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ADD: Wino_bias dataset
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"merging since the CI is fixed on master"
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| 2020-12-13T19:13:57Z
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Updated PR to counter messed up history of previous one (https://github.com/huggingface/datasets/pull/1235) due to rebase.
Removed manual downloading of dataset.
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Replace data URL in big_patent dataset and support streaming
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"_The documentation is not available anymore as the PR was closed or merged._",
"I first uploaded the data files to the Hub: I think it is a good option because we have git lfs to track versions and changes. Moreover people will be able to make PRs to propose updates on the data files.\r\n- I would have preferred to upload it it to the \"data\" org namespace, but it is already taken (although not used): might be possible to take it?\r\n\r\nAs an alternative (and to be consistent with previous datasets), I also uploaded the data files to our AWS bucket.\r\n\r\nWe should decide which to use (now and for future datasets) and set it here before merging. We should remove the data files for the non-chosen option.\r\n\r\nCC: @lhoestq @mariosasko @polinaeterna ",
"Would it make sense to make the dataset a community one (so, create an organization for it) and store the script and the data in a single repository? Just as it is for most of the datasets. That way we can also access the data using a relative path inside the repo (that's not the point though). The point is that to me it seems a bit more straightforward to store everything in one place. \r\n\r\nI guess the strong argument against this logic is that in this case the canonical version won't work... But maybe there is some redirecting mechanism I don't know about? :)\r\n\r\nAnyway, I'm in favor of hosting data on the Hub instead of AWS :) ",
"I also think storing everything in one place/single repository is the best option.\r\n\r\n@polinaeterna Canonical datasets also support data files (see the [`red_caps` repo](https://huggingface.co/datasets/red_caps/tree/main) for instance) ",
"Thanks @polinaeterna and @mariosasko for your comments.\r\n\r\nYes, definitely it is much better to have everything in the same repo. \r\n\r\nI'm transferring their data files to the Hub under \"big_patent\" and deleting them from the other repo and AWS."
] | 2022-04-27T10:01:13Z
| 2022-06-10T08:10:55Z
| 2022-05-02T18:21:15Z
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This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub.
Moreover, this PR makes the dataset streamable.
Fix #4217.
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sync logging utils with transformers
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"Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging. Happy to reorganize logging between DEBUG/INFO/WARNING to make it less verbose by default though.",
"The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n\r\nWould you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\n",
"> Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging.\r\n\r\nIf you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.",
"> The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n> \r\n> Would you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\nFor now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\n> If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n\r\nActually we configured some progress bars to be disabled depending on the logging level ^^'\r\n",
"> For now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\nIf it is logical then by all means. \r\n\r\n> > If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n> \r\n> Actually we configured some progress bars to be disabled depending on the logging level ^^'\r\n\r\nThis is very smart!\r\n\r\nI reverted s/WARNINGS/INFO/.\r\n\r\nThank you!",
"Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.",
"> Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.\r\n\r\nThat's good to know, @thomwolf - thank you!\r\n\r\nI see that it's controlled in `trainer.py`, but in `examples` it's not - since that's where I usually see the progressbars (and they are great!). But I suppose they aren't API, so `examples` can behave differently.",
"BTW, this is what I'm talking about:\r\n```\r\npython -c \"import transformers\"\r\n2020-09-14 21:00:58.032658: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\nPyTorch version 1.7.0.dev20200910 available.\r\nTensorFlow version 2.3.0 available.\r\nApache Beam available.\r\n```\r\nwhy does the user need to see this? Especially, if they aren't even using `datasets` directly?",
"Yes you are right, we should re-think the logging level of various elements.\r\nI also think that the `set_format` messages are confusing when they are the results of our internal operations (as mentioned [here](https://discuss.huggingface.co/t/pipeline-with-custom-dataset-tokenizer-when-to-save-load-manually/1084/7?u=thomwolf))",
"Actually I continued this PR in #635 to set the level to warning and update the logging level of some messages.\r\n\r\nLet me know if it sounds good to you",
"Closing this one sice #635 got merged",
"Awesome! Thank you!\r\n\r\nAny ideas how to eliminate this remaining log line from tensorflow (I know it's not `datasets` related, but perhaps you know).\r\n```\r\npython -c \"import transformers\"\r\n2020-09-17 08:38:34.718410: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n```"
] | 2020-09-11T19:46:13Z
| 2020-09-17T15:40:59Z
| 2020-09-17T09:53:47Z
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sync the docs/code with the recent changes in transformers' `logging` utils:
1. change the default level to `WARNING`
2. add `DATASETS_VERBOSITY` env var
3. expand docs
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Dataset Viewer issue for Yaxin/SemEval2015
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"Closing since it's a duplicate of https://github.com/huggingface/datasets/issues/4453"
] | 2022-06-07T03:31:46Z
| 2022-06-07T11:53:11Z
| 2022-06-07T11:53:11Z
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NONE
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### Link
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### Description
the link could not visit
### Owner
_No response_
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adding meta_woz dataset
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Add parallel module using joblib for Spark
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"Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.",
"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```",
"@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.",
"Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)",
"Hi maintainers, I've just addressed most of the comments, please take another look, thank you.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008422 / 0.011353 (-0.002931) | 0.005658 / 0.011008 (-0.005350) | 0.135372 / 0.038508 (0.096864) | 0.044766 / 0.023109 (0.021657) | 0.417876 / 0.275898 (0.141978) | 0.462785 / 0.323480 (0.139305) | 0.005485 / 0.007986 (-0.002501) | 0.005640 / 0.004328 (0.001311) | 0.105020 / 0.004250 (0.100770) | 0.049114 / 0.037052 (0.012062) | 0.490450 / 0.258489 (0.231961) | 0.467693 / 0.293841 (0.173852) | 0.050929 / 0.128546 (-0.077617) | 0.014644 / 0.075646 (-0.061002) | 0.452373 / 0.419271 (0.033101) | 0.074897 / 0.043533 (0.031364) | 0.425816 / 0.255139 (0.170677) | 0.420415 / 0.283200 (0.137215) | 0.134121 / 0.141683 (-0.007561) | 1.927744 / 1.452155 (0.475589) | 2.014417 / 1.492716 (0.521701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254811 / 0.018006 (0.236805) | 0.550011 / 0.000490 (0.549521) | 0.004913 / 0.000200 (0.004714) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032644 / 0.037411 (-0.004768) | 0.135672 / 0.014526 (0.121146) | 0.158984 / 0.176557 (-0.017572) | 0.218267 / 0.737135 (-0.518869) | 0.150348 / 0.296338 (-0.145991) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.625723 / 0.215209 (0.410514) | 6.247559 / 2.077655 (4.169905) | 2.626785 / 1.504120 (1.122666) | 2.195224 / 1.541195 (0.654030) | 2.232140 / 1.468490 (0.763650) | 0.943082 / 4.584777 (-3.641695) | 5.799262 / 3.745712 (2.053550) | 2.849411 / 5.269862 (-2.420450) | 1.744160 / 4.565676 (-2.821516) | 0.119056 / 0.424275 (-0.305219) | 0.014233 / 0.007607 (0.006626) | 0.795238 / 0.226044 (0.569194) | 7.569586 / 2.268929 (5.300657) | 3.179481 / 55.444624 (-52.265143) | 2.519772 / 6.876477 (-4.356704) | 2.714570 / 2.142072 (0.572498) | 1.107197 / 4.805227 (-3.698030) | 0.229986 / 6.500664 (-6.270678) | 0.087993 / 0.075469 (0.012524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.535610 / 1.841788 (-0.306178) | 18.639369 / 8.074308 (10.565061) | 21.081844 / 10.191392 (10.890452) | 0.253247 / 0.680424 (-0.427177) | 0.026711 / 0.534201 (-0.507490) | 0.503790 / 0.579283 (-0.075493) | 0.600124 / 0.434364 (0.165760) | 0.617944 / 0.540337 (0.077607) | 0.766947 / 1.386936 (-0.619989) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007885 / 0.011353 (-0.003468) | 0.004761 / 0.011008 (-0.006248) | 0.097995 / 0.038508 (0.059487) | 0.033624 / 0.023109 (0.010515) | 0.504307 / 0.275898 (0.228409) | 0.534803 / 0.323480 (0.211323) | 0.006048 / 0.007986 (-0.001937) | 0.005042 / 0.004328 (0.000714) | 0.102288 / 0.004250 (0.098038) | 0.048695 / 0.037052 (0.011643) | 0.559086 / 0.258489 (0.300597) | 0.553233 / 0.293841 (0.259392) | 0.044596 / 0.128546 (-0.083950) | 0.013696 / 0.075646 (-0.061950) | 0.109875 / 0.419271 (-0.309397) | 0.059993 / 0.043533 (0.016460) | 0.485579 / 0.255139 (0.230440) | 0.519835 / 0.283200 (0.236635) | 0.123504 / 0.141683 (-0.018179) | 1.820506 / 1.452155 (0.368351) | 1.963448 / 1.492716 (0.470732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292663 / 0.018006 (0.274656) | 0.557783 / 0.000490 (0.557293) | 0.001330 / 0.000200 (0.001130) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036890 / 0.037411 (-0.000522) | 0.140373 / 0.014526 (0.125847) | 0.140176 / 0.176557 (-0.036381) | 0.237378 / 0.737135 (-0.499757) | 0.160186 / 0.296338 (-0.136152) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.673599 / 0.215209 (0.458390) | 6.510280 / 2.077655 (4.432625) | 2.981617 / 1.504120 (1.477497) | 2.684664 / 1.541195 (1.143469) | 2.760471 / 1.468490 (1.291981) | 0.975413 / 4.584777 (-3.609364) | 5.708933 / 3.745712 (1.963220) | 2.772069 / 5.269862 (-2.497793) | 1.763627 / 4.565676 (-2.802049) | 0.111632 / 0.424275 (-0.312643) | 0.013223 / 0.007607 (0.005616) | 0.791545 / 0.226044 (0.565500) | 8.063287 / 2.268929 (5.794359) | 3.671920 / 55.444624 (-51.772704) | 3.057248 / 6.876477 (-3.819229) | 3.083569 / 2.142072 (0.941497) | 1.118136 / 4.805227 (-3.687092) | 0.214655 / 6.500664 (-6.286009) | 0.083074 / 0.075469 (0.007605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.761731 / 1.841788 (-0.080056) | 18.874200 / 8.074308 (10.799892) | 22.383693 / 10.191392 (12.192301) | 0.240292 / 0.680424 (-0.440132) | 0.028850 / 0.534201 (-0.505351) | 0.557334 / 0.579283 (-0.021949) | 0.627732 / 0.434364 (0.193369) | 0.634484 / 0.540337 (0.094146) | 0.767372 / 1.386936 (-0.619564) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n"
] | 2023-06-02T22:25:25Z
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Discussion in https://github.com/huggingface/datasets/issues/5798
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PR_kwDODunzps47cLkm
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Update create dataset card docs
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-15T00:41:29Z
| 2022-07-18T17:26:00Z
| 2022-07-18T13:24:10Z
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This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input.
Let me know what you think! :)
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ValueError when rename_column on splitted dataset
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[
"Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__` is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use the named splits API (for now):\r\n```python\r\ntrain_ds, test_ds = load_dataset(\r\n path='csv', \r\n delimiter='\\t', \r\n data_files=text_files, \r\n split=['train[:90%]', 'train[-10%:]'],\r\n)\r\n\r\ntrain_ds = train_ds.rename_column('sentence', 'text')\r\n```",
"This has been fixed in #2043 , thanks @mariosasko \r\nThe fix is available on master and we'll do a new release soon :)\r\n\r\nfeel free to re-open if you still have issues"
] | 2021-03-10T09:40:38Z
| 2021-03-16T14:06:08Z
| 2021-03-16T14:05:05Z
|
NONE
| null | null | null |
Hi there,
I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:
```python
split = {
'train': ReadInstruction('train', to=90, unit='%'),
'test': ReadInstruction('train', from_=-10, unit='%')
}
dataset = load_dataset(
path='csv', # use 'text' loading script to load from local txt-files
delimiter='\t', # xxx
data_files=text_files, # list of paths to local text files
split=split, # xxx
)
dataset
```
Part of output:
```python
DatasetDict({
train: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 900
})
test: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 100
})
})
```
Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:
```python
dataset['train'].rename_column('sentence', 'text')
```
```python
/usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name)
353 for split_name in split_names_from_instruction:
354 if not re.match(_split_re, split_name):
--> 355 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.")
356
357 def __str__(self):
ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('.
```
In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.
Thanks in advance! :)
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"_The documentation is not available anymore as the PR was closed or merged._"
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Unable to download `wiki_dpr`
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[
"Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix",
"I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !",
"Closing since this has been fixed by #1925"
] | 2021-02-19T18:11:32Z
| 2021-03-03T17:40:48Z
| 2021-03-03T17:40:48Z
|
NONE
| null | null | null |
I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings/no index. In order to do so, I ran:
`curr_dataset = load_dataset("wiki_dpr", embeddings_name="multiset", index_name="no_index")`
However, I got the following error:
`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`
I tried adding in flags `with_embeddings=False` and `with_index=False`:
`curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False, embeddings_name="multiset", index_name="no_index")`
But I got the following error:
`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_5’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_15’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_30’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_36’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_18’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_41’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_13’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_48’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_10’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_23’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_14’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_34’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_43’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_40’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_47’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_3’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_24’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_7’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_33’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_46’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_42’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_27’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_29’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_26’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_22’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_4’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_20’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_39’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_6’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_16’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_8’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_35’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_49’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_17’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_25’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_0’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_38’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_12’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_44’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_1’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_32’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_19’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_31’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_37’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_9’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_11’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_21’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_28’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_45’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_2’}`
Is there anything else I need to set to download the dataset?
**UPDATE**: just running `curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False)` gives me the same error.
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update saving and loading methods for faiss index so to accept path l…
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[
"Seems ok for me, what do you think @lhoestq ?"
] | 2020-12-29T14:15:37Z
| 2021-01-18T09:27:23Z
| 2021-01-18T09:27:23Z
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CONTRIBUTOR
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- Update saving and loading methods for faiss index so to accept path like objects from pathlib
The current code only supports using a string type to save and load a faiss index. This change makes it possible to use a string type OR a Path from [pathlib](https://docs.python.org/3/library/pathlib.html). The codes becomes a more intuitive this way I think.
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|
Test in memory and on disk
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| 2020-09-09T13:50:04Z
| 2020-09-09T13:50:03Z
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I added test parameters to do every test both in memory and on disk.
I also found a bug in concatenate_dataset thanks to the new tests and fixed it.
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MDExOlB1bGxSZXF1ZXN0NTM0MzAzNDk0
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Update coqa dataset url
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| 2020-12-08T18:19:09Z
| 2020-12-08T18:19:09Z
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`datasets.stanford.edu` is invalid.
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MDU6SXNzdWU2MzEyNjAxNDM=
| 238
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[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.
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"description": "A bug in a metric script",
"id": 2067393914,
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"This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https://github.com/Tiiiger/bert_score) ?"
] | 2020-06-05T02:14:47Z
| 2020-06-29T17:10:19Z
| 2020-06-29T17:10:19Z
|
NONE
| null | null | null |
When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```
---
**What am I doing wrong / How can I hide this warning ?**
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Fix use_dataset.mdx
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007718 / 0.011353 (-0.003635) | 0.004730 / 0.011008 (-0.006278) | 0.097262 / 0.038508 (0.058754) | 0.077880 / 0.023109 (0.054771) | 0.363855 / 0.275898 (0.087957) | 0.394470 / 0.323480 (0.070990) | 0.006416 / 0.007986 (-0.001570) | 0.003596 / 0.004328 (-0.000732) | 0.076494 / 0.004250 (0.072243) | 0.062656 / 0.037052 (0.025603) | 0.366160 / 0.258489 (0.107671) | 0.421383 / 0.293841 (0.127542) | 0.035756 / 0.128546 (-0.092791) | 0.009430 / 0.075646 (-0.066217) | 0.327722 / 0.419271 (-0.091550) | 0.061252 / 0.043533 (0.017719) | 0.352167 / 0.255139 (0.097028) | 0.385166 / 0.283200 (0.101966) | 0.026656 / 0.141683 (-0.115027) | 1.718533 / 1.452155 (0.266378) | 1.886646 / 1.492716 (0.393930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254564 / 0.018006 (0.236558) | 0.490942 / 0.000490 (0.490452) | 0.011656 / 0.000200 (0.011456) | 0.000313 / 0.000054 (0.000259) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028753 / 0.037411 (-0.008659) | 0.093076 / 0.014526 (0.078550) | 0.096441 / 0.176557 (-0.080116) | 0.154848 / 0.737135 (-0.582287) | 0.092903 / 0.296338 (-0.203435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395611 / 0.215209 (0.180402) | 3.860736 / 2.077655 (1.783082) | 1.908808 / 1.504120 (0.404688) | 1.708975 / 1.541195 (0.167781) | 1.848173 / 1.468490 (0.379683) | 0.527022 / 4.584777 (-4.057755) | 3.815171 / 3.745712 (0.069459) | 3.621132 / 5.269862 (-1.648730) | 2.220238 / 4.565676 (-2.345439) | 0.063169 / 0.424275 (-0.361106) | 0.008906 / 0.007607 (0.001299) | 0.510478 / 0.226044 (0.284433) | 4.828116 / 2.268929 (2.559187) | 2.340801 / 55.444624 (-53.103824) | 2.040834 / 6.876477 (-4.835642) | 2.092316 / 2.142072 (-0.049757) | 0.579194 / 4.805227 (-4.226033) | 0.135525 / 6.500664 (-6.365139) | 0.062720 / 0.075469 (-0.012749) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.393091 / 1.841788 (-0.448697) | 19.751526 / 8.074308 (11.677218) | 14.161795 / 10.191392 (3.970403) | 0.163340 / 0.680424 (-0.517084) | 0.021504 / 0.534201 (-0.512697) | 0.393183 / 0.579283 (-0.186100) | 0.448407 / 0.434364 (0.014043) | 0.504169 / 0.540337 (-0.036169) | 0.663698 / 1.386936 (-0.723238) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007390 / 0.011353 (-0.003962) | 0.004381 / 0.011008 (-0.006628) | 0.074501 / 0.038508 (0.035993) | 0.078242 / 0.023109 (0.055133) | 0.481108 / 0.275898 (0.205210) | 0.512111 / 0.323480 (0.188631) | 0.006280 / 0.007986 (-0.001705) | 0.003820 / 0.004328 (-0.000509) | 0.071602 / 0.004250 (0.067351) | 0.068359 / 0.037052 (0.031307) | 0.478484 / 0.258489 (0.219995) | 0.519543 / 0.293841 (0.225702) | 0.036211 / 0.128546 (-0.092335) | 0.009433 / 0.075646 (-0.066213) | 0.086140 / 0.419271 (-0.333132) | 0.054177 / 0.043533 (0.010644) | 0.466726 / 0.255139 (0.211587) | 0.514085 / 0.283200 (0.230885) | 0.026729 / 0.141683 (-0.114954) | 1.743770 / 1.452155 (0.291615) | 1.833469 / 1.492716 (0.340753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251339 / 0.018006 (0.233333) | 0.472294 / 0.000490 (0.471804) | 0.013381 / 0.000200 (0.013181) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037845 / 0.037411 (0.000433) | 0.105977 / 0.014526 (0.091451) | 0.124446 / 0.176557 (-0.052111) | 0.180432 / 0.737135 (-0.556703) | 0.120844 / 0.296338 (-0.175495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470928 / 0.215209 (0.255719) | 4.738154 / 2.077655 (2.660499) | 2.558618 / 1.504120 (1.054498) | 2.359745 / 1.541195 (0.818550) | 2.458438 / 1.468490 (0.989948) | 0.548580 / 4.584777 (-4.036197) | 3.912145 / 3.745712 (0.166433) | 3.764174 / 5.269862 (-1.505687) | 2.325265 / 4.565676 (-2.240411) | 0.078022 / 0.424275 (-0.346254) | 0.008279 / 0.007607 (0.000672) | 0.571635 / 0.226044 (0.345590) | 5.672445 / 2.268929 (3.403517) | 2.760577 / 55.444624 (-52.684047) | 2.544229 / 6.876477 (-4.332248) | 2.537509 / 2.142072 (0.395436) | 0.609858 / 4.805227 (-4.195369) | 0.131053 / 6.500664 (-6.369611) | 0.056433 / 0.075469 (-0.019036) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567231 / 1.841788 (-0.274556) | 21.415586 / 8.074308 (13.341278) | 15.982328 / 10.191392 (5.790936) | 0.167648 / 0.680424 (-0.512776) | 0.023562 / 0.534201 (-0.510639) | 0.477307 / 0.579283 (-0.101976) | 0.471929 / 0.434364 (0.037566) | 0.549996 / 0.540337 (0.009659) | 0.753927 / 1.386936 (-0.633009) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n"
] | 2023-10-25T18:21:08Z
| 2023-10-26T17:19:49Z
| 2023-10-26T17:10:27Z
|
CONTRIBUTOR
| null | 0
|
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The current example isn't working because it can't find `labels` inside the Dataset object. So I've added an extra step to the process. Tested and working in Colab.
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| 982,789,593
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MDExOlB1bGxSZXF1ZXN0NzIyNDg4MDY2
| 2,851
|
Update `column_names` showed as `:func:` in exploring.st
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[] | 2021-08-30T13:21:46Z
| 2021-09-01T08:42:11Z
| 2021-08-31T14:45:46Z
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CONTRIBUTOR
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Hi,
One mention of `column_names` in exploring.st was showing it as `:func:` instead of `:attr:`.
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Remove compression from xopen
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"After discussing with @lhoestq, a reasonable alternative:\r\n- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats: \r\n `bz2::http://domain.org/filename.bz2`\r\n- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:\r\n `fsspec.open(\"http://domain.org/filename.bz2\", compression=\"bz2\")`\r\n\r\nPros:\r\n- clean solution that continues giving support to all compression formats\r\n- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)\r\n\r\nCons:\r\n- we create a \"private\" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` argument"
] | 2021-08-18T09:35:59Z
| 2021-08-23T15:59:14Z
| 2021-08-23T15:59:14Z
|
MEMBER
| null | null | null |
We implemented support for streaming with 2 requirements:
- transparent use for the end user: just needs to pass the parameter `streaming=True`
- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming
In order to fulfill these requirements, streaming implementation patched some Python functions:
- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`
- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip://`), which are required by `fsspec.open`
Recently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...
Under the hood, the implementation:
- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`
Some concerns have been raised about passing the parameter `compression` to `fsspec.open`:
- https://github.com/huggingface/datasets/pull/2786#discussion_r689550254
- #2811
The main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:
```python
gzip.open(open(urlpath
```
While this is true:
- it is not natural/usual to call `open` inside `gzip.open` (never seen this before)
- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)
In this particular case, there is a natural fix solution: #2811:
- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`
- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression="gzip"`
Are there other issues apart from this?
Note that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just
- `gzip.open`
- `open` (after having called dl_manager.download_and_extract)
TODO:
- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.
- For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:
- oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)
- In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming
- [ ] If this is indeed an issue, which are the possible alternatives? Pros/cons?
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I_kwDODunzps5CDF-_
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|
Readme info not being parsed to show on Dataset card page
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[
"i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?",
"# Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\" \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https://huggingface.co/docs/datasets/v1.12.0/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https://huggingface.co/datasets/ritwikraha/Test)",
"Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.",
"Thanks, if this solves your issue, can you please close it?"
] | 2022-01-19T13:32:29Z
| 2022-01-21T10:20:01Z
| 2022-01-21T10:20:01Z
|
NONE
| null | null | null |
## Describe the bug
The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.
## Steps to reproduce the bug
# Sample code to reproduce the bug
The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md
## Expected results
README info should appear in the Dataset card page.
## Actual results
Nothing is shown. However, labels are parsed and shown successfully.
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|
Style change
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[
"What about the other PR #419 ?",
"Oh this is the PR where I ran make quality and make style and some previous files from master were changed",
"Oh right ! Let me fix the style myself if you don't mind"
] | 2020-07-20T20:08:29Z
| 2020-07-22T16:08:40Z
| 2020-07-22T16:08:39Z
|
CONTRIBUTOR
| null | 0
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make quality and make style ran on scripts
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|
[Tests] refactor tests
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[
"Nice !"
] | 2020-05-19T11:43:32Z
| 2020-05-19T16:17:12Z
| 2020-05-19T16:17:10Z
|
MEMBER
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This PR separates AWS and Local tests to remove these ugly statements in the script:
```python
if "/" not in dataset_name:
logging.info("Skip {} because it is a canonical dataset")
return
```
To run a `aws` test, one should now run the following command:
```python
pytest -s tests/test_dataset_common.py::AWSDatasetTest::test_builder_class_wmt14
```
The same `local` test, can be run with:
```python
pytest -s tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_wmt14
```
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| 1,159,106,603
|
I_kwDODunzps5FFpAr
| 3,820
|
`pubmed_qa` checksum mismatch
|
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[
"Hi @jon-tow, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today.\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```"
] | 2022-03-04T00:28:08Z
| 2022-03-04T09:42:32Z
| 2022-03-04T09:42:32Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets
try:
datasets.load_dataset("pubmed_qa", "pqa_labeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_unlabeled")
except Exception as e:
print(e)
try:
datasets.load_dataset("pubmed_qa", "pqa_artificial")
except Exception as e:
print(e)
```
## Expected results
Successful download.
## Actual results
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare
verify_checksums(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: macOS
- Python version: 3.8.1
- PyArrow version: 3.0.0
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PR_kwDODunzps4ymQMQ
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|
Fix CI code quality issue
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Fix CI code quality issue introduced by #3695.
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Improve example in rounding docs
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| 2021-05-21T12:53:22Z
| 2021-05-21T12:36:29Z
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Improves the example in the rounding subsection of the Split API docs. With this change, it should more clear what's the difference between the `closest` and the `pct1_dropremainder` rounding.
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Add a robustness benchmark dataset for vision
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"Ccing @nazneenrajani @lvwerra @osanseviero "
] | 2022-12-17T12:35:13Z
| 2022-12-20T06:21:41Z
| null |
MEMBER
| null | null | null |
### Name
ImageNet-C
### Paper
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
### Data
https://github.com/hendrycks/robustness
### Motivation
It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models.
Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them.
Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting.
ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts.
Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499
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Optional per-dataset default config name
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"I like the idea ! And the approach is right imo\r\n\r\nNote that by changing this we will have to add a way for users to get the config lists of a dataset. In the current user workflow, the user could see the list of the config when the missing config error is raised but now it won't be the case because of the default config.",
"Maybe let's add a test in the test_builder.py test script ?",
"@lhoestq Okay great, I added a test as well as two new inspect functions: `get_dataset_config_names` and `get_dataset_infos` (the latter is something I've been wanting anyway). As a quick hack, you can also just pass a random config name (e.g. an empty string) to `load_dataset` to get the config names in the error msg as before. Also added a couple paragraphs to the adding new datasets doc.\r\n\r\nI'll send a separate PR incorporating this in existing datasets so we can get this merged before our sprint on Monday.\r\n\r\nAny ideas on the failing tests? I'm having trouble making sense of it. **Edit**: nvm, it was master."
] | 2020-11-25T21:02:30Z
| 2020-11-30T17:27:33Z
| 2020-11-30T17:27:27Z
|
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| null | 0
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This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following:
```python
ds = load_dataset("polyglot_ner")
```
which is equivalent to,
```python
ds = load_dataset("polyglot_ner", "combined")
```
In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages.
Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual.
Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets.
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wikitext description wrong
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[
"Hi @hongyuanmei, thanks for reporting.\r\n\r\nI'm fixing it.",
"Duplicate of:\r\n- #795"
] | 2021-11-09T04:06:52Z
| 2022-02-14T15:45:11Z
| 2021-11-09T13:49:28Z
|
NONE
| null | null | null |
## Describe the bug
Descriptions of the wikitext datasests are wrong.
## Steps to reproduce the bug
Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50
## Expected results
The descriptions for raw-v1 and v1 should be switched.
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Fix iter_archive generator
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| 2021-12-20T10:05:00Z
| 2021-12-20T10:04:59Z
|
MEMBER
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This PR:
- Adds tests to DownloadManager and StreamingDownloadManager `iter_archive` for both path and file inputs
- Fixes bugs in `iter_archive` introduced in:
- #3443
Fix #3453.
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`to_json` reporting enhancements
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[] | 2021-07-15T23:32:18Z
| 2021-07-15T23:33:53Z
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CONTRIBUTOR
| null | null | null |
While using `to_json` 2 things came to mind that would have made the experience easier on the user:
1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.
2. It took me a while to make sense of the reported numbers:
```
22%|██▏ | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
22%|██▏ | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or
```
22%|██▏ | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)
I forget if tqdm lets you add a magnitude identifier to the running count.
Thank you!
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[firewalled env] OFFLINE mode
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[
"Thanks for reporting and for all the details and suggestions.\r\n\r\nI'm totally in favor of having a HF_DATASETS_OFFLINE env variable to disable manually all the connection checks, remove retries etc.\r\n\r\nMoreover you may know that the use case that you are mentioning is already supported from `datasets` 1.3.0, i.e. you already can:\r\n- first load datasets and metrics from an instance with internet connection\r\n- then be able to reload datasets and metrics from another instance without connection (as long as the filesystem is shared)\r\n\r\nThis is already implemented, but currently it only works if the requests return a `ConnectionError` (or any error actually). Not sure why it would hang instead of returning an error.\r\n\r\nMaybe this is just a issue with the timeout value being not set or too high ?\r\nIs there a way I can have access to one of the instances on which there's this issue (we can discuss this offline) ?\r\n",
"I'm on master, so using all the available bells and whistles already.\r\n\r\nIf you look at the common issues - it for example tries to look up files if they appear in `_PACKAGED_DATASETS_MODULES` which it shouldn't do.\r\n\r\n--------------\r\n\r\nYes, there is a nuance to it. As I mentioned it's firewalled - that is it has a network but making any calls outside - it just hangs in:\r\n\r\n```\r\nsin_addr=inet_addr(\"xx.xx.xx.xx\")}, [28->16]) = 0\r\nclose(5) = 0\r\nsocket(AF_INET, SOCK_STREAM|SOCK_CLOEXEC, IPPROTO_TCP) = 5\r\nconnect(5, {sa_family=AF_INET, sin_port=htons(3128), sin_addr=inet_addr(\"yy.yy.yy.yy\")}, 16^C) = ? ERESTARTSYS (To be restarted if SA_RESTART is set)\r\n```\r\nuntil it times out.\r\n\r\nThat's why we need to be able to tell the software that there is no network to rely on even if there is one (good for testing too).\r\n\r\nSo what I'm thinking is that this is a simple matter of pre-ambling any network call wrappers with:\r\n\r\n```\r\nif HF_DATASETS_OFFLINE:\r\n assert \"Attempting to make a network call under Offline mode\"\r\n```\r\n\r\nand then fixing up if there is anything else to fix to make it work.\r\n\r\n--------------\r\n\r\nOtherwise I think the only other problem I encountered is that we need to find a way to pre-cache metrics, for some reason it's not caching it and wanting to fetch it from online.\r\n\r\nWhich is extra strange since it already has those files in the `datasets` repo itself that is on the filesystem.\r\n\r\nThe workaround I had to do is to copy `rouge/rouge.py` (with the parent folder) from the datasets repo to the current dir - and then it proceeded.",
"Ok understand better the hanging issue.\r\nI guess catching connection errors is not enough, we should also avoid all the hangings.\r\nCurrently the offline mode tests are only done by simulating an instant connection fail that returns an error, let's have another connection mock that hangs instead.\r\n\r\nI'll also take a look at why you had to do this for `rouge`.\r\n",
"FWIW, I think instant failure on the behalf of a network call is the simplest solution to correctly represent the environment and having the caller to sort it out is the next thing to do, since here it is the case of having no functional network, it's just that the software doesn't know this is the case, because there is some network. So we just need to help it to bail out instantly rather than hang waiting for it to time out. And afterwards everything else you said.",
"Update on this: \r\n\r\nI managed to create a mock environment for tests that makes the connections hang until timeout.\r\nI managed to reproduce the issue you're having in this environment.\r\n\r\nI'll update the offline test cases to also test the robustness to connection hangings, and make sure we set proper timeouts where it's needed in the code. This should cover the _automatic_ section you mentioned.",
"Fabulous! I'm glad you were able to reproduce the issues, @lhoestq!",
"I lost access to the firewalled setup, but I emulated it with:\r\n\r\n```\r\nsudo ufw enable\r\nsudo ufw default deny outgoing\r\n```\r\n(thanks @mfuntowicz)\r\n\r\nI was able to test `HF_DATASETS_OFFLINE=1` and it worked great - i.e. didn't try to reach out with it and used the cached files instead.\r\n\r\nThank you!"
] | 2021-02-24T17:13:42Z
| 2021-03-05T05:09:54Z
| 2021-03-05T05:09:54Z
|
CONTRIBUTOR
| null | null | null |
This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.
I propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.
## 1. Manual
manually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:
```
DATASETS_OFFLINE=1 run_seq2seq.py --train_file xyz.csv --validation_file xyz.csv ...
```
`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.
## 2. Automatic
In some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:
1. on the non-firewalled instance:
```
run_seq2seq.py --dataset_name wmt16 --dataset_config ro-en ...
```
which should download and cached everything.
2. and then immediately after on the firewalled instance, which shares the same filesystem
```
DATASETS_OFFLINE=1 run_seq2seq.py --dataset_name wmt16 --dataset_config ro-en ...
```
and the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.
## Common Issues
1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided
```
if dataset and path in _PACKAGED_DATASETS_MODULES:
```
2. it has an issue with metrics. e.g. I had to manually copy `rouge/rouge.py` from the `datasets` repo to the current dir - or it was hanging.
I had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`
Here is the corresponding issue for `transformers`: https://github.com/huggingface/transformers/issues/10379
Thanks.
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Add SentiWS dataset for pos-tagging and sentiment-scoring (German)
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[
"Hi @lhoestq @yjernite, requesting you to review this for any changes needed. Thanks! :)",
"Hi @lhoestq , I have updated the PR"
] | 2020-12-12T12:17:53Z
| 2020-12-15T18:32:38Z
| 2020-12-15T18:32:38Z
|
CONTRIBUTOR
| null | 0
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Cannot load beans dataset (Couldn't reach the dataset)
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[
"Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)"
] | 2022-03-10T16:34:08Z
| 2022-03-15T15:26:47Z
| 2022-03-15T15:26:47Z
|
NONE
| null | null | null |
## Describe the bug
The beans dataset is unavailable to download.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('beans')
```
## Expected results
The dataset would be downloaded with no issue.
## Actual results
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ibeans/train.zip (error 403)
```
[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https://storage.googleapis.com/ibeans/train.zip )
## Environment info
Google Colab
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Remove Google Drive URL in spider dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-26T06:17:35Z
| 2022-05-26T06:48:42Z
| 2022-05-26T06:40:12Z
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MEMBER
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The `spider` dataset is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license.
Fix #4401.
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009885 / 0.011353 (-0.001468) | 0.005338 / 0.011008 (-0.005670) | 0.099967 / 0.038508 (0.061459) | 0.036860 / 0.023109 (0.013751) | 0.295283 / 0.275898 (0.019385) | 0.369504 / 0.323480 (0.046024) | 0.008267 / 0.007986 (0.000281) | 0.004375 / 0.004328 (0.000046) | 0.076294 / 0.004250 (0.072043) | 0.047058 / 0.037052 (0.010006) | 0.314463 / 0.258489 (0.055974) | 0.348125 / 0.293841 (0.054284) | 0.038334 / 0.128546 (-0.090213) | 0.012102 / 0.075646 (-0.063544) | 0.333049 / 0.419271 (-0.086223) | 0.050727 / 0.043533 (0.007195) | 0.299244 / 0.255139 (0.044105) | 0.318210 / 0.283200 (0.035010) | 0.112609 / 0.141683 (-0.029074) | 1.450377 / 1.452155 (-0.001778) | 1.485177 / 1.492716 (-0.007539) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287083 / 0.018006 (0.269077) | 0.564268 / 0.000490 (0.563778) | 0.003578 / 0.000200 (0.003378) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026755 / 0.037411 (-0.010657) | 0.105857 / 0.014526 (0.091331) | 0.118291 / 0.176557 (-0.058266) | 0.155735 / 0.737135 (-0.581401) | 0.122527 / 0.296338 (-0.173812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396992 / 0.215209 (0.181783) | 3.958562 / 2.077655 (1.880908) | 1.781570 / 1.504120 (0.277451) | 1.617743 / 1.541195 (0.076549) | 1.753504 / 1.468490 (0.285013) | 0.681509 / 4.584777 (-3.903268) | 3.816910 / 3.745712 (0.071198) | 2.087359 / 5.269862 (-3.182503) | 1.328380 / 4.565676 (-3.237297) | 0.083542 / 0.424275 (-0.340733) | 0.012081 / 0.007607 (0.004473) | 0.505127 / 0.226044 (0.279082) | 5.075136 / 2.268929 (2.806208) | 2.259871 / 55.444624 (-53.184753) | 1.944302 / 6.876477 (-4.932175) | 2.102624 / 2.142072 (-0.039449) | 0.819779 / 4.805227 (-3.985448) | 0.165584 / 6.500664 (-6.335080) | 0.061774 / 0.075469 (-0.013695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208258 / 1.841788 (-0.633530) | 14.841635 / 8.074308 (6.767327) | 14.484515 / 10.191392 (4.293123) | 0.156464 / 0.680424 (-0.523959) | 0.028839 / 0.534201 (-0.505362) | 0.440860 / 0.579283 (-0.138423) | 0.433892 / 0.434364 (-0.000472) | 0.515339 / 0.540337 (-0.024998) | 0.608838 / 1.386936 (-0.778098) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007548 / 0.011353 (-0.003804) | 0.005464 / 0.011008 (-0.005544) | 0.096987 / 0.038508 (0.058479) | 0.034472 / 0.023109 (0.011363) | 0.391249 / 0.275898 (0.115351) | 0.432779 / 0.323480 (0.109299) | 0.006170 / 0.007986 (-0.001816) | 0.004316 / 0.004328 (-0.000013) | 0.074184 / 0.004250 (0.069934) | 0.054254 / 0.037052 (0.017202) | 0.397947 / 0.258489 (0.139458) | 0.451253 / 0.293841 (0.157412) | 0.037098 / 0.128546 (-0.091449) | 0.012649 / 0.075646 (-0.062997) | 0.333533 / 0.419271 (-0.085739) | 0.050247 / 0.043533 (0.006714) | 0.390446 / 0.255139 (0.135307) | 0.410547 / 0.283200 (0.127347) | 0.110888 / 0.141683 (-0.030795) | 1.452160 / 1.452155 (0.000006) | 1.596331 / 1.492716 (0.103615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256061 / 0.018006 (0.238055) | 0.552674 / 0.000490 (0.552184) | 0.003362 / 0.000200 (0.003162) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030199 / 0.037411 (-0.007213) | 0.110288 / 0.014526 (0.095762) | 0.127412 / 0.176557 (-0.049145) | 0.165428 / 0.737135 (-0.571707) | 0.131658 / 0.296338 (-0.164680) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441946 / 0.215209 (0.226737) | 4.414209 / 2.077655 (2.336555) | 2.284530 / 1.504120 (0.780410) | 2.110752 / 1.541195 (0.569557) | 2.210751 / 1.468490 (0.742260) | 0.698829 / 4.584777 (-3.885948) | 3.819044 / 3.745712 (0.073332) | 3.274021 / 5.269862 (-1.995840) | 1.781284 / 4.565676 (-2.784393) | 0.085264 / 0.424275 (-0.339011) | 0.012360 / 0.007607 (0.004753) | 0.553519 / 0.226044 (0.327475) | 5.466395 / 2.268929 (3.197467) | 2.825839 / 55.444624 (-52.618786) | 2.439451 / 6.876477 (-4.437026) | 2.582534 / 2.142072 (0.440462) | 0.841644 / 4.805227 (-3.963583) | 0.172288 / 6.500664 (-6.328376) | 0.067215 / 0.075469 (-0.008254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283623 / 1.841788 (-0.558165) | 15.753163 / 8.074308 (7.678855) | 14.983263 / 10.191392 (4.791871) | 0.187584 / 0.680424 (-0.492840) | 0.017999 / 0.534201 (-0.516202) | 0.427157 / 0.579283 (-0.152126) | 0.435456 / 0.434364 (0.001092) | 0.496800 / 0.540337 (-0.043537) | 0.592557 / 1.386936 (-0.794379) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8a72676689a4a3fb466cc5077884446c7302e605 \"CML watermark\")\n"
] | 2023-01-20T01:09:14Z
| 2023-01-20T15:44:45Z
| 2023-01-20T15:37:30Z
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Based on feedback from discussion #5423, this PR updates the sharing tutorial with a mention of writing your own dataset loading script to support more advanced dataset creation options like multiple configs.
I'll open a separate PR to update the *Create a Dataset card* with the new Hub metadata UI update 😄
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Doc2dial update data_infos and data_loaders
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[] | 2021-03-12T14:39:29Z
| 2021-03-16T11:09:20Z
| 2021-03-16T11:09:20Z
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MDU6SXNzdWU3MDk1NzU1Mjc=
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Questions about XSUM
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[
"We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is updated",
"Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking issue for us; would appreciate any progress on this front. We can also help with the fix, if you deem it appropriately. ",
"I just started the generation on my side, I'll let you know how it goes :) ",
"Hmm after a first run I'm still missing 136668/226711 urls.\r\nI'll relaunch it tomorrow to try to get the remaining ones.",
"Update: I'm missing 36/226711 urls but I haven't managed to download them yet",
"Thanks! That sounds like a reasonable number! ",
"So I managed to download them all but when parsing only 226,181/226,711 worked.\r\nNot sure if it's worth digging and debugging parsing at this point :/ ",
"Maybe @sshleifer can help, I think he's already played with xsum at one point",
"Thanks @lhoestq\r\nIt would be great to improve coverage, but IDs are the really crucial part for us. We'd really appreciate an update to the dataset with IDs either way!",
"I gave up at an even earlier point. The dataset I use has 204,017 train examples.",
"@lhoestq @sshleifer like @jbragg said earlier, the main issue for us is that the current XSUM dataset (in your package) does not have IDs suggested by the original dataset ([here is the file](https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json).) Would appreciate if you update the XSUM dataset to include the instance IDs. \r\n\r\nThe missing instances is also a problem, but likely not worth pursuing given its relatively small scale. ",
">So I managed to download them all but when parsing only 226,181/226,711 worked.\r\n\r\n@lhoestq any chance we could update the HF-hosted dataset with the IDs in your new version? Happy to help if there's something I can do.",
"Well I couldn't parse what I downloaded.\r\nUnfortunately I think I won't be able to take a look at it this week.\r\nI can try to send you what I got if you want to give it a shot @jbragg \r\nOtherwise feel free to re-run the xsum download script, maybe you'll be luckier than me",
"Resolved via #754"
] | 2020-09-26T17:16:24Z
| 2022-10-04T17:30:17Z
| 2022-10-04T17:30:17Z
|
CONTRIBUTOR
| null | null | null |
Hi there ✋
I'm looking into your `xsum` dataset and I have several questions on that.
So here is how I loaded the data:
```
>>> data = datasets.load_dataset('xsum', version='1.0.1')
>>> data['train']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)
>>> data['test']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)
```
The first issue is, the instance counts don’t match what I see on [the dataset's website](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)
```
… training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.
```
Any thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https://github.com/huggingface/datasets/pull/289 (reviewed by @patrickvonplaten)
Another issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https://github.com/EdinburghNLP/XSum/blob/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match.
CC @jbragg
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Allow loading community metrics from the hub, just like datasets
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[
"Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https://github.com/huggingface/huggingface_hub/blob/main/docs/hub/how-to-downstream.md#cached_download))",
"This is a great solution in the meantime, thanks!",
"Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n # repo_id: for models \"username/model_name\", for datasets \"datasets/username/model_name\"\r\n local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n shutil.copy(local_metric_path, updated_local_metric_path)\r\n return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```",
"Solved with https://github.com/huggingface/evaluate 🤗 ",
"Yay!! cc @lvwerra @sashavor @douwekiela \r\n\r\nPlease share your feedback @eladsegal =)"
] | 2022-01-06T11:26:26Z
| 2022-05-31T20:59:14Z
| 2022-05-31T20:53:37Z
|
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.
However, there is no option to do it with the metric uploaded to the hub.
This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.
**Describe the solution you'd like**
Load metrics from the hub just like datasets are loaded.
In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
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add bswac
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PR_kwDODunzps49HaWT
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Fix typo in streaming docs
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| 2022-08-14T11:02:09Z
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Call fs.makedirs in save_to_disk
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007490 / 0.011353 (-0.003862) | 0.004957 / 0.011008 (-0.006051) | 0.096952 / 0.038508 (0.058444) | 0.034125 / 0.023109 (0.011016) | 0.301926 / 0.275898 (0.026028) | 0.330538 / 0.323480 (0.007058) | 0.005999 / 0.007986 (-0.001987) | 0.003948 / 0.004328 (-0.000380) | 0.073024 / 0.004250 (0.068773) | 0.050020 / 0.037052 (0.012967) | 0.299987 / 0.258489 (0.041498) | 0.336077 / 0.293841 (0.042237) | 0.035781 / 0.128546 (-0.092765) | 0.012159 / 0.075646 (-0.063487) | 0.333311 / 0.419271 (-0.085960) | 0.059925 / 0.043533 (0.016392) | 0.297772 / 0.255139 (0.042633) | 0.313447 / 0.283200 (0.030247) | 0.100991 / 0.141683 (-0.040692) | 1.472182 / 1.452155 (0.020027) | 1.553010 / 1.492716 (0.060294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214222 / 0.018006 (0.196216) | 0.441579 / 0.000490 (0.441090) | 0.001030 / 0.000200 (0.000830) | 0.000194 / 0.000054 (0.000140) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026149 / 0.037411 (-0.011262) | 0.107324 / 0.014526 (0.092798) | 0.113390 / 0.176557 (-0.063167) | 0.170282 / 0.737135 (-0.566854) | 0.120601 / 0.296338 (-0.175737) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411795 / 0.215209 (0.196585) | 4.091412 / 2.077655 (2.013757) | 1.819597 / 1.504120 (0.315477) | 1.623413 / 1.541195 (0.082218) | 1.658959 / 1.468490 (0.190469) | 0.697671 / 4.584777 (-3.887106) | 3.868855 / 3.745712 (0.123143) | 3.220448 / 5.269862 (-2.049414) | 1.796472 / 4.565676 (-2.769204) | 0.085817 / 0.424275 (-0.338458) | 0.012422 / 0.007607 (0.004815) | 0.520302 / 0.226044 (0.294258) | 5.062477 / 2.268929 (2.793548) | 2.275065 / 55.444624 (-53.169560) | 1.936717 / 6.876477 (-4.939759) | 2.069924 / 2.142072 (-0.072148) | 0.838964 / 4.805227 (-3.966264) | 0.170632 / 6.500664 (-6.330032) | 0.066011 / 0.075469 (-0.009458) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190673 / 1.841788 (-0.651114) | 14.679478 / 8.074308 (6.605169) | 14.099743 / 10.191392 (3.908351) | 0.142556 / 0.680424 (-0.537868) | 0.017601 / 0.534201 (-0.516600) | 0.421301 / 0.579283 (-0.157982) | 0.418035 / 0.434364 (-0.016329) | 0.503799 / 0.540337 (-0.036539) | 0.588809 / 1.386936 (-0.798127) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007556 / 0.011353 (-0.003797) | 0.005283 / 0.011008 (-0.005725) | 0.075616 / 0.038508 (0.037107) | 0.034127 / 0.023109 (0.011018) | 0.345145 / 0.275898 (0.069247) | 0.377490 / 0.323480 (0.054010) | 0.006532 / 0.007986 (-0.001454) | 0.004145 / 0.004328 (-0.000183) | 0.074724 / 0.004250 (0.070473) | 0.048658 / 0.037052 (0.011605) | 0.339989 / 0.258489 (0.081500) | 0.398240 / 0.293841 (0.104399) | 0.037433 / 0.128546 (-0.091114) | 0.012410 / 0.075646 (-0.063237) | 0.088110 / 0.419271 (-0.331162) | 0.050635 / 0.043533 (0.007103) | 0.351878 / 0.255139 (0.096739) | 0.365707 / 0.283200 (0.082508) | 0.104342 / 0.141683 (-0.037341) | 1.438009 / 1.452155 (-0.014145) | 1.533616 / 1.492716 (0.040900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225570 / 0.018006 (0.207563) | 0.442482 / 0.000490 (0.441992) | 0.000402 / 0.000200 (0.000202) | 0.000063 / 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) | 0.030348 / 0.037411 (-0.007063) | 0.111402 / 0.014526 (0.096877) | 0.123365 / 0.176557 (-0.053192) | 0.175604 / 0.737135 (-0.561531) | 0.128458 / 0.296338 (-0.167881) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426054 / 0.215209 (0.210845) | 4.255050 / 2.077655 (2.177395) | 2.039568 / 1.504120 (0.535448) | 1.856842 / 1.541195 (0.315647) | 1.923792 / 1.468490 (0.455301) | 0.701023 / 4.584777 (-3.883754) | 3.746632 / 3.745712 (0.000920) | 2.055563 / 5.269862 (-3.214298) | 1.308068 / 4.565676 (-3.257608) | 0.085524 / 0.424275 (-0.338751) | 0.012103 / 0.007607 (0.004496) | 0.522929 / 0.226044 (0.296885) | 5.258133 / 2.268929 (2.989205) | 2.458440 / 55.444624 (-52.986185) | 2.141681 / 6.876477 (-4.734796) | 2.258667 / 2.142072 (0.116595) | 0.842533 / 4.805227 (-3.962694) | 0.168089 / 6.500664 (-6.332575) | 0.063707 / 0.075469 (-0.011762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312252 / 1.841788 (-0.529536) | 14.939185 / 8.074308 (6.864877) | 14.479845 / 10.191392 (4.288453) | 0.162557 / 0.680424 (-0.517867) | 0.017660 / 0.534201 (-0.516541) | 0.423261 / 0.579283 (-0.156023) | 0.417693 / 0.434364 (-0.016671) | 0.495440 / 0.540337 (-0.044897) | 0.589932 / 1.386936 (-0.797004) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4e3c86574155961097b367d5cddda5bd13c42b09 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008796 / 0.011353 (-0.002557) | 0.005828 / 0.011008 (-0.005180) | 0.118629 / 0.038508 (0.080121) | 0.042435 / 0.023109 (0.019326) | 0.383780 / 0.275898 (0.107882) | 0.420344 / 0.323480 (0.096864) | 0.006855 / 0.007986 (-0.001130) | 0.006290 / 0.004328 (0.001962) | 0.087160 / 0.004250 (0.082910) | 0.057568 / 0.037052 (0.020516) | 0.378761 / 0.258489 (0.120272) | 0.426496 / 0.293841 (0.132655) | 0.041772 / 0.128546 (-0.086774) | 0.014226 / 0.075646 (-0.061420) | 0.400097 / 0.419271 (-0.019174) | 0.060402 / 0.043533 (0.016870) | 0.381955 / 0.255139 (0.126816) | 0.399110 / 0.283200 (0.115911) | 0.124608 / 0.141683 (-0.017075) | 1.737856 / 1.452155 (0.285702) | 1.829034 / 1.492716 (0.336318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219941 / 0.018006 (0.201934) | 0.497156 / 0.000490 (0.496666) | 0.005094 / 0.000200 (0.004894) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032144 / 0.037411 (-0.005268) | 0.131782 / 0.014526 (0.117256) | 0.141543 / 0.176557 (-0.035014) | 0.211419 / 0.737135 (-0.525716) | 0.147338 / 0.296338 (-0.149001) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478345 / 0.215209 (0.263136) | 4.749506 / 2.077655 (2.671851) | 2.195794 / 1.504120 (0.691674) | 1.978126 / 1.541195 (0.436932) | 2.059941 / 1.468490 (0.591451) | 0.821959 / 4.584777 (-3.762818) | 5.737479 / 3.745712 (1.991767) | 2.507125 / 5.269862 (-2.762737) | 2.051772 / 4.565676 (-2.513905) | 0.100619 / 0.424275 (-0.323656) | 0.014437 / 0.007607 (0.006830) | 0.599484 / 0.226044 (0.373440) | 5.977579 / 2.268929 (3.708651) | 2.708143 / 55.444624 (-52.736482) | 2.320279 / 6.876477 (-4.556198) | 2.510172 / 2.142072 (0.368100) | 1.006279 / 4.805227 (-3.798948) | 0.199812 / 6.500664 (-6.300853) | 0.077967 / 0.075469 (0.002498) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.510171 / 1.841788 (-0.331616) | 21.099446 / 8.074308 (13.025138) | 17.634225 / 10.191392 (7.442833) | 0.223506 / 0.680424 (-0.456918) | 0.023845 / 0.534201 (-0.510356) | 0.613489 / 0.579283 (0.034206) | 0.685735 / 0.434364 (0.251371) | 0.652485 / 0.540337 (0.112148) | 0.734756 / 1.386936 (-0.652180) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008444 / 0.011353 (-0.002909) | 0.005789 / 0.011008 (-0.005220) | 0.088297 / 0.038508 (0.049789) | 0.040847 / 0.023109 (0.017737) | 0.411748 / 0.275898 (0.135850) | 0.452320 / 0.323480 (0.128841) | 0.006689 / 0.007986 (-0.001296) | 0.006029 / 0.004328 (0.001701) | 0.086080 / 0.004250 (0.081830) | 0.053310 / 0.037052 (0.016257) | 0.402568 / 0.258489 (0.144079) | 0.459047 / 0.293841 (0.165206) | 0.041203 / 0.128546 (-0.087343) | 0.014216 / 0.075646 (-0.061431) | 0.102729 / 0.419271 (-0.316543) | 0.057170 / 0.043533 (0.013637) | 0.407137 / 0.255139 (0.151998) | 0.429703 / 0.283200 (0.146503) | 0.123528 / 0.141683 (-0.018155) | 1.690026 / 1.452155 (0.237872) | 1.797793 / 1.492716 (0.305077) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264581 / 0.018006 (0.246575) | 0.498981 / 0.000490 (0.498492) | 0.000462 / 0.000200 (0.000262) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034613 / 0.037411 (-0.002798) | 0.136596 / 0.014526 (0.122070) | 0.142183 / 0.176557 (-0.034374) | 0.201816 / 0.737135 (-0.535320) | 0.148843 / 0.296338 (-0.147496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506708 / 0.215209 (0.291499) | 5.042829 / 2.077655 (2.965175) | 2.448414 / 1.504120 (0.944295) | 2.213251 / 1.541195 (0.672056) | 2.255805 / 1.468490 (0.787315) | 0.829929 / 4.584777 (-3.754848) | 5.145717 / 3.745712 (1.400004) | 2.493947 / 5.269862 (-2.775915) | 1.676171 / 4.565676 (-2.889506) | 0.102097 / 0.424275 (-0.322178) | 0.014545 / 0.007607 (0.006938) | 0.635473 / 0.226044 (0.409429) | 6.306767 / 2.268929 (4.037839) | 3.050284 / 55.444624 (-52.394341) | 2.653175 / 6.876477 (-4.223302) | 2.850569 / 2.142072 (0.708496) | 1.355280 / 4.805227 (-3.449947) | 0.248112 / 6.500664 (-6.252552) | 0.091993 / 0.075469 (0.016524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.837509 / 1.841788 (-0.004279) | 21.268838 / 8.074308 (13.194530) | 17.338053 / 10.191392 (7.146660) | 0.232263 / 0.680424 (-0.448161) | 0.029093 / 0.534201 (-0.505108) | 0.651056 / 0.579283 (0.071773) | 0.617623 / 0.434364 (0.183259) | 0.773921 / 0.540337 (0.233584) | 0.705118 / 1.386936 (-0.681818) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35846fd54fa16aa72ff344d15c98b5e08c5effe0 \"CML watermark\")\n"
] | 2023-04-21T15:04:28Z
| 2023-04-26T12:20:01Z
| 2023-04-26T12:11:15Z
|
MEMBER
| null | 0
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We need to call `fs.makedirs` when saving a dataset using `save_to_disk`, because some fs implementations have actual directories (S3 and others don't)
Close https://github.com/huggingface/datasets/issues/5775
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Datasets streaming + map doesn't work for `Audio`
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"This seems related to https://github.com/huggingface/datasets/issues/3505."
] | 2022-01-17T12:55:42Z
| 2022-01-20T13:28:00Z
| 2022-01-20T13:28:00Z
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MEMBER
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## Describe the bug
When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("common_voice", "en", streaming=True, split="train")
def map_fn(batch):
print("audio keys", batch["audio"].keys())
batch["audio"] = batch["audio"]["array"][:100]
return batch
ds = ds.map(map_fn)
sample = next(iter(ds))
```
I think the audio is somehow decoded before `.map(...)` is actually called.
## Expected results
IMO, the above code snippet should work.
## Actual results
```bash
audio keys dict_keys(['path', 'bytes'])
Traceback (most recent call last):
File "./run_audio.py", line 15, in <module>
sample = next(iter(ds))
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "./run_audio.py", line 9, in map_fn
batch["input"] = batch["audio"]["array"][:100]
KeyError: 'array'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
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[
"Hi @echatzikyriakidis, thanks for submitting the issue.\r\nWhich shell are you using exactly? I tried to run the command you sent, but I don't see colors at all 🧐\r\n\r\nI tried from bash and zsh as well.",
"Hi @david1542 ,\r\n\r\nI use Google Colab.\r\n",
"Got it. I [created a PR](https://github.com/huggingface/datasets/pull/5120) that fixes this issue. Turns out that the wrapping logic for the inner loop was slightly incorrect.",
"Thank you!"
] | 2022-10-14T16:12:30Z
| 2022-10-23T12:58:41Z
| 2022-10-23T12:58:41Z
|
NONE
| null | null | null |
## Describe the bug
Progress bars after transformative operations turn in red and never be completed to 100%
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('rotten_tomatoes', split='test').filter(lambda o: True)
```
## Expected results
Progress bar should be 100% and green
## Actual results
Progress bar turn in red and never completed to 100%
## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.14
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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Add support for `Sequence(Audio/Image)` feature in `push_to_hub`
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[
"This issue stems from https://github.com/huggingface/datasets/blob/6d2f2a5e0fea3827eccfd1717d8021c15fc4292a/src/datasets/table.py#L2203-L2205\r\n\r\nI'll address it as part of https://github.com/huggingface/datasets/pull/6283.\r\n\r\nIn the meantime, this should work\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets import Image\r\n\r\ndataset = dataset.with_format(\"arrow\")\r\n\r\ndef embed_images(pa_table):\r\n images_arr = pa.chunked_array(\r\n [\r\n pa.ListArray.from_arrays(chunk.offsets, Image().embed_storage(chunk.values), mask=chunk.is_null())\r\n for chunk in pa_table[\"images\"].chunks\r\n ]\r\n )\r\n return pa_table.set_column(pa_table.schema.get_field_index(\"images\"), \"images\", images_arr)\r\n\r\ndataset = dataset.map(embed_images, batched=True)\r\n\r\ndataset = dataset.with_format(\"python\")\r\n\r\ndataset.push_to_hub(...)\r\n```"
] | 2023-10-27T14:39:57Z
| 2023-11-02T17:49:28Z
| null |
CONTRIBUTOR
| null | null | null |
### Feature request
Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards.
### Motivation
Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files.
I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse).
### Your contribution
I'll submit a PR if I find a way to add this feature
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Install tensorflow-macos dependency conditionally
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-17T08:45:08Z
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Fix #5118.
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"@lhoestq done! The failing testes don't seem to be related, it seems to be a connection issue, if I understand it correctly.",
"@lhoestq done!",
"merging since the CI is fixed on master"
] | 2020-12-09T20:22:12Z
| 2020-12-14T18:06:44Z
| 2020-12-14T16:25:31Z
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Add [Hate Speech and Offensive Language Detection dataset](https://github.com/t-davidson/hate-speech-and-offensive-language) from [this paper](https://arxiv.org/abs/1703.04009).
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[Windows] Fix Access Denied when using os.rename()
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[
"Cool thank you ! Maybe we can just replace `os.rename` by `shutil.move` instead ?",
"> Cool thank you ! Maybe we can just replace `os.rename` by `shutil.move` instead ?\r\n\r\nYes, I think that could be a better solution, but I didn't test it in Linux (e.g. Ubuntu) to guarantee that `os.rename()` could be completely replaced by `shutil.move()`.",
"AFAIK `shutil.move` does call `os.rename` first before doing extra work to make it work on windows, so this is should be a safe safe change for linux ;)",
"> AFAIK `shutil.move` does call `os.rename` first before doing extra work to make it work on windows, so this is should be a safe safe change for linux ;)\r\n\r\nalright, let me change the PR then.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4825). All of your documentation changes will be reflected on that endpoint.",
"Hi @lhoestq looks like one of the tests failed, but is not related to this change, do I need to do something from my side?"
] | 2022-08-11T11:57:15Z
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In this PR, we are including an additional step when `os.rename()` raises a PermissionError.
Basically, we will use `shutil.move()` on the temp files.
Fix #2937
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This PR adds the Schema Guided Dialogue dataset created for the DSTC8 challenge
- https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
A bit simpler than MultiWOZ, the only tricky thing is the sequence of dictionaries that had to be linearized. There is a config for the data proper, and a config for the schemas.
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Can you please add the Stanford dog dataset?
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[
"would you like to give it a try, @dgrnd4? (maybe with the help of the dataset author?)",
"@julien-c i am sorry but I have no idea about how it works: can I add the dataset by myself, following \"instructions to add a new dataset\"?\r\nCan I add a dataset even if it's not mine? (it's public in the link that I wrote on the post)\r\n",
"Hi! The [ADD NEW DATASET](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md) instructions are indeed the best place to start. It's also perfectly fine to add a dataset if it's public, even if it's not yours. Let me know if you need some additional pointers.",
"If no one is working on this, I could take this up!",
"@khushmeeet this is the [link](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset) where I added the dataset already. If you can I would ask you to do this:\r\n1) The dataset it's all in TRAINING SET: can you please divide it in Training,Test and Validation Set? If you can for each class, take the 80% for the Training set and the 10% for Test and 10% Validation\r\n2) The images has different size, can you please resize all the images in 224,224,3? Look even at the last dimension \"3\" because some images has dimension 4!\r\n\r\nThank you!!",
"Hi @khushmeeet! Thanks for the interest. You can self-assign the issue by commenting `#self-assign` on it. \r\n\r\nAlso, I think we can skip @dgrnd4's steps as we try to avoid any custom processing on top of raw data. One can later copy the script and override `_post_process` in it to perform such processing on the generated dataset.",
"Thanks @mariosasko \r\n\r\n@dgrnd4 As dataset is there on Hub, and preprocessing is not recommended. I am not sure if there is any other task to do. However, I can't seem to find relevant `.py` files for this dataset in GitHub repo.",
"@khushmeeet @mariosasko The point is that the images must be processed and must have the same size in order to can be used for things for example \"Training\". ",
"@dgrnd4 Yes, but this can be done after loading (`map` to resize images and `train_test_split` to create extra splits)\r\n\r\n@khushmeeet The linked version is implemented as a no-code dataset and is generated directly from the ZIP archive, but our \"GitHub\" datasets (these are datasets without a user/org namespace on the Hub) need a generation script, and you can find one [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/stanford_dogs.py). `datasets` started as a fork of TFDS, so we share similar script structure, which makes it trivial to adapt it.",
"@mariosasko The point is that if I use something like this:\r\nx_train, x_test = train_test_split(dataset, test_size=0.1) \r\n\r\nto get Train 90% and Test 10%, and then to get the Validation Set (10% of the whole 100%):\r\n\r\n```\r\ntrain_ratio = 0.80\r\nvalidation_ratio = 0.10\r\ntest_ratio = 0.10\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size=1 - train_ratio)\r\nx_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio)) \r\n\r\n```\r\n\r\nThe point is that the structure of the data is:\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['image', 'label'],\r\n num_rows: 20580\r\n })\r\n})\r\n\r\n```\r\n\r\nSo how to extract images and labels?\r\n\r\nEDIT --> Split of the dataset in Train-Test-Validation:\r\n```\r\nimport datasets\r\nfrom datasets.dataset_dict import DatasetDict\r\nfrom datasets import Dataset\r\n\r\npercentage_divison_test = int(len(dataset['train'])/100 *10) # 10% --> 2058 \r\npercentage_divison_validation = int(len(dataset['train'])/100 *20) # 20% --> 4116\r\n\r\ndataset_ = datasets.DatasetDict({\"train\": Dataset.from_dict({\r\n\r\n 'image': dataset['train'][0 : len(dataset['train']) ]['image'], \r\n 'labels': dataset['train'][0 : len(dataset['train']) ]['label'] }), \r\n \r\n \"test\": Dataset.from_dict({ #20580-4116 (validation) ,20580-2058 (test)\r\n 'image': dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['image'], \r\n 'labels': dataset['train'][len(dataset['train']) - percentage_divison_validation : len(dataset['train']) - percentage_divison_test]['label'] }), \r\n \r\n \"validation\": Dataset.from_dict({ # 20580-2058 (test)\r\n 'image': dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['image'], \r\n 'labels': dataset['train'][len(dataset['train']) - percentage_divison_test : len(dataset['train'])]['label'] }), \r\n })\r\n```",
"@mariosasko in order to resize images I'm trying this method: \r\n```\r\nfor i in range(0,len(dataset['train'])): #len(dataset['train'])\r\n\r\n ex = dataset['train'][i] #i\r\n image = ex['image']\r\n image = image.convert(\"RGB\") # <class 'PIL.Image.Image'> <PIL.Image.Image image mode=RGB size=500x333 at 0x7F84F1948150>\r\n image_resized = image.resize(size_to_resize) # <PIL.Image.Image image mode=RGB size=224x224 at 0x7F84F17885D0>\r\n\r\n dataset['train'][i]['image'] = image_resized \r\n```\r\n\r\nBecause the DatasetDict is formed by arrows that are immutable, the changing assignment in the last line of code, doesn't work!\r\nDo you have any idea in order to get a valid result?",
"#self-assign",
"I have raised PR for adding stanford-dog dataset. I have not added any data preprocessing code. Only dataset generation script is there. Let me know any changes required, or anything to add to README.",
"Is this issue still open, i am new to open source thus want to take this one as my start.",
"@zutarich This issue should have been closed since the dataset in question is available on the Hub [here](https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset)."
] | 2022-06-15T15:39:35Z
| 2023-10-18T18:55:30Z
| 2023-10-18T18:55:30Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *Stanford dog dataset*
- **Description:** *The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Paper:** *http://vision.stanford.edu/aditya86/ImageNetDogs/*
- **Data:** *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/ImageNetDogs/)*
- **Motivation:** *The dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. It is useful for fine-grain purpose *
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
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[
"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.",
"If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you."
] | 2021-07-04T15:39:49Z
| 2021-07-07T13:18:51Z
| 2021-07-07T13:18:51Z
|
NONE
| null | null | null |
## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].
For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.
## Steps to reproduce the bug
```python
import datasets
def check_context_answer_alignment(example):
for a_idx in range(len(example['answers']['text'])):
# check raw dataset for answer consistency between context and answer
answer_text = example['answers']['text'][a_idx]
a_st_idx = example['answers']['answer_start'][a_idx]
a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
answer_text_from_context = example['context'][a_st_idx:a_end_idx]
if answer_text != answer_text_from_context:
#print(example['id'])
return False
return True
dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)
start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
num_proc=1,
keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```
## Expected results
This code should result in 0 rows being filtered out from the dataset.
## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.
This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."
## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y
# activate the virtual environment
conda activate round8
# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia
pip install jsonpickle transformers datasets matplotlib
```
OS: Ubuntu 20.04
Python 3.8
Result of `conda env export`:
```
name: round8
channels:
- pytorch-lts
- nvidia
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- blas=1.0=mkl
- brotlipy=0.7.0=py38h27cfd23_1003
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- pip:
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- cycler==0.10.0
- datasets==1.8.0
- dill==0.3.4
- filelock==3.0.12
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- sacremoses==0.0.45
- tokenizers==0.10.3
- tqdm==4.49.0
- transformers==4.6.1
- xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```
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VoxPopuli
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"duplicate of https://github.com/huggingface/datasets/issues/2300"
] | 2022-02-15T23:04:55Z
| 2022-02-16T18:49:12Z
| 2022-02-16T18:49:12Z
|
MEMBER
| null | null | null |
## Adding a Dataset
- **Name:** VoxPopuli
- **Description:** A Large-Scale Multilingual Speech Corpus
- **Paper:** https://arxiv.org/pdf/2101.00390.pdf
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** one of the largest (if not the largest) multilingual speech corpus: 400K hours of multilingual unlabeled speech + 17k hours of labeled speech
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
👀 @kahne @Molugan
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Import error
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[
"Hi ! Can you \r\n```python\r\nimport platform\r\nprint(platform.python_version())\r\n```\r\nto see that it returns ?",
"Hi,\n\n3.8.13\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:37:02 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nHi ! Can you\n\nimport platform\nprint(platform.python_version())\n\nto see that it returns ?\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323691385>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F5YGG32W6WABYC25NJTWJTD75ANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"Then it should work as expected if you use the same python when using `datasets`\r\n\r\nPlease make sure you're running your code in the right environment",
"It's the right environment. But in if statement I have\n\"3.8.13\" < 3.7\nAnd in the error message is Python>=3.7 which is true in my case (3.8.13 is greater then 3.7), so I don't understand my python should be below the 3.7 which case the if statement is right, but the message is wrong, or above 3.7 which case if statement is wrong, error message is right.\n\nGet Outlook for Android<https://aka.ms/AAb9ysg>\n________________________________\nFrom: Quentin Lhoest ***@***.***>\nSent: Tuesday, November 22, 2022 2:41:43 PM\nTo: huggingface/datasets ***@***.***>\nCc: feketedavid1012 ***@***.***>; Author ***@***.***>\nSubject: Re: [huggingface/datasets] Import error (Issue #5280)\n\n\nThen it should work as expected if you use the same python when using datasets\n\nPlease make sure you're running your code in the right environment\n\n—\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5280#issuecomment-1323697094>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AJW7F54JURTAJJWWDO2QGI3WJTERPANCNFSM6AAAAAASHZJ2AU>.\nYou are receiving this because you authored the thread.Message ID: ***@***.***>\n",
"If you're having an error then you're not running your code in the right environment."
] | 2022-11-22T12:56:43Z
| 2022-12-15T19:57:40Z
| 2022-12-15T19:57:40Z
|
NONE
| null | null | null |
https://github.com/huggingface/datasets/blob/cd3d8e637cfab62d352a3f4e5e60e96597b5f0e9/src/datasets/__init__.py#L28
Hy,
I have error at the above line. I have python version 3.8.13, the message says I need python>=3.7, which is True, but I think the if statement not working properly (or the message wrong)
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Update remote checksums instead of overwrite
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When the user uploads a dataset on S3, checksums are also uploaded with the `--upload_checksums` parameter.
If the user uploads the dataset in several steps, then the remote checksums file was previously overwritten. Now it's going to be updated with the new checksums.
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Column of arrays (2D+) are using unreasonably high memory
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"note i have tried the same code with `datasets` version 2.4.0, the outcome is the very same as described above.",
"Seems related to issues #4623 and #4802 so it would appear this issue has been around for a few months.",
"Hi ! `Dataset.from_dict` keeps the data in memory. You can write on disk and reload them with\r\n```python\r\ndataset.save_to_disk(\"path/to/local\")\r\ndataset = load_from_disk(\"path/to/local\")\r\n```\r\nthis way you'll end up with a dataset loaded from your disk using memory mapping, and it won't fill up your RAM :)\r\n\r\nrelated to https://github.com/huggingface/datasets/issues/4861",
"@lhoestq thnx for getting back to me! i've tested the suggested method, but unfortunately the memory consumption is the very same:\r\n\r\n```\r\nfrom datasets import Dataset, Features, Array2D, Array3D, load_from_disk\r\nimport numpy as np\r\n\r\ncolumn_name = \"a\"\r\narray_shape = (64, 64, 3)\r\n\r\ndata = np.random.random((10000,) + array_shape)\r\ndataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype=\"float64\")}))\r\ndataset.save_to_disk(\"foo\")\r\n\r\nfoo_db = load_from_disk(\"foo\")\r\ncolum_value = foo_db[column_name]\r\n```\r\n\r\nthe very same happens when you create the dataset, but dont specify the feature type.\r\n\r\ni've tried running this on different envs (macOS, linux) and it's behaving the very same way.",
"When you call `colum_value = foo_db[column_name]`, you load the full column in memory.\r\n\r\nIf you want to avoid filling up your memory, you can access chunks of data instead\r\n```python\r\nembeddings = dataset[i:i + chunk_size][\"embeddings\"]\r\n```",
"@lhoestq yeah that's intentional, i.e. i really want to load the whole column into the memory. but as said above there's an unreasonable amount of overhead for the memory. the np array itself is using about 1G of memory:\r\n```\r\n>>> getsizeof(data)/1024/1024\r\n937.5001525878906\r\n```\r\nthat accessing of column above is using 10x memory compared to the original numpy array.",
"The dataset must be twice as big because we use regular arrow ListArray under the hood and not FixedSizeListArray. Basically we store unnecessary offsets.\r\n\r\nAnd this should affect performance as well. When we developed this, FixedSizeListArray still had some issues but they should be resolved on the PyArrow side now",
"A doubling would be fine. My very basic understanding of PyArrow is that using ListArray is probably related to the issue though. Using a multi-dimensional array in datasets is storing everything as strange nested 1d object arrays, which I imagine is creating the massive overhead.\r\n\r\nI think it should be a PyArrow Tensor, no?",
"PyArrow tensors are not part of the Arrow format AFAIK:\r\n\r\n> There is no direct support in the arrow columnar format to store Tensors as column values.\r\n\r\nsource: https://github.com/apache/arrow/issues/4802#issuecomment-508494694",
"That's... unfortunate. I didn't realize that."
] | 2022-09-10T13:07:22Z
| 2022-09-22T18:29:22Z
| null |
CONTRIBUTOR
| null | null | null |
## Describe the bug
When trying to store `Array2D, Array3D, etc` as column values in a dataset, accessing that column (or creating depending on how you create it, see code below) will cause more than 10 fold of memory usage.
## Steps to reproduce the bug
```python
from datasets import Dataset, Features, Array2D, Array3D
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype="float64")}))
```
the code above will use about 10Gb of RAM while constructing the `dataset` object.
The code below will use roughly the same amount of memory (and time) when trying to actually access the data itself of that column.
```python
from datasets import Dataset
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data})
dataset[column_name]
```
## Expected results
Some memory overhead, but not like as it is now and certainly not an overhead of such runtime that is currently happening.
## Actual results
Enormous memory- and runtime overhead.
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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(`from_spark()`) Unable to connect HDFS in pyspark YARN setting
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[] | 2023-08-10T11:03:08Z
| 2023-08-10T11:03:08Z
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NONE
| null | null | null |
### Describe the bug
related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613
---
Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script.
Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process.
These are some peculiarities in my environment that might caused this issue.
* **Cluster requires kerberos authentication**
* But I think the error message implies that's not the problem in this case
* **The user that runs the worker process is different from that built the docker image**
* To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone
* **Pyspark-part of my code has no problem interacting with HDFS.**
* Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker)
* The problem occurs only when it runs as cluster's worker runtime
Hope I could get some help. Thanks.
```bash
2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
### Steps to reproduce the bug
Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path.
### Expected behavior
Work as described in document
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
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Add msr_text_compression
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[
"the `RemoteDatasetTest ` error in the CI is fixed on master so it's fine"
] | 2020-12-06T09:06:11Z
| 2020-12-09T10:53:45Z
| 2020-12-09T10:53:45Z
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-12T01:20:34Z
| 2022-04-12T08:32:24Z
| 2022-04-12T08:26:02Z
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The link in the How-To overview page to the Datasets tutorials is currently broken. This is just a small adjustment to make it match the format used in https://github.com/huggingface/datasets/blob/master/docs/source/tutorial.md.
(Edit to add: The link in the PR deployment (https://moon-ci-docs.huggingface.co/docs/datasets/pr_4147/en/how_to) is also broken since it's actually hardcoded to `master` and not dynamic to the branch name, but other links seem to behave similarly.)
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This code fails:
```python
from datasets import Dataset
ds = Dataset.from_json(path_to_file)
ds.data.validate()
```
raises
```python
ArrowInvalid: Column 2: In chunk 1: Invalid: Struct child array #3 invalid: Invalid: Length spanned by list offsets (4064) larger than values array (length 4063)
```
This causes many issues for @TevenLeScao:
- `map` fails because it fails to rewrite invalid arrow arrays
```python
~/Desktop/hf/datasets/src/datasets/arrow_writer.py in write_examples_on_file(self)
438 if all(isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) for row in self.current_examples):
439 arrays = [row[0][col] for row in self.current_examples]
--> 440 batch_examples[col] = array_concat(arrays)
441 else:
442 batch_examples[col] = [
~/Desktop/hf/datasets/src/datasets/table.py in array_concat(arrays)
1885
1886 if not _is_extension_type(array_type):
-> 1887 return pa.concat_arrays(arrays)
1888
1889 def _offsets_concat(offsets):
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.concat_arrays()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowIndexError: array slice would exceed array length
```
- `to_dict()` **segfaults** ⚠️
```python
/Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/array/data.cc:99: Check failed: (off) <= (length) Slice offset greater
than array length
```
To reproduce: unzip the archive and run the above code using `sanity_oscar_en.jsonl`
[sanity_oscar_en.jsonl.zip](https://github.com/huggingface/datasets/files/10734124/sanity_oscar_en.jsonl.zip)
PS: reading using pandas and converting to Arrow works though (note that the dataset lives in RAM in this case):
```python
ds = Dataset.from_pandas(pd.read_json(path_to_file, lines=True))
ds.data.validate()
```
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Adding myPOS2017 dataset
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[
"I've updated the code and Readme to reflect your comments.\r\nThank you very much,",
"looks like this PR includes changes about many other files than the ones for myPOS2017\r\n\r\nCould you open another branch and another PR please ?\r\n(or fix this branch)",
"Hi @hungluumfc ! Have you had a chance to fix this PR so that it only includes the changes for `mypos` ? \r\n\r\nFeel free to ping me if you have questions or if I can help :) ",
"Thanks for your contribution, @hungluumfc. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help."
] | 2020-12-22T04:04:55Z
| 2022-10-03T09:38:23Z
| 2022-10-03T09:38:23Z
|
NONE
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myPOS Corpus (Myanmar Part-of-Speech Corpus) for Myanmar language NLP Research and Developments
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:fist: ¡Viva la Independencia!
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[
"I've added the changes / fixes - ready for a second pass :)"
] | 2020-12-08T20:43:43Z
| 2020-12-14T10:36:01Z
| 2020-12-14T10:36:01Z
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Adds the Catalonia Independence Corpus for stance-detection of Tweets.
Ready for review!
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Generate metadata JSON for reclor dataset
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Related to #2743.
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PR_kwDODunzps5AVXwm
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IterableDataset formatting in numpy/torch/tf/jax
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084). All of your documentation changes will be reflected on that endpoint.",
"Actually I'm not happy with this implementation. It always require the iterable dataset to have definite `features`, which removes a lot of flexibility. So I think we need an actual formatting from python objects, not from arrow data.",
"Closing this one since it has too many conflicts and still require some work - it will be easier to open a new PR"
] | 2022-10-06T16:53:38Z
| 2023-09-24T10:06:51Z
| 2022-12-20T17:19:52Z
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This code now returns a numpy array:
```python
from datasets import load_dataset
ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```
It also works with "arrow", "pandas", "torch", "tf" and "jax"
### Implementation details:
I'm using the existing code to format an Arrow Table to the right output format for simplicity.
Therefore it's probbaly not the most optimized approach.
For example to output PyTorch tensors it does this for every example:
python data -> arrow table -> numpy extracted data -> pytorch formatted data
### Releasing this feature
Even though I consider this as a bug/inconsistency, this change is a breaking change.
And I'm sure some users were relying on the torch iterable dataset to return PIL Image and used data collators to convert to pytorch.
So I guess this is `datasets` 3.0 ?
### TODO
- [x] merge https://github.com/huggingface/datasets/pull/5072
- [ ] docs
- [ ] tests
Close https://github.com/huggingface/datasets/issues/5083
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[Tests] skip beam dataset tests for now
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[
"@lhoestq - I moved the wkipedia file to the \"correct\" folder. ",
"Nice thanks !"
] | 2020-05-12T16:00:58Z
| 2020-05-12T16:16:24Z
| 2020-05-12T16:16:22Z
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For now we will skip tests for Beam Datasets
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Fix unused DatasetInfosDict code in push_to_hub
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008634 / 0.011353 (-0.002719) | 0.005147 / 0.011008 (-0.005861) | 0.102865 / 0.038508 (0.064357) | 0.080245 / 0.023109 (0.057136) | 0.401288 / 0.275898 (0.125390) | 0.419708 / 0.323480 (0.096228) | 0.006342 / 0.007986 (-0.001644) | 0.003998 / 0.004328 (-0.000330) | 0.078880 / 0.004250 (0.074630) | 0.068199 / 0.037052 (0.031147) | 0.389573 / 0.258489 (0.131084) | 0.417292 / 0.293841 (0.123451) | 0.048856 / 0.128546 (-0.079691) | 0.014165 / 0.075646 (-0.061481) | 0.348063 / 0.419271 (-0.071209) | 0.067547 / 0.043533 (0.024014) | 0.402251 / 0.255139 (0.147112) | 0.419478 / 0.283200 (0.136278) | 0.034846 / 0.141683 (-0.106837) | 1.773493 / 1.452155 (0.321338) | 1.930546 / 1.492716 (0.437830) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211835 / 0.018006 (0.193829) | 0.545311 / 0.000490 (0.544821) | 0.006766 / 0.000200 (0.006566) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035406 / 0.037411 (-0.002006) | 0.100769 / 0.014526 (0.086243) | 0.108667 / 0.176557 (-0.067890) | 0.193099 / 0.737135 (-0.544036) | 0.113539 / 0.296338 (-0.182799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586935 / 0.215209 (0.371726) | 5.895245 / 2.077655 (3.817591) | 2.528375 / 1.504120 (1.024255) | 2.228617 / 1.541195 (0.687423) | 2.295799 / 1.468490 (0.827309) | 0.859272 / 4.584777 (-3.725505) | 5.033434 / 3.745712 (1.287722) | 7.546587 / 5.269862 (2.276726) | 4.457137 / 4.565676 (-0.108539) | 0.099626 / 0.424275 (-0.324649) | 0.009296 / 0.007607 (0.001689) | 0.713498 / 0.226044 (0.487454) | 7.409385 / 2.268929 (5.140456) | 3.361418 / 55.444624 (-52.083206) | 2.681111 / 6.876477 (-4.195366) | 2.849598 / 2.142072 (0.707526) | 1.114863 / 4.805227 (-3.690364) | 0.215494 / 6.500664 (-6.285170) | 0.075807 / 0.075469 (0.000338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606458 / 1.841788 (-0.235330) | 23.751096 / 8.074308 (15.676788) | 21.279110 / 10.191392 (11.087718) | 0.220785 / 0.680424 (-0.459639) | 0.032688 / 0.534201 (-0.501513) | 0.530948 / 0.579283 (-0.048335) | 0.630056 / 0.434364 (0.195693) | 0.572743 / 0.540337 (0.032405) | 0.771853 / 1.386936 (-0.615083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008693 / 0.011353 (-0.002660) | 0.004750 / 0.011008 (-0.006259) | 0.079764 / 0.038508 (0.041256) | 0.082096 / 0.023109 (0.058987) | 0.467198 / 0.275898 (0.191300) | 0.532361 / 0.323480 (0.208881) | 0.005836 / 0.007986 (-0.002149) | 0.004333 / 0.004328 (0.000005) | 0.080444 / 0.004250 (0.076194) | 0.065883 / 0.037052 (0.028831) | 0.464871 / 0.258489 (0.206382) | 0.575026 / 0.293841 (0.281185) | 0.057807 / 0.128546 (-0.070739) | 0.017462 / 0.075646 (-0.058185) | 0.093667 / 0.419271 (-0.325605) | 0.071466 / 0.043533 (0.027933) | 0.495846 / 0.255139 (0.240707) | 0.526100 / 0.283200 (0.242900) | 0.034852 / 0.141683 (-0.106831) | 1.884152 / 1.452155 (0.431998) | 1.922681 / 1.492716 (0.429965) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250969 / 0.018006 (0.232963) | 0.504979 / 0.000490 (0.504489) | 0.000466 / 0.000200 (0.000266) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032411 / 0.037411 (-0.005000) | 0.093184 / 0.014526 (0.078658) | 0.110798 / 0.176557 (-0.065759) | 0.165741 / 0.737135 (-0.571394) | 0.111022 / 0.296338 (-0.185317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.661284 / 0.215209 (0.446075) | 6.622388 / 2.077655 (4.544733) | 3.095705 / 1.504120 (1.591585) | 2.745698 / 1.541195 (1.204503) | 2.694103 / 1.468490 (1.225612) | 0.862154 / 4.584777 (-3.722623) | 5.109985 / 3.745712 (1.364273) | 5.040362 / 5.269862 (-0.229499) | 3.072837 / 4.565676 (-1.492840) | 0.110421 / 0.424275 (-0.313854) | 0.008476 / 0.007607 (0.000869) | 0.910020 / 0.226044 (0.683975) | 8.123626 / 2.268929 (5.854698) | 3.813811 / 55.444624 (-51.630813) | 3.017244 / 6.876477 (-3.859232) | 3.061222 / 2.142072 (0.919150) | 1.073548 / 4.805227 (-3.731680) | 0.216327 / 6.500664 (-6.284338) | 0.072977 / 0.075469 (-0.002492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.722482 / 1.841788 (-0.119305) | 23.706716 / 8.074308 (15.632407) | 23.192134 / 10.191392 (13.000742) | 0.276733 / 0.680424 (-0.403691) | 0.033538 / 0.534201 (-0.500663) | 0.602083 / 0.579283 (0.022799) | 0.578718 / 0.434364 (0.144354) | 0.558311 / 0.540337 (0.017974) | 0.740341 / 1.386936 (-0.646595) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac575b8ed57dac60d7ba33a616894f38601f84a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006862 / 0.011353 (-0.004491) | 0.004223 / 0.011008 (-0.006786) | 0.085931 / 0.038508 (0.047423) | 0.081437 / 0.023109 (0.058328) | 0.349542 / 0.275898 (0.073644) | 0.379881 / 0.323480 (0.056401) | 0.005651 / 0.007986 (-0.002334) | 0.003662 / 0.004328 (-0.000666) | 0.065251 / 0.004250 (0.061001) | 0.061599 / 0.037052 (0.024547) | 0.359681 / 0.258489 (0.101192) | 0.392502 / 0.293841 (0.098661) | 0.031300 / 0.128546 (-0.097246) | 0.008591 / 0.075646 (-0.067055) | 0.288577 / 0.419271 (-0.130694) | 0.062920 / 0.043533 (0.019388) | 0.348989 / 0.255139 (0.093850) | 0.362769 / 0.283200 (0.079569) | 0.030087 / 0.141683 (-0.111596) | 1.480748 / 1.452155 (0.028594) | 1.580413 / 1.492716 (0.087697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205804 / 0.018006 (0.187798) | 0.455386 / 0.000490 (0.454897) | 0.003134 / 0.000200 (0.002934) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030252 / 0.037411 (-0.007159) | 0.087566 / 0.014526 (0.073041) | 0.098209 / 0.176557 (-0.078347) | 0.155816 / 0.737135 (-0.581319) | 0.098938 / 0.296338 (-0.197401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386688 / 0.215209 (0.171479) | 3.852777 / 2.077655 (1.775123) | 1.938688 / 1.504120 (0.434568) | 1.779234 / 1.541195 (0.238039) | 1.864262 / 1.468490 (0.395772) | 0.482472 / 4.584777 (-4.102305) | 3.658060 / 3.745712 (-0.087652) | 5.206489 / 5.269862 (-0.063373) | 3.262498 / 4.565676 (-1.303179) | 0.057523 / 0.424275 (-0.366752) | 0.007365 / 0.007607 (-0.000242) | 0.466886 / 0.226044 (0.240841) | 4.671026 / 2.268929 (2.402097) | 2.380357 / 55.444624 (-53.064268) | 2.096590 / 6.876477 (-4.779887) | 2.274415 / 2.142072 (0.132342) | 0.579705 / 4.805227 (-4.225522) | 0.134522 / 6.500664 (-6.366142) | 0.062232 / 0.075469 (-0.013237) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245965 / 1.841788 (-0.595823) | 20.115180 / 8.074308 (12.040872) | 14.602983 / 10.191392 (4.411591) | 0.146890 / 0.680424 (-0.533533) | 0.018424 / 0.534201 (-0.515777) | 0.393941 / 0.579283 (-0.185342) | 0.413785 / 0.434364 (-0.020579) | 0.453344 / 0.540337 (-0.086993) | 0.655446 / 1.386936 (-0.731490) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006807 / 0.011353 (-0.004546) | 0.004083 / 0.011008 (-0.006925) | 0.065389 / 0.038508 (0.026881) | 0.081056 / 0.023109 (0.057947) | 0.362823 / 0.275898 (0.086925) | 0.401928 / 0.323480 (0.078448) | 0.005452 / 0.007986 (-0.002533) | 0.003413 / 0.004328 (-0.000915) | 0.065238 / 0.004250 (0.060987) | 0.057264 / 0.037052 (0.020211) | 0.375713 / 0.258489 (0.117224) | 0.407858 / 0.293841 (0.114017) | 0.031580 / 0.128546 (-0.096966) | 0.008643 / 0.075646 (-0.067003) | 0.071693 / 0.419271 (-0.347578) | 0.049392 / 0.043533 (0.005859) | 0.370194 / 0.255139 (0.115055) | 0.384647 / 0.283200 (0.101447) | 0.024805 / 0.141683 (-0.116877) | 1.509511 / 1.452155 (0.057356) | 1.560193 / 1.492716 (0.067477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234442 / 0.018006 (0.216436) | 0.458818 / 0.000490 (0.458329) | 0.000407 / 0.000200 (0.000207) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031661 / 0.037411 (-0.005750) | 0.093143 / 0.014526 (0.078618) | 0.102205 / 0.176557 (-0.074352) | 0.155850 / 0.737135 (-0.581286) | 0.104345 / 0.296338 (-0.191994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419641 / 0.215209 (0.204432) | 4.200808 / 2.077655 (2.123153) | 2.218227 / 1.504120 (0.714107) | 2.052604 / 1.541195 (0.511409) | 2.150611 / 1.468490 (0.682121) | 0.482665 / 4.584777 (-4.102112) | 3.606541 / 3.745712 (-0.139172) | 3.310637 / 5.269862 (-1.959224) | 2.070200 / 4.565676 (-2.495476) | 0.056586 / 0.424275 (-0.367689) | 0.007826 / 0.007607 (0.000218) | 0.491037 / 0.226044 (0.264992) | 4.901538 / 2.268929 (2.632610) | 2.676402 / 55.444624 (-52.768223) | 2.363935 / 6.876477 (-4.512542) | 2.587813 / 2.142072 (0.445741) | 0.579302 / 4.805227 (-4.225926) | 0.132792 / 6.500664 (-6.367873) | 0.061865 / 0.075469 (-0.013604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354315 / 1.841788 (-0.487473) | 20.874516 / 8.074308 (12.800208) | 14.863559 / 10.191392 (4.672167) | 0.183635 / 0.680424 (-0.496789) | 0.018636 / 0.534201 (-0.515565) | 0.395317 / 0.579283 (-0.183966) | 0.410598 / 0.434364 (-0.023766) | 0.476485 / 0.540337 (-0.063853) | 0.643246 / 1.386936 (-0.743690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4472a8795c603a95eef7c8f15cb04f1290cc8d11 \"CML watermark\")\n"
] | 2023-07-17T11:03:09Z
| 2023-07-18T16:17:52Z
| 2023-07-18T16:08:42Z
|
MEMBER
| null | 0
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Adding COVID QA dataset in Chinese and English from UC SanDiego
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"Changed the pre-processing based on the comments raised in [PR-1482](https://github.com/huggingface/datasets/pull/1482).The below command is passing in my local environment:\r\n\r\n`python datasets-cli test datasets/covid_qa_ucsd/ --save_infos --all_configs --data_dir ~/Downloads/Medical-Dialogue-Dataset/CovidDailogue/`\r\n\r\n"
] | 2020-12-12T12:02:48Z
| 2021-02-16T05:29:18Z
| 2020-12-17T15:29:26Z
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PR_kwDODunzps47lCFt
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Skip tests only for lz4/zstd params if not installed
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-18T15:41:40Z
| 2022-07-19T13:02:31Z
| 2022-07-19T12:49:18Z
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Currently, if `zstandard` or `lz4` are not installed, `test_compression_filesystems` and `test_streaming_dl_manager_extract_all_supported_single_file_compression_types` are skipped for all compression format parameters.
This PR fixes these tests, so that if `zstandard` or `lz4` are not installed, the tests are skipped only for the corresponding compression parameters (`zstd` or `lz4`), whereas the tests are not skipped for all the other compression parameters (`gzip`, `xz` and `bz2`).
Related to:
- #4688
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Fix bug with labels of eurlex config of lex_glue dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"@JamesLYC88 here is the fix! Thanks again!",
"Thanks, @albertvillanova. When do you expect that this change will take effect when someone downloads the dataset?",
"The change is immediately available now, since this change we made to our library:\r\n- #4059"
] | 2022-09-30T09:47:12Z
| 2022-09-30T16:30:25Z
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Fix for a critical bug in the EURLEX dataset label list to make LexGLUE EURLEX results replicable.
In LexGLUE (Chalkidis et al., 2022), the following is mentioned w.r.t. EUR-LEX: _"It supports four different label granularities, comprising 21, 127, 567, 7390 EuroVoc concepts, respectively. We use the 100 most frequent concepts from level 2 [...]”._ The current label list has all 127 labels, which leads to different (lower) results, as communicated by users.
Thanks!
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Patching fails for modules that are not installed or don't exist
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Reported in https://github.com/huggingface/huggingface_hub/runs/6894703718?check_suite_focus=true
When trying to patch `scipy.io.loadmat`:
```python
ModuleNotFoundError: No module named 'scipy'
```
Instead it shouldn't raise an error and do nothing
We use patching to extend such functions to support remote URLs and work in streaming mode
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Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')
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[
"cc @yjernite is this expected ?",
"Yes: TriviaQA has a private test set for the leaderboard [here](https://competitions.codalab.org/competitions/17208)\r\n\r\nFor the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here:\r\nhttps://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md",
"Oh ok, I guess I read the paper too fast 😅, thank you for your answer!"
] | 2020-11-05T11:38:01Z
| 2020-11-09T14:14:59Z
| 2020-11-09T14:14:58Z
|
CONTRIBUTOR
| null | null | null |
# The issue
It's all in the title, it appears to be fine on the train and validation sets.
Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ?
# How to reproduce
```py
from datasets import load_dataset
kilt_tasks = load_dataset("kilt_tasks")
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
# both in "kilt_tasks"
In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])
Out[18]: False
# and "trivia_qa"
In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])
Out[13]: True
# appears to be fine on the train and validation sets.
In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])
Out[14]: False
In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])
Out[15]: False
In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])
Out[16]: True
In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])
Out[17]: True
```
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'Dataset' object has no attribute 'rename_column'
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"Hi ! `rename_column` has been added recently and will be available in the next release"
] | 2021-03-02T08:01:49Z
| 2022-06-01T16:08:47Z
| 2022-06-01T16:08:47Z
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Add repo_id to DatasetInfo
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6268). All of your documentation changes will be reflected on that endpoint.",
"In https://github.com/huggingface/datasets/issues/4129 we want to track the origin of a dataset, e.g. if it comes from multiple datasets.\r\n\r\nI think it's out of scope of DatasetInfo alone, which has info for one dataset only.\r\nTherefore it makes sense to add repo_id, which is for one dataset only.\r\n\r\nIMO if we want to track multiple origins we will need a new DatasetInfo that would have fields relevant to a mix of datasets (out of scope of this PR)\r\n\r\ncc @mariosasko I'd like your opinion on this",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009009 / 0.011353 (-0.002344) | 0.004169 / 0.011008 (-0.006840) | 0.098634 / 0.038508 (0.060126) | 0.069526 / 0.023109 (0.046417) | 0.337963 / 0.275898 (0.062065) | 0.379737 / 0.323480 (0.056257) | 0.004318 / 0.007986 (-0.003668) | 0.005347 / 0.004328 (0.001019) | 0.069875 / 0.004250 (0.065624) | 0.055964 / 0.037052 (0.018912) | 0.340305 / 0.258489 (0.081816) | 0.429718 / 0.293841 (0.135877) | 0.045101 / 0.128546 (-0.083445) | 0.012610 / 0.075646 (-0.063036) | 0.312366 / 0.419271 (-0.106905) | 0.064711 / 0.043533 (0.021178) | 0.345216 / 0.255139 (0.090077) | 0.367245 / 0.283200 (0.084046) | 0.034638 / 0.141683 (-0.107045) | 1.541947 / 1.452155 (0.089793) | 1.645268 / 1.492716 (0.152551) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233501 / 0.018006 (0.215495) | 0.514207 / 0.000490 (0.513717) | 0.014271 / 0.000200 (0.014072) | 0.000366 / 0.000054 (0.000311) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026288 / 0.037411 (-0.011124) | 0.083206 / 0.014526 (0.068680) | 0.098172 / 0.176557 (-0.078385) | 0.158529 / 0.737135 (-0.578606) | 0.095183 / 0.296338 (-0.201155) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.538300 / 0.215209 (0.323091) | 5.486939 / 2.077655 (3.409285) | 2.321812 / 1.504120 (0.817692) | 2.002124 / 1.541195 (0.460929) | 2.045043 / 1.468490 (0.576553) | 0.852772 / 4.584777 (-3.732005) | 5.014897 / 3.745712 (1.269185) | 4.428115 / 5.269862 (-0.841746) | 2.750126 / 4.565676 (-1.815550) | 0.099028 / 0.424275 (-0.325247) | 0.007678 / 0.007607 (0.000070) | 0.664463 / 0.226044 (0.438418) | 6.617811 / 2.268929 (4.348883) | 2.888382 / 55.444624 (-52.556242) | 2.190753 / 6.876477 (-4.685724) | 2.414586 / 2.142072 (0.272513) | 1.010302 / 4.805227 (-3.794925) | 0.194925 / 6.500664 (-6.305739) | 0.063490 / 0.075469 (-0.011979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.543464 / 1.841788 (-0.298323) | 20.566666 / 8.074308 (12.492358) | 19.410745 / 10.191392 (9.219353) | 0.207077 / 0.680424 (-0.473347) | 0.028895 / 0.534201 (-0.505306) | 0.427525 / 0.579283 (-0.151758) | 0.535450 / 0.434364 (0.101086) | 0.494632 / 0.540337 (-0.045705) | 0.723705 / 1.386936 (-0.663231) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008209 / 0.011353 (-0.003144) | 0.004184 / 0.011008 (-0.006824) | 0.072420 / 0.038508 (0.033912) | 0.066851 / 0.023109 (0.043742) | 0.424137 / 0.275898 (0.148239) | 0.473156 / 0.323480 (0.149676) | 0.005394 / 0.007986 (-0.002591) | 0.003898 / 0.004328 (-0.000430) | 0.069996 / 0.004250 (0.065746) | 0.053113 / 0.037052 (0.016061) | 0.453214 / 0.258489 (0.194725) | 0.495921 / 0.293841 (0.202080) | 0.043028 / 0.128546 (-0.085519) | 0.012320 / 0.075646 (-0.063326) | 0.080270 / 0.419271 (-0.339002) | 0.053337 / 0.043533 (0.009804) | 0.436604 / 0.255139 (0.181465) | 0.463422 / 0.283200 (0.180223) | 0.030277 / 0.141683 (-0.111406) | 1.560261 / 1.452155 (0.108106) | 1.647209 / 1.492716 (0.154493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232556 / 0.018006 (0.214550) | 0.502387 / 0.000490 (0.501897) | 0.006688 / 0.000200 (0.006488) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030204 / 0.037411 (-0.007207) | 0.089438 / 0.014526 (0.074912) | 0.118939 / 0.176557 (-0.057617) | 0.160537 / 0.737135 (-0.576598) | 0.113432 / 0.296338 (-0.182906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586469 / 0.215209 (0.371260) | 5.916156 / 2.077655 (3.838502) | 2.904960 / 1.504120 (1.400840) | 2.346838 / 1.541195 (0.805644) | 2.373688 / 1.468490 (0.905198) | 0.829917 / 4.584777 (-3.754860) | 4.851283 / 3.745712 (1.105571) | 4.220103 / 5.269862 (-1.049758) | 2.706139 / 4.565676 (-1.859538) | 0.094095 / 0.424275 (-0.330180) | 0.008201 / 0.007607 (0.000594) | 0.699099 / 0.226044 (0.473054) | 7.046940 / 2.268929 (4.778011) | 3.374837 / 55.444624 (-52.069788) | 2.690839 / 6.876477 (-4.185638) | 2.845717 / 2.142072 (0.703645) | 0.989698 / 4.805227 (-3.815529) | 0.190413 / 6.500664 (-6.310251) | 0.066233 / 0.075469 (-0.009236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.513607 / 1.841788 (-0.328180) | 21.544200 / 8.074308 (13.469892) | 20.297337 / 10.191392 (10.105945) | 0.216390 / 0.680424 (-0.464034) | 0.029962 / 0.534201 (-0.504239) | 0.451531 / 0.579283 (-0.127752) | 0.530147 / 0.434364 (0.095783) | 0.520739 / 0.540337 (-0.019598) | 0.716431 / 1.386936 (-0.670505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fcaa9f218ad1505bb5474060889b4b9578e24423 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006509 / 0.011353 (-0.004844) | 0.003987 / 0.011008 (-0.007022) | 0.085233 / 0.038508 (0.046725) | 0.077765 / 0.023109 (0.054656) | 0.310467 / 0.275898 (0.034569) | 0.343363 / 0.323480 (0.019883) | 0.005557 / 0.007986 (-0.002429) | 0.003430 / 0.004328 (-0.000898) | 0.064948 / 0.004250 (0.060697) | 0.056864 / 0.037052 (0.019812) | 0.314005 / 0.258489 (0.055516) | 0.360638 / 0.293841 (0.066798) | 0.031134 / 0.128546 (-0.097412) | 0.008869 / 0.075646 (-0.066777) | 0.286409 / 0.419271 (-0.132862) | 0.051338 / 0.043533 (0.007805) | 0.311329 / 0.255139 (0.056190) | 0.334373 / 0.283200 (0.051174) | 0.024816 / 0.141683 (-0.116867) | 1.502872 / 1.452155 (0.050718) | 1.569941 / 1.492716 (0.077224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269639 / 0.018006 (0.251633) | 0.558510 / 0.000490 (0.558020) | 0.011748 / 0.000200 (0.011548) | 0.000234 / 0.000054 (0.000180) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029139 / 0.037411 (-0.008272) | 0.083586 / 0.014526 (0.069060) | 0.102426 / 0.176557 (-0.074131) | 0.162398 / 0.737135 (-0.574737) | 0.101364 / 0.296338 (-0.194975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382281 / 0.215209 (0.167072) | 3.826412 / 2.077655 (1.748758) | 1.815911 / 1.504120 (0.311791) | 1.644539 / 1.541195 (0.103344) | 1.688487 / 1.468490 (0.219996) | 0.482115 / 4.584777 (-4.102662) | 3.574773 / 3.745712 (-0.170939) | 3.262733 / 5.269862 (-2.007129) | 2.058115 / 4.565676 (-2.507562) | 0.056367 / 0.424275 (-0.367908) | 0.007233 / 0.007607 (-0.000374) | 0.456859 / 0.226044 (0.230815) | 4.565935 / 2.268929 (2.297006) | 2.311802 / 55.444624 (-53.132823) | 1.943936 / 6.876477 (-4.932541) | 2.129811 / 2.142072 (-0.012261) | 0.575098 / 4.805227 (-4.230129) | 0.130495 / 6.500664 (-6.370169) | 0.059757 / 0.075469 (-0.015712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238495 / 1.841788 (-0.603293) | 18.940000 / 8.074308 (10.865692) | 14.034240 / 10.191392 (3.842848) | 0.166418 / 0.680424 (-0.514006) | 0.018420 / 0.534201 (-0.515781) | 0.395330 / 0.579283 (-0.183953) | 0.413518 / 0.434364 (-0.020846) | 0.461499 / 0.540337 (-0.078838) | 0.661371 / 1.386936 (-0.725565) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006673 / 0.011353 (-0.004680) | 0.004335 / 0.011008 (-0.006673) | 0.064568 / 0.038508 (0.026060) | 0.072763 / 0.023109 (0.049653) | 0.429488 / 0.275898 (0.153590) | 0.456900 / 0.323480 (0.133420) | 0.005481 / 0.007986 (-0.002505) | 0.003649 / 0.004328 (-0.000680) | 0.064975 / 0.004250 (0.060724) | 0.056839 / 0.037052 (0.019786) | 0.439451 / 0.258489 (0.180962) | 0.461691 / 0.293841 (0.167850) | 0.031455 / 0.128546 (-0.097092) | 0.008848 / 0.075646 (-0.066798) | 0.071719 / 0.419271 (-0.347553) | 0.047116 / 0.043533 (0.003583) | 0.429055 / 0.255139 (0.173916) | 0.434204 / 0.283200 (0.151004) | 0.022594 / 0.141683 (-0.119089) | 1.539231 / 1.452155 (0.087077) | 1.568111 / 1.492716 (0.075394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267374 / 0.018006 (0.249368) | 0.553202 / 0.000490 (0.552712) | 0.005410 / 0.000200 (0.005210) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031478 / 0.037411 (-0.005933) | 0.092438 / 0.014526 (0.077912) | 0.103874 / 0.176557 (-0.072682) | 0.158428 / 0.737135 (-0.578708) | 0.111617 / 0.296338 (-0.184721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434783 / 0.215209 (0.219574) | 4.332536 / 2.077655 (2.254881) | 2.354522 / 1.504120 (0.850402) | 2.220271 / 1.541195 (0.679076) | 2.338524 / 1.468490 (0.870034) | 0.494508 / 4.584777 (-4.090269) | 3.619592 / 3.745712 (-0.126120) | 3.320897 / 5.269862 (-1.948964) | 2.107475 / 4.565676 (-2.458202) | 0.058479 / 0.424275 (-0.365796) | 0.007427 / 0.007607 (-0.000180) | 0.509298 / 0.226044 (0.283254) | 5.067940 / 2.268929 (2.799012) | 2.815336 / 55.444624 (-52.629288) | 2.470958 / 6.876477 (-4.405519) | 2.672801 / 2.142072 (0.530728) | 0.588199 / 4.805227 (-4.217028) | 0.134062 / 6.500664 (-6.366602) | 0.060951 / 0.075469 (-0.014518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353955 / 1.841788 (-0.487832) | 20.386012 / 8.074308 (12.311704) | 15.032463 / 10.191392 (4.841071) | 0.167243 / 0.680424 (-0.513181) | 0.020426 / 0.534201 (-0.513775) | 0.396815 / 0.579283 (-0.182469) | 0.421806 / 0.434364 (-0.012558) | 0.471866 / 0.540337 (-0.068471) | 0.667206 / 1.386936 (-0.719730) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aade5a0c79398c84632a3ff253111e694c7b598b \"CML watermark\")\n",
"Really happy to see this! It could also be helpful to track some other metadata about how the dataset was built in the future. i.e. for the Stack loaded like this:\r\n\r\n```\r\nds = load_dataset(\"bigcode/the-stack\", data_dir=\"data/dockerfile\", split=\"train\")\r\n```\r\nIt could be helpful to have easy access to the `data_dir` argument used during loading since that changes the training data quite a bit vs. loading the full dataset. You can also recover this from `download_checksums`, which seems a bit hacky. That is not necessary for this PR, though.\r\n",
"Perhaps it is also interesting to track the revision? I suppose the version also kind of covers that.\r\n\r\nThat said, this is looking great already! I'm quite excited about this. Losing the `repo_id` after merging (different) datasets also makes sense to me, well done.",
"One other thought. Is it worth tracking if a `token` was passed during loading? \r\n\r\nThe Hub ID for private datasets could in some cases contain information someone wouldn't want to make public i.e. `davanstrien/super_secret_dataset_using_GPT_created_data`. \r\n\r\nAdding a bool like `is_private` could then be used by another library to determine if the dataset ID should be shared or not (or default to not sharing the ID for private datasets). i.e. in SpanMarker @tomaarsen might do a check like \r\n\r\n```python\r\nif ds.is_private and not push_hub_id_for_private_ds:\r\n\tds_name = None\r\n```\r\nPotentially this is overkill but could be useful for downstream libraries who might use this information for creating automatic model cards. \r\n\r\n\r\n",
"We should probably find a way to remove `DatasetInfo`, as (most of) its attributes are outdated (homepage, description, etc.), not introduce new ones :). But I guess storing `repo_id` there is the simplest solution for now, so I'm OK with it.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007757 / 0.011353 (-0.003595) | 0.004543 / 0.011008 (-0.006465) | 0.100193 / 0.038508 (0.061685) | 0.082333 / 0.023109 (0.059224) | 0.374586 / 0.275898 (0.098688) | 0.412617 / 0.323480 (0.089137) | 0.006148 / 0.007986 (-0.001838) | 0.003826 / 0.004328 (-0.000503) | 0.077077 / 0.004250 (0.072827) | 0.064057 / 0.037052 (0.027005) | 0.391435 / 0.258489 (0.132946) | 0.436439 / 0.293841 (0.142599) | 0.036534 / 0.128546 (-0.092012) | 0.009986 / 0.075646 (-0.065660) | 0.344243 / 0.419271 (-0.075028) | 0.062013 / 0.043533 (0.018480) | 0.378113 / 0.255139 (0.122974) | 0.398476 / 0.283200 (0.115276) | 0.026552 / 0.141683 (-0.115131) | 1.740505 / 1.452155 (0.288350) | 1.835684 / 1.492716 (0.342968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267917 / 0.018006 (0.249911) | 0.510676 / 0.000490 (0.510186) | 0.010810 / 0.000200 (0.010610) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032113 / 0.037411 (-0.005299) | 0.097679 / 0.014526 (0.083153) | 0.113213 / 0.176557 (-0.063344) | 0.177897 / 0.737135 (-0.559238) | 0.111761 / 0.296338 (-0.184577) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450544 / 0.215209 (0.235335) | 4.476746 / 2.077655 (2.399091) | 2.205391 / 1.504120 (0.701271) | 2.006457 / 1.541195 (0.465262) | 2.058859 / 1.468490 (0.590369) | 0.571549 / 4.584777 (-4.013228) | 4.175039 / 3.745712 (0.429327) | 3.815445 / 5.269862 (-1.454416) | 2.376673 / 4.565676 (-2.189004) | 0.067048 / 0.424275 (-0.357227) | 0.008544 / 0.007607 (0.000937) | 0.536384 / 0.226044 (0.310340) | 5.386232 / 2.268929 (3.117304) | 2.825620 / 55.444624 (-52.619004) | 2.339821 / 6.876477 (-4.536656) | 2.535736 / 2.142072 (0.393663) | 0.679572 / 4.805227 (-4.125655) | 0.156799 / 6.500664 (-6.343865) | 0.071667 / 0.075469 (-0.003802) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512198 / 1.841788 (-0.329590) | 21.786760 / 8.074308 (13.712452) | 16.386274 / 10.191392 (6.194882) | 0.169108 / 0.680424 (-0.511316) | 0.021312 / 0.534201 (-0.512889) | 0.466153 / 0.579283 (-0.113130) | 0.496192 / 0.434364 (0.061829) | 0.549420 / 0.540337 (0.009082) | 0.780974 / 1.386936 (-0.605962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007644 / 0.011353 (-0.003709) | 0.004654 / 0.011008 (-0.006354) | 0.075280 / 0.038508 (0.036772) | 0.083044 / 0.023109 (0.059935) | 0.481704 / 0.275898 (0.205805) | 0.514828 / 0.323480 (0.191348) | 0.006245 / 0.007986 (-0.001740) | 0.003715 / 0.004328 (-0.000614) | 0.074498 / 0.004250 (0.070248) | 0.064406 / 0.037052 (0.027353) | 0.481874 / 0.258489 (0.223385) | 0.518527 / 0.293841 (0.224686) | 0.037549 / 0.128546 (-0.090997) | 0.010106 / 0.075646 (-0.065541) | 0.084266 / 0.419271 (-0.335006) | 0.056659 / 0.043533 (0.013126) | 0.497707 / 0.255139 (0.242568) | 0.503201 / 0.283200 (0.220001) | 0.027086 / 0.141683 (-0.114597) | 1.834715 / 1.452155 (0.382560) | 1.919927 / 1.492716 (0.427210) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249288 / 0.018006 (0.231282) | 0.500950 / 0.000490 (0.500460) | 0.005856 / 0.000200 (0.005656) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037674 / 0.037411 (0.000263) | 0.111141 / 0.014526 (0.096615) | 0.123408 / 0.176557 (-0.053149) | 0.186604 / 0.737135 (-0.550531) | 0.125360 / 0.296338 (-0.170979) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520480 / 0.215209 (0.305271) | 5.171108 / 2.077655 (3.093453) | 2.812746 / 1.504120 (1.308626) | 2.602941 / 1.541195 (1.061746) | 2.666196 / 1.468490 (1.197706) | 0.578684 / 4.584777 (-4.006092) | 4.238722 / 3.745712 (0.493010) | 3.844361 / 5.269862 (-1.425501) | 2.369214 / 4.565676 (-2.196462) | 0.068543 / 0.424275 (-0.355732) | 0.008695 / 0.007607 (0.001088) | 0.621869 / 0.226044 (0.395825) | 6.200566 / 2.268929 (3.931637) | 3.340846 / 55.444624 (-52.103779) | 2.920691 / 6.876477 (-3.955786) | 3.132438 / 2.142072 (0.990366) | 0.697394 / 4.805227 (-4.107834) | 0.158385 / 6.500664 (-6.342280) | 0.072566 / 0.075469 (-0.002903) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599070 / 1.841788 (-0.242717) | 22.767139 / 8.074308 (14.692831) | 17.053988 / 10.191392 (6.862596) | 0.188414 / 0.680424 (-0.492009) | 0.023409 / 0.534201 (-0.510792) | 0.472092 / 0.579283 (-0.107191) | 0.486107 / 0.434364 (0.051743) | 0.562190 / 0.540337 (0.021852) | 0.791606 / 1.386936 (-0.595330) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aacbaf45c93f88e8c95924f6224153fb37c3064a \"CML watermark\")\n"
] | 2023-09-29T10:24:55Z
| 2023-10-01T15:29:45Z
| null |
MEMBER
| null | 1
|
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```python
from datasets import load_dataset
ds = load_dataset("lhoestq/demo1", split="train")
ds = ds.map(lambda x: {}, num_proc=2).filter(lambda x: True).remove_columns(["id"])
print(ds.repo_id)
# lhoestq/demo1
```
- repo_id is None when the dataset doesn't come from the Hub, e.g. from Dataset.from_dict
- repo_id is set to None when concatenating datasets with different repo ids
related to https://github.com/huggingface/datasets/issues/4129
TODO:
- [ ] discuss if it's ok for now
- [ ] tests
|
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PR_kwDODunzps4wWL5U
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update `pretty_name` for first 200 datasets
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[] | 2021-12-28T19:50:07Z
| 2022-07-10T14:36:53Z
| 2022-01-05T16:38:21Z
|
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I made a script some time back to fetch `pretty_names` from `papers_with_code` dataset along with some other rules incase that dataset wasn't available on `papers_with_code`. Updating them in the `README` of `datasets`. Took only the first 200 datasets into consideration and after some eyeballing, most of them were looking good to me!
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FewRel
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[
"+1",
"@dspoka Please check the following link : https://github.com/thunlp/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.",
"Hi @lhoestq,\r\n\r\nThis issue can be closed, I guess.",
"Yes :) closing\r\nThanks again for adding FewRel !",
"Thanks for adding this @gchhablani ! Sorry didn't see the email notifications sooner!"
] | 2021-01-20T23:56:03Z
| 2021-03-09T02:52:05Z
| 2021-03-08T14:34:52Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** FewRel
- **Description:** Large-Scale Supervised Few-Shot Relation Classification Dataset
- **Paper:** @inproceedings{han2018fewrel,
title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},
author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},
booktitle={EMNLP},
year={2018}}
- **Data:** https://github.com/ProKil/FewRel
- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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I_kwDODunzps5t8QMf
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Dataset is slower after calling `to_iterable_dataset`
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[
"@lhoestq ",
"It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?"
] | 2023-08-10T06:36:23Z
| 2023-08-16T09:18:54Z
| null |
CONTRIBUTOR
| null | null | null |
### Describe the bug
Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset`
### Steps to reproduce the bug
Any dataset after converting to `IterableDataset`
### Expected behavior
Maybe it should be faster on big dataset? I only test on small dataset
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
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MDExOlB1bGxSZXF1ZXN0NDgxNTE2Mzkx
| 587
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Support pathlike obj in load dataset
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Fix #582
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PR_kwDODunzps5LefAf
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Bump pyarrow to 8.0.0
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009873 / 0.011353 (-0.001480) | 0.005180 / 0.011008 (-0.005828) | 0.099587 / 0.038508 (0.061079) | 0.035674 / 0.023109 (0.012565) | 0.299156 / 0.275898 (0.023258) | 0.361253 / 0.323480 (0.037773) | 0.008159 / 0.007986 (0.000173) | 0.004245 / 0.004328 (-0.000084) | 0.076809 / 0.004250 (0.072559) | 0.045251 / 0.037052 (0.008199) | 0.306002 / 0.258489 (0.047513) | 0.345758 / 0.293841 (0.051917) | 0.037826 / 0.128546 (-0.090721) | 0.011887 / 0.075646 (-0.063759) | 0.333804 / 0.419271 (-0.085467) | 0.047859 / 0.043533 (0.004326) | 0.291866 / 0.255139 (0.036727) | 0.319356 / 0.283200 (0.036157) | 0.104241 / 0.141683 (-0.037442) | 1.443816 / 1.452155 (-0.008338) | 1.514654 / 1.492716 (0.021938) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009846 / 0.018006 (-0.008160) | 0.439488 / 0.000490 (0.438999) | 0.003227 / 0.000200 (0.003028) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027553 / 0.037411 (-0.009858) | 0.105337 / 0.014526 (0.090811) | 0.116203 / 0.176557 (-0.060354) | 0.161140 / 0.737135 (-0.575995) | 0.123002 / 0.296338 (-0.173336) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400102 / 0.215209 (0.184893) | 3.976748 / 2.077655 (1.899094) | 1.794763 / 1.504120 (0.290643) | 1.602477 / 1.541195 (0.061282) | 1.703689 / 1.468490 (0.235199) | 0.696751 / 4.584777 (-3.888026) | 3.713832 / 3.745712 (-0.031880) | 2.124536 / 5.269862 (-3.145326) | 1.313005 / 4.565676 (-3.252671) | 0.086130 / 0.424275 (-0.338146) | 0.012085 / 0.007607 (0.004477) | 0.512976 / 0.226044 (0.286932) | 5.135313 / 2.268929 (2.866384) | 2.318173 / 55.444624 (-53.126451) | 1.996360 / 6.876477 (-4.880117) | 2.060150 / 2.142072 (-0.081922) | 0.853534 / 4.805227 (-3.951693) | 0.165586 / 6.500664 (-6.335078) | 0.062365 / 0.075469 (-0.013104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.178843 / 1.841788 (-0.662945) | 14.541639 / 8.074308 (6.467331) | 14.090782 / 10.191392 (3.899390) | 0.158717 / 0.680424 (-0.521707) | 0.028825 / 0.534201 (-0.505376) | 0.441427 / 0.579283 (-0.137856) | 0.439856 / 0.434364 (0.005492) | 0.530610 / 0.540337 (-0.009727) | 0.634044 / 1.386936 (-0.752892) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007502 / 0.011353 (-0.003851) | 0.005208 / 0.011008 (-0.005801) | 0.075020 / 0.038508 (0.036512) | 0.033297 / 0.023109 (0.010188) | 0.342218 / 0.275898 (0.066320) | 0.376716 / 0.323480 (0.053236) | 0.005906 / 0.007986 (-0.002080) | 0.005320 / 0.004328 (0.000992) | 0.073531 / 0.004250 (0.069281) | 0.049091 / 0.037052 (0.012039) | 0.344202 / 0.258489 (0.085713) | 0.380556 / 0.293841 (0.086715) | 0.037500 / 0.128546 (-0.091047) | 0.012404 / 0.075646 (-0.063242) | 0.087254 / 0.419271 (-0.332017) | 0.055145 / 0.043533 (0.011612) | 0.344112 / 0.255139 (0.088973) | 0.359052 / 0.283200 (0.075852) | 0.108337 / 0.141683 (-0.033345) | 1.450332 / 1.452155 (-0.001822) | 1.553607 / 1.492716 (0.060891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216335 / 0.018006 (0.198329) | 0.436813 / 0.000490 (0.436323) | 0.005055 / 0.000200 (0.004855) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030037 / 0.037411 (-0.007374) | 0.110854 / 0.014526 (0.096329) | 0.121967 / 0.176557 (-0.054589) | 0.174029 / 0.737135 (-0.563107) | 0.128340 / 0.296338 (-0.167998) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424463 / 0.215209 (0.209254) | 4.201822 / 2.077655 (2.124167) | 2.043075 / 1.504120 (0.538956) | 1.851841 / 1.541195 (0.310647) | 1.947790 / 1.468490 (0.479300) | 0.684110 / 4.584777 (-3.900667) | 3.763536 / 3.745712 (0.017824) | 3.106988 / 5.269862 (-2.162873) | 1.498305 / 4.565676 (-3.067372) | 0.085079 / 0.424275 (-0.339196) | 0.012241 / 0.007607 (0.004634) | 0.520877 / 0.226044 (0.294832) | 5.181455 / 2.268929 (2.912527) | 2.443038 / 55.444624 (-53.001586) | 2.130823 / 6.876477 (-4.745654) | 2.217901 / 2.142072 (0.075829) | 0.837116 / 4.805227 (-3.968111) | 0.166581 / 6.500664 (-6.334083) | 0.065510 / 0.075469 (-0.009959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289317 / 1.841788 (-0.552471) | 15.122019 / 8.074308 (7.047710) | 13.919670 / 10.191392 (3.728278) | 0.150047 / 0.680424 (-0.530377) | 0.017612 / 0.534201 (-0.516589) | 0.426239 / 0.579283 (-0.153044) | 0.425686 / 0.434364 (-0.008678) | 0.521436 / 0.540337 (-0.018901) | 0.618217 / 1.386936 (-0.768719) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#879fc6d5186ce593fe819f1e9e67897a1873766b \"CML watermark\")\n",
"We haven't updated the minimal version requirement for PyArrow in a while, so it's ok to make a bigger leap IMO, e.g., PyArrow 8.0 (Colab installs 9.0). With this change, we should also remove the PyArrow version check in `folder_based_builder.py`, and the ones in `table.py`/`arrow_dataset.py` regarding the `to_reader` API if we decide to bump PyArrow to version 8.0.",
"I think it's a good opportunity to bump the version to 8.0 which offers higher performance anyway, I wouldn't bother trying to support 6.0.1 anymore. Only 1% of users based on 6.0.1 use the latest `datasets` version 2.10.1\r\n\r\nBumping to 8.0 if it sounds good to you",
"Sure, it is OK for those other reasons. I would just not stress that the increase of the minimum version is to support pandas 2.0 though...",
"If requiring min 8.0, do you know the percentage of people using 7.0 and latest datasets version?",
"Around 10% of users have 7.0.0, and 25% among them use the latest datasets version",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006744 / 0.011353 (-0.004609) | 0.004585 / 0.011008 (-0.006423) | 0.097828 / 0.038508 (0.059320) | 0.028230 / 0.023109 (0.005121) | 0.302190 / 0.275898 (0.026292) | 0.335022 / 0.323480 (0.011542) | 0.005107 / 0.007986 (-0.002878) | 0.004648 / 0.004328 (0.000320) | 0.076842 / 0.004250 (0.072592) | 0.038291 / 0.037052 (0.001239) | 0.313286 / 0.258489 (0.054797) | 0.342534 / 0.293841 (0.048693) | 0.031325 / 0.128546 (-0.097221) | 0.011632 / 0.075646 (-0.064014) | 0.321879 / 0.419271 (-0.097392) | 0.042204 / 0.043533 (-0.001329) | 0.304442 / 0.255139 (0.049303) | 0.330912 / 0.283200 (0.047712) | 0.085446 / 0.141683 (-0.056237) | 1.469990 / 1.452155 (0.017835) | 1.551147 / 1.492716 (0.058431) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185961 / 0.018006 (0.167955) | 0.404675 / 0.000490 (0.404186) | 0.003212 / 0.000200 (0.003012) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023876 / 0.037411 (-0.013535) | 0.097820 / 0.014526 (0.083295) | 0.107382 / 0.176557 (-0.069174) | 0.167598 / 0.737135 (-0.569537) | 0.108789 / 0.296338 (-0.187550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455004 / 0.215209 (0.239795) | 4.529104 / 2.077655 (2.451449) | 2.180068 / 1.504120 (0.675948) | 1.982109 / 1.541195 (0.440914) | 2.041856 / 1.468490 (0.573366) | 0.702029 / 4.584777 (-3.882747) | 3.368613 / 3.745712 (-0.377099) | 1.932303 / 5.269862 (-3.337559) | 1.278340 / 4.565676 (-3.287336) | 0.082836 / 0.424275 (-0.341439) | 0.012349 / 0.007607 (0.004742) | 0.548197 / 0.226044 (0.322153) | 5.509982 / 2.268929 (3.241053) | 2.612889 / 55.444624 (-52.831736) | 2.278157 / 6.876477 (-4.598320) | 2.386923 / 2.142072 (0.244851) | 0.803332 / 4.805227 (-4.001896) | 0.151222 / 6.500664 (-6.349442) | 0.066673 / 0.075469 (-0.008796) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209453 / 1.841788 (-0.632335) | 13.649733 / 8.074308 (5.575424) | 14.065917 / 10.191392 (3.874525) | 0.128872 / 0.680424 (-0.551551) | 0.016773 / 0.534201 (-0.517428) | 0.385475 / 0.579283 (-0.193809) | 0.386208 / 0.434364 (-0.048156) | 0.475144 / 0.540337 (-0.065194) | 0.564183 / 1.386936 (-0.822753) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006629 / 0.011353 (-0.004724) | 0.004433 / 0.011008 (-0.006575) | 0.076008 / 0.038508 (0.037500) | 0.027471 / 0.023109 (0.004362) | 0.339837 / 0.275898 (0.063939) | 0.376857 / 0.323480 (0.053377) | 0.004930 / 0.007986 (-0.003055) | 0.003312 / 0.004328 (-0.001016) | 0.075070 / 0.004250 (0.070820) | 0.035897 / 0.037052 (-0.001156) | 0.342398 / 0.258489 (0.083909) | 0.380202 / 0.293841 (0.086361) | 0.031781 / 0.128546 (-0.096766) | 0.011697 / 0.075646 (-0.063950) | 0.085926 / 0.419271 (-0.333345) | 0.041599 / 0.043533 (-0.001934) | 0.343098 / 0.255139 (0.087959) | 0.371275 / 0.283200 (0.088076) | 0.090489 / 0.141683 (-0.051194) | 1.483738 / 1.452155 (0.031584) | 1.554973 / 1.492716 (0.062256) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183703 / 0.018006 (0.165697) | 0.395105 / 0.000490 (0.394616) | 0.002162 / 0.000200 (0.001963) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025432 / 0.037411 (-0.011979) | 0.101322 / 0.014526 (0.086796) | 0.107839 / 0.176557 (-0.068718) | 0.160328 / 0.737135 (-0.576807) | 0.109899 / 0.296338 (-0.186440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448001 / 0.215209 (0.232792) | 4.485321 / 2.077655 (2.407666) | 2.157064 / 1.504120 (0.652944) | 1.966141 / 1.541195 (0.424947) | 2.032808 / 1.468490 (0.564318) | 0.705684 / 4.584777 (-3.879093) | 3.359802 / 3.745712 (-0.385910) | 2.694952 / 5.269862 (-2.574910) | 1.471309 / 4.565676 (-3.094368) | 0.084185 / 0.424275 (-0.340090) | 0.012330 / 0.007607 (0.004723) | 0.554083 / 0.226044 (0.328038) | 5.569137 / 2.268929 (3.300208) | 2.586009 / 55.444624 (-52.858615) | 2.234920 / 6.876477 (-4.641557) | 2.285128 / 2.142072 (0.143056) | 0.818825 / 4.805227 (-3.986402) | 0.152604 / 6.500664 (-6.348060) | 0.067722 / 0.075469 (-0.007747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305571 / 1.841788 (-0.536217) | 13.687471 / 8.074308 (5.613163) | 13.305401 / 10.191392 (3.114009) | 0.140477 / 0.680424 (-0.539947) | 0.018138 / 0.534201 (-0.516063) | 0.377255 / 0.579283 (-0.202028) | 0.379522 / 0.434364 (-0.054842) | 0.458489 / 0.540337 (-0.081849) | 0.543767 / 1.386936 (-0.843169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02570894db6ecc46bf25b7fa1cb1bcdc1dede853 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009606 / 0.011353 (-0.001747) | 0.006795 / 0.011008 (-0.004213) | 0.133738 / 0.038508 (0.095230) | 0.043379 / 0.023109 (0.020270) | 0.412917 / 0.275898 (0.137019) | 0.418790 / 0.323480 (0.095310) | 0.007290 / 0.007986 (-0.000696) | 0.004960 / 0.004328 (0.000632) | 0.095496 / 0.004250 (0.091246) | 0.057607 / 0.037052 (0.020555) | 0.402638 / 0.258489 (0.144149) | 0.436206 / 0.293841 (0.142365) | 0.056023 / 0.128546 (-0.072523) | 0.019909 / 0.075646 (-0.055737) | 0.463958 / 0.419271 (0.044687) | 0.064073 / 0.043533 (0.020541) | 0.398337 / 0.255139 (0.143198) | 0.421786 / 0.283200 (0.138586) | 0.131563 / 0.141683 (-0.010120) | 1.840217 / 1.452155 (0.388063) | 1.912013 / 1.492716 (0.419296) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230519 / 0.018006 (0.212513) | 0.550506 / 0.000490 (0.550017) | 0.003649 / 0.000200 (0.003449) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029713 / 0.037411 (-0.007698) | 0.129913 / 0.014526 (0.115387) | 0.131543 / 0.176557 (-0.045013) | 0.203571 / 0.737135 (-0.533565) | 0.141483 / 0.296338 (-0.154856) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626383 / 0.215209 (0.411174) | 6.193043 / 2.077655 (4.115388) | 2.442728 / 1.504120 (0.938608) | 2.079049 / 1.541195 (0.537855) | 2.117761 / 1.468490 (0.649271) | 1.315296 / 4.584777 (-3.269481) | 5.643709 / 3.745712 (1.897997) | 5.245789 / 5.269862 (-0.024073) | 2.757442 / 4.565676 (-1.808235) | 0.151655 / 0.424275 (-0.272620) | 0.014686 / 0.007607 (0.007079) | 0.779937 / 0.226044 (0.553893) | 7.796685 / 2.268929 (5.527756) | 3.349580 / 55.444624 (-52.095045) | 2.493750 / 6.876477 (-4.382727) | 2.506200 / 2.142072 (0.364128) | 1.534964 / 4.805227 (-3.270263) | 0.260001 / 6.500664 (-6.240663) | 0.080543 / 0.075469 (0.005074) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541940 / 1.841788 (-0.299848) | 17.851935 / 8.074308 (9.777627) | 22.418859 / 10.191392 (12.227467) | 0.258602 / 0.680424 (-0.421822) | 0.027679 / 0.534201 (-0.506522) | 0.548379 / 0.579283 (-0.030904) | 0.625505 / 0.434364 (0.191141) | 0.664074 / 0.540337 (0.123737) | 0.797418 / 1.386936 (-0.589518) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009800 / 0.011353 (-0.001553) | 0.006178 / 0.011008 (-0.004830) | 0.105667 / 0.038508 (0.067159) | 0.039380 / 0.023109 (0.016271) | 0.419528 / 0.275898 (0.143630) | 0.469857 / 0.323480 (0.146377) | 0.006672 / 0.007986 (-0.001314) | 0.004745 / 0.004328 (0.000417) | 0.101647 / 0.004250 (0.097397) | 0.048531 / 0.037052 (0.011478) | 0.433364 / 0.258489 (0.174875) | 0.459719 / 0.293841 (0.165878) | 0.054291 / 0.128546 (-0.074256) | 0.020406 / 0.075646 (-0.055240) | 0.122321 / 0.419271 (-0.296951) | 0.059719 / 0.043533 (0.016186) | 0.416083 / 0.255139 (0.160944) | 0.455277 / 0.283200 (0.172077) | 0.119342 / 0.141683 (-0.022341) | 1.862544 / 1.452155 (0.410390) | 2.001428 / 1.492716 (0.508712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240951 / 0.018006 (0.222945) | 0.516958 / 0.000490 (0.516468) | 0.000449 / 0.000200 (0.000249) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032725 / 0.037411 (-0.004686) | 0.130291 / 0.014526 (0.115765) | 0.139834 / 0.176557 (-0.036723) | 0.214995 / 0.737135 (-0.522140) | 0.150925 / 0.296338 (-0.145414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.652062 / 0.215209 (0.436853) | 6.584447 / 2.077655 (4.506793) | 2.654838 / 1.504120 (1.150718) | 2.297209 / 1.541195 (0.756015) | 2.420394 / 1.468490 (0.951904) | 1.299285 / 4.584777 (-3.285492) | 5.605849 / 3.745712 (1.860137) | 3.166103 / 5.269862 (-2.103759) | 2.138123 / 4.565676 (-2.427554) | 0.152562 / 0.424275 (-0.271713) | 0.015499 / 0.007607 (0.007892) | 0.816300 / 0.226044 (0.590256) | 8.308746 / 2.268929 (6.039817) | 3.482982 / 55.444624 (-51.961642) | 2.689247 / 6.876477 (-4.187229) | 2.792728 / 2.142072 (0.650656) | 1.566320 / 4.805227 (-3.238907) | 0.264110 / 6.500664 (-6.236554) | 0.083652 / 0.075469 (0.008183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.643027 / 1.841788 (-0.198760) | 18.612349 / 8.074308 (10.538041) | 19.460644 / 10.191392 (9.269252) | 0.260795 / 0.680424 (-0.419629) | 0.026050 / 0.534201 (-0.508151) | 0.539750 / 0.579283 (-0.039533) | 0.620791 / 0.434364 (0.186428) | 0.645023 / 0.540337 (0.104686) | 0.765604 / 1.386936 (-0.621332) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e6dcf4c50e14ee6dbc6d763ed1b7ce3501460863 \"CML watermark\")\n",
"ready for re-review :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006388 / 0.011353 (-0.004965) | 0.004469 / 0.011008 (-0.006540) | 0.097082 / 0.038508 (0.058573) | 0.028005 / 0.023109 (0.004895) | 0.364797 / 0.275898 (0.088899) | 0.399671 / 0.323480 (0.076191) | 0.005062 / 0.007986 (-0.002923) | 0.004580 / 0.004328 (0.000252) | 0.075670 / 0.004250 (0.071420) | 0.038328 / 0.037052 (0.001276) | 0.365948 / 0.258489 (0.107459) | 0.402631 / 0.293841 (0.108790) | 0.031378 / 0.128546 (-0.097168) | 0.011443 / 0.075646 (-0.064203) | 0.321590 / 0.419271 (-0.097682) | 0.042263 / 0.043533 (-0.001270) | 0.368238 / 0.255139 (0.113099) | 0.389928 / 0.283200 (0.106728) | 0.085203 / 0.141683 (-0.056480) | 1.462820 / 1.452155 (0.010665) | 1.529207 / 1.492716 (0.036490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197194 / 0.018006 (0.179188) | 0.410897 / 0.000490 (0.410407) | 0.003394 / 0.000200 (0.003194) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022911 / 0.037411 (-0.014500) | 0.097012 / 0.014526 (0.082486) | 0.102247 / 0.176557 (-0.074309) | 0.163363 / 0.737135 (-0.573772) | 0.106897 / 0.296338 (-0.189441) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416303 / 0.215209 (0.201094) | 4.159325 / 2.077655 (2.081671) | 1.844893 / 1.504120 (0.340773) | 1.646131 / 1.541195 (0.104936) | 1.706763 / 1.468490 (0.238273) | 0.699607 / 4.584777 (-3.885170) | 3.462048 / 3.745712 (-0.283664) | 1.939076 / 5.269862 (-3.330786) | 1.324744 / 4.565676 (-3.240932) | 0.082949 / 0.424275 (-0.341326) | 0.012327 / 0.007607 (0.004720) | 0.513812 / 0.226044 (0.287768) | 5.171021 / 2.268929 (2.902093) | 2.288039 / 55.444624 (-53.156585) | 1.957403 / 6.876477 (-4.919074) | 1.990060 / 2.142072 (-0.152013) | 0.805571 / 4.805227 (-3.999656) | 0.152641 / 6.500664 (-6.348023) | 0.068169 / 0.075469 (-0.007300) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.200624 / 1.841788 (-0.641164) | 13.836334 / 8.074308 (5.762026) | 14.065340 / 10.191392 (3.873948) | 0.143406 / 0.680424 (-0.537018) | 0.016709 / 0.534201 (-0.517492) | 0.380080 / 0.579283 (-0.199204) | 0.398414 / 0.434364 (-0.035950) | 0.479192 / 0.540337 (-0.061145) | 0.572508 / 1.386936 (-0.814428) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006622 / 0.011353 (-0.004731) | 0.004511 / 0.011008 (-0.006497) | 0.076454 / 0.038508 (0.037946) | 0.027431 / 0.023109 (0.004322) | 0.339041 / 0.275898 (0.063143) | 0.375691 / 0.323480 (0.052211) | 0.004854 / 0.007986 (-0.003131) | 0.004654 / 0.004328 (0.000325) | 0.075300 / 0.004250 (0.071049) | 0.036469 / 0.037052 (-0.000583) | 0.341357 / 0.258489 (0.082868) | 0.381561 / 0.293841 (0.087720) | 0.031754 / 0.128546 (-0.096792) | 0.011544 / 0.075646 (-0.064102) | 0.085956 / 0.419271 (-0.333315) | 0.041704 / 0.043533 (-0.001828) | 0.340088 / 0.255139 (0.084950) | 0.364037 / 0.283200 (0.080838) | 0.091016 / 0.141683 (-0.050667) | 1.483515 / 1.452155 (0.031360) | 1.562878 / 1.492716 (0.070162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228019 / 0.018006 (0.210013) | 0.404809 / 0.000490 (0.404320) | 0.000384 / 0.000200 (0.000184) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025230 / 0.037411 (-0.012181) | 0.099790 / 0.014526 (0.085264) | 0.107923 / 0.176557 (-0.068634) | 0.157651 / 0.737135 (-0.579484) | 0.112525 / 0.296338 (-0.183813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440360 / 0.215209 (0.225151) | 4.387749 / 2.077655 (2.310094) | 2.077592 / 1.504120 (0.573472) | 1.872532 / 1.541195 (0.331337) | 1.941607 / 1.468490 (0.473117) | 0.699394 / 4.584777 (-3.885383) | 3.411210 / 3.745712 (-0.334502) | 1.901816 / 5.269862 (-3.368046) | 1.177042 / 4.565676 (-3.388634) | 0.083536 / 0.424275 (-0.340739) | 0.012418 / 0.007607 (0.004811) | 0.548463 / 0.226044 (0.322419) | 5.487107 / 2.268929 (3.218178) | 2.548076 / 55.444624 (-52.896548) | 2.215012 / 6.876477 (-4.661465) | 2.253472 / 2.142072 (0.111400) | 0.812925 / 4.805227 (-3.992302) | 0.152935 / 6.500664 (-6.347729) | 0.068144 / 0.075469 (-0.007325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267914 / 1.841788 (-0.573873) | 14.015185 / 8.074308 (5.940877) | 13.153967 / 10.191392 (2.962575) | 0.140666 / 0.680424 (-0.539758) | 0.016718 / 0.534201 (-0.517483) | 0.383411 / 0.579283 (-0.195872) | 0.395424 / 0.434364 (-0.038940) | 0.466069 / 0.540337 (-0.074269) | 0.553825 / 1.386936 (-0.833111) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14568bf072b38e3b295f29774c874c8e78b9fe37 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007463 / 0.011353 (-0.003890) | 0.005017 / 0.011008 (-0.005991) | 0.098777 / 0.038508 (0.060269) | 0.033859 / 0.023109 (0.010750) | 0.298569 / 0.275898 (0.022670) | 0.343717 / 0.323480 (0.020237) | 0.005806 / 0.007986 (-0.002180) | 0.005403 / 0.004328 (0.001074) | 0.075840 / 0.004250 (0.071590) | 0.046539 / 0.037052 (0.009487) | 0.300058 / 0.258489 (0.041569) | 0.345036 / 0.293841 (0.051195) | 0.036258 / 0.128546 (-0.092288) | 0.011992 / 0.075646 (-0.063654) | 0.334986 / 0.419271 (-0.084286) | 0.050427 / 0.043533 (0.006894) | 0.295319 / 0.255139 (0.040180) | 0.318980 / 0.283200 (0.035780) | 0.098407 / 0.141683 (-0.043276) | 1.437626 / 1.452155 (-0.014529) | 1.562548 / 1.492716 (0.069832) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231502 / 0.018006 (0.213496) | 0.441550 / 0.000490 (0.441060) | 0.005863 / 0.000200 (0.005663) | 0.000724 / 0.000054 (0.000670) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027501 / 0.037411 (-0.009911) | 0.111490 / 0.014526 (0.096964) | 0.117503 / 0.176557 (-0.059054) | 0.173849 / 0.737135 (-0.563286) | 0.124521 / 0.296338 (-0.171818) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419266 / 0.215209 (0.204057) | 4.170337 / 2.077655 (2.092683) | 2.015883 / 1.504120 (0.511763) | 1.832683 / 1.541195 (0.291488) | 1.950195 / 1.468490 (0.481705) | 0.698150 / 4.584777 (-3.886627) | 3.775601 / 3.745712 (0.029889) | 2.094581 / 5.269862 (-3.175281) | 1.325437 / 4.565676 (-3.240240) | 0.085382 / 0.424275 (-0.338894) | 0.012151 / 0.007607 (0.004544) | 0.526441 / 0.226044 (0.300397) | 5.256124 / 2.268929 (2.987196) | 2.488408 / 55.444624 (-52.956216) | 2.157228 / 6.876477 (-4.719249) | 2.228991 / 2.142072 (0.086919) | 0.837002 / 4.805227 (-3.968225) | 0.167520 / 6.500664 (-6.333144) | 0.066435 / 0.075469 (-0.009035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.174544 / 1.841788 (-0.667243) | 14.684207 / 8.074308 (6.609899) | 14.494676 / 10.191392 (4.303284) | 0.143423 / 0.680424 (-0.537001) | 0.017289 / 0.534201 (-0.516912) | 0.424727 / 0.579283 (-0.154556) | 0.417077 / 0.434364 (-0.017287) | 0.498955 / 0.540337 (-0.041383) | 0.584838 / 1.386936 (-0.802098) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007666 / 0.011353 (-0.003687) | 0.005269 / 0.011008 (-0.005739) | 0.073548 / 0.038508 (0.035040) | 0.033683 / 0.023109 (0.010573) | 0.342646 / 0.275898 (0.066747) | 0.380948 / 0.323480 (0.057468) | 0.005737 / 0.007986 (-0.002248) | 0.005366 / 0.004328 (0.001038) | 0.073228 / 0.004250 (0.068978) | 0.050065 / 0.037052 (0.013013) | 0.348593 / 0.258489 (0.090104) | 0.393930 / 0.293841 (0.100089) | 0.037411 / 0.128546 (-0.091135) | 0.012476 / 0.075646 (-0.063170) | 0.084884 / 0.419271 (-0.334387) | 0.049368 / 0.043533 (0.005835) | 0.343142 / 0.255139 (0.088003) | 0.362828 / 0.283200 (0.079628) | 0.102962 / 0.141683 (-0.038721) | 1.505703 / 1.452155 (0.053549) | 1.580695 / 1.492716 (0.087979) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207621 / 0.018006 (0.189615) | 0.437678 / 0.000490 (0.437188) | 0.003931 / 0.000200 (0.003731) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029079 / 0.037411 (-0.008332) | 0.108600 / 0.014526 (0.094074) | 0.124787 / 0.176557 (-0.051770) | 0.173354 / 0.737135 (-0.563781) | 0.126124 / 0.296338 (-0.170214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427911 / 0.215209 (0.212702) | 4.254227 / 2.077655 (2.176572) | 2.052142 / 1.504120 (0.548022) | 1.857042 / 1.541195 (0.315848) | 1.965244 / 1.468490 (0.496754) | 0.707994 / 4.584777 (-3.876783) | 3.807593 / 3.745712 (0.061880) | 3.387588 / 5.269862 (-1.882274) | 1.844853 / 4.565676 (-2.720824) | 0.088548 / 0.424275 (-0.335727) | 0.012398 / 0.007607 (0.004791) | 0.565896 / 0.226044 (0.339851) | 5.228024 / 2.268929 (2.959095) | 2.467220 / 55.444624 (-52.977405) | 2.144413 / 6.876477 (-4.732064) | 2.214049 / 2.142072 (0.071977) | 0.869381 / 4.805227 (-3.935846) | 0.170991 / 6.500664 (-6.329673) | 0.064932 / 0.075469 (-0.010537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246661 / 1.841788 (-0.595127) | 14.902743 / 8.074308 (6.828435) | 13.264294 / 10.191392 (3.072902) | 0.165328 / 0.680424 (-0.515095) | 0.017567 / 0.534201 (-0.516634) | 0.425491 / 0.579283 (-0.153792) | 0.427327 / 0.434364 (-0.007037) | 0.526475 / 0.540337 (-0.013862) | 0.627309 / 1.386936 (-0.759627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd31bce76b554447bccb2b1447440e1f8ddba035 \"CML watermark\")\n"
] | 2023-03-07T13:31:53Z
| 2023-03-08T14:01:27Z
| 2023-03-08T13:54:22Z
|
MEMBER
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Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()`
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Incorrect Russian filenames encoding after extraction by datasets.DownloadManager.download_and_extract()
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"To reproduce:\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.get_dataset_split_names('MalakhovIlya/RuREBus', config_name='raw_txt')\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 280, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 101, in _split_generators\r\n decode_file_names(folder)\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/MalakhovIlya--RuREBus/21046f5f1a0cf91187d68c30918d78d934ec7113ec435e146776d4f28f12c4ed/RuREBus.py\", line 26, in decode_file_names\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/streaming.py\", line 66, in wrapper\r\n return function(*args, use_auth_token=use_auth_token, **kwargs)\r\nTypeError: xwalk() got an unexpected keyword argument 'topdown'\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 323, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 285, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nIt's not related to the dataset viewer. Maybe @albertvillanova or @lhoestq could help more on this issue.",
"Hi! This issue stems from the fact that `xwalk`, which is a streamable version of `os.walk`, doesn't support the `topdown` param due to `fsspec`'s `walk` also not supporting it, so fixing this issue could be tricky. \r\n\r\n@MalakhovIlyaPavlovich You can avoid the error by tweaking your data processing and not using this param. (and `Path.rename`, which also cannot be streamed) ",
"@mariosasko thank you for your reply. I couldn't reproduce error showed by @severo either on Ubuntu 20.04.3 LTS, Windows 10 and Google Colab environments. But trying to avoid using os.walk(topdown=False) and Path.rename(), In _split_generators I replaced\r\n```\r\ndef decode_file_names(folder):\r\n for root, dirs, files in os.walk(folder, topdown=False):\r\n root = Path(root)\r\n for file in files:\r\n old_name = root / Path(file)\r\n new_name = root / Path(\r\n file.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n for dir in dirs:\r\n old_name = root / Path(dir)\r\n new_name = root / Path(dir.encode('cp437').decode('cp866'))\r\n old_name.rename(new_name)\r\n\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\ndecode_file_names(folder)\r\n```\r\nby\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nif not is_url(zip_file):\r\n folder = extract(zip_file)\r\nelse:\r\n folder = None\r\n```\r\nand now everything works well except data viewer for \"raw_txt\" subset: dataset preview on hub shows \"No data.\". As far as I understand dl_manager.download returns original URL when we are calling datasets.get_dataset_split_names and my suspicions are that dataset viewer can do smth similar. I couldn't find information about how it works. I would be very grateful, if you could tell me how to fix this)",
"This is what I get when I try to stream the `raw_txt` subset:\r\n```python\r\n>>> dset = load_dataset(\"MalakhovIlya/RuREBus\", \"raw_txt\", split=\"raw_txt\", streaming=True)\r\n>>> next(iter(dset))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\nStopIteration\r\n```\r\nSo there is a bug in your script.",
"streaming=True helped me to find solution. I fixed\r\n```\r\ndef extract(zip_file_path):\r\n p = Path(zip_file_path)\r\n dest_dir = str(p.parent / 'extracted' / p.stem)\r\n os.makedirs(dest_dir, exist_ok=True)\r\n with zipfile.ZipFile(zip_file_path) as archive:\r\n for file_info in tqdm(archive.infolist(), desc='Extracting'):\r\n filename = file_info.filename.encode('cp437').decode('cp866')\r\n target = os.path.join(dest_dir, *filename.split('/'))\r\n os.makedirs(os.path.dirname(target), exist_ok=True)\r\n if not file_info.is_dir():\r\n with archive.open(file_info) as source, open(target, 'wb') as dest:\r\n shutil.copyfileobj(source, dest)\r\n return dest_dir\r\n\r\nzip_file = dl_manager.download(self._RAW_TXT_URLS)['raw_txt']\r\nfolder = extract(zip_file)\r\n```\r\nby \r\n```\r\nfolder = dl_manager.download_and_extract(self._RAW_TXT_URLS)['raw_txt']\r\npath = os.path.join(folder, 'MED_txt/unparsed_txt')\r\nfor root, dirs, files in os.walk(path):\r\n decoded_root_name = Path(root).name.encode('cp437').decode('cp866')\r\n```\r\n@mariosasko thank you for your help :)"
] | 2022-04-11T02:07:13Z
| 2022-04-19T03:15:46Z
| 2022-04-16T15:46:29Z
|
NONE
| null | null | null |
## Dataset viewer issue for 'MalakhovIlya/RuREBus'
**Link:** https://huggingface.co/datasets/MalakhovIlya/RuREBus
**Description**
Using os.walk(topdown=False) in DatasetBuilder causes following error:
Status code: 400
Exception: TypeError
Message: xwalk() got an unexpected keyword argument 'topdown'
Couldn't find where "xwalk" come from. How can I fix this?
Am I the one who added this dataset ? Yes
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Trouble loading files defined in YAML explicitly
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[
"There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ",
"wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?"
] | 2023-11-22T15:18:10Z
| 2023-11-23T09:06:20Z
| null |
CONTRIBUTOR
| null | null | null |
Look at https://huggingface.co/datasets/severo/doc-yaml-2
It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration
```
You can select multiple files per split using a list of paths:
my_dataset_repository/
├── README.md
├── data/
│ ├── abc.csv
│ └── def.csv
└── holdout/
└── ghi.csv
---
configs:
- config_name: default
data_files:
- split: train
path:
- "data/abc.csv"
- "data/def.csv"
- split: test
path: "holdout/ghi.csv"
---
```
It raises the following error:
```
Error code: ConfigNamesError
Exception: FileNotFoundError
Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip']
```
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For data-only datasets, streaming and non-streaming don't behave the same
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[
"Note that we might change the heuristic and create a different config per file, at least in that case.",
"Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting is not done while yielding item by item in streaming mode.\r\n\r\nMaybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?",
"Why do we want to concatenate the files? Is it the expected behavior for most datasets that lack a script and dataset info?",
"These files are two different dataset configurations since they don't share the same schema.\r\n\r\nIMO the streaming mode should fail in this case, as @albertvillanova said.\r\n\r\nThere is one challenge though: inferring the schema from the first example is not robust enough in the general case - especially if some fields are nullable. I guess we can at least make sure that no new columns are added",
"OK. So, if we make the streaming also fail, the dataset https://huggingface.co/datasets/huggingface/transformers-metadata will never be [viewable](https://github.com/huggingface/datasets-preview-backend/issues/144) (be it using streaming or fallback to downloading the files), right?\r\n",
"Yes, until we have a way for the user to specify explicitly that those two files are different configurations.\r\n\r\nWe can maybe have some rule to detect this automatically, maybe checking the first line of each file ? That would mean that for dataset of 10,000+ files we would have to verify every single one of them just to know if there is one ore more configurations, so I'm not sure if this is a good idea",
"i think requiring the user to specify that those two files are different configurations is in that case perfectly reasonable.\r\n\r\n(Maybe at some point we could however detect this type of case and prompt them to define a config mapping etc)",
"OK, so, before closing the issue, what do you think should be done?\r\n\r\n> Maybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?\r\n\r\nor nothing?",
"We should at least raise an error if a new sample has column names that are missing, or if it has extra columns. No need to check for the type for now.\r\n\r\nI'm in favor of having an error especially because we want to avoid silent issues as much as possible - i.e. when something goes wrong (when schemas don't match or some data are missing) and no errors/warnings are raised.\r\n\r\nConsistency between streaming and non-streaming is also important."
] | 2022-02-16T15:20:57Z
| 2022-02-21T14:24:55Z
| null |
CONTRIBUTOR
| null | null | null |
See https://huggingface.co/datasets/huggingface/transformers-metadata: it only contains two JSON files.
In streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:
```python
import datasets as ds
iterable_dataset = ds.load_dataset("huggingface/transformers-metadata", split="train", streaming=True);
rows = list(iterable_dataset.take(100))
rows[0]
# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}
rows[99]
# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}
```
In normal mode, an exception is thrown:
```python
import datasets as ds
dataset = ds.load_dataset("huggingface/transformers-metadata", split="train");
```
```
ValueError: Couldn't cast
model_class: string
pipeline_tag: string
auto_class: string
to
{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}
because column names don't match
```
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[
"Hi, thanks for reporting! I've opened a PR with the fix.",
"Wow, that was quick! Thank you very much 🙏 "
] | 2022-06-16T12:36:20Z
| 2022-06-20T08:18:46Z
| 2022-06-16T17:11:40Z
|
NONE
| null | null | null |
## Describe the bug
When loading image data, HF datasets seems to recognize `.jpg` and `.jpeg` file extensions, but not e.g. .JPEG. As the naming convention .JPEG is used in important datasets such as imagenet, I would welcome if according extensions like .JPEG or .JPG would be allowed.
## Steps to reproduce the bug
```python
# use bash to create 2 sham datasets with jpeg and JPEG ext
!mkdir dataset_a
!mkdir dataset_b
!wget https://upload.wikimedia.org/wikipedia/commons/7/71/Dsc_%28179253513%29.jpeg -O example_img.jpeg
!cp example_img.jpeg ./dataset_a/
!mv example_img.jpeg ./dataset_b/example_img.JPEG
from datasets import load_dataset
# working
df1 = load_dataset("./dataset_a", ignore_verifications=True)
#not working
df2 = load_dataset("./dataset_b", ignore_verifications=True)
# show
print(df1, df2)
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
}) DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 1
})
})
```
## Actual results
```
FileNotFoundError: Unable to resolve any data file that matches '['**']' at /..PATH../dataset_b with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
I know that it can be annoying to allow seemingly arbitrary numbers of file extensions. But I think this one would be really welcome.
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[
"Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.",
"Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path=\"json\", data_files={nlp.Split.TRAIN: [\"path/to/train.json\"], nlp.Split.TEST: [\"path/to/test.json\"]})\r\n```\r\n\r\nFor CSV/TSV datasets, you have to replace `json` by `csv`.",
"Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again."
] | 2020-06-18T09:47:27Z
| 2020-06-20T13:15:12Z
| 2020-06-20T13:15:12Z
|
CONTRIBUTOR
| null | null | null |
Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?
Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines.
Many thanks for your responses ahead of time and kind regards,
MFreidank
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[
"Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n name=datasets.Split.VALIDATION,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"filepath\": os.path.join(data_dir, \"dev.jsonl\"),\r\n \"split\": \"dev\",\r\n },\r\n),\r\n```\r\n\r\nNotice the `gen_kwargs` argument passed to the constructor of `SplitGenerator`: this dict will be unpacked as keyword arguments to pass to the `_generat_examples` method (in this case the `filepath` and `split` arguments).\r\n\r\nLet me know if that helps!",
"Oh ok I hadn't made the connection between those two, will offer a tweak to the comment and the template then - thanks!"
] | 2021-03-09T08:42:11Z
| 2021-03-12T15:01:34Z
| 2021-03-12T15:01:34Z
|
CONTRIBUTOR
| null | null | null |
https://github.com/huggingface/datasets/blob/2ac9a0d24a091989f869af55f9f6411b37ff5188/templates/new_dataset_script.py#L156-L158
Looking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.
Happy to push a PR with a clearer statement when I understand the meaning.
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Add OSCAR
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[
"Hi @lhoestq, on the OSCAR dataset, the document boundaries are defined by an empty line. Are there any chances to keep this empty line or explicitly group the sentences of a document? I'm asking for this 'cause I need to know if some sentences belong to the same document on my current OSCAR dataset usage.",
"Indeed currently it yields one example per line and ignore the empty lines.\r\nMaybe the best is to group them by paragraph then, and yield one example when an empty line is found.\r\nWhat do you think ?",
"I think to group them is the best choice indeed, I actually did this on [brwac](https://github.com/huggingface/datasets/tree/master/datasets/brwac) dataset too, it's another huge textual dataset.",
"Ok I just launched the computation of the dataset_infos.json again by grouping lines in paragraphs.\r\nThe new _generate_examples is\r\n```python\r\n def _generate_examples(self, filepaths):\r\n \"\"\"This function returns the examples in the raw (text) form.\"\"\"\r\n id_ = 0\r\n current_lines = []\r\n for filepath in filepaths:\r\n logging.info(\"generating examples from = %s\", filepath)\r\n with gzip.open(filepath, \"rt\") as f:\r\n for line in f:\r\n if len(line.strip()) > 0:\r\n current_lines.append(line)\r\n else:\r\n feature = id_, {\"id\": id_, \"text\": \"\".join(current_lines)}\r\n yield feature\r\n id_ += 1\r\n current_lines = []\r\n # last paragraph\r\n if current_lines:\r\n feature = id_, {\"id\": id_, \"text\": \"\".join(current_lines)}\r\n yield feature\r\n```",
"Is there any chance to also keep the sentences raw (without the `\"\".join()`)?. This is useful if you wanna train models where one of the tasks you perform is document sentence permutation... that's my case :)",
"They are raw in the sense that nothing is changed from the raw file for each paragraph.\r\nYou can split sentences on new lines `\\n` for example.\r\n\r\nThe first example for the unshuffled deduplicated english is going to be \r\n> Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visit to Malawi. Chief Napoleon conveyed the desperate need for a program to intervene and care for the orphans and vulnerable children (OVC) in Malawi, and John committed to help.\r\n> Established in honor of John & Lindy’s son, Christopher Blanchard, this particular program is very dear to the Blanchard family. Dana Blanchard, or Mama Dana as she is more commonly referred to at Mtendere, lived on site during the initial development, and she returns each summer to spend the season with her Malawian family. The heart of the program is to be His hands and feet by caring for the children at Mtendere, and meeting their spiritual, physical, academic, and emotional needs.\r\n> [...]\r\n> 100X Development Foundation, Inc. is registered 503 (c)(3) nonprofit organization. Donations are deductable to the full extent allowable under IRS regulations.",
"I thought the line reader would omit the `\\n` character. I can easily split the sentences as you suggested. Thanks @lhoestq! 😃 ",
"The recomputation of the metadata finished a few days ago, I'll update the PR soon :) ",
"Let me know if you have comments @pjox @jonatasgrosman :) \r\n\r\nOtherwise we can merge it",
"Everything seems fine to me 😄 "
] | 2021-01-06T10:21:08Z
| 2021-01-25T09:10:33Z
| 2021-01-25T09:10:32Z
|
MEMBER
| null | 0
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Continuation of #348
The files have been moved to S3 and only the unshuffled version is available.
Both original and deduplicated versions of each language are available.
Example of usage:
```python
from datasets import load_dataset
oscar_dedup_en = load_dataset("oscar", "unshuffled_deduplicated_en", split="train")
oscar_orig_fr = load_dataset("oscar", "unshuffled_original_fr", split="train")
```
cc @pjox @jonatasgrosman
-------------
To make the metadata generation work in parallel I did a few changes in the `datasets-cli test` command to add the `num_proc` and `proc_rank` arguments. This way you can run multiple processes for the metadata computation.
```
datasets-cli test ./datasets/oscar --save_infos --all_configs --num_proc 4 --proc_rank 0 --clear_cache --cache_dir tmp0
```
-------------
ToDo: add the dummy_data
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"Thanks for the PR @purvimisal. \r\n\r\nFew comments below. ",
"Hi, @lhoestq Thank you for the review. I have made all the changes. PTAL! ",
"the CI error is not related to your dataset, merging"
] | 2020-12-13T23:52:11Z
| 2020-12-21T14:38:20Z
| 2020-12-21T14:38:20Z
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This PR adds the Igbo NER dataset.
Data: https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner
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[
"Hi :) \r\nThe slicing API from https://huggingface.co/docs/datasets/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows",
"thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https://huggingface.co/docs/datasets/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n"
] | 2021-01-18T14:08:55Z
| 2022-10-05T12:37:27Z
| 2022-10-05T12:37:27Z
|
NONE
| null | null | null |
Hi
I need to slice a dataset with random seed, I looked into documentation here https://huggingface.co/docs/datasets/splits.html
I could not find a seed option, could you assist me please how I can get a slice for different seeds?
thank you.
@lhoestq
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Bug in xjoin with Windows pathnames
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| 2022-11-29T08:05:13Z
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MEMBER
| null | null | null |
Currently, `xjoin` function has a bug with local Windows pathnames: instead of returning the OS-dependent join pathname, it always returns it in POSIX format.
```python
from datasets.download.streaming_download_manager import xjoin
path = xjoin("C:\\Users\\USERNAME", "filename.txt")
```
Join path should be:
```python
"C:\\Users\\USERNAME\\filename.txt"
```
However it is:
```python
"C:/Users/USERNAME/filename.txt"
```
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Simple Wikipedia
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[
"uploaded dataset [here](https://huggingface.co/datasets/embedding-data/simple-wiki)."
] | 2022-07-07T02:51:26Z
| 2022-07-14T02:16:33Z
| 2022-07-14T02:16:33Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *Simple Wikipedia*
- **Description:** *Two different versions of the data set now exist. Both were generated by aligning Simple English Wikipedia and English Wikipedia. A complete description of the extraction process can be found in "Simple English Wikipedia: A New Simplification Task", William Coster and David Kauchak (2011).*
- **Paper:** *https://aclanthology.org/P11-2117/*
- **Data:** *https://huggingface.co/datasets/sentence-transformers/embedding-training-data/resolve/main/SimpleWiki.jsonl.gz*
- **Motivation:** *Dataset for training and evaluating models of conversational response*
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Fix: wikicorpus - fix keys
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[
"The CI error is unrelated to this PR\r\n\r\n... merging !"
] | 2021-08-27T15:56:06Z
| 2021-09-06T14:07:28Z
| 2021-09-06T14:07:27Z
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MEMBER
| null | 0
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As mentioned in https://github.com/huggingface/datasets/issues/2552, there is a duplicate keys error in `wikicorpus`.
I fixed that by taking into account the file index in the keys
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`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`
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[] | 2023-11-14T03:12:08Z
| 2023-11-16T06:24:10Z
| null |
NONE
| null | null | null |
### Describe the bug
IterableDataset lost but not keep columns when map function adding columns with names in remove_columns,
Dataset not.
May be related to the code below:
https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756
### Steps to reproduce the bug
```python
dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train")
column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected']
# map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx}
dataset = dataset.map(map_fn, batched=True, remove_columns=column_names)
next(iter(dataset))
# output
# {'prompt': 'xxx, 'history': xxx}
```
```python
# when load_dataset with streaming=False, the column_names are kept:
dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train")
column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected']
# map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx}
dataset = dataset.map(map_fn, batched=True, remove_columns=column_names)
next(iter(dataset))
# output
# {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx}
```
### Expected behavior
IterableDataset keep columns when map function adding columns with names in remove_columns
### Environment info
datasets==2.14.6
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added arxiv paper inswiss_judgment_prediction dataset card
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| 2021-10-08T16:01:44Z
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|
CONTRIBUTOR
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Handle empty audio
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[
"Hi! Can you share one of the problematic audio files with us?\r\n\r\nI tried to reproduce the error with the following code: \r\n```python\r\nimport soundfile as sf\r\nimport numpy as np\r\nfrom datasets import Audio\r\n\r\nsf.write(\"empty.wav\", np.array([]), 16000)\r\nAudio(sampling_rate=24000).decode_example({\"path\": \"empty.wav\", \"bytes\": None})\r\n```\r\nBut without success.\r\n\r\nAlso, what version of `librosa` is installed in your env? (You can get this info with `python -c \"import librosa; print(librosa.__version__)`)\r\n\r\n",
"I'm closing this issue as the reproducer hasn't been provided."
] | 2023-04-07T09:51:40Z
| 2023-09-27T17:47:08Z
| 2023-09-27T17:47:08Z
|
NONE
| null | null | null |
Some audio paths exist, but they are empty, and an error will be reported when reading the audio path.How to use the filter function to avoid the empty audio path?
when a audio is empty, when do resample , it will break:
`array, sampling_rate = sf.read(f) array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate)`
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"Hi @jacobbieker, thanks for your request and study of possible alternatives.\r\n\r\nWe are very interested in finding a way to make `datasets` useful to you.\r\n\r\nLooking at the Zarr docs, I saw that among its storage alternatives, there is the ZIP file format: https://zarr.readthedocs.io/en/stable/api/storage.html#zarr.storage.ZipStore\r\n\r\nThis might be convenient for many reasons:\r\n- On the one hand, we avoid the Git issue with huge number of small files: chunks files are compressed into a single ZIP file\r\n- On the other hand, the ZIP file format is specially suited for streaming data because it allows random access to its component files (i.e. it supports random access to its chunks)\r\n\r\nAnyway, I think that a Python loading script will be necessary: you need to implement additional logic to select certain chunks (based on date or other criteria).\r\n\r\nPlease, let me know if this makes sense to you.",
"Ah okay, I missed the option of zip files for zarr, I'll try that with our repos and see if it works! Thanks a lot!",
"Hi @jacobbieker, does the Zarr ZipStore work for your use case?",
"Hi,\r\n\r\nYes, it seems to! I got it working for https://huggingface.co/datasets/openclimatefix/mrms thanks for the help! ",
"On behalf of the Zarr developers, let me say THANK YOU for working to support Zarr on HF! 🙏 Zarr is a 100% open-source and community driven project (fiscally sponsored by NumFocus). We see it as an ideal format for ML training datasets, particularly in scientific domains.\r\n\r\nI think the solution of zipping the Zarr store is a reasonable way to balance the constraints of Git LFS with the structure of Zarr.\r\n\r\nIt would be amazing to get something on the [Hugging Face Datasets Docs](https://huggingface.co/docs/datasets/index) about how to best work with Zarr. Let me know if there's a way I could help with that effort.",
"Also just noting here that I was able to lazily open @jacobbieker's dataset over the internet from HF hub 🚀 !\r\n\r\n```python\r\nimport xarray as xr\r\nurl = \"https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\"\r\nzip_url = 'zip:///::' + url\r\nds = xr.open_dataset(zip_url, engine='zarr', chunks={})\r\n```\r\n\r\n<img width=\"740\" alt=\"image\" src=\"https://user-images.githubusercontent.com/1197350/164508663-bc75cdc0-734d-44f4-9562-2877ecfdf433.png\">\r\n",
"However, I wasn't able to get streaming working using the Datasets api:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"openclimatefix/mrms\", streaming=True, split='train')\r\nitem = next(iter(ds))\r\n```\r\n\r\n<details>\r\n<summary>FileNotFoundError traceback</summary>\r\n\r\n```\r\nNo config specified, defaulting to: mrms/2021\r\nzip://::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\r\ndata/2016_001.zarr.zip\r\nzip://2016_001.zarr.zip::https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip\r\n---------------------------------------------------------------------------\r\nFileNotFoundError Traceback (most recent call last)\r\nInput In [1], in <cell line: 3>()\r\n 1 from datasets import load_dataset\r\n 2 ds = load_dataset(\"openclimatefix/mrms\", streaming=True, split='train')\r\n----> 3 item = next(iter(ds))\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:497, in IterableDataset.__iter__(self)\r\n 496 def __iter__(self):\r\n--> 497 for key, example in self._iter():\r\n 498 if self.features:\r\n 499 # we encode the example for ClassLabel feature types for example\r\n 500 encoded_example = self.features.encode_example(example)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:494, in IterableDataset._iter(self)\r\n 492 else:\r\n 493 ex_iterable = self._ex_iterable\r\n--> 494 yield from ex_iterable\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/datasets/iterable_dataset.py:87, in ExamplesIterable.__iter__(self)\r\n 86 def __iter__(self):\r\n---> 87 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/openclimatefix--mrms/2a6f697014d7eb3caf586ca137d47ca38785ae2fe36248611b021f8248b59936/mrms.py:150, in MRMS._generate_examples(self, filepath, split)\r\n 147 filepath = \"[https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip](https://huggingface.co/datasets/openclimatefix/mrms/resolve/main/data/2016_001.zarr.zip%3C/span%3E%3Cspan) style=\"color:rgb(175,0,0)\">\"\r\n 148 # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.\r\n 149 # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.\r\n--> 150 with zarr.storage.FSStore(fsspec.open(\"zip::\" + filepath, mode='r'), mode='r') as store:\r\n 151 data = xr.open_zarr(store)\r\n 152 for key, row in enumerate(data[\"time\"].values):\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/zarr/storage.py:1120, in FSStore.__init__(self, url, normalize_keys, key_separator, mode, exceptions, dimension_separator, **storage_options)\r\n 1117 import fsspec\r\n 1118 self.normalize_keys = normalize_keys\r\n-> 1120 protocol, _ = fsspec.core.split_protocol(url)\r\n 1121 # set auto_mkdir to True for local file system\r\n 1122 if protocol in (None, \"file\") and not storage_options.get(\"auto_mkdir\"):\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:514, in split_protocol(urlpath)\r\n 512 def split_protocol(urlpath):\r\n 513 \"\"\"Return protocol, path pair\"\"\"\r\n--> 514 urlpath = stringify_path(urlpath)\r\n 515 if \"://\" in urlpath:\r\n 516 protocol, path = urlpath.split(\"://\", 1)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/utils.py:315, in stringify_path(filepath)\r\n 313 return filepath\r\n 314 elif hasattr(filepath, \"__fspath__\"):\r\n--> 315 return filepath.__fspath__()\r\n 316 elif isinstance(filepath, pathlib.Path):\r\n 317 return str(filepath)\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:98, in OpenFile.__fspath__(self)\r\n 96 def __fspath__(self):\r\n 97 # may raise if cannot be resolved to local file\r\n---> 98 return self.open().__fspath__()\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self)\r\n 132 def open(self):\r\n 133 \"\"\"Materialise this as a real open file without context\r\n 134 \r\n 135 The file should be explicitly closed to avoid enclosed file\r\n (...)\r\n 138 been deleted; but a with-context is better style.\r\n 139 \"\"\"\r\n--> 140 out = self.__enter__()\r\n 141 closer = out.close\r\n 142 fobjects = self.fobjects.copy()[:-1]\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1007 else:\r\n 1008 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1009 f = self._open(\r\n 1010 path,\r\n 1011 mode=mode,\r\n 1012 block_size=block_size,\r\n 1013 autocommit=ac,\r\n 1014 cache_options=cache_options,\r\n 1015 **kwargs,\r\n 1016 )\r\n 1017 if compression is not None:\r\n 1018 from fsspec.compression import compr\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/implementations/zip.py:96, in ZipFileSystem._open(self, path, mode, block_size, autocommit, cache_options, **kwargs)\r\n 94 if mode != \"rb\":\r\n 95 raise NotImplementedError\r\n---> 96 info = self.info(path)\r\n 97 out = self.zip.open(path, \"r\")\r\n 98 out.size = info[\"size\"]\r\n\r\nFile /opt/miniconda3/envs/hugginface/lib/python3.9/site-packages/fsspec/archive.py:42, in AbstractArchiveFileSystem.info(self, path, **kwargs)\r\n 40 return self.dir_cache[path + \"/\"]\r\n 41 else:\r\n---> 42 raise FileNotFoundError(path)\r\n\r\nFileNotFoundError:\r\n```\r\n\r\n</details>\r\n\r\nIs this a bug? Or am I just doing it wrong...",
"I'm still messing around with that dataset, so the data might have moved. I currently have each year of MRMS precipitation rate data as it's own zarr, but as they are quite large (on order of 100GB each) I'm working to split them into single days, and as such they are still being moved around, I was just trying to get a proof of concept working originally. ",
"I've mostly finished rearranging the data now and uploading some more, so this works now:\r\n```python\r\nimport datasets\r\nds = datasets.load_dataset(\"openclimatefix/mrms\", streaming=True, split=\"train\")\r\nitem = next(iter(ds))\r\nprint(item.keys())\r\nprint(item[\"timestamp\"])\r\n```\r\n\r\nThe MRMS data now goes most of 2016-2022, with quite a few gaps I'm working on filling in",
"Hi @albertvillanova, I noticed there is now the [HFFileSystem](https://huggingface.co/docs/huggingface_hub/main/en/guides/hf_file_system), where the docs show an example of writing a Zarr store directly to the Hub, and no mention of having too many files. Is there still the restriction on lots of files in `datasets`? It would be more convenient to be able to have the geospatial data in one large Zarr store, rather than in multiple smaller ones, but happy to continue using zipped Zarrs if thats the recommended way.",
"Hi @jacobbieker.\r\n\r\nThanks for coming back to this pending issue. \r\n\r\nIn fact, we are now using the `fsspec` API in our `HFFileSystem`, which was not the case when you created this issue.\r\nOn the other hand, I am not sure of the current limitations, both in terms of the number of files or performance when loading.\r\n- If I remember correctly, I think there is a limit in the maximum number of files per directory: 10k\r\n\r\nI think it would be best to try a POC again and discuss any issues that arise and whether we can fix them on our end (both `datasets` and the Hub).\r\nWe would really like to support the Zarr format 100% and that the Hub is really convenient for your use case. So do not hesitate to report any problem: you can ping me on the Hub as @albertvillanova"
] | 2022-04-05T13:38:32Z
| 2023-12-07T09:01:49Z
| 2022-04-21T08:12:58Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily.
**Describe the solution you'd like**
A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec.
**Describe alternatives you've considered**
Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users.
Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
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"It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005357 / 0.011353 (-0.005996) | 0.003295 / 0.011008 (-0.007713) | 0.062354 / 0.038508 (0.023846) | 0.054207 / 0.023109 (0.031098) | 0.240030 / 0.275898 (-0.035869) | 0.267863 / 0.323480 (-0.055617) | 0.002925 / 0.007986 (-0.005061) | 0.002634 / 0.004328 (-0.001695) | 0.047952 / 0.004250 (0.043702) | 0.038424 / 0.037052 (0.001372) | 0.248059 / 0.258489 (-0.010430) | 0.271923 / 0.293841 (-0.021918) | 0.027513 / 0.128546 (-0.101034) | 0.010344 / 0.075646 (-0.065302) | 0.210864 / 0.419271 (-0.208407) | 0.035911 / 0.043533 (-0.007622) | 0.245166 / 0.255139 (-0.009973) | 0.260914 / 0.283200 (-0.022285) | 0.016709 / 0.141683 (-0.124974) | 1.098324 / 1.452155 (-0.353830) | 1.162638 / 1.492716 (-0.330079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094419 / 0.018006 (0.076413) | 0.303209 / 0.000490 (0.302719) | 0.000214 / 0.000200 (0.000014) | 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) | 0.018350 / 0.037411 (-0.019061) | 0.060625 / 0.014526 (0.046099) | 0.072545 / 0.176557 (-0.104012) | 0.120905 / 0.737135 (-0.616231) | 0.073858 / 0.296338 (-0.222480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282011 / 0.215209 (0.066802) | 2.758741 / 2.077655 (0.681086) | 1.431691 / 1.504120 (-0.072429) | 1.315883 / 1.541195 (-0.225312) | 1.344235 / 1.468490 (-0.124255) | 0.562117 / 4.584777 (-4.022660) | 2.385641 / 3.745712 (-1.360071) | 2.785402 / 5.269862 (-2.484460) | 1.753912 / 4.565676 (-2.811764) | 0.064054 / 0.424275 (-0.360221) | 0.005050 / 0.007607 (-0.002557) | 0.336452 / 0.226044 (0.110407) | 3.302481 / 2.268929 (1.033553) | 1.794105 / 55.444624 (-53.650519) | 1.519346 / 6.876477 (-5.357131) | 1.514911 / 2.142072 (-0.627161) | 0.655779 / 4.805227 (-4.149449) | 0.117913 / 6.500664 (-6.382751) | 0.042229 / 0.075469 (-0.033240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935196 / 1.841788 (-0.906591) | 11.490113 / 8.074308 (3.415805) | 10.542446 / 10.191392 (0.351054) | 0.129614 / 0.680424 (-0.550810) | 0.014919 / 0.534201 (-0.519282) | 0.288448 / 0.579283 (-0.290835) | 0.266929 / 0.434364 (-0.167435) | 0.328830 / 0.540337 (-0.211507) | 0.475510 / 1.386936 (-0.911426) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005469 / 0.011353 (-0.005884) | 0.003798 / 0.011008 (-0.007210) | 0.049129 / 0.038508 (0.010621) | 0.055490 / 0.023109 (0.032380) | 0.265828 / 0.275898 (-0.010070) | 0.286031 / 0.323480 (-0.037448) | 0.004075 / 0.007986 (-0.003910) | 0.002668 / 0.004328 (-0.001660) | 0.047823 / 0.004250 (0.043573) | 0.041946 / 0.037052 (0.004894) | 0.270359 / 0.258489 (0.011869) | 0.294287 / 0.293841 (0.000446) | 0.029643 / 0.128546 (-0.098903) | 0.010523 / 0.075646 (-0.065123) | 0.057370 / 0.419271 (-0.361902) | 0.033149 / 0.043533 (-0.010384) | 0.264408 / 0.255139 (0.009269) | 0.280413 / 0.283200 (-0.002787) | 0.018313 / 0.141683 (-0.123370) | 1.105982 / 1.452155 (-0.346173) | 1.182486 / 1.492716 (-0.310230) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092643 / 0.018006 (0.074637) | 0.301320 / 0.000490 (0.300831) | 0.000221 / 0.000200 (0.000021) | 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) | 0.021253 / 0.037411 (-0.016158) | 0.068052 / 0.014526 (0.053527) | 0.080821 / 0.176557 (-0.095736) | 0.119320 / 0.737135 (-0.617816) | 0.081952 / 0.296338 (-0.214387) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288536 / 0.215209 (0.073327) | 2.819900 / 2.077655 (0.742245) | 1.545210 / 1.504120 (0.041090) | 1.422047 / 1.541195 (-0.119147) | 1.439158 / 1.468490 (-0.029332) | 0.564910 / 4.584777 (-4.019867) | 2.430474 / 3.745712 (-1.315238) | 2.763979 / 5.269862 (-2.505882) | 1.732203 / 4.565676 (-2.833474) | 0.062692 / 0.424275 (-0.361583) | 0.004936 / 0.007607 (-0.002671) | 0.341626 / 0.226044 (0.115582) | 3.366623 / 2.268929 (1.097694) | 1.917198 / 55.444624 (-53.527426) | 1.637635 / 6.876477 (-5.238842) | 1.625953 / 2.142072 (-0.516119) | 0.634936 / 4.805227 (-4.170291) | 0.115336 / 6.500664 (-6.385328) | 0.040946 / 0.075469 (-0.034524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964865 / 1.841788 (-0.876922) | 12.077233 / 8.074308 (4.002925) | 10.664120 / 10.191392 (0.472728) | 0.132084 / 0.680424 (-0.548340) | 0.015931 / 0.534201 (-0.518270) | 0.289181 / 0.579283 (-0.290102) | 0.276943 / 0.434364 (-0.157420) | 0.324884 / 0.540337 (-0.215453) | 0.552570 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e \"CML watermark\")\n"
] | 2023-12-01T11:35:30Z
| 2023-12-01T12:09:09Z
| 2023-12-01T12:03:04Z
|
MEMBER
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In order to keep PR pages less spammy / more readable.
Having the benchmarks on commits on `main` is enough imo
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PR_kwDODunzps5MI9zO
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Support PyArrow arrays as column values in `from_dict`
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006665 / 0.011353 (-0.004688) | 0.004842 / 0.011008 (-0.006166) | 0.097802 / 0.038508 (0.059294) | 0.032292 / 0.023109 (0.009182) | 0.327522 / 0.275898 (0.051624) | 0.351851 / 0.323480 (0.028371) | 0.005197 / 0.007986 (-0.002789) | 0.003781 / 0.004328 (-0.000547) | 0.073213 / 0.004250 (0.068963) | 0.045819 / 0.037052 (0.008767) | 0.331323 / 0.258489 (0.072834) | 0.376978 / 0.293841 (0.083137) | 0.035014 / 0.128546 (-0.093532) | 0.011853 / 0.075646 (-0.063793) | 0.344031 / 0.419271 (-0.075240) | 0.049094 / 0.043533 (0.005561) | 0.327054 / 0.255139 (0.071915) | 0.349053 / 0.283200 (0.065853) | 0.095413 / 0.141683 (-0.046269) | 1.451593 / 1.452155 (-0.000562) | 1.505568 / 1.492716 (0.012851) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211624 / 0.018006 (0.193618) | 0.437569 / 0.000490 (0.437079) | 0.003775 / 0.000200 (0.003575) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025915 / 0.037411 (-0.011496) | 0.104085 / 0.014526 (0.089559) | 0.111064 / 0.176557 (-0.065493) | 0.167316 / 0.737135 (-0.569819) | 0.117255 / 0.296338 (-0.179084) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424241 / 0.215209 (0.209032) | 4.251365 / 2.077655 (2.173710) | 2.074036 / 1.504120 (0.569916) | 1.858022 / 1.541195 (0.316828) | 1.819929 / 1.468490 (0.351439) | 0.704153 / 4.584777 (-3.880624) | 3.750506 / 3.745712 (0.004794) | 3.149836 / 5.269862 (-2.120026) | 1.729540 / 4.565676 (-2.836137) | 0.087287 / 0.424275 (-0.336988) | 0.012304 / 0.007607 (0.004697) | 0.513811 / 0.226044 (0.287767) | 5.129427 / 2.268929 (2.860498) | 2.489253 / 55.444624 (-52.955371) | 2.122746 / 6.876477 (-4.753730) | 2.208528 / 2.142072 (0.066456) | 0.843386 / 4.805227 (-3.961841) | 0.169320 / 6.500664 (-6.331344) | 0.064085 / 0.075469 (-0.011384) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.184361 / 1.841788 (-0.657427) | 14.013478 / 8.074308 (5.939170) | 13.936774 / 10.191392 (3.745382) | 0.138009 / 0.680424 (-0.542415) | 0.017192 / 0.534201 (-0.517009) | 0.420938 / 0.579283 (-0.158345) | 0.413390 / 0.434364 (-0.020974) | 0.500244 / 0.540337 (-0.040094) | 0.582499 / 1.386936 (-0.804437) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006709 / 0.011353 (-0.004643) | 0.004847 / 0.011008 (-0.006161) | 0.074740 / 0.038508 (0.036232) | 0.032126 / 0.023109 (0.009017) | 0.343248 / 0.275898 (0.067350) | 0.376822 / 0.323480 (0.053342) | 0.005547 / 0.007986 (-0.002439) | 0.005080 / 0.004328 (0.000752) | 0.074634 / 0.004250 (0.070384) | 0.044735 / 0.037052 (0.007682) | 0.357895 / 0.258489 (0.099406) | 0.401150 / 0.293841 (0.107310) | 0.035485 / 0.128546 (-0.093061) | 0.011978 / 0.075646 (-0.063668) | 0.087567 / 0.419271 (-0.331704) | 0.050233 / 0.043533 (0.006701) | 0.337476 / 0.255139 (0.082337) | 0.385064 / 0.283200 (0.101865) | 0.102733 / 0.141683 (-0.038950) | 1.456238 / 1.452155 (0.004083) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203156 / 0.018006 (0.185149) | 0.448898 / 0.000490 (0.448408) | 0.002843 / 0.000200 (0.002644) | 0.000222 / 0.000054 (0.000168) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027836 / 0.037411 (-0.009576) | 0.109889 / 0.014526 (0.095364) | 0.119378 / 0.176557 (-0.057179) | 0.171208 / 0.737135 (-0.565927) | 0.124240 / 0.296338 (-0.172098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425374 / 0.215209 (0.210165) | 4.252994 / 2.077655 (2.175339) | 2.006410 / 1.504120 (0.502290) | 1.812821 / 1.541195 (0.271626) | 1.857618 / 1.468490 (0.389128) | 0.714564 / 4.584777 (-3.870213) | 3.803040 / 3.745712 (0.057328) | 2.075452 / 5.269862 (-3.194410) | 1.344868 / 4.565676 (-3.220809) | 0.088705 / 0.424275 (-0.335570) | 0.012481 / 0.007607 (0.004874) | 0.528022 / 0.226044 (0.301977) | 5.268878 / 2.268929 (2.999949) | 2.467858 / 55.444624 (-52.976767) | 2.138681 / 6.876477 (-4.737796) | 2.134928 / 2.142072 (-0.007145) | 0.851518 / 4.805227 (-3.953709) | 0.175085 / 6.500664 (-6.325579) | 0.063555 / 0.075469 (-0.011914) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265788 / 1.841788 (-0.576000) | 14.683444 / 8.074308 (6.609136) | 14.055848 / 10.191392 (3.864456) | 0.145260 / 0.680424 (-0.535164) | 0.017064 / 0.534201 (-0.517137) | 0.424836 / 0.579283 (-0.154447) | 0.418345 / 0.434364 (-0.016019) | 0.491408 / 0.540337 (-0.048930) | 0.594387 / 1.386936 (-0.792549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10c3f32c228cc7011ce456498942e6a2a5dc3086 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006870 / 0.011353 (-0.004483) | 0.004602 / 0.011008 (-0.006406) | 0.100075 / 0.038508 (0.061567) | 0.028720 / 0.023109 (0.005611) | 0.304212 / 0.275898 (0.028314) | 0.348423 / 0.323480 (0.024943) | 0.005266 / 0.007986 (-0.002720) | 0.003473 / 0.004328 (-0.000855) | 0.077563 / 0.004250 (0.073313) | 0.040066 / 0.037052 (0.003013) | 0.304039 / 0.258489 (0.045550) | 0.348721 / 0.293841 (0.054881) | 0.032127 / 0.128546 (-0.096419) | 0.011583 / 0.075646 (-0.064063) | 0.326853 / 0.419271 (-0.092418) | 0.043158 / 0.043533 (-0.000375) | 0.310111 / 0.255139 (0.054973) | 0.332869 / 0.283200 (0.049670) | 0.088384 / 0.141683 (-0.053299) | 1.509245 / 1.452155 (0.057091) | 1.575393 / 1.492716 (0.082677) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212839 / 0.018006 (0.194833) | 0.431407 / 0.000490 (0.430918) | 0.002639 / 0.000200 (0.002439) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024945 / 0.037411 (-0.012466) | 0.101312 / 0.014526 (0.086787) | 0.107873 / 0.176557 (-0.068683) | 0.169579 / 0.737135 (-0.567556) | 0.109922 / 0.296338 (-0.186417) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422091 / 0.215209 (0.206882) | 4.227174 / 2.077655 (2.149519) | 1.957964 / 1.504120 (0.453844) | 1.812076 / 1.541195 (0.270882) | 1.966666 / 1.468490 (0.498176) | 0.698710 / 4.584777 (-3.886067) | 3.431824 / 3.745712 (-0.313888) | 1.898646 / 5.269862 (-3.371215) | 1.172096 / 4.565676 (-3.393581) | 0.083383 / 0.424275 (-0.340892) | 0.012793 / 0.007607 (0.005186) | 0.522501 / 0.226044 (0.296457) | 5.240049 / 2.268929 (2.971121) | 2.349286 / 55.444624 (-53.095338) | 2.051117 / 6.876477 (-4.825360) | 2.255652 / 2.142072 (0.113580) | 0.813668 / 4.805227 (-3.991560) | 0.153770 / 6.500664 (-6.346894) | 0.068323 / 0.075469 (-0.007146) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197204 / 1.841788 (-0.644584) | 14.146212 / 8.074308 (6.071904) | 14.469765 / 10.191392 (4.278373) | 0.130024 / 0.680424 (-0.550400) | 0.016858 / 0.534201 (-0.517343) | 0.382949 / 0.579283 (-0.196334) | 0.393414 / 0.434364 (-0.040950) | 0.447910 / 0.540337 (-0.092427) | 0.529842 / 1.386936 (-0.857094) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006903 / 0.011353 (-0.004450) | 0.004695 / 0.011008 (-0.006313) | 0.077457 / 0.038508 (0.038949) | 0.028624 / 0.023109 (0.005514) | 0.340767 / 0.275898 (0.064869) | 0.378811 / 0.323480 (0.055331) | 0.005996 / 0.007986 (-0.001990) | 0.003481 / 0.004328 (-0.000848) | 0.076284 / 0.004250 (0.072034) | 0.042564 / 0.037052 (0.005511) | 0.340908 / 0.258489 (0.082419) | 0.384952 / 0.293841 (0.091111) | 0.032057 / 0.128546 (-0.096489) | 0.011697 / 0.075646 (-0.063949) | 0.085941 / 0.419271 (-0.333331) | 0.042464 / 0.043533 (-0.001069) | 0.339309 / 0.255139 (0.084170) | 0.368105 / 0.283200 (0.084905) | 0.093382 / 0.141683 (-0.048301) | 1.467220 / 1.452155 (0.015065) | 1.563105 / 1.492716 (0.070389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260631 / 0.018006 (0.242625) | 0.418155 / 0.000490 (0.417665) | 0.009539 / 0.000200 (0.009339) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025494 / 0.037411 (-0.011917) | 0.106034 / 0.014526 (0.091508) | 0.109878 / 0.176557 (-0.066678) | 0.160754 / 0.737135 (-0.576382) | 0.113226 / 0.296338 (-0.183112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442989 / 0.215209 (0.227780) | 4.447040 / 2.077655 (2.369385) | 2.082529 / 1.504120 (0.578409) | 1.876952 / 1.541195 (0.335757) | 1.968341 / 1.468490 (0.499851) | 0.704317 / 4.584777 (-3.880460) | 3.466190 / 3.745712 (-0.279523) | 1.924954 / 5.269862 (-3.344908) | 1.199763 / 4.565676 (-3.365913) | 0.084320 / 0.424275 (-0.339955) | 0.012956 / 0.007607 (0.005349) | 0.538905 / 0.226044 (0.312861) | 5.426593 / 2.268929 (3.157665) | 2.509287 / 55.444624 (-52.935338) | 2.174829 / 6.876477 (-4.701648) | 2.239214 / 2.142072 (0.097141) | 0.810031 / 4.805227 (-3.995196) | 0.153534 / 6.500664 (-6.347130) | 0.069578 / 0.075469 (-0.005891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294068 / 1.841788 (-0.547720) | 14.601899 / 8.074308 (6.527591) | 14.469282 / 10.191392 (4.277890) | 0.130024 / 0.680424 (-0.550400) | 0.016895 / 0.534201 (-0.517306) | 0.382583 / 0.579283 (-0.196700) | 0.388938 / 0.434364 (-0.045426) | 0.448416 / 0.540337 (-0.091922) | 0.533261 / 1.386936 (-0.853675) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b2af47647152d39a3acade256da898cb396e4d9 \"CML watermark\")\n"
] | 2023-03-15T19:32:40Z
| 2023-03-16T17:23:06Z
| 2023-03-16T17:15:40Z
|
CONTRIBUTOR
| null | 0
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For consistency with `pa.Table.from_pydict`, which supports both Python lists and PyArrow arrays as column values.
"Fixes" https://discuss.huggingface.co/t/pyarrow-lib-floatarray-did-not-recognize-python-value-type-when-inferring-an-arrow-data-type/33417
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PR_kwDODunzps5Icn9C
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Document that removing all the columns returns an empty document and the num_row is lost
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011812 / 0.011353 (0.000459) | 0.006878 / 0.011008 (-0.004130) | 0.128720 / 0.038508 (0.090212) | 0.038506 / 0.023109 (0.015397) | 0.359670 / 0.275898 (0.083772) | 0.422908 / 0.323480 (0.099428) | 0.010115 / 0.007986 (0.002129) | 0.004332 / 0.004328 (0.000004) | 0.096281 / 0.004250 (0.092031) | 0.048850 / 0.037052 (0.011798) | 0.373795 / 0.258489 (0.115306) | 0.414643 / 0.293841 (0.120802) | 0.057568 / 0.128546 (-0.070978) | 0.024135 / 0.075646 (-0.051512) | 0.411764 / 0.419271 (-0.007507) | 0.060167 / 0.043533 (0.016634) | 0.367119 / 0.255139 (0.111980) | 0.391813 / 0.283200 (0.108613) | 0.112125 / 0.141683 (-0.029558) | 1.869560 / 1.452155 (0.417406) | 1.845649 / 1.492716 (0.352932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211449 / 0.018006 (0.193443) | 0.522453 / 0.000490 (0.521963) | 0.003984 / 0.000200 (0.003784) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026015 / 0.037411 (-0.011397) | 0.117747 / 0.014526 (0.103221) | 0.125037 / 0.176557 (-0.051520) | 0.168351 / 0.737135 (-0.568785) | 0.132390 / 0.296338 (-0.163949) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605653 / 0.215209 (0.390444) | 5.883452 / 2.077655 (3.805798) | 2.367052 / 1.504120 (0.862932) | 2.137671 / 1.541195 (0.596476) | 2.042370 / 1.468490 (0.573880) | 1.168442 / 4.584777 (-3.416335) | 5.205236 / 3.745712 (1.459524) | 2.992514 / 5.269862 (-2.277348) | 2.191829 / 4.565676 (-2.373847) | 0.137702 / 0.424275 (-0.286574) | 0.015898 / 0.007607 (0.008291) | 0.783987 / 0.226044 (0.557942) | 7.768965 / 2.268929 (5.500036) | 3.249149 / 55.444624 (-52.195476) | 2.530687 / 6.876477 (-4.345790) | 2.675212 / 2.142072 (0.533140) | 1.482804 / 4.805227 (-3.322423) | 0.276845 / 6.500664 (-6.223819) | 0.080597 / 0.075469 (0.005128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.519086 / 1.841788 (-0.322701) | 17.394093 / 8.074308 (9.319785) | 19.613554 / 10.191392 (9.422162) | 0.253291 / 0.680424 (-0.427133) | 0.047746 / 0.534201 (-0.486455) | 0.547114 / 0.579283 (-0.032170) | 0.623873 / 0.434364 (0.189509) | 0.631924 / 0.540337 (0.091586) | 0.744390 / 1.386936 (-0.642546) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009229 / 0.011353 (-0.002124) | 0.006206 / 0.011008 (-0.004802) | 0.121866 / 0.038508 (0.083357) | 0.033629 / 0.023109 (0.010519) | 0.435172 / 0.275898 (0.159274) | 0.472093 / 0.323480 (0.148613) | 0.006946 / 0.007986 (-0.001039) | 0.004848 / 0.004328 (0.000519) | 0.097289 / 0.004250 (0.093038) | 0.046982 / 0.037052 (0.009930) | 0.447365 / 0.258489 (0.188876) | 0.491213 / 0.293841 (0.197372) | 0.055486 / 0.128546 (-0.073060) | 0.019788 / 0.075646 (-0.055858) | 0.399830 / 0.419271 (-0.019441) | 0.058943 / 0.043533 (0.015411) | 0.447658 / 0.255139 (0.192519) | 0.465752 / 0.283200 (0.182552) | 0.110441 / 0.141683 (-0.031242) | 1.773155 / 1.452155 (0.321001) | 1.899370 / 1.492716 (0.406653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191188 / 0.018006 (0.173181) | 0.523721 / 0.000490 (0.523232) | 0.004008 / 0.000200 (0.003808) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032579 / 0.037411 (-0.004833) | 0.120870 / 0.014526 (0.106344) | 0.154991 / 0.176557 (-0.021565) | 0.175450 / 0.737135 (-0.561685) | 0.136526 / 0.296338 (-0.159813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627262 / 0.215209 (0.412052) | 6.457989 / 2.077655 (4.380334) | 2.935188 / 1.504120 (1.431068) | 2.558705 / 1.541195 (1.017510) | 2.669455 / 1.468490 (1.200965) | 1.228791 / 4.584777 (-3.355985) | 5.621262 / 3.745712 (1.875549) | 3.181775 / 5.269862 (-2.088086) | 2.115116 / 4.565676 (-2.450560) | 0.159348 / 0.424275 (-0.264927) | 0.013598 / 0.007607 (0.005991) | 0.834732 / 0.226044 (0.608687) | 8.051097 / 2.268929 (5.782168) | 3.761681 / 55.444624 (-51.682943) | 2.898158 / 6.876477 (-3.978319) | 2.936289 / 2.142072 (0.794217) | 1.476307 / 4.805227 (-3.328920) | 0.269845 / 6.500664 (-6.230819) | 0.087225 / 0.075469 (0.011756) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632522 / 1.841788 (-0.209266) | 17.615297 / 8.074308 (9.540989) | 20.501172 / 10.191392 (10.309780) | 0.248845 / 0.680424 (-0.431579) | 0.024852 / 0.534201 (-0.509349) | 0.498957 / 0.579283 (-0.080326) | 0.588566 / 0.434364 (0.154202) | 0.611051 / 0.540337 (0.070714) | 0.726321 / 1.386936 (-0.660615) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adaaf0b5ad596538c744d41bb56ce472834b6573 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008920 / 0.011353 (-0.002433) | 0.004666 / 0.011008 (-0.006342) | 0.098584 / 0.038508 (0.060076) | 0.030213 / 0.023109 (0.007103) | 0.298180 / 0.275898 (0.022282) | 0.358932 / 0.323480 (0.035452) | 0.007182 / 0.007986 (-0.000804) | 0.005430 / 0.004328 (0.001102) | 0.077962 / 0.004250 (0.073712) | 0.038516 / 0.037052 (0.001463) | 0.308840 / 0.258489 (0.050351) | 0.343678 / 0.293841 (0.049837) | 0.033701 / 0.128546 (-0.094845) | 0.011460 / 0.075646 (-0.064186) | 0.319809 / 0.419271 (-0.099462) | 0.040731 / 0.043533 (-0.002802) | 0.299772 / 0.255139 (0.044633) | 0.324292 / 0.283200 (0.041092) | 0.087755 / 0.141683 (-0.053928) | 1.493077 / 1.452155 (0.040922) | 1.527462 / 1.492716 (0.034746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187927 / 0.018006 (0.169921) | 0.412785 / 0.000490 (0.412296) | 0.003235 / 0.000200 (0.003035) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023313 / 0.037411 (-0.014098) | 0.095663 / 0.014526 (0.081137) | 0.105094 / 0.176557 (-0.071463) | 0.140389 / 0.737135 (-0.596746) | 0.108477 / 0.296338 (-0.187861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410680 / 0.215209 (0.195471) | 4.109287 / 2.077655 (2.031632) | 1.833214 / 1.504120 (0.329094) | 1.622837 / 1.541195 (0.081642) | 1.679899 / 1.468490 (0.211409) | 0.686920 / 4.584777 (-3.897857) | 3.463267 / 3.745712 (-0.282445) | 1.867035 / 5.269862 (-3.402826) | 1.150631 / 4.565676 (-3.415046) | 0.081209 / 0.424275 (-0.343066) | 0.012384 / 0.007607 (0.004777) | 0.521070 / 0.226044 (0.295026) | 5.208829 / 2.268929 (2.939900) | 2.289032 / 55.444624 (-53.155592) | 1.942976 / 6.876477 (-4.933501) | 1.990660 / 2.142072 (-0.151413) | 0.802976 / 4.805227 (-4.002252) | 0.148199 / 6.500664 (-6.352465) | 0.064644 / 0.075469 (-0.010825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277029 / 1.841788 (-0.564759) | 13.915489 / 8.074308 (5.841181) | 14.035486 / 10.191392 (3.844094) | 0.138205 / 0.680424 (-0.542219) | 0.028968 / 0.534201 (-0.505232) | 0.394275 / 0.579283 (-0.185008) | 0.399967 / 0.434364 (-0.034397) | 0.460595 / 0.540337 (-0.079742) | 0.537625 / 1.386936 (-0.849311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006485 / 0.011353 (-0.004868) | 0.004534 / 0.011008 (-0.006474) | 0.097742 / 0.038508 (0.059234) | 0.027231 / 0.023109 (0.004122) | 0.431321 / 0.275898 (0.155423) | 0.469212 / 0.323480 (0.145732) | 0.004894 / 0.007986 (-0.003092) | 0.004147 / 0.004328 (-0.000181) | 0.073650 / 0.004250 (0.069400) | 0.037052 / 0.037052 (-0.000000) | 0.434196 / 0.258489 (0.175707) | 0.480539 / 0.293841 (0.186698) | 0.031923 / 0.128546 (-0.096623) | 0.011522 / 0.075646 (-0.064124) | 0.317062 / 0.419271 (-0.102209) | 0.041124 / 0.043533 (-0.002409) | 0.432013 / 0.255139 (0.176874) | 0.456760 / 0.283200 (0.173560) | 0.089757 / 0.141683 (-0.051925) | 1.497752 / 1.452155 (0.045597) | 1.585342 / 1.492716 (0.092626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227784 / 0.018006 (0.209778) | 0.404570 / 0.000490 (0.404080) | 0.000556 / 0.000200 (0.000356) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025201 / 0.037411 (-0.012210) | 0.099348 / 0.014526 (0.084822) | 0.114984 / 0.176557 (-0.061573) | 0.147039 / 0.737135 (-0.590097) | 0.109727 / 0.296338 (-0.186611) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468415 / 0.215209 (0.253206) | 4.692228 / 2.077655 (2.614573) | 2.403382 / 1.504120 (0.899262) | 2.196026 / 1.541195 (0.654832) | 2.234736 / 1.468490 (0.766246) | 0.703011 / 4.584777 (-3.881766) | 3.451513 / 3.745712 (-0.294199) | 2.596811 / 5.269862 (-2.673051) | 1.544079 / 4.565676 (-3.021598) | 0.083153 / 0.424275 (-0.341123) | 0.012605 / 0.007607 (0.004998) | 0.570265 / 0.226044 (0.344220) | 5.735996 / 2.268929 (3.467067) | 2.865336 / 55.444624 (-52.579288) | 2.508340 / 6.876477 (-4.368137) | 2.547144 / 2.142072 (0.405072) | 0.813018 / 4.805227 (-3.992210) | 0.150327 / 6.500664 (-6.350337) | 0.065837 / 0.075469 (-0.009632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268941 / 1.841788 (-0.572847) | 13.835698 / 8.074308 (5.761390) | 13.992726 / 10.191392 (3.801334) | 0.127751 / 0.680424 (-0.552673) | 0.016673 / 0.534201 (-0.517528) | 0.381921 / 0.579283 (-0.197362) | 0.390688 / 0.434364 (-0.043676) | 0.446234 / 0.540337 (-0.094103) | 0.532631 / 1.386936 (-0.854305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1492df3311bfeac55aaedf34c93c014630c4403e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008486 / 0.011353 (-0.002867) | 0.004573 / 0.011008 (-0.006435) | 0.100096 / 0.038508 (0.061588) | 0.029449 / 0.023109 (0.006340) | 0.298384 / 0.275898 (0.022486) | 0.361886 / 0.323480 (0.038406) | 0.006813 / 0.007986 (-0.001173) | 0.003394 / 0.004328 (-0.000935) | 0.077563 / 0.004250 (0.073312) | 0.035605 / 0.037052 (-0.001447) | 0.306864 / 0.258489 (0.048375) | 0.346438 / 0.293841 (0.052597) | 0.033156 / 0.128546 (-0.095390) | 0.011567 / 0.075646 (-0.064079) | 0.322189 / 0.419271 (-0.097083) | 0.040161 / 0.043533 (-0.003372) | 0.299329 / 0.255139 (0.044190) | 0.326375 / 0.283200 (0.043175) | 0.086572 / 0.141683 (-0.055111) | 1.502473 / 1.452155 (0.050319) | 1.528539 / 1.492716 (0.035823) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008502 / 0.018006 (-0.009505) | 0.411045 / 0.000490 (0.410555) | 0.003179 / 0.000200 (0.002980) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023177 / 0.037411 (-0.014234) | 0.096948 / 0.014526 (0.082422) | 0.104068 / 0.176557 (-0.072489) | 0.138739 / 0.737135 (-0.598396) | 0.108241 / 0.296338 (-0.188097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411156 / 0.215209 (0.195947) | 4.092992 / 2.077655 (2.015337) | 1.841903 / 1.504120 (0.337783) | 1.637449 / 1.541195 (0.096254) | 1.670968 / 1.468490 (0.202478) | 0.697301 / 4.584777 (-3.887476) | 3.354717 / 3.745712 (-0.390995) | 1.851518 / 5.269862 (-3.418344) | 1.160367 / 4.565676 (-3.405309) | 0.082613 / 0.424275 (-0.341662) | 0.012477 / 0.007607 (0.004870) | 0.524839 / 0.226044 (0.298795) | 5.264173 / 2.268929 (2.995245) | 2.294530 / 55.444624 (-53.150094) | 1.933233 / 6.876477 (-4.943244) | 1.968959 / 2.142072 (-0.173113) | 0.817104 / 4.805227 (-3.988123) | 0.149072 / 6.500664 (-6.351592) | 0.064911 / 0.075469 (-0.010558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.222215 / 1.841788 (-0.619573) | 13.607545 / 8.074308 (5.533237) | 13.990230 / 10.191392 (3.798838) | 0.150855 / 0.680424 (-0.529568) | 0.028844 / 0.534201 (-0.505357) | 0.396169 / 0.579283 (-0.183114) | 0.406957 / 0.434364 (-0.027407) | 0.464069 / 0.540337 (-0.076268) | 0.554027 / 1.386936 (-0.832909) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006296 / 0.011353 (-0.005057) | 0.004563 / 0.011008 (-0.006445) | 0.097719 / 0.038508 (0.059211) | 0.027106 / 0.023109 (0.003996) | 0.409333 / 0.275898 (0.133435) | 0.445397 / 0.323480 (0.121917) | 0.004906 / 0.007986 (-0.003080) | 0.003316 / 0.004328 (-0.001012) | 0.075363 / 0.004250 (0.071112) | 0.039366 / 0.037052 (0.002314) | 0.412710 / 0.258489 (0.154221) | 0.451789 / 0.293841 (0.157948) | 0.031810 / 0.128546 (-0.096736) | 0.011681 / 0.075646 (-0.063965) | 0.318484 / 0.419271 (-0.100788) | 0.046741 / 0.043533 (0.003208) | 0.411631 / 0.255139 (0.156492) | 0.435274 / 0.283200 (0.152074) | 0.092366 / 0.141683 (-0.049317) | 1.492243 / 1.452155 (0.040089) | 1.617603 / 1.492716 (0.124887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217376 / 0.018006 (0.199369) | 0.400940 / 0.000490 (0.400450) | 0.003700 / 0.000200 (0.003500) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023733 / 0.037411 (-0.013678) | 0.098553 / 0.014526 (0.084027) | 0.105790 / 0.176557 (-0.070767) | 0.139537 / 0.737135 (-0.597598) | 0.109862 / 0.296338 (-0.186477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476562 / 0.215209 (0.261353) | 4.773469 / 2.077655 (2.695814) | 2.447302 / 1.504120 (0.943182) | 2.240596 / 1.541195 (0.699401) | 2.271370 / 1.468490 (0.802880) | 0.698913 / 4.584777 (-3.885864) | 3.345648 / 3.745712 (-0.400064) | 1.845008 / 5.269862 (-3.424854) | 1.163213 / 4.565676 (-3.402464) | 0.082456 / 0.424275 (-0.341819) | 0.012315 / 0.007607 (0.004708) | 0.575881 / 0.226044 (0.349836) | 5.769575 / 2.268929 (3.500647) | 2.909759 / 55.444624 (-52.534865) | 2.580259 / 6.876477 (-4.296218) | 2.590473 / 2.142072 (0.448401) | 0.802765 / 4.805227 (-4.002462) | 0.151514 / 6.500664 (-6.349150) | 0.067718 / 0.075469 (-0.007751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293014 / 1.841788 (-0.548773) | 13.934072 / 8.074308 (5.859763) | 13.538760 / 10.191392 (3.347368) | 0.126490 / 0.680424 (-0.553934) | 0.016653 / 0.534201 (-0.517548) | 0.381220 / 0.579283 (-0.198064) | 0.387571 / 0.434364 (-0.046793) | 0.444674 / 0.540337 (-0.095663) | 0.550802 / 1.386936 (-0.836134) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bed576f2205c96f6cb26b5c6522345cb8b06ecfc \"CML watermark\")\n"
] | 2023-01-24T17:33:38Z
| 2023-01-25T16:11:10Z
| 2023-01-25T16:04:03Z
|
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resolving a weird tar extract issue
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011815 / 0.011353 (0.000463) | 0.006407 / 0.011008 (-0.004601) | 0.132937 / 0.038508 (0.094429) | 0.040634 / 0.023109 (0.017525) | 0.398049 / 0.275898 (0.122151) | 0.498207 / 0.323480 (0.174727) | 0.010111 / 0.007986 (0.002126) | 0.007282 / 0.004328 (0.002954) | 0.103661 / 0.004250 (0.099411) | 0.046223 / 0.037052 (0.009171) | 0.411490 / 0.258489 (0.153001) | 0.480973 / 0.293841 (0.187132) | 0.058397 / 0.128546 (-0.070149) | 0.019952 / 0.075646 (-0.055695) | 0.440734 / 0.419271 (0.021463) | 0.064585 / 0.043533 (0.021052) | 0.392556 / 0.255139 (0.137417) | 0.437842 / 0.283200 (0.154643) | 0.130684 / 0.141683 (-0.010999) | 1.910552 / 1.452155 (0.458397) | 1.984644 / 1.492716 (0.491927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264417 / 0.018006 (0.246411) | 0.676519 / 0.000490 (0.676030) | 0.003369 / 0.000200 (0.003169) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034558 / 0.037411 (-0.002854) | 0.126561 / 0.014526 (0.112035) | 0.134478 / 0.176557 (-0.042079) | 0.202125 / 0.737135 (-0.535010) | 0.143273 / 0.296338 (-0.153066) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618592 / 0.215209 (0.403383) | 6.224435 / 2.077655 (4.146780) | 2.636689 / 1.504120 (1.132569) | 2.243507 / 1.541195 (0.702313) | 2.312449 / 1.468490 (0.843959) | 1.188499 / 4.584777 (-3.396277) | 5.738347 / 3.745712 (1.992635) | 4.891933 / 5.269862 (-0.377929) | 2.697631 / 4.565676 (-1.868046) | 0.140200 / 0.424275 (-0.284076) | 0.015484 / 0.007607 (0.007877) | 0.781947 / 0.226044 (0.555903) | 7.946600 / 2.268929 (5.677671) | 3.365574 / 55.444624 (-52.079050) | 2.783443 / 6.876477 (-4.093034) | 2.738634 / 2.142072 (0.596561) | 1.487247 / 4.805227 (-3.317980) | 0.255681 / 6.500664 (-6.244983) | 0.084607 / 0.075469 (0.009138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.717846 / 1.841788 (-0.123941) | 18.405566 / 8.074308 (10.331258) | 20.508578 / 10.191392 (10.317186) | 0.262364 / 0.680424 (-0.418060) | 0.050881 / 0.534201 (-0.483319) | 0.587516 / 0.579283 (0.008232) | 0.650900 / 0.434364 (0.216536) | 0.656168 / 0.540337 (0.115830) | 0.778876 / 1.386936 (-0.608061) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010817 / 0.011353 (-0.000536) | 0.007338 / 0.011008 (-0.003670) | 0.131949 / 0.038508 (0.093441) | 0.037244 / 0.023109 (0.014135) | 0.565994 / 0.275898 (0.290096) | 0.567434 / 0.323480 (0.243954) | 0.007733 / 0.007986 (-0.000252) | 0.005216 / 0.004328 (0.000887) | 0.096578 / 0.004250 (0.092328) | 0.056001 / 0.037052 (0.018949) | 0.538209 / 0.258489 (0.279720) | 0.580385 / 0.293841 (0.286544) | 0.053654 / 0.128546 (-0.074892) | 0.019471 / 0.075646 (-0.056176) | 0.448781 / 0.419271 (0.029509) | 0.064774 / 0.043533 (0.021241) | 0.540222 / 0.255139 (0.285083) | 0.563058 / 0.283200 (0.279858) | 0.122716 / 0.141683 (-0.018967) | 1.839402 / 1.452155 (0.387247) | 1.915523 / 1.492716 (0.422806) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310448 / 0.018006 (0.292442) | 0.603664 / 0.000490 (0.603175) | 0.004833 / 0.000200 (0.004633) | 0.000145 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032340 / 0.037411 (-0.005072) | 0.130115 / 0.014526 (0.115589) | 0.154192 / 0.176557 (-0.022364) | 0.200655 / 0.737135 (-0.536480) | 0.144961 / 0.296338 (-0.151377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671588 / 0.215209 (0.456379) | 6.691642 / 2.077655 (4.613988) | 2.915230 / 1.504120 (1.411110) | 2.573337 / 1.541195 (1.032143) | 2.578204 / 1.468490 (1.109714) | 1.249028 / 4.584777 (-3.335749) | 5.808539 / 3.745712 (2.062827) | 3.079317 / 5.269862 (-2.190545) | 2.033308 / 4.565676 (-2.532369) | 0.142411 / 0.424275 (-0.281864) | 0.015525 / 0.007607 (0.007918) | 0.800389 / 0.226044 (0.574345) | 8.228236 / 2.268929 (5.959308) | 3.660207 / 55.444624 (-51.784417) | 3.021033 / 6.876477 (-3.855444) | 3.088335 / 2.142072 (0.946263) | 1.380137 / 4.805227 (-3.425091) | 0.252065 / 6.500664 (-6.248599) | 0.084302 / 0.075469 (0.008833) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.709429 / 1.841788 (-0.132359) | 18.358770 / 8.074308 (10.284462) | 21.109844 / 10.191392 (10.918452) | 0.231549 / 0.680424 (-0.448875) | 0.029251 / 0.534201 (-0.504950) | 0.560719 / 0.579283 (-0.018564) | 0.610125 / 0.434364 (0.175761) | 0.630015 / 0.540337 (0.089678) | 0.751656 / 1.386936 (-0.635280) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18baf4eebf71c0db1d9980f7ee164f1272ff8f26 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5441). All of your documentation changes will be reflected on that endpoint.",
"I think I managed to reproduce it:\r\n\r\n```\r\nrm -rf ~/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing\r\nmkdir -p /tmp/xxx/hf-data\r\nsudo ln -s /tmp/xxx /test\r\nmkdir -p /tmp/yyy\r\nln -sf /test/hf-data /tmp/yyy/data\r\ncd /tmp/yyy\r\npython -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/cm4-synthetic-testing\r\n```\r\n\r\nPlease note it includes a creation of a symlink from the `/` (so `sudo`) - may be there is a simpler way but I'm just trying to replicate the real setup. Of course please be careful - it's mostly under `/tmp` not to destroy anything if you try to run this.\r\n\r\nthis fails with:\r\n\r\n```\r\nNo config specified, defaulting to: cm4-synthetic-testing/100.unique\r\nDownloading and preparing dataset cm4-synthetic-testing/100.unique (download: 20.71 KiB, generated: 49.99 MiB, post-processed: Unknown size, total: 50.01 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing/100.unique/1.1.1/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b...\r\nExtraction of data is blocked (illegal path: /tmp/yyy)\r\n[...]\r\nExtraction of data/115/texts_03.txt is blocked (illegal path: /tmp/yyy)\r\nGenerating 100.unique split: 0%| | 0/100 [00:00<?, ? examples/s]Generating 100-long unique records split\r\n\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1571, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/stas/.cache/huggingface/modules/datasets_modules/datasets/HuggingFaceM4--cm4-synthetic-testing/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b/cm4-synthetic-testing.py\", line 190, in _generate_examples\r\n raise ValueError(f\"can't find any data - check {data_path}\")\r\nValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/load.py\", line 1757, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 860, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1612, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 953, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1450, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1607, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nnote that `illegal path: /tmp/yyy` is now with the mods of this PR.\r\n\r\n----------------------\r\n\r\nAlso I think the whole thing should have failed at the first `illegal path` and not continue running. But as it continued and gave:\r\n\r\n\r\n> ValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nwhat can a user do with that other than confirming that that dir is indeed empty, but no clue is given to why and it's far from obvious that one needs to scroll up and discover earlier issues. Most users won't do that.\r\n\r\n(my apologies for writing out so much - was trying to make the situation clear)",
"Thank you, Albert, for the explanation.\r\n\r\nTo summarize I think what's needed is:\r\n\r\n1. add a comment in the code to why this is done for someone being puzzled over the odd code\r\n2. and to use an actionable by the user error message\r\n3. perform an untrapped assert on that tar extract error and not continue, so that the user will not get a later misleading error that the folder is empty and is completely not actionable and it's is far from obvious that one needs to scroll up to find earlier errors, which were trapped.\r\n\r\nAfter reading the advisory I'm still not sure why `cwd` is used and not a designated `~/.cache/huggingface/datasets/downloads/extracted`, I can't see what difference does it make since I could `chdir` to the designated directory and it would be `cwd`. The security solution is trying to ensure that `/etc/passwd` won't get overriden. So why is the check done in `.` and not the real target base directory, since the extraction isn't done in the current working dir. By not using `.` you lower the chances that the user will have all sorts of local symlinks that could trigger the issue since `datasets` typically is the only one managing it's `~/.cache/huggingface/datasets` domain and 99.9% of the time the user won't manually create files in it.\r\n\r\nthank you!\r\n"
] | 2023-01-19T02:17:21Z
| 2023-01-20T16:49:22Z
| null |
CONTRIBUTOR
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ok, every so often, I have been getting a strange failure on dataset install:
```
$ python -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/general-pmd-synthetic-testing
No config specified, defaulting to: general-pmd-synthetic-testing/100.unique
Downloading and preparing dataset general-pmd-synthetic-testing/100.unique (download: 3.21 KiB, generated: 16.01 MiB, post-processed: Unknown size, total: 16.02 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___general-pmd-synthetic-testing/100.unique/1.1.1/86bc445e3e48cb5ef79de109eb4e54ff85b318cd55c3835c4ee8f86eae33d9d2...
Extraction of data is blocked (illegal path)
Extraction of data/1 is blocked (illegal path)
Extraction of data/1/text.null is blocked (illegal path)
[...]
```
I had no idea what to do with that - what in the world does **illegal path** mean?
I started looking at the code in `TarExtractor` and added a debug print of `base` so that told me that there was a problem with the current directory - which was a clone of one of the hf repos.
This particular dataset extracts into a directory `data` and the current dir I was running the tests from already had `data` in it which was a symbolic link to another partition and somehow all that `badpath` code was blowing up there.
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L113-L114
I tried hard to come up with a repro, but no matter what I tried it only fails in that particular clone directory that has a `data` symlink and not anywhere else.
In any case, in this PR I'm proposing to at least give a user a hint of what seems to be an issue.
I'm not at all happy with the info I got with this proposed change, but at least it gave me a hint that `TarExtractor` tries to extract into the current directory without any respect to pre-existing files. Say what?
https://github.com/huggingface/datasets/blob/80eb8db74f49b7ee9c0f73a819c22177fabd61db/src/datasets/utils/extract.py#L110
why won't it use the `datasets` designated directory for that? There would never be a problem if it were to do that.
I had to look at all those `resolved`, `badpath` calls and see what it did and why it failed, since it was far from obvious. It appeared like it resolved a symlink and compared it to the original path which of course wasn't matching.
So perhaps you have a better solution than what I proposed in this PR. I think that code line I quoted is the one that should be fixed instead.
But if you can't think of a better solution let's merge this at least so that the user will have a clue that the current dir is somehow involved.
p.s. I double checked that if I remove the pre-existing `data` symlink in the current dir I'm running the dataset install command from, the problem goes away too.
Thanks.
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006103 / 0.011353 (-0.005250) | 0.003588 / 0.011008 (-0.007420) | 0.080335 / 0.038508 (0.041827) | 0.059634 / 0.023109 (0.036525) | 0.356093 / 0.275898 (0.080195) | 0.407376 / 0.323480 (0.083896) | 0.005343 / 0.007986 (-0.002643) | 0.002928 / 0.004328 (-0.001400) | 0.062580 / 0.004250 (0.058330) | 0.047544 / 0.037052 (0.010491) | 0.364305 / 0.258489 (0.105816) | 0.421463 / 0.293841 (0.127623) | 0.027249 / 0.128546 (-0.101298) | 0.008010 / 0.075646 (-0.067636) | 0.262543 / 0.419271 (-0.156728) | 0.044978 / 0.043533 (0.001445) | 0.339344 / 0.255139 (0.084205) | 0.395288 / 0.283200 (0.112088) | 0.021425 / 0.141683 (-0.120258) | 1.439767 / 1.452155 (-0.012387) | 1.498081 / 1.492716 (0.005365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196976 / 0.018006 (0.178970) | 0.435383 / 0.000490 (0.434893) | 0.004559 / 0.000200 (0.004359) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023653 / 0.037411 (-0.013759) | 0.072944 / 0.014526 (0.058418) | 0.083651 / 0.176557 (-0.092906) | 0.144590 / 0.737135 (-0.592545) | 0.084844 / 0.296338 (-0.211494) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398752 / 0.215209 (0.183543) | 3.959539 / 2.077655 (1.881884) | 1.935277 / 1.504120 (0.431157) | 1.751994 / 1.541195 (0.210799) | 1.828386 / 1.468490 (0.359896) | 0.500492 / 4.584777 (-4.084284) | 3.086630 / 3.745712 (-0.659082) | 2.851664 / 5.269862 (-2.418198) | 1.869792 / 4.565676 (-2.695885) | 0.058509 / 0.424275 (-0.365766) | 0.006500 / 0.007607 (-0.001107) | 0.467468 / 0.226044 (0.241424) | 4.686168 / 2.268929 (2.417240) | 2.427632 / 55.444624 (-53.016993) | 2.193194 / 6.876477 (-4.683283) | 2.408574 / 2.142072 (0.266501) | 0.592173 / 4.805227 (-4.213054) | 0.125381 / 6.500664 (-6.375283) | 0.060679 / 0.075469 (-0.014790) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236066 / 1.841788 (-0.605722) | 18.591689 / 8.074308 (10.517381) | 14.138774 / 10.191392 (3.947382) | 0.147455 / 0.680424 (-0.532968) | 0.016921 / 0.534201 (-0.517280) | 0.328129 / 0.579283 (-0.251154) | 0.348872 / 0.434364 (-0.085491) | 0.380311 / 0.540337 (-0.160026) | 0.532901 / 1.386936 (-0.854035) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005914 / 0.011353 (-0.005438) | 0.003614 / 0.011008 (-0.007394) | 0.062857 / 0.038508 (0.024349) | 0.060633 / 0.023109 (0.037524) | 0.419684 / 0.275898 (0.143786) | 0.449025 / 0.323480 (0.125546) | 0.004595 / 0.007986 (-0.003391) | 0.002861 / 0.004328 (-0.001467) | 0.063253 / 0.004250 (0.059003) | 0.048770 / 0.037052 (0.011718) | 0.419838 / 0.258489 (0.161349) | 0.465183 / 0.293841 (0.171342) | 0.027350 / 0.128546 (-0.101196) | 0.008065 / 0.075646 (-0.067582) | 0.068321 / 0.419271 (-0.350950) | 0.041083 / 0.043533 (-0.002449) | 0.400831 / 0.255139 (0.145692) | 0.449286 / 0.283200 (0.166086) | 0.020472 / 0.141683 (-0.121210) | 1.437215 / 1.452155 (-0.014940) | 1.503679 / 1.492716 (0.010963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230764 / 0.018006 (0.212758) | 0.420774 / 0.000490 (0.420285) | 0.004012 / 0.000200 (0.003812) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026009 / 0.037411 (-0.011402) | 0.077943 / 0.014526 (0.063417) | 0.087281 / 0.176557 (-0.089276) | 0.139422 / 0.737135 (-0.597713) | 0.089090 / 0.296338 (-0.207248) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417298 / 0.215209 (0.202088) | 4.152303 / 2.077655 (2.074648) | 2.179996 / 1.504120 (0.675877) | 2.020619 / 1.541195 (0.479424) | 2.085241 / 1.468490 (0.616751) | 0.501111 / 4.584777 (-4.083666) | 3.079849 / 3.745712 (-0.665863) | 2.820607 / 5.269862 (-2.449255) | 1.863988 / 4.565676 (-2.701688) | 0.057662 / 0.424275 (-0.366613) | 0.006778 / 0.007607 (-0.000830) | 0.498661 / 0.226044 (0.272616) | 4.986503 / 2.268929 (2.717574) | 2.620676 / 55.444624 (-52.823949) | 2.297546 / 6.876477 (-4.578931) | 2.458148 / 2.142072 (0.316075) | 0.599490 / 4.805227 (-4.205738) | 0.125102 / 6.500664 (-6.375562) | 0.061411 / 0.075469 (-0.014059) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.323816 / 1.841788 (-0.517971) | 18.462614 / 8.074308 (10.388306) | 13.845826 / 10.191392 (3.654434) | 0.146115 / 0.680424 (-0.534309) | 0.016862 / 0.534201 (-0.517339) | 0.335449 / 0.579283 (-0.243834) | 0.343792 / 0.434364 (-0.090572) | 0.394068 / 0.540337 (-0.146269) | 0.536378 / 1.386936 (-0.850558) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de3f00368c9236e9410821f5fddb95d6069883c1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006825 / 0.011353 (-0.004527) | 0.004005 / 0.011008 (-0.007003) | 0.085504 / 0.038508 (0.046996) | 0.077252 / 0.023109 (0.054143) | 0.351891 / 0.275898 (0.075993) | 0.383404 / 0.323480 (0.059924) | 0.004153 / 0.007986 (-0.003833) | 0.003344 / 0.004328 (-0.000985) | 0.064936 / 0.004250 (0.060685) | 0.057653 / 0.037052 (0.020601) | 0.368155 / 0.258489 (0.109666) | 0.406122 / 0.293841 (0.112282) | 0.032049 / 0.128546 (-0.096497) | 0.008698 / 0.075646 (-0.066949) | 0.292394 / 0.419271 (-0.126878) | 0.053634 / 0.043533 (0.010101) | 0.358273 / 0.255139 (0.103134) | 0.378441 / 0.283200 (0.095242) | 0.026928 / 0.141683 (-0.114755) | 1.458718 / 1.452155 (0.006563) | 1.536231 / 1.492716 (0.043515) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213956 / 0.018006 (0.195950) | 0.458620 / 0.000490 (0.458130) | 0.002718 / 0.000200 (0.002519) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027870 / 0.037411 (-0.009541) | 0.083922 / 0.014526 (0.069396) | 0.152056 / 0.176557 (-0.024501) | 0.151584 / 0.737135 (-0.585552) | 0.095698 / 0.296338 (-0.200641) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407762 / 0.215209 (0.192553) | 4.074324 / 2.077655 (1.996669) | 2.089929 / 1.504120 (0.585809) | 1.920024 / 1.541195 (0.378829) | 2.013410 / 1.468490 (0.544920) | 0.486056 / 4.584777 (-4.098721) | 3.656869 / 3.745712 (-0.088843) | 3.304008 / 5.269862 (-1.965854) | 2.074363 / 4.565676 (-2.491313) | 0.057293 / 0.424275 (-0.366982) | 0.007240 / 0.007607 (-0.000367) | 0.482696 / 0.226044 (0.256652) | 4.833251 / 2.268929 (2.564322) | 2.570391 / 55.444624 (-52.874233) | 2.220619 / 6.876477 (-4.655857) | 2.426316 / 2.142072 (0.284243) | 0.584811 / 4.805227 (-4.220416) | 0.134907 / 6.500664 (-6.365757) | 0.061115 / 0.075469 (-0.014354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251969 / 1.841788 (-0.589818) | 19.601611 / 8.074308 (11.527303) | 14.190217 / 10.191392 (3.998825) | 0.166296 / 0.680424 (-0.514128) | 0.018334 / 0.534201 (-0.515867) | 0.395172 / 0.579283 (-0.184111) | 0.410440 / 0.434364 (-0.023924) | 0.462263 / 0.540337 (-0.078074) | 0.645504 / 1.386936 (-0.741432) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006991 / 0.011353 (-0.004362) | 0.004084 / 0.011008 (-0.006924) | 0.065208 / 0.038508 (0.026700) | 0.077809 / 0.023109 (0.054699) | 0.386472 / 0.275898 (0.110574) | 0.418686 / 0.323480 (0.095206) | 0.005346 / 0.007986 (-0.002640) | 0.003416 / 0.004328 (-0.000912) | 0.066209 / 0.004250 (0.061958) | 0.057517 / 0.037052 (0.020465) | 0.407684 / 0.258489 (0.149195) | 0.425438 / 0.293841 (0.131597) | 0.032166 / 0.128546 (-0.096380) | 0.008662 / 0.075646 (-0.066985) | 0.071712 / 0.419271 (-0.347560) | 0.049764 / 0.043533 (0.006231) | 0.394882 / 0.255139 (0.139743) | 0.403589 / 0.283200 (0.120389) | 0.023688 / 0.141683 (-0.117995) | 1.468488 / 1.452155 (0.016334) | 1.533118 / 1.492716 (0.040401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252949 / 0.018006 (0.234943) | 0.447355 / 0.000490 (0.446865) | 0.011721 / 0.000200 (0.011521) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031444 / 0.037411 (-0.005968) | 0.089390 / 0.014526 (0.074864) | 0.100103 / 0.176557 (-0.076454) | 0.153301 / 0.737135 (-0.583835) | 0.101336 / 0.296338 (-0.195003) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408574 / 0.215209 (0.193365) | 4.073135 / 2.077655 (1.995480) | 2.086550 / 1.504120 (0.582430) | 1.930651 / 1.541195 (0.389457) | 2.013548 / 1.468490 (0.545058) | 0.477235 / 4.584777 (-4.107542) | 3.547545 / 3.745712 (-0.198167) | 3.321957 / 5.269862 (-1.947905) | 2.057705 / 4.565676 (-2.507971) | 0.056730 / 0.424275 (-0.367545) | 0.007882 / 0.007607 (0.000275) | 0.487297 / 0.226044 (0.261253) | 4.874184 / 2.268929 (2.605255) | 2.631129 / 55.444624 (-52.813496) | 2.235755 / 6.876477 (-4.640722) | 2.463329 / 2.142072 (0.321257) | 0.578308 / 4.805227 (-4.226919) | 0.132726 / 6.500664 (-6.367938) | 0.064883 / 0.075469 (-0.010586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.347564 / 1.841788 (-0.494223) | 20.192973 / 8.074308 (12.118665) | 14.563553 / 10.191392 (4.372161) | 0.168244 / 0.680424 (-0.512180) | 0.018638 / 0.534201 (-0.515563) | 0.394789 / 0.579283 (-0.184494) | 0.419677 / 0.434364 (-0.014687) | 0.480274 / 0.540337 (-0.060063) | 0.641204 / 1.386936 (-0.745732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c7a0d56b60bf700d6a491fa30eaf66500969315 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005939 / 0.011353 (-0.005413) | 0.003457 / 0.011008 (-0.007551) | 0.079985 / 0.038508 (0.041477) | 0.056492 / 0.023109 (0.033383) | 0.312356 / 0.275898 (0.036458) | 0.354038 / 0.323480 (0.030558) | 0.004551 / 0.007986 (-0.003435) | 0.002828 / 0.004328 (-0.001501) | 0.062369 / 0.004250 (0.058119) | 0.044712 / 0.037052 (0.007660) | 0.318244 / 0.258489 (0.059755) | 0.361977 / 0.293841 (0.068136) | 0.026460 / 0.128546 (-0.102086) | 0.007928 / 0.075646 (-0.067719) | 0.261378 / 0.419271 (-0.157894) | 0.044209 / 0.043533 (0.000676) | 0.313931 / 0.255139 (0.058792) | 0.339553 / 0.283200 (0.056354) | 0.019776 / 0.141683 (-0.121907) | 1.443126 / 1.452155 (-0.009029) | 1.508149 / 1.492716 (0.015432) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183801 / 0.018006 (0.165795) | 0.427967 / 0.000490 (0.427477) | 0.002028 / 0.000200 (0.001828) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023697 / 0.037411 (-0.013715) | 0.072128 / 0.014526 (0.057602) | 0.083701 / 0.176557 (-0.092855) | 0.142821 / 0.737135 (-0.594315) | 0.082276 / 0.296338 (-0.214063) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434427 / 0.215209 (0.219218) | 4.325962 / 2.077655 (2.248308) | 2.277115 / 1.504120 (0.772995) | 2.093736 / 1.541195 (0.552541) | 2.127984 / 1.468490 (0.659494) | 0.502336 / 4.584777 (-4.082441) | 3.023243 / 3.745712 (-0.722469) | 2.805154 / 5.269862 (-2.464708) | 1.821273 / 4.565676 (-2.744403) | 0.057480 / 0.424275 (-0.366795) | 0.006365 / 0.007607 (-0.001242) | 0.508258 / 0.226044 (0.282213) | 5.087950 / 2.268929 (2.819022) | 2.705029 / 55.444624 (-52.739596) | 2.378392 / 6.876477 (-4.498085) | 2.515380 / 2.142072 (0.373307) | 0.589283 / 4.805227 (-4.215944) | 0.125719 / 6.500664 (-6.374945) | 0.061074 / 0.075469 (-0.014395) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221895 / 1.841788 (-0.619893) | 18.025917 / 8.074308 (9.951609) | 13.556901 / 10.191392 (3.365509) | 0.142614 / 0.680424 (-0.537809) | 0.016731 / 0.534201 (-0.517469) | 0.328374 / 0.579283 (-0.250910) | 0.342553 / 0.434364 (-0.091811) | 0.374502 / 0.540337 (-0.165836) | 0.534173 / 1.386936 (-0.852763) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005817 / 0.011353 (-0.005536) | 0.003500 / 0.011008 (-0.007509) | 0.062240 / 0.038508 (0.023732) | 0.058128 / 0.023109 (0.035019) | 0.424014 / 0.275898 (0.148116) | 0.468453 / 0.323480 (0.144973) | 0.004641 / 0.007986 (-0.003345) | 0.002821 / 0.004328 (-0.001508) | 0.062180 / 0.004250 (0.057930) | 0.047578 / 0.037052 (0.010526) | 0.427367 / 0.258489 (0.168878) | 0.467889 / 0.293841 (0.174048) | 0.027144 / 0.128546 (-0.101403) | 0.007969 / 0.075646 (-0.067678) | 0.067764 / 0.419271 (-0.351508) | 0.040719 / 0.043533 (-0.002814) | 0.423663 / 0.255139 (0.168524) | 0.458556 / 0.283200 (0.175356) | 0.019196 / 0.141683 (-0.122487) | 1.471546 / 1.452155 (0.019392) | 1.547541 / 1.492716 (0.054825) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228777 / 0.018006 (0.210770) | 0.406663 / 0.000490 (0.406173) | 0.003688 / 0.000200 (0.003488) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025494 / 0.037411 (-0.011917) | 0.076339 / 0.014526 (0.061814) | 0.084233 / 0.176557 (-0.092324) | 0.136995 / 0.737135 (-0.600140) | 0.085443 / 0.296338 (-0.210895) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420441 / 0.215209 (0.205232) | 4.187018 / 2.077655 (2.109363) | 2.142139 / 1.504120 (0.638019) | 1.974530 / 1.541195 (0.433335) | 2.027321 / 1.468490 (0.558831) | 0.498116 / 4.584777 (-4.086661) | 2.988514 / 3.745712 (-0.757198) | 2.782046 / 5.269862 (-2.487816) | 1.821725 / 4.565676 (-2.743951) | 0.057711 / 0.424275 (-0.366564) | 0.006664 / 0.007607 (-0.000944) | 0.491015 / 0.226044 (0.264971) | 4.921037 / 2.268929 (2.652108) | 2.574964 / 55.444624 (-52.869661) | 2.251703 / 6.876477 (-4.624774) | 2.361154 / 2.142072 (0.219082) | 0.593362 / 4.805227 (-4.211865) | 0.126107 / 6.500664 (-6.374557) | 0.061840 / 0.075469 (-0.013630) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.327459 / 1.841788 (-0.514328) | 18.062960 / 8.074308 (9.988652) | 13.669253 / 10.191392 (3.477861) | 0.130719 / 0.680424 (-0.549705) | 0.016564 / 0.534201 (-0.517637) | 0.335821 / 0.579283 (-0.243462) | 0.341691 / 0.434364 (-0.092673) | 0.392651 / 0.540337 (-0.147686) | 0.529650 / 1.386936 (-0.857286) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65806b0542996e56825ab46a3ce8f9c07ab0df3 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009625 / 0.011353 (-0.001728) | 0.005354 / 0.011008 (-0.005654) | 0.114350 / 0.038508 (0.075842) | 0.086637 / 0.023109 (0.063528) | 0.465381 / 0.275898 (0.189483) | 0.490411 / 0.323480 (0.166931) | 0.006575 / 0.007986 (-0.001411) | 0.004287 / 0.004328 (-0.000041) | 0.093134 / 0.004250 (0.088884) | 0.060209 / 0.037052 (0.023156) | 0.459570 / 0.258489 (0.201080) | 0.523320 / 0.293841 (0.229479) | 0.047943 / 0.128546 (-0.080603) | 0.014764 / 0.075646 (-0.060882) | 0.383887 / 0.419271 (-0.035384) | 0.069864 / 0.043533 (0.026331) | 0.469122 / 0.255139 (0.213983) | 0.509953 / 0.283200 (0.226753) | 0.037800 / 0.141683 (-0.103883) | 1.877589 / 1.452155 (0.425434) | 2.014913 / 1.492716 (0.522197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.309146 / 0.018006 (0.291140) | 0.644390 / 0.000490 (0.643900) | 0.005017 / 0.000200 (0.004817) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032964 / 0.037411 (-0.004447) | 0.103236 / 0.014526 (0.088711) | 0.119950 / 0.176557 (-0.056607) | 0.207674 / 0.737135 (-0.529461) | 0.117278 / 0.296338 (-0.179060) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605464 / 0.215209 (0.390255) | 6.027805 / 2.077655 (3.950150) | 2.719725 / 1.504120 (1.215605) | 2.262752 / 1.541195 (0.721558) | 2.330310 / 1.468490 (0.861820) | 0.862537 / 4.584777 (-3.722240) | 5.347080 / 3.745712 (1.601368) | 4.792170 / 5.269862 (-0.477691) | 3.103694 / 4.565676 (-1.461983) | 0.103646 / 0.424275 (-0.320629) | 0.009411 / 0.007607 (0.001804) | 0.743052 / 0.226044 (0.517008) | 7.289684 / 2.268929 (5.020755) | 3.436530 / 55.444624 (-52.008094) | 2.722440 / 6.876477 (-4.154036) | 2.952380 / 2.142072 (0.810308) | 1.047688 / 4.805227 (-3.757539) | 0.212724 / 6.500664 (-6.287940) | 0.081473 / 0.075469 (0.006004) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.714437 / 1.841788 (-0.127351) | 24.384330 / 8.074308 (16.310022) | 22.444162 / 10.191392 (12.252770) | 0.226264 / 0.680424 (-0.454160) | 0.030530 / 0.534201 (-0.503671) | 0.473999 / 0.579283 (-0.105284) | 0.575005 / 0.434364 (0.140641) | 0.542789 / 0.540337 (0.002451) | 0.776079 / 1.386936 (-0.610857) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009366 / 0.011353 (-0.001987) | 0.005239 / 0.011008 (-0.005769) | 0.085116 / 0.038508 (0.046608) | 0.089600 / 0.023109 (0.066491) | 0.485778 / 0.275898 (0.209880) | 0.540054 / 0.323480 (0.216574) | 0.006290 / 0.007986 (-0.001695) | 0.004054 / 0.004328 (-0.000274) | 0.083535 / 0.004250 (0.079284) | 0.067200 / 0.037052 (0.030148) | 0.519520 / 0.258489 (0.261031) | 0.544049 / 0.293841 (0.250208) | 0.054300 / 0.128546 (-0.074246) | 0.013650 / 0.075646 (-0.061996) | 0.102515 / 0.419271 (-0.316757) | 0.063054 / 0.043533 (0.019522) | 0.491724 / 0.255139 (0.236585) | 0.547498 / 0.283200 (0.264298) | 0.039266 / 0.141683 (-0.102416) | 1.801226 / 1.452155 (0.349071) | 1.861778 / 1.492716 (0.369061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313009 / 0.018006 (0.295003) | 0.587695 / 0.000490 (0.587205) | 0.004972 / 0.000200 (0.004772) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029230 / 0.037411 (-0.008181) | 0.091154 / 0.014526 (0.076628) | 0.110505 / 0.176557 (-0.066052) | 0.164204 / 0.737135 (-0.572932) | 0.107812 / 0.296338 (-0.188526) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.610535 / 0.215209 (0.395326) | 6.162517 / 2.077655 (4.084862) | 2.866718 / 1.504120 (1.362598) | 2.542412 / 1.541195 (1.001218) | 2.584136 / 1.468490 (1.115645) | 0.874319 / 4.584777 (-3.710458) | 5.257184 / 3.745712 (1.511472) | 4.705840 / 5.269862 (-0.564022) | 2.971708 / 4.565676 (-1.593969) | 0.099026 / 0.424275 (-0.325249) | 0.009142 / 0.007607 (0.001535) | 0.728660 / 0.226044 (0.502615) | 7.560922 / 2.268929 (5.291994) | 3.439521 / 55.444624 (-52.005103) | 2.854730 / 6.876477 (-4.021746) | 3.088951 / 2.142072 (0.946879) | 0.973621 / 4.805227 (-3.831606) | 0.209792 / 6.500664 (-6.290872) | 0.081107 / 0.075469 (0.005638) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.716809 / 1.841788 (-0.124978) | 24.386927 / 8.074308 (16.312619) | 20.715524 / 10.191392 (10.524131) | 0.260831 / 0.680424 (-0.419592) | 0.030701 / 0.534201 (-0.503500) | 0.490018 / 0.579283 (-0.089265) | 0.590424 / 0.434364 (0.156060) | 0.589942 / 0.540337 (0.049604) | 0.798094 / 1.386936 (-0.588842) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0a77dc943de68a17f23f141517028c734c78623 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006592 / 0.011353 (-0.004761) | 0.003880 / 0.011008 (-0.007128) | 0.083761 / 0.038508 (0.045253) | 0.075966 / 0.023109 (0.052857) | 0.315291 / 0.275898 (0.039393) | 0.355920 / 0.323480 (0.032440) | 0.004972 / 0.007986 (-0.003014) | 0.003053 / 0.004328 (-0.001275) | 0.063553 / 0.004250 (0.059302) | 0.050794 / 0.037052 (0.013742) | 0.317681 / 0.258489 (0.059192) | 0.361991 / 0.293841 (0.068150) | 0.028119 / 0.128546 (-0.100427) | 0.008203 / 0.075646 (-0.067443) | 0.271756 / 0.419271 (-0.147516) | 0.046701 / 0.043533 (0.003168) | 0.316520 / 0.255139 (0.061381) | 0.350499 / 0.283200 (0.067300) | 0.022399 / 0.141683 (-0.119284) | 1.416017 / 1.452155 (-0.036138) | 1.503087 / 1.492716 (0.010371) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208250 / 0.018006 (0.190244) | 0.470345 / 0.000490 (0.469856) | 0.003687 / 0.000200 (0.003487) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026163 / 0.037411 (-0.011248) | 0.083315 / 0.014526 (0.068789) | 0.088541 / 0.176557 (-0.088015) | 0.150078 / 0.737135 (-0.587057) | 0.088862 / 0.296338 (-0.207476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404911 / 0.215209 (0.189702) | 4.059257 / 2.077655 (1.981602) | 1.890987 / 1.504120 (0.386867) | 1.726608 / 1.541195 (0.185413) | 1.767479 / 1.468490 (0.298989) | 0.518826 / 4.584777 (-4.065951) | 3.212145 / 3.745712 (-0.533567) | 3.029933 / 5.269862 (-2.239929) | 2.000203 / 4.565676 (-2.565474) | 0.059631 / 0.424275 (-0.364644) | 0.006707 / 0.007607 (-0.000900) | 0.485741 / 0.226044 (0.259697) | 4.871938 / 2.268929 (2.603010) | 2.418856 / 55.444624 (-53.025769) | 2.084847 / 6.876477 (-4.791630) | 2.207992 / 2.142072 (0.065920) | 0.614354 / 4.805227 (-4.190873) | 0.128932 / 6.500664 (-6.371732) | 0.062342 / 0.075469 (-0.013127) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.325792 / 1.841788 (-0.515995) | 19.718995 / 8.074308 (11.644687) | 15.278535 / 10.191392 (5.087143) | 0.146719 / 0.680424 (-0.533705) | 0.017718 / 0.534201 (-0.516483) | 0.335709 / 0.579283 (-0.243574) | 0.378060 / 0.434364 (-0.056304) | 0.391135 / 0.540337 (-0.149202) | 0.548045 / 1.386936 (-0.838891) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006504 / 0.011353 (-0.004849) | 0.003742 / 0.011008 (-0.007266) | 0.064405 / 0.038508 (0.025897) | 0.077618 / 0.023109 (0.054509) | 0.365325 / 0.275898 (0.089427) | 0.408109 / 0.323480 (0.084629) | 0.004909 / 0.007986 (-0.003076) | 0.002972 / 0.004328 (-0.001356) | 0.063933 / 0.004250 (0.059682) | 0.052916 / 0.037052 (0.015863) | 0.370891 / 0.258489 (0.112402) | 0.412134 / 0.293841 (0.118293) | 0.028171 / 0.128546 (-0.100375) | 0.008150 / 0.075646 (-0.067497) | 0.069248 / 0.419271 (-0.350024) | 0.042353 / 0.043533 (-0.001180) | 0.368117 / 0.255139 (0.112978) | 0.397548 / 0.283200 (0.114348) | 0.022967 / 0.141683 (-0.118716) | 1.472740 / 1.452155 (0.020586) | 1.524028 / 1.492716 (0.031311) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256854 / 0.018006 (0.238848) | 0.471499 / 0.000490 (0.471009) | 0.009609 / 0.000200 (0.009409) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027978 / 0.037411 (-0.009433) | 0.086741 / 0.014526 (0.072215) | 0.091189 / 0.176557 (-0.085368) | 0.146117 / 0.737135 (-0.591018) | 0.092358 / 0.296338 (-0.203980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426356 / 0.215209 (0.211147) | 4.263782 / 2.077655 (2.186127) | 2.178198 / 1.504120 (0.674078) | 2.015405 / 1.541195 (0.474211) | 2.055966 / 1.468490 (0.587476) | 0.507531 / 4.584777 (-4.077246) | 3.175967 / 3.745712 (-0.569745) | 3.055697 / 5.269862 (-2.214165) | 1.987663 / 4.565676 (-2.578014) | 0.058452 / 0.424275 (-0.365823) | 0.006944 / 0.007607 (-0.000663) | 0.502534 / 0.226044 (0.276489) | 5.024693 / 2.268929 (2.755765) | 2.754971 / 55.444624 (-52.689653) | 2.470845 / 6.876477 (-4.405632) | 2.698675 / 2.142072 (0.556602) | 0.602357 / 4.805227 (-4.202871) | 0.129490 / 6.500664 (-6.371174) | 0.065127 / 0.075469 (-0.010342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.398487 / 1.841788 (-0.443301) | 19.692279 / 8.074308 (11.617971) | 15.124064 / 10.191392 (4.932672) | 0.148938 / 0.680424 (-0.531486) | 0.017418 / 0.534201 (-0.516783) | 0.340480 / 0.579283 (-0.238803) | 0.377223 / 0.434364 (-0.057141) | 0.405303 / 0.540337 (-0.135034) | 0.548923 / 1.386936 (-0.838013) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58e62af004b6b8b84dcfd897a4bc71637cfa6c3f \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006433 / 0.011353 (-0.004920) | 0.004002 / 0.011008 (-0.007006) | 0.084130 / 0.038508 (0.045622) | 0.070628 / 0.023109 (0.047519) | 0.312372 / 0.275898 (0.036474) | 0.343993 / 0.323480 (0.020513) | 0.003936 / 0.007986 (-0.004050) | 0.003336 / 0.004328 (-0.000993) | 0.064715 / 0.004250 (0.060465) | 0.052511 / 0.037052 (0.015458) | 0.314092 / 0.258489 (0.055603) | 0.363152 / 0.293841 (0.069311) | 0.030898 / 0.128546 (-0.097648) | 0.008396 / 0.075646 (-0.067250) | 0.288083 / 0.419271 (-0.131188) | 0.051654 / 0.043533 (0.008122) | 0.315252 / 0.255139 (0.060113) | 0.346756 / 0.283200 (0.063556) | 0.025167 / 0.141683 (-0.116515) | 1.487265 / 1.452155 (0.035110) | 1.557528 / 1.492716 (0.064812) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206517 / 0.018006 (0.188510) | 0.458359 / 0.000490 (0.457869) | 0.003719 / 0.000200 (0.003519) | 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) | 0.029631 / 0.037411 (-0.007780) | 0.083856 / 0.014526 (0.069330) | 0.340431 / 0.176557 (0.163875) | 0.153864 / 0.737135 (-0.583271) | 0.095951 / 0.296338 (-0.200388) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379182 / 0.215209 (0.163973) | 3.783396 / 2.077655 (1.705741) | 1.835932 / 1.504120 (0.331813) | 1.667563 / 1.541195 (0.126369) | 1.739309 / 1.468490 (0.270818) | 0.478957 / 4.584777 (-4.105820) | 3.521974 / 3.745712 (-0.223738) | 3.237635 / 5.269862 (-2.032227) | 2.000300 / 4.565676 (-2.565377) | 0.056389 / 0.424275 (-0.367887) | 0.007242 / 0.007607 (-0.000365) | 0.452642 / 0.226044 (0.226598) | 4.524339 / 2.268929 (2.255411) | 2.346210 / 55.444624 (-53.098414) | 1.957196 / 6.876477 (-4.919281) | 2.180051 / 2.142072 (0.037979) | 0.570205 / 4.805227 (-4.235022) | 0.131346 / 6.500664 (-6.369318) | 0.059327 / 0.075469 (-0.016142) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244709 / 1.841788 (-0.597079) | 19.566277 / 8.074308 (11.491969) | 14.172598 / 10.191392 (3.981206) | 0.166493 / 0.680424 (-0.513931) | 0.018281 / 0.534201 (-0.515920) | 0.391608 / 0.579283 (-0.187675) | 0.402642 / 0.434364 (-0.031722) | 0.464974 / 0.540337 (-0.075364) | 0.637565 / 1.386936 (-0.749371) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006929 / 0.011353 (-0.004424) | 0.004114 / 0.011008 (-0.006894) | 0.064589 / 0.038508 (0.026081) | 0.083334 / 0.023109 (0.060225) | 0.391280 / 0.275898 (0.115382) | 0.426157 / 0.323480 (0.102678) | 0.005336 / 0.007986 (-0.002650) | 0.003395 / 0.004328 (-0.000934) | 0.064560 / 0.004250 (0.060310) | 0.057094 / 0.037052 (0.020042) | 0.398959 / 0.258489 (0.140470) | 0.432470 / 0.293841 (0.138629) | 0.031412 / 0.128546 (-0.097134) | 0.008670 / 0.075646 (-0.066976) | 0.071249 / 0.419271 (-0.348022) | 0.048934 / 0.043533 (0.005401) | 0.384207 / 0.255139 (0.129068) | 0.407992 / 0.283200 (0.124792) | 0.024492 / 0.141683 (-0.117191) | 1.467788 / 1.452155 (0.015634) | 1.541011 / 1.492716 (0.048295) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279607 / 0.018006 (0.261600) | 0.448899 / 0.000490 (0.448410) | 0.020990 / 0.000200 (0.020790) | 0.000132 / 0.000054 (0.000078) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030313 / 0.037411 (-0.007099) | 0.089209 / 0.014526 (0.074684) | 0.101024 / 0.176557 (-0.075532) | 0.153468 / 0.737135 (-0.583667) | 0.103219 / 0.296338 (-0.193120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429176 / 0.215209 (0.213967) | 4.302234 / 2.077655 (2.224580) | 2.291103 / 1.504120 (0.786983) | 2.126257 / 1.541195 (0.585062) | 2.207090 / 1.468490 (0.738600) | 0.484643 / 4.584777 (-4.100134) | 3.557429 / 3.745712 (-0.188283) | 3.253804 / 5.269862 (-2.016058) | 2.026087 / 4.565676 (-2.539589) | 0.057793 / 0.424275 (-0.366482) | 0.007761 / 0.007607 (0.000154) | 0.504819 / 0.226044 (0.278775) | 5.046868 / 2.268929 (2.777940) | 2.773149 / 55.444624 (-52.671475) | 2.398036 / 6.876477 (-4.478440) | 2.608094 / 2.142072 (0.466021) | 0.630499 / 4.805227 (-4.174729) | 0.135496 / 6.500664 (-6.365168) | 0.061329 / 0.075469 (-0.014140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.327124 / 1.841788 (-0.514664) | 19.889796 / 8.074308 (11.815488) | 14.196100 / 10.191392 (4.004708) | 0.161963 / 0.680424 (-0.518461) | 0.018529 / 0.534201 (-0.515672) | 0.392325 / 0.579283 (-0.186958) | 0.404836 / 0.434364 (-0.029528) | 0.475898 / 0.540337 (-0.064439) | 0.633563 / 1.386936 (-0.753373) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4684fc1032321abf0d494b0c130ea7c82ebda80 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006390 / 0.011353 (-0.004963) | 0.003683 / 0.011008 (-0.007325) | 0.081274 / 0.038508 (0.042766) | 0.062193 / 0.023109 (0.039083) | 0.355360 / 0.275898 (0.079462) | 0.396471 / 0.323480 (0.072992) | 0.003569 / 0.007986 (-0.004416) | 0.003928 / 0.004328 (-0.000400) | 0.062292 / 0.004250 (0.058041) | 0.049700 / 0.037052 (0.012648) | 0.354604 / 0.258489 (0.096115) | 0.419436 / 0.293841 (0.125595) | 0.027151 / 0.128546 (-0.101395) | 0.007954 / 0.075646 (-0.067692) | 0.262231 / 0.419271 (-0.157041) | 0.045483 / 0.043533 (0.001950) | 0.354285 / 0.255139 (0.099146) | 0.385178 / 0.283200 (0.101978) | 0.021183 / 0.141683 (-0.120500) | 1.420785 / 1.452155 (-0.031370) | 1.531545 / 1.492716 (0.038829) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202298 / 0.018006 (0.184292) | 0.442172 / 0.000490 (0.441683) | 0.003565 / 0.000200 (0.003366) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024229 / 0.037411 (-0.013183) | 0.074352 / 0.014526 (0.059826) | 0.087530 / 0.176557 (-0.089026) | 0.146478 / 0.737135 (-0.590658) | 0.085145 / 0.296338 (-0.211194) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388395 / 0.215209 (0.173186) | 3.877623 / 2.077655 (1.799968) | 1.882444 / 1.504120 (0.378324) | 1.707871 / 1.541195 (0.166676) | 1.772132 / 1.468490 (0.303642) | 0.491937 / 4.584777 (-4.092840) | 3.057947 / 3.745712 (-0.687765) | 2.822390 / 5.269862 (-2.447471) | 1.879719 / 4.565676 (-2.685957) | 0.056830 / 0.424275 (-0.367445) | 0.006415 / 0.007607 (-0.001192) | 0.458945 / 0.226044 (0.232900) | 4.594502 / 2.268929 (2.325574) | 2.339677 / 55.444624 (-53.104948) | 1.983750 / 6.876477 (-4.892727) | 2.173792 / 2.142072 (0.031719) | 0.580390 / 4.805227 (-4.224838) | 0.124568 / 6.500664 (-6.376096) | 0.061694 / 0.075469 (-0.013775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265108 / 1.841788 (-0.576680) | 18.415254 / 8.074308 (10.340946) | 13.963829 / 10.191392 (3.772437) | 0.148926 / 0.680424 (-0.531498) | 0.016919 / 0.534201 (-0.517282) | 0.331082 / 0.579283 (-0.248201) | 0.345777 / 0.434364 (-0.088587) | 0.381123 / 0.540337 (-0.159214) | 0.543297 / 1.386936 (-0.843639) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006121 / 0.011353 (-0.005232) | 0.003717 / 0.011008 (-0.007291) | 0.063653 / 0.038508 (0.025144) | 0.063723 / 0.023109 (0.040613) | 0.360233 / 0.275898 (0.084335) | 0.398353 / 0.323480 (0.074873) | 0.004696 / 0.007986 (-0.003290) | 0.002876 / 0.004328 (-0.001452) | 0.063057 / 0.004250 (0.058806) | 0.050258 / 0.037052 (0.013206) | 0.362946 / 0.258489 (0.104457) | 0.403260 / 0.293841 (0.109419) | 0.027738 / 0.128546 (-0.100809) | 0.008025 / 0.075646 (-0.067621) | 0.068781 / 0.419271 (-0.350491) | 0.042114 / 0.043533 (-0.001419) | 0.363546 / 0.255139 (0.108407) | 0.385640 / 0.283200 (0.102440) | 0.021757 / 0.141683 (-0.119926) | 1.482364 / 1.452155 (0.030209) | 1.571859 / 1.492716 (0.079143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235628 / 0.018006 (0.217622) | 0.439909 / 0.000490 (0.439419) | 0.003070 / 0.000200 (0.002870) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027045 / 0.037411 (-0.010366) | 0.080413 / 0.014526 (0.065887) | 0.088953 / 0.176557 (-0.087603) | 0.141907 / 0.737135 (-0.595228) | 0.090604 / 0.296338 (-0.205735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423250 / 0.215209 (0.208041) | 4.216510 / 2.077655 (2.138855) | 2.162946 / 1.504120 (0.658826) | 2.014561 / 1.541195 (0.473366) | 2.086347 / 1.468490 (0.617857) | 0.496591 / 4.584777 (-4.088186) | 3.089594 / 3.745712 (-0.656118) | 2.853640 / 5.269862 (-2.416221) | 1.878149 / 4.565676 (-2.687527) | 0.056914 / 0.424275 (-0.367361) | 0.006762 / 0.007607 (-0.000845) | 0.493470 / 0.226044 (0.267426) | 4.929966 / 2.268929 (2.661037) | 2.640885 / 55.444624 (-52.803739) | 2.335950 / 6.876477 (-4.540527) | 2.565866 / 2.142072 (0.423793) | 0.585433 / 4.805227 (-4.219794) | 0.124969 / 6.500664 (-6.375695) | 0.062361 / 0.075469 (-0.013108) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369144 / 1.841788 (-0.472644) | 19.037582 / 8.074308 (10.963274) | 14.069141 / 10.191392 (3.877749) | 0.146469 / 0.680424 (-0.533954) | 0.016911 / 0.534201 (-0.517290) | 0.336802 / 0.579283 (-0.242482) | 0.336411 / 0.434364 (-0.097953) | 0.392360 / 0.540337 (-0.147977) | 0.536078 / 1.386936 (-0.850858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12cfc1196e62847e2e8239fbd727a02cbc86ddec \"CML watermark\")\n"
] | 2023-08-07T15:41:25Z
| 2023-08-08T15:24:59Z
| 2023-08-08T15:16:22Z
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This PR fixes 3 authentication issues:
- Fix authentication when passing `token`.
- Fix authentication in `Audio.decode_example` and `Image.decode_example`.
- Fix authentication to resolve `data_files` in repositories without script.
This PR also fixes our CI so that we properly test when passing `token` and we do not use the token stored in `HfFolder`.
Fix #6126.
## Details
### Fix authentication when passing `token`
See c0a77dc943de68a17f23f141517028c734c78623
The root issue was caused when the `token` was set in an already instantiated `DownloadConfig` and thus not propagated to `self._storage_options`:
```python
download_config.token = token
```
As this usage pattern is very common, the fix consists in overriding `DownloadConfig.__setattr__`.
This fixes authentication issues in the following functions:
- `load_dataset` and `load_dataset_builder`
- `Dataset.push_to_hub` and `Dataset.push_to_hub`
- `inspect.get_dataset_config_info`, `inspect.get_dataset_infos` and `inspect.get_dataset_split_names`
### Fix authentication in `Audio.decode_example` and `Image.decode_example`.
See: 58e62af004b6b8b84dcfd897a4bc71637cfa6c3f
The `token` was not set because the `repo_id` was wrongly tried to be parsed from an HTTP URL (`"http://..."`), instead of an HFFileSystem URL (`"hf://"`)
### Fix authentication to resolve `data_files` in repositories without script
See: e4684fc1032321abf0d494b0c130ea7c82ebda80
This is fixed by passing `download_config` to the function `create_builder_configs_from_metadata_configs`
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Update README.md
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This PR avoids the creation of empty `LICENSE` files.
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Add Rico Dataset
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"Hi ! Thanks for adding this dataset :)\r\n\r\nRegarding your questions:\r\n1. We can have them as different configuration of the `rico` dataset\r\n2. Yes please use the path to the image and not open the image directly, so that we can let users open the image one at at time during training if they want to for example. In the future we'll have an Image feature type that will decode the encoded image data on the fly when accessing the examples.\r\n3. Feel free to keep the hierarchies as strings if they don't follow a fixed format\r\n4. You can just return the path\r\n\r\n",
"Thanks for your contribution, @ncoop57. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help."
] | 2021-06-11T20:17:41Z
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Hi there!
I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib.
1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset?
You can see the datasets available for Rico here: http://interactionmining.org/rico
2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset?
2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image?
3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently?
4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string?
I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: !
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Better messages regarding split naming
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I made explicit the error message when a bad split name is used.
Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol.
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MDExOlB1bGxSZXF1ZXN0NTMxNjE3MDI2
| 1,039
|
Update ADD NEW DATASET
|
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[] |
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[] | null |
[] | 2020-12-03T08:58:32Z
| 2020-12-03T09:18:28Z
| 2020-12-03T09:18:10Z
|
CONTRIBUTOR
| null | 0
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This PR adds a couple of detail on cloning/rebasing the repo.
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