to prevent errors that arrise when using evaluate.py on only a portion of the squad 2.0 dataset.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6601", "html_url": "https://github.com/huggingface/datasets/pull/6601", "diff_url": "https://github.com/huggingface/datasets/pull/6601.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6601.patch", "merged_at": null }	true	[ "Hi ! The metrics in `datasets` are deprecated in favor of https://github.com/huggingface/evaluate\r\n\r\nYou can open a PR here instead: https://huggingface.co/spaces/evaluate-metric/squad_v2/tree/main" ]
6,600	Loading CSV exported dataset has unexpected format	### Describe the bug I wanted to be able to save a HF dataset for translations and load it again in another script, but I'm a bit confused with the documentation and the result I've got so I'm opening this issue to ask if this behavior is as expected. ### Steps to reproduce the bug The documentation I've mainly consulted is https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/loading_methods#datasets.load_dataset and https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset (where I've found `.to_csv()`) ```python # Load a dataset of translations test_dataset = load_dataset("opus100", name="en-fr", split="test") # Save with .to_csv() test_csv_path = "try_testset_save.csv" test_dataset.to_csv(test_csv_path) # Load dataset from the CSV loaded_dataset = load_dataset("csv", data_files=test_csv_path) print(test_dataset_fromfile[0]["translation"]) print(test_dataset_fromfile[0]["translation"]["en"]) ``` ``` Creating CSV from Arrow format: 100% 2/2 [00:00<00:00, 47.99ba/s] Downloading data files: 100% 1/1 [00:00<00:00, 65.33it/s] Extracting data files: 100% 1/1 [00:00<00:00, 42.10it/s] Generating train split: 2000/0 [00:00<00:00, 47486.09 examples/s] {'en': "She wasn't going to vaccinate her kid against polio, no way.", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'} --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[29], line 11 9 loaded_dataset = load_dataset("csv", data_files=test_csv_path) 10 print(test_dataset_fromfile[0]["translation"]) ---> 11 print(test_dataset_fromfile[0]["translation"]["en"]) TypeError: string indices must be integers, not 'str' ``` ### Expected behavior Each translation was saved as a stringified dict like `"{'en': ""She wasn't going to vaccinate her kid against polio, no way."", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}"` where I would have expected 2 columns (1st with English segments, and 2nd with French segments), and I was expecting `load_dataset` to infer the type of feature automatically as I haven't seen anything about it in the documentation. Do you have an example of how to effectively save and load datasets of translations ? ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.5 - `huggingface_hub` version: 0.16.4 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Parquet is the only format that supports complex/nested features such as `Translation`. So, this should work:\r\n```python\r\ntest_dataset = load_dataset(\"opus100\", name=\"en-fr\", split=\"test\")\r\n\r\n# Save with .to_parquet()\r\ntest_parquet_path = \"try_testset_save.parquet\"\r\ntest_dataset.to_parquet(test_parquet_path)\r\n\r\n# Load dataset from the Parquet\r\nloaded_dataset = load_dataset(\"parquet\", data_files=test_parquet_path)\r\nprint(test_dataset_fromfile[0][\"translation\"])\r\nprint(test_dataset_fromfile[0][\"translation\"][\"en\"])\r\n```", "Indeed this works great, thank you !" ]
6,599	Easy way to segment into 30s snippets given an m4a file and a vtt file	### Feature request Uploading datasets is straightforward thanks to the ability to push Audio to hub. However, it would be nice if the data (text and audio) could be segmented when being pushed (if not possible already). ### Motivation It's easy to create a vtt file from an audio file. If there could be auto-segmenting, this would make the creation of datasets much faster. ### Your contribution I have made a custom script to do this but it's not all that clean - uses librosa and pydub.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Non-generic data processing is out of this library's scope, so it's downstream libraries/users' responsibility to implement such logic.", "That's fair. Thanks" ]
6,598	Unexpected keyword argument 'hf' when downloading CSV dataset from S3	### Describe the bug I receive this error message when using `load_dataset` with "csv" path and `dataset_files=s3://...`: ``` TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` I found a similar issue here: https://stackoverflow.com/questions/77596258/aws-issue-load-dataset-from-s3-fails-with-unexpected-keyword-argument-error-in Full stacktrace: ``` .../site-packages/datasets/load.py:2549: in load_dataset builder_instance.download_and_prepare( .../site-packages/datasets/builder.py:1005: in download_and_prepare self._download_and_prepare( .../site-packages/datasets/builder.py:1078: in _download_and_prepare split_generators = self._split_generators(dl_manager, split_generators_kwargs) .../site-packages/datasets/packaged_modules/csv/csv.py:147: in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) .../site-packages/datasets/download/download_manager.py:562: in download_and_extract return self.extract(self.download(url_or_urls)) .../site-packages/datasets/download/download_manager.py:426: in download downloaded_path_or_paths = map_nested( .../site-packages/datasets/utils/py_utils.py:466: in map_nested mapped = [ .../site-packages/datasets/utils/py_utils.py:467: in <listcomp> _single_map_nested((function, obj, types, None, True, None)) .../site-packages/datasets/utils/py_utils.py:387: in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:387: in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:370: in _single_map_nested return function(data_struct) .../site-packages/datasets/download/download_manager.py:451: in _download out = cached_path(url_or_filename, download_config=download_config) .../site-packages/datasets/utils/file_utils.py:188: in cached_path output_path = get_from_cache( ...1/site-packages/datasets/utils/file_utils.py:511: in get_from_cache response = fsspec_head(url, storage_options=storage_options) .../site-packages/datasets/utils/file_utils.py:316: in fsspec_head fs, _, paths = fsspec.get_fs_token_paths(url, storage_options=storage_options) .../site-packages/fsspec/core.py:622: in get_fs_token_paths fs = filesystem(protocol, inkwargs) .../site-packages/fsspec/registry.py:290: in filesystem return cls(*storage_options) .../site-packages/fsspec/spec.py:79: in __call__ obj = super().__call__(args, kwargs) .../site-packages/s3fs/core.py:187: in __init__ self.s3 = self.connect() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <s3fs.core.S3FileSystem object at 0x1500a1310>, refresh = True def connect(self, refresh=True): """ Establish S3 connection object. Parameters ---------- refresh : bool Whether to create new session/client, even if a previous one with the same parameters already exists. If False (default), an existing one will be used if possible """ if refresh is False: # back compat: we store whole FS instance now return self.s3 anon, key, secret, kwargs, ckwargs, token, ssl = ( self.anon, self.key, self.secret, self.kwargs, self.client_kwargs, self.token, self.use_ssl) if not self.passed_in_session: > self.session = botocore.session.Session(self.kwargs) E TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` ### Steps to reproduce the bug 1. Assuming a valid CSV file located at `s3://bucket/data.csv` 2. Run the below code: ``` storage_options = { "key": "...", "secret": "...", "client_kwargs": { "endpoint_url": "...", } } load_dataset("csv", data_files="s3://bucket/data.csv", storage_options=storage_options) ``` Encountered in version `2.16.1` but also reproduced in `2.16.0` and `2.15.0`. Note: I encountered this in a unit test using a `moto` mock for S3, however since the error occurs before the session is instantiated, it should not be the issue. ### Expected behavior No exception is raised, the boto3 session is created successfully, and the CSV file is downloaded successfully and returned as a dataset. === After some research I found that `DownloadConfig` has a `__post_init__` method that always forces this value to be set in its `storage_options`, even though in case of an S3 location the storage options get passed on to the S3 Session which does not expect this parameter. I assume this parameter is needed when reading from the huggingface hub and should not be set in this context. Unfortunately there is nothing the user can do to work around it. Even if you manually do something like: ``` download_config = DownloadConfig() del download_config.storage_options["hf"] load_dataset("csv", data_files="s3://bucket/data.csv", download_config=download_config) ``` the library will still reinsert this parameter when `download_config = self.download_config.copy()` in line 418 of `download_manager.py` (`DownloadManager.download`). Therefore `load_dataset` currently cannot be used to read a dataset in CSV format from an S3 location. ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.11.7 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "I am facing similar issue while reading a csv file from s3. Wondering if somebody has found a workaround. ", "same thing happened to other formats like parquet", "I am facing similar issue while reading a parquet file from s3.\r\ni try with every version between 2.14 to 2.16.1 but it dosen't work ", "Re-define the DownloadConfig might work:\r\n\r\n```\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\n def copy(self):\r\n return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()})\r\n\r\ndownloadconfig = ReviseDownloadConfig()\r\n```\r\n", "> Re-define the DownloadConfig might work:\r\n> \r\n> ```\r\n> class ReviseDownloadConfig(DownloadConfig):\r\n> def __post_init__(self, use_auth_token):\r\n> if use_auth_token != \"deprecated\":\r\n> warnings.warn(\r\n> \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n> f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n> FutureWarning,\r\n> )\r\n> self.token = use_auth_token\r\n> ```\r\nThis seemed to work for me.\r\n", "use pandas and then convert to `Dataset`", "I am currently facing the same issue while using a custom loading script with files located in a remote S3 instance. I was using the `download_custom` functionality but now it is deprecated mentioning that I should use the native S3 loading, which is not working. \r\n\r\nAs stated before, the library forces the existence of a `hf` key in the `storage_options` variable, which is not accepted by `s3fs` : \r\n\r\n```python\r\n.../site-packages/s3fs/core.py\", line 516, in set_session\r\n self.session = aiobotocore.session.AioSession(self.kwargs)\r\nTypeError: __init__() got an unexpected keyword argument 'hf'.\r\n````\r\n\r\nMeanwhile, if my `storage_options` var stays like:\r\n```python\r\n{'key': '...',\r\n 'secret': '...',\r\n 'client_kwargs': {'endpoint_url': '...'}}\r\n```\r\nit works alright. " ]
6,597	Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace	While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace. ## Steps to reproduce the bug The command: ```python commit_info = ds.push_to_hub( "caner", config_name="default", commit_message="Convert dataset to Parquet", commit_description="Convert dataset to Parquet.", create_pr=True, token=token, ) ``` creates the additional dataset `albertvillanova/caner`.	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694", "Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585\r\n\r\n> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\r\n\r\nThis behavior was \"reverted\" by the PR: \r\n- #6519\r\n\r\nWe have therefore contradictory requirements. We should decide:\r\n- whether to support passing dataset_namespace without user/org that defaults to the logged-in user (and not support canonical datasets)\r\n- or vice-versa, to support canonical datasets and not support passing only dataset_name\r\n\r\nAs canonical datasets are \"deprecated\" (and will eventually disappear), I would choose the first option. However, if so, the Space to convert datasets to Parquet will not work for canonical datasets: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet", "IIUC, this could also be \"fixed\" by `create_repo(\"dataset_name\")` not defaulting to `create_repo(\"user/dataset_name\")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but justified to make the API more consistent.", "I tag @Wauplin to have his opinion as well.", "Hmm, creating repo with implicit namespace (e.g. `create_repo(\"dataset_name\")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace/repo_name`) that has to be passed to every `HfApi` method. Otherwise, libraries/scripts would often need to do a `whoami` call to get the namespace before creating a repo.\r\n\r\n Another solution for https://github.com/huggingface/datasets/issues/6597#issuecomment-1893746690 could be that implicit namespace is allowed (same as today) except if the `repo_id` is in a hard-coded list of canonical datasets. This list can be maintained automatically and should be slowly decreasing. Caveat: as a normal user I wouldn't be able to implicitly push to `imagenet-1k` if I wanted to push to `Wauplin/imagenet-1k`. Shouldn't be too problematic, no? Worse case, would need to add a `whoami` call and allow implicit-canonical-name for non-HF users for instance (a bit too over-engineered IMO but doable). ", "As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.\r\n\r\nI propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: \"Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\"\r\n\r\nI'm opening a PR." ]
6,596	Drop redundant None guard.	`xxx if xxx is not None else None` is no-op.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6596", "html_url": "https://github.com/huggingface/datasets/pull/6596", "diff_url": "https://github.com/huggingface/datasets/pull/6596.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6596.patch", "merged_at": "2024-01-16T17:05:52" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6596). 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) \| 0.004768 / 0.011353 (-0.006585) \| 0.003084 / 0.011008 (-0.007924) \| 0.062775 / 0.038508 (0.024267) \| 0.029909 / 0.023109 (0.006800) \| 0.242905 / 0.275898 (-0.032993) \| 0.265609 / 0.323480 (-0.057871) \| 0.003856 / 0.007986 (-0.004130) \| 0.002610 / 0.004328 (-0.001718) \| 0.048631 / 0.004250 (0.044381) \| 0.040464 / 0.037052 (0.003412) \| 0.256023 / 0.258489 (-0.002467) \| 0.285914 / 0.293841 (-0.007927) \| 0.027305 / 0.128546 (-0.101241) \| 0.010345 / 0.075646 (-0.065301) \| 0.206264 / 0.419271 (-0.213008) \| 0.035290 / 0.043533 (-0.008243) \| 0.247785 / 0.255139 (-0.007353) \| 0.267053 / 0.283200 (-0.016147) \| 0.017910 / 0.141683 (-0.123773) \| 1.166096 / 1.452155 (-0.286059) \| 1.210717 / 1.492716 (-0.281999) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095759 / 0.018006 (0.077753) \| 0.311030 / 0.000490 (0.310540) \| 0.000234 / 0.000200 (0.000034) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017828 / 0.037411 (-0.019583) \| 0.060123 / 0.014526 (0.045597) \| 0.071947 / 0.176557 (-0.104610) \| 0.119353 / 0.737135 (-0.617782) \| 0.073529 / 0.296338 (-0.222809) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282737 / 0.215209 (0.067528) \| 2.761914 / 2.077655 (0.684260) \| 1.480310 / 1.504120 (-0.023810) \| 1.329977 / 1.541195 (-0.211218) \| 1.332686 / 1.468490 (-0.135804) \| 0.566309 / 4.584777 (-4.018468) \| 2.361838 / 3.745712 (-1.383874) \| 2.775613 / 5.269862 (-2.494249) \| 1.744985 / 4.565676 (-2.820692) \| 0.063038 / 0.424275 (-0.361237) \| 0.004969 / 0.007607 (-0.002638) \| 0.335543 / 0.226044 (0.109499) \| 3.293779 / 2.268929 (1.024851) \| 1.816093 / 55.444624 (-53.628532) \| 1.562658 / 6.876477 (-5.313819) \| 1.544888 / 2.142072 (-0.597185) \| 0.641762 / 4.805227 (-4.163465) \| 0.117904 / 6.500664 (-6.382760) \| 0.042534 / 0.075469 (-0.032935) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935577 / 1.841788 (-0.906211) \| 11.565833 / 8.074308 (3.491525) \| 10.314723 / 10.191392 (0.123331) \| 0.138912 / 0.680424 (-0.541512) \| 0.013968 / 0.534201 (-0.520233) \| 0.296270 / 0.579283 (-0.283013) \| 0.266106 / 0.434364 (-0.168258) \| 0.334729 / 0.540337 (-0.205609) \| 0.443191 / 1.386936 (-0.943745) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004865 / 0.011353 (-0.006488) \| 0.003523 / 0.011008 (-0.007485) \| 0.049303 / 0.038508 (0.010795) \| 0.029252 / 0.023109 (0.006143) \| 0.271288 / 0.275898 (-0.004610) \| 0.290529 / 0.323480 (-0.032951) \| 0.003982 / 0.007986 (-0.004004) \| 0.002740 / 0.004328 (-0.001589) \| 0.048513 / 0.004250 (0.044262) \| 0.044473 / 0.037052 (0.007420) \| 0.282072 / 0.258489 (0.023583) \| 0.311321 / 0.293841 (0.017480) \| 0.028825 / 0.128546 (-0.099721) \| 0.010311 / 0.075646 (-0.065335) \| 0.057071 / 0.419271 (-0.362200) \| 0.052629 / 0.043533 (0.009097) \| 0.273134 / 0.255139 (0.017995) \| 0.290989 / 0.283200 (0.007789) \| 0.018074 / 0.141683 (-0.123609) \| 1.171724 / 1.452155 (-0.280431) \| 1.236178 / 1.492716 (-0.256538) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097099 / 0.018006 (0.079093) \| 0.309788 / 0.000490 (0.309298) \| 0.000221 / 0.000200 (0.000021) \| 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) \| 0.021703 / 0.037411 (-0.015708) \| 0.076104 / 0.014526 (0.061578) \| 0.088202 / 0.176557 (-0.088355) \| 0.127351 / 0.737135 (-0.609784) \| 0.089754 / 0.296338 (-0.206585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294574 / 0.215209 (0.079365) \| 2.851581 / 2.077655 (0.773926) \| 1.599117 / 1.504120 (0.094997) \| 1.476183 / 1.541195 (-0.065012) \| 1.512309 / 1.468490 (0.043819) \| 0.559785 / 4.584777 (-4.024992) \| 2.453287 / 3.745712 (-1.292425) \| 2.660101 / 5.269862 (-2.609760) \| 1.743043 / 4.565676 (-2.822633) \| 0.063450 / 0.424275 (-0.360825) \| 0.005019 / 0.007607 (-0.002589) \| 0.351507 / 0.226044 (0.125462) \| 3.431587 / 2.268929 (1.162658) \| 1.943349 / 55.444624 (-53.501275) \| 1.658706 / 6.876477 (-5.217771) \| 1.780042 / 2.142072 (-0.362030) \| 0.641364 / 4.805227 (-4.163863) \| 0.118052 / 6.500664 (-6.382612) \| 0.040961 / 0.075469 (-0.034508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974219 / 1.841788 (-0.867568) \| 12.257824 / 8.074308 (4.183516) \| 10.821225 / 10.191392 (0.629833) \| 0.139399 / 0.680424 (-0.541025) \| 0.015277 / 0.534201 (-0.518924) \| 0.286975 / 0.579283 (-0.292309) \| 0.283419 / 0.434364 (-0.150945) \| 0.324299 / 0.540337 (-0.216039) \| 0.424538 / 1.386936 (-0.962398) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2ba3f30bae5ff75ac48f0e653240b924d7982d5 \"CML watermark\")\n" ]
6,595	Loading big dataset raises pyarrow.lib.ArrowNotImplementedError 2	### Describe the bug I'm aware of the issue #5695 . I'm using a modified SDXL trainer: https://github.com/kopyl/diffusers/blob/5e70f604155aeecee254a5c63c5e4236ad4a0d3d/examples/text_to_image/train_text_to_image_sdxl.py#L1027C16-L1027C16 So i 1. Map dataset 2. Save to disk 3. Try to upload: ``` import datasets from datasets import load_from_disk dataset = load_from_disk("ds") datasets.config.DEFAULT_MAX_BATCH_SIZE = 1 dataset.push_to_hub("kopyl/ds", private=True, max_shard_size="500MB") ``` And i get this error: `pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat` Full traceback: ``` >>> dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, max_shard_size="500MB") Map: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1451/1451 [00:00<00:00, 6827.40 examples/s] Uploading the dataset shards: 0%\| \| 0/2099 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py", line 1705, in push_to_hub split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 5208, in _push_parquet_shards_to_hub shard.to_parquet(buffer) File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 4931, in to_parquet return ParquetDatasetWriter(self, path_or_buf, batch_size=batch_size, parquet_writer_kwargs).write() File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 129, in write written = self._write(file_obj=self.path_or_buf, batch_size=batch_size, self.parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 141, in _write writer = pq.ParquetWriter(file_obj, schema=schema, *parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py", line 1016, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat ``` Smaller datasets with the same way of saving and pushing work wonders. Big ones are not. I'm currently trying to upload dataset like this: `HfApi().upload_folder...` But i'm not sure that in this case "load_dataset" would work well. This setting num_shards does not help too: ``` dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, num_shards={'train': 500}) ``` Tried 3000, 500, 478, 100 Also do you know if it's possible to push a dataset with multiple processes? It would take an eternity pushing 1TB... ### Steps to reproduce the bug Described above ### Expected behavior Should be able to upload... ### Environment info Total dataset size: 978G Amount of `.arrow` files: 2101 Each `.arrow` file size: 477M (i know 477 megabytes 2101 does not equal 978G, but i just checked the size of a couple `.arrow` files, i don't know if some might have different size) Some files: - "ds/train/state.json": https://pastebin.com/tJ3ZLGAg - "ds/train/dataset_info.json": https://pastebin.com/JdXMQ5ih	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi ! I think the issue comes from the \"float16\" features that are not supported yet in Parquet\r\n\r\nFeel free to open an issue in `pyarrow` about this. In the meantime, I'd encourage you to use \"float32\" for your \"pooled_prompt_embeds\" and \"prompt_embeds\" features.\r\n\r\nYou can cast them to \"float32\" using\r\n\r\n```python\r\nfrom datasets import Value\r\n\r\nds = ds.cast_column(\"pooled_prompt_embeds\", Value(\"float32\"))\r\nds = ds.cast_column(\"prompt_embeds\", Value(\"float32\"))\r\n```", "@lhoestq hm. Thank you very much.\r\n\r\nDo you think it won't have any impact on the training? That it won't break it or the quality won't degrade because of this?\r\n\r\nI need to use it for [SDXL training](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)", "Increasing the precision should not degrade training (it only increases the precision), but make sure that it doesn't break your pytorch code (e.g. if it expects a float16 instead of a float32 somewhere)", "@lhoestq just fyi pyarrow 15.0.0 (just released) supports float16 as the underlying parquetcpp does as well now :)", "Oh that's amazing ! (and great timing ^^)\r\n\r\n@kopyl can you try to update `pyarrow` and try again ?\r\n\r\nBtw @assignUser there seems to be some casting implementations missing with float16 in 15.0.0, e.g.\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\n```\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float\r\n```", "Ah you are right casting is not implemented yet, it's even mentioned in the docs. This pr references the relevant issues if you'd like to track them\nhttps://github.com/apache/arrow/pull/38494", "Cool thank you :)", "@lhoestq i just recently found out that it's supported in 15.0.0, but wanted to try it first before telling you...\r\n\r\nTrying this right now and it seemingly works (although i need to wait till the end to make sure there is nothing wrong). Will update you when it's finished.\r\n\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/4821e215-e782-4736-8c76-d06187078175\">\r\n\r\nA couple of questions though:\r\n\r\n1. What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n2. Do you know how to `push_to_hub` with multiple processes?", "@lhoestq also it's strange that there was no error for a dataset with the same features, same data type, but smaller (much smaller).\r\n\r\nAltho i'm not sure about this, but chances are the dataset was loaded directly, not `load_from_disk`.... Maybe because of this.", "> What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n\r\nNothing for you, just that casting to float16 using `.cast_column(\"my_column_name\", Value(\"float16\"))` raises an error\r\n\r\n> Do you know how to push_to_hub with multiple processes?\r\n\r\nIt's not possible (yet ?). Mostly because we haven't implemented yet how to do parallel uploads to the Hub from `datasets`.\r\nThough if you want faster uploads you can already enable `hf_transfer` \r\n\r\n```\r\npip install hf_transfer\r\n```\r\n\r\nand setting `HF_HUB_ENABLE_HF_TRANSFER=1` as an environment variable\r\n\r\nsee https://huggingface.co/docs/huggingface_hub/guides/upload#tips-and-tricks-for-large-uploads", "@lhoestq thank you very much.\r\n\r\nThat would be amazing, I need to create a feature request for this :)\r\n\r\nBy the way, in short, how does hf_transfer improves the upload speed under the hood?", "@lhoestq i was just able to successfully upload without the dataset with the new pyarrow update and without increasing the precision :)", "Awesome !\r\n\r\nRegarding hf_transfer: it's been optimized in rust ;)", "@lhoestq wow, cool :)" ]
6,594	IterableDataset sharding logic needs improvement	### Describe the bug The sharding of IterableDatasets with respect to distributed and dataloader worker processes appears problematic with significant performance traps and inconsistencies wrt to distributed train processes vs worker processes. Splitting across num_workers (per train process loader processes) and world_size (distributed training processes) appears inconsistent. * worker split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1266-L1283 * distributed split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1335-L1356 In the case of the distributed split, there is a modulus check that flips between two very different behaviours, why is this different than splitting across the data loader workers? For IterableDatasets the DataLoaders worker processes are independent, so whether it's workers within one train process or across a distributed world the shards should be distributed the same, across `world_size * num_worker` independent workers in either case... Further, the fallback case when the `n_shards % world_size == 0` check fails is a rather extreme change. I argue it is not desirable to do that implicitly, it should be an explicit case for specific scenarios (ie reliable validation). A train scenario would likely be much better handled with improved wrapping / stopping behaviour to eg also fix #6437. Changing from stepping shards to stepping samples means that every single process reads ALL of the shards. This was never an intended default for sharded training, shards gain their performance advantage in large scale distributed training by explicitly avoiding the need to have every process overlapping in the data they read, by default, only the data allocated to each process via their assigned shards should be read in each pass of the dataset. Using a large scale CLIP example, some of the larger datasets have 10-20k shards across 100+TB of data. Training with 1000 GPUs we are switching between reading 100 terabytes per epoch to 100 petabytes if say change 20k % 1000 and drop one gpu-node to 20k % 992. The 'step over samples' case might be worth the overhead in specific validation scenarios where gaurantees of at least/most once samples seen are more important and do not make up a significant portion of train time or are done in smaller world sizes outside of train. ### Steps to reproduce the bug N/A ### Expected behavior We have an iterable dataset with N shards, to split across workers * shuffle shards (same seed across all train processes) * step shard iterator across distributed processes * step shard iterator across dataloader worker processes * shuffle samples in every worker via shuffle buffer (different seed in each worker, but ideally controllable (based on base seed + worker id + epoch). * end up with (possibly uneven) number of shards per worker but each shard only ever accessed by 1 worker per pass (epoch) ### Environment info N/A	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,592	Logs are delayed when doing .map when `docker logs`	### Describe the bug When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed. It's updating every few percent. When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken ### Steps to reproduce the bug 1. Run any huge dataset processing as a Docker image 2. `docker logs image_name` to it ### Expected behavior ... ### Environment info ...	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress." ]
6,591	The datasets models housed in Dropbox can't support a lot of users downloading them	### Describe the bug I'm using the datasets ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` And it seems that sometimes when I imagine a lot of users are accessing the same resources, the Dropbox host fails: `raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://www.dropbox.com/s/e2us0hcs3ilr20e/MInDS-14.zip?dl=1 (error 429)` My question is if we can somehow host these files elsewhere or can you change the limit of simultaneous users accessing those resources or any other solution? Also, has anyone had this issue before? Thanks ### Steps to reproduce the bug 1: Create a python script like so: ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` 2: Execute this by a certain number of users at the same time ### Expected behavior I woudl expect that this shouldnt happen unless its a huge amount of users, which it is not the case ### Environment info This was done in an Ubuntu 22 environment.	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi! Indeed, Dropbox is not a reliable host. I've just merged https://huggingface.co/datasets/PolyAI/minds14/discussions/24 to fix this by hosting the data files inside the repo." ]
6,590	Feature request: Multi-GPU dataset mapping for SDXL training	### Feature request We need to speed up SDXL dataset pre-process. Please make it possible to use multiple GPUs for the [official SDXL trainer](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) :) ### Motivation Pre-computing 3 million of images takes around 2 days. Would be nice to be able to be able to do multi-GPU (or even better – multi-GPU + multi-node) vae and embedding precompute... ### Your contribution I'm not sure i can wrap my head around the multi-GPU mapping... Plus it's too expensive for me to take x2 A100 and spend a day just figuring out the staff since I don't have a job right now.	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,589	After `2.16.0` version, there are `PermissionError` when users use shared cache_dir	### Describe the bug - We use shared `cache_dir` using `HF_HOME="{shared_directory}"` - After dataset version 2.16.0, datasets uses `filelock` package for file locking #6445 - But, `filelock` package make `.lock` file with `644` permission - Dataset is not available to other users except the user who created the lock file via `load_dataset`. ### Steps to reproduce the bug 1. `pip install datasets==2.16.0` 2. `export HF_HOME="{shared_directory}"` 3. download dataset with `load_dataset` 4. logout and login another user 5. `pip install datasets==2.16.0` 6. `export HF_HOME="{shared_directory}"` 7. download dataset with `load_dataset` 8. `PermissionError` occurs ### Expected behavior - Users can share `cache_dir` using environment variable `HF_HOME` ### Environment info - python == 3.9.10 - datasets == 2.16.0 - ubuntu 22.04 - shared_directory has ACL ![image (1)](https://github.com/huggingface/datasets/assets/106717516/5ca759db-ad0c-4883-9a97-9c8fccd00d8a) - users are same group (developers)	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "We'll do a new release of `datasets` in the coming days with a fix !", "@lhoestq Thank you very much!" ]
6,588	fix os.listdir return name is empty string	### Describe the bug xlistdir return name is empty string Overloaded os.listdir ### Steps to reproduce the bug ```python from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) ``` in xlistdir function Obj ["name"] ends with "/" last return "" ### Expected behavior Obj ["name"] ends with "/" return folder name ### Environment info no	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[]
6,587	Allow concatenation of datasets with mixed structs	Fixes #6466 The idea is to do a recursive check for structs. PyArrow handles it well enough. For a demo you can do: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) print(concatenate_datasets([ds, ds2]).features) print(concatenate_datasets([ds, ds2]).to_dict()) ``` Now both the features and the rows are fixed. I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged.	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6587", "html_url": "https://github.com/huggingface/datasets/pull/6587", "diff_url": "https://github.com/huggingface/datasets/pull/6587.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6587.patch", "merged_at": "2024-02-08T14:38:32" }	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "friendly bump", "<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) \| 0.005403 / 0.011353 (-0.005950) \| 0.003807 / 0.011008 (-0.007201) \| 0.063850 / 0.038508 (0.025342) \| 0.028242 / 0.023109 (0.005132) \| 0.242866 / 0.275898 (-0.033032) \| 0.266015 / 0.323480 (-0.057464) \| 0.004111 / 0.007986 (-0.003875) \| 0.002816 / 0.004328 (-0.001513) \| 0.048862 / 0.004250 (0.044611) \| 0.043036 / 0.037052 (0.005984) \| 0.255149 / 0.258489 (-0.003340) \| 0.280105 / 0.293841 (-0.013736) \| 0.028182 / 0.128546 (-0.100365) \| 0.010997 / 0.075646 (-0.064649) \| 0.208131 / 0.419271 (-0.211141) \| 0.036030 / 0.043533 (-0.007502) \| 0.241551 / 0.255139 (-0.013588) \| 0.260741 / 0.283200 (-0.022459) \| 0.018045 / 0.141683 (-0.123638) \| 1.175308 / 1.452155 (-0.276847) \| 1.192160 / 1.492716 (-0.300556) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094579 / 0.018006 (0.076573) \| 0.309850 / 0.000490 (0.309360) \| 0.000232 / 0.000200 (0.000032) \| 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) \| 0.019519 / 0.037411 (-0.017892) \| 0.062201 / 0.014526 (0.047675) \| 0.074017 / 0.176557 (-0.102539) \| 0.121987 / 0.737135 (-0.615148) \| 0.078958 / 0.296338 (-0.217380) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286306 / 0.215209 (0.071097) \| 2.777004 / 2.077655 (0.699350) \| 1.481445 / 1.504120 (-0.022675) \| 1.348643 / 1.541195 (-0.192552) \| 1.382257 / 1.468490 (-0.086234) \| 0.571436 / 4.584777 (-4.013341) \| 2.373279 / 3.745712 (-1.372433) \| 2.749366 / 5.269862 (-2.520496) \| 1.724937 / 4.565676 (-2.840739) \| 0.062233 / 0.424275 (-0.362042) \| 0.005013 / 0.007607 (-0.002594) \| 0.339623 / 0.226044 (0.113579) \| 3.385770 / 2.268929 (1.116842) \| 1.832023 / 55.444624 (-53.612601) \| 1.556172 / 6.876477 (-5.320305) \| 1.573301 / 2.142072 (-0.568772) \| 0.648866 / 4.805227 (-4.156361) \| 0.121228 / 6.500664 (-6.379436) \| 0.041684 / 0.075469 (-0.033786) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974595 / 1.841788 (-0.867192) \| 11.519692 / 8.074308 (3.445383) \| 9.773075 / 10.191392 (-0.418317) \| 0.138149 / 0.680424 (-0.542274) \| 0.014068 / 0.534201 (-0.520133) \| 0.288161 / 0.579283 (-0.291122) \| 0.272832 / 0.434364 (-0.161532) \| 0.324476 / 0.540337 (-0.215862) \| 0.419962 / 1.386936 (-0.966974) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005668 / 0.011353 (-0.005685) \| 0.003637 / 0.011008 (-0.007371) \| 0.049582 / 0.038508 (0.011074) \| 0.030982 / 0.023109 (0.007872) \| 0.273036 / 0.275898 (-0.002862) \| 0.297562 / 0.323480 (-0.025918) \| 0.004382 / 0.007986 (-0.003603) \| 0.002763 / 0.004328 (-0.001566) \| 0.050807 / 0.004250 (0.046556) \| 0.046914 / 0.037052 (0.009862) \| 0.287443 / 0.258489 (0.028954) \| 0.319694 / 0.293841 (0.025853) \| 0.051110 / 0.128546 (-0.077436) \| 0.010650 / 0.075646 (-0.064997) \| 0.058254 / 0.419271 (-0.361018) \| 0.033419 / 0.043533 (-0.010114) \| 0.275634 / 0.255139 (0.020495) \| 0.288618 / 0.283200 (0.005419) \| 0.018004 / 0.141683 (-0.123678) \| 1.134166 / 1.452155 (-0.317989) \| 1.192533 / 1.492716 (-0.300183) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098573 / 0.018006 (0.080566) \| 0.308152 / 0.000490 (0.307662) \| 0.000249 / 0.000200 (0.000049) \| 0.000045 / 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) \| 0.022443 / 0.037411 (-0.014968) \| 0.075628 / 0.014526 (0.061103) \| 0.088807 / 0.176557 (-0.087750) \| 0.127519 / 0.737135 (-0.609617) \| 0.090156 / 0.296338 (-0.206182) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294493 / 0.215209 (0.079284) \| 2.862084 / 2.077655 (0.784429) \| 1.585962 / 1.504120 (0.081842) \| 1.466366 / 1.541195 (-0.074829) \| 1.503306 / 1.468490 (0.034816) \| 0.581524 / 4.584777 (-4.003253) \| 2.475593 / 3.745712 (-1.270120) \| 2.852014 / 5.269862 (-2.417847) \| 1.834047 / 4.565676 (-2.731630) \| 0.064009 / 0.424275 (-0.360266) \| 0.005094 / 0.007607 (-0.002514) \| 0.355960 / 0.226044 (0.129916) \| 3.428849 / 2.268929 (1.159920) \| 1.958501 / 55.444624 (-53.486124) \| 1.675448 / 6.876477 (-5.201029) \| 1.719960 / 2.142072 (-0.422113) \| 0.659609 / 4.805227 (-4.145618) \| 0.119036 / 6.500664 (-6.381628) \| 0.041800 / 0.075469 (-0.033669) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.025955 / 1.841788 (-0.815833) \| 12.432417 / 8.074308 (4.358108) \| 10.444854 / 10.191392 (0.253462) \| 0.130106 / 0.680424 (-0.550318) \| 0.015655 / 0.534201 (-0.518546) \| 0.288184 / 0.579283 (-0.291099) \| 0.285023 / 0.434364 (-0.149340) \| 0.329244 / 0.540337 (-0.211093) \| 0.415484 / 1.386936 (-0.971452) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262b060525efd973cac3f2073ba3944f3ddd7e3 \"CML watermark\")\n" ]
6,586	keep more info in DatasetInfo.from_merge #6585	* try not to merge DatasetInfos if they're equal * fixes losing DatasetInfo during parallel Dataset.map	[]	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6586", "html_url": "https://github.com/huggingface/datasets/pull/6586", "diff_url": "https://github.com/huggingface/datasets/pull/6586.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6586.patch", "merged_at": "2024-01-26T15:53:28" }	true	[ "@JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`.\r\nThere is short discussion about it [here](https://github.com/huggingface/blog/issues/1623).\r\nWould be great if you can check if your PR would also fix that!", "> @JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`. There is short discussion about it [here](https://github.com/huggingface/blog/issues/1623). Would be great if you can check if your PR would also fix that!\r\n\r\nHi @thiagobarbosa, it might be related but I didn't worked with `push_to_hub` yet. I don't see a minimal example reproducing the specific error in your link. However, if you have a running version producing the error locally you can test it by pulling this PR and run your specific example locally. ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6586). 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) \| 0.004729 / 0.011353 (-0.006624) \| 0.002983 / 0.011008 (-0.008025) \| 0.062482 / 0.038508 (0.023974) \| 0.028406 / 0.023109 (0.005297) \| 0.255896 / 0.275898 (-0.020002) \| 0.276423 / 0.323480 (-0.047057) \| 0.003828 / 0.007986 (-0.004157) \| 0.002601 / 0.004328 (-0.001728) \| 0.048954 / 0.004250 (0.044704) \| 0.040661 / 0.037052 (0.003609) \| 0.277710 / 0.258489 (0.019221) \| 0.290360 / 0.293841 (-0.003481) \| 0.027105 / 0.128546 (-0.101441) \| 0.010168 / 0.075646 (-0.065478) \| 0.206835 / 0.419271 (-0.212436) \| 0.035226 / 0.043533 (-0.008306) \| 0.262567 / 0.255139 (0.007428) \| 0.273979 / 0.283200 (-0.009221) \| 0.017576 / 0.141683 (-0.124106) \| 1.125588 / 1.452155 (-0.326566) \| 1.185018 / 1.492716 (-0.307698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092192 / 0.018006 (0.074186) \| 0.298350 / 0.000490 (0.297861) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.017925 / 0.037411 (-0.019486) \| 0.060285 / 0.014526 (0.045759) \| 0.076579 / 0.176557 (-0.099978) \| 0.118830 / 0.737135 (-0.618305) \| 0.073017 / 0.296338 (-0.223322) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288149 / 0.215209 (0.072940) \| 2.840004 / 2.077655 (0.762349) \| 1.495758 / 1.504120 (-0.008361) \| 1.362338 / 1.541195 (-0.178857) \| 1.389746 / 1.468490 (-0.078744) \| 0.576891 / 4.584777 (-4.007886) \| 2.375724 / 3.745712 (-1.369988) \| 2.707405 / 5.269862 (-2.562457) \| 1.719850 / 4.565676 (-2.845826) \| 0.067055 / 0.424275 (-0.357220) \| 0.005039 / 0.007607 (-0.002568) \| 0.346626 / 0.226044 (0.120581) \| 3.468346 / 2.268929 (1.199418) \| 1.860686 / 55.444624 (-53.583938) \| 1.582929 / 6.876477 (-5.293548) \| 1.613131 / 2.142072 (-0.528941) \| 0.659022 / 4.805227 (-4.146206) \| 0.118477 / 6.500664 (-6.382187) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005062 / 1.841788 (-0.836726) \| 11.203210 / 8.074308 (3.128902) \| 10.320764 / 10.191392 (0.129372) \| 0.128541 / 0.680424 (-0.551883) \| 0.014646 / 0.534201 (-0.519555) \| 0.285280 / 0.579283 (-0.294003) \| 0.263613 / 0.434364 (-0.170751) \| 0.321161 / 0.540337 (-0.219177) \| 0.420565 / 1.386936 (-0.966371) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005288 / 0.011353 (-0.006065) \| 0.003048 / 0.011008 (-0.007960) \| 0.049196 / 0.038508 (0.010688) \| 0.032104 / 0.023109 (0.008994) \| 0.279345 / 0.275898 (0.003447) \| 0.300194 / 0.323480 (-0.023286) \| 0.004045 / 0.007986 (-0.003941) \| 0.002594 / 0.004328 (-0.001735) \| 0.047680 / 0.004250 (0.043430) \| 0.044294 / 0.037052 (0.007241) \| 0.292330 / 0.258489 (0.033841) \| 0.318610 / 0.293841 (0.024769) \| 0.050417 / 0.128546 (-0.078129) \| 0.010326 / 0.075646 (-0.065320) \| 0.057372 / 0.419271 (-0.361899) \| 0.032985 / 0.043533 (-0.010548) \| 0.277717 / 0.255139 (0.022579) \| 0.295692 / 0.283200 (0.012493) \| 0.017756 / 0.141683 (-0.123927) \| 1.166277 / 1.452155 (-0.285877) \| 1.213337 / 1.492716 (-0.279380) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091365 / 0.018006 (0.073359) \| 0.296261 / 0.000490 (0.295772) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.021973 / 0.037411 (-0.015438) \| 0.074631 / 0.014526 (0.060106) \| 0.085645 / 0.176557 (-0.090911) \| 0.125181 / 0.737135 (-0.611955) \| 0.086893 / 0.296338 (-0.209445) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294110 / 0.215209 (0.078901) \| 2.855531 / 2.077655 (0.777876) \| 1.583204 / 1.504120 (0.079084) \| 1.453911 / 1.541195 (-0.087284) \| 1.467031 / 1.468490 (-0.001460) \| 0.581214 / 4.584777 (-4.003562) \| 2.423626 / 3.745712 (-1.322086) \| 2.736665 / 5.269862 (-2.533197) \| 1.707000 / 4.565676 (-2.858676) \| 0.061171 / 0.424275 (-0.363104) \| 0.004789 / 0.007607 (-0.002818) \| 0.344546 / 0.226044 (0.118502) \| 3.530955 / 2.268929 (1.262027) \| 1.962532 / 55.444624 (-53.482092) \| 1.670207 / 6.876477 (-5.206270) \| 1.669041 / 2.142072 (-0.473031) \| 0.642298 / 4.805227 (-4.162929) \| 0.115503 / 6.500664 (-6.385161) \| 0.040729 / 0.075469 (-0.034740) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973101 / 1.841788 (-0.868687) \| 11.823894 / 8.074308 (3.749586) \| 10.664592 / 10.191392 (0.473200) \| 0.139848 / 0.680424 (-0.540576) \| 0.015728 / 0.534201 (-0.518473) \| 0.289135 / 0.579283 (-0.290148) \| 0.271325 / 0.434364 (-0.163039) \| 0.332253 / 0.540337 (-0.208085) \| 0.416982 / 1.386936 (-0.969954) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca76ca1152fce82bfeaab9f9a33849d4d7f9dd63 \"CML watermark\")\n" ]
6,585	losing DatasetInfo in Dataset.map when num_proc > 1	### Describe the bug Hello and thanks for developing this package! When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset. ### Steps to reproduce the bug ```python from datasets import Dataset, DatasetInfo def run_map(num_proc): dataset = Dataset.from_dict( {"col1": [0, 1], "col2": [3, 4]}, info=DatasetInfo( dataset_name="my_dataset", ), ) ds = dataset.map(lambda x: x, num_proc=num_proc) print(ds.info.dataset_name) run_map(1) run_map(2) ``` This puts out: ```bash Map: 100%\|██████████\| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%\|██████████\| 2/2 [00:00<00:00, 18.25 examples/s] None ``` ### Expected behavior I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2. Expected output: ```bash Map: 100%\|██████████\| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%\|██████████\| 2/2 [00:00<00:00, 18.25 examples/s] my_dataset ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17 - Python version: 3.8.18 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.9.2	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270", "#self-assign" ]
6,584	np.fromfile not supported	How to do np.fromfile to use it like np.load ```python def xnumpy_fromfile(filepath_or_buffer, args, download_config: Optional[DownloadConfig] = None, kwargs): import numpy as np if hasattr(filepath_or_buffer, "read"): return np.fromfile(filepath_or_buffer, args, *kwargs) else: filepath_or_buffer = str(filepath_or_buffer) return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), args, **kwargs) ``` this is not work	[]	{ "url": null, "html_url": null, "diff_url": null, "patch_url": null, "merged_at": null }	false	[ "@lhoestq\r\nCan you provide me with some ideas？", "Hi ! What's the error ?", "@lhoestq \r\n```\r\nTraceback (most recent call last):\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/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/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/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/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/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/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/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/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/example/test.py\", line 83, in <module>\r\n data = xnumpy_fromfile(current_dir, download_config=config,dtype=numpy.float32,)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/src/datasets/download/streaming_download_manager.py\", line 765, in xnumpy_fromfile\r\n return np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config).read(), args, kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: embedded null byte\r\n```", " not add read() \r\nthe error is \r\n\r\nreturn np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config), args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nio.UnsupportedOperation: fileno", "xopen return obj do not have fileno function\r\nI don't know why?", "I used this method to read point cloud data in the script\r\n\r\n\r\n```python\r\nwith open(velodyne_filepath,\"rb\") as obj:\r\n velodyne_data = numpy.frombuffer(obj.read(), dtype=numpy.float32).reshape([-1, 4])\r\n```" ]

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