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2025-08-23 00:18:43
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timestamp[s]date 2021-07-26 13:27:59
2025-08-23 12:34:39
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2025-08-20 16:35:55
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1,640,840,012
| 5,671
|
How to use `load_dataset('glue', 'cola')`
|
closed
| 2023-03-26T09:40:34
| 2023-03-28T07:43:44
| 2023-03-28T07:43:43
|
https://github.com/huggingface/datasets/issues/5671
| null |
makinzm
| false
|
[
"Sounds like an issue with incompatible `transformers` dependencies versions.\r\n\r\nCan you try to update `transformers` ?\r\n\r\nEDIT: I checked the `transformers` dependencies and it seems like you need `tokenizers>=0.10.1,<0.11` with `transformers==4.5.1`\r\n\r\nEDIT2: this old version of `datasets` seems to import `transformers` but it's no longer the case, so you could also simply update `datasets` and `transformers` won't be imported",
"Thank you for advising me to update these libraries versions.\r\n\r\nI can implement codes using `datasets==2.10.1` and `transformers==4.27.3`"
] |
1,640,607,045
| 5,670
|
Unable to load multi class classification datasets
|
closed
| 2023-03-25T18:06:15
| 2023-03-27T22:54:56
| 2023-03-27T22:54:56
|
https://github.com/huggingface/datasets/issues/5670
| null |
ysahil97
| false
|
[
"Hi ! This sounds related to https://github.com/huggingface/datasets/issues/5406\r\n\r\nUpdating `datasets` fixes the issue ;)",
"Thanks @lhoestq!\r\n\r\nI'll close this issue now."
] |
1,638,070,046
| 5,669
|
Almost identical datasets, huge performance difference
|
open
| 2023-03-23T18:20:20
| 2023-04-09T18:56:23
| null |
https://github.com/huggingface/datasets/issues/5669
| null |
eli-osherovich
| false
|
[
"Do I miss something here?",
"Hi! \r\n\r\nThe first dataset stores images as bytes (the \"image\" column type is `datasets.Image()`) and decodes them as `PIL.Image` objects and the second dataset stores them as variable-length lists (the \"image\" column type is `datasets.Sequence(...)`)), so I guess going from `arrow bytes -> NumPy -> decoding as PIL.Image -> PyTorch` is faster than going from `arrow list -> NumPy -> PyTorch`. \r\n\r\nTo store image bytes in the second example, you can do the following:\r\n\r\n```python\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"])\r\n return example\r\n\r\nfeatures = dataset.features.copy()\r\ndel features[\"image\"]\r\nfeatures[\"image2\"] = datasets.Image()\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"], features=features)\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```",
"Thanks, @mariosasko. I could not understand why a (decoded) sequence should be MUCH slower than an encoded image (that must be decoded every time). At any rate, I tried you suggestion. It made the `map` step to run extremely slow (consumes all the 16GB of memory and starts swapping)\r\n\r\nI tried also the easiest (as I see it) scenario, where images are kept as bytes, but it made things even worse: not only it was extremely slow, but also crashes\r\n\r\n```python\r\n\r\ndef transform(example):\r\n example[\"image2\"] = cv2.imread(example[\"image_file_path\"]).tobytes()\r\n return example\r\n\r\ndataset2 = dataset.map(transform, remove_columns=[\"image\"])\r\n\r\nfor x in DataLoader(dataset2.with_format(\"torch\"), batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n\r\n\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nOutput exceeds the size limit. Open the full output data in a text editor\r\n---------------------------------------------------------------------------\r\nRuntimeError Traceback (most recent call last)\r\nFile ~/virtenvs/py310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1133, in _MultiProcessingDataLoaderIter._try_get_data(self, timeout)\r\n 1132 try:\r\n-> 1133 data = self._data_queue.get(timeout=timeout)\r\n 1134 return (True, data)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/queues.py:113, in Queue.get(self, block, timeout)\r\n 112 timeout = deadline - time.monotonic()\r\n--> 113 if not self._poll(timeout):\r\n 114 raise Empty\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:257, in _ConnectionBase.poll(self, timeout)\r\n 256 self._check_readable()\r\n--> 257 return self._poll(timeout)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:424, in Connection._poll(self, timeout)\r\n 423 def _poll(self, timeout):\r\n--> 424 r = wait([self], timeout)\r\n 425 return bool(r)\r\n\r\nFile ~/virtenvs/py310/lib/python3.10/multiprocessing/connection.py:931, in wait(object_list, timeout)\r\n 930 while True:\r\n--> 931 ready = selector.select(timeout)\r\n 932 if ready:\r\n...\r\n-> 1146 raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e\r\n 1147 if isinstance(e, queue.Empty):\r\n 1148 return (False, None)\r\n\r\nRuntimeError: DataLoader worker (pid(s) 195393) exited unexpectedly\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\nResource temporarily unavailable (src/thread.cpp:269)\r\n```\r\n",
"Correction: the `beans` dataset stores the image file paths, not the bytes.\r\n\r\nFor your use case, I think it makes more sense to use `with_tranform` than `map` and lazily decode images with `cv2.imread` when indexing an example/batch:\r\n```python\r\nimport cv2\r\n\r\ndef transform(batch):\r\n batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\r\n return batch\r\n\r\ndataset = dataset.with_transform(transform)\r\n```\r\n",
"This is incorrect.\n\nDid you try to run it? dataset[0] returns a tensor of numbers. dataset2[0]\nreturns the same tensor, but after a few long seconds. Looping over a\nthousand of images cannot take 15 minutes.\n\nOn Fri, 24 Mar 2023 at 19:28 Mario Šaško ***@***.***> wrote:\n\n> Correction: the beans dataset stores the image file paths, not the bytes.\n>\n> For your use case, I think it makes more sense to use with_tranform than\n> map and lazily decode images with cv2.imread when accessing an\n> example/batch:\n>\n> import cv2\n> def transform(batch):\n> batch[\"image2\"] = np.stack([cv2.imread(image_file_path) for image_file_path in batch[\"image_file_path\"]])\n> return batch\n> dataset = dataset.with_transform(transform)\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5669#issuecomment-1483084347>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73SHRWXIQX6SCYCJ7ITW5XDUDANCNFSM6AAAAAAWFSHWEM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n",
"I updated the transform with the NumPy -> PyTorch conversion.\r\n\r\nI'm sharing the entire code:\r\n```python\r\nimport cv2\r\nimport numpy as np\r\nimport datasets\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"beans\", split=\"train\")\r\n\r\ndef transform(batch):\r\n # # Pillow decodes as RGB\r\n # batch[\"image\"] = torch.stack([torch.from_numpy(cv2.cvtColor(cv2.imread(image_file_path), cv2.COLOR_BGR2RGB)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"image\"] = torch.stack([torch.from_numpy(cv2.imread(image_file_path)) for image_file_path in batch[\"image_file_path\"]])\r\n batch[\"labels\"] = torch.tensor(batch[\"labels\"])\r\n return batch\r\n\r\ndataset2 = dataset.cast_column(\"image\", datasets.Image(decode=False)).with_transform(transform)\r\n\r\nfor x in DataLoader(dataset2, batch_size=16, shuffle=True, num_workers=8):\r\n pass\r\n```\r\n\r\nThis code is ≈ 10% faster on my machine than the default decoding with Pillow and `.with_format(\"torch\")`.",
"Thanks, @mariosasko \r\nMy question remain unanswered though. Why is the `map`ed dataset so slow? My understanding is that a dataset of numpy arrays should be must faster than a dataset that has to decode images into numpy arrays every time one accesses an item. "
] |
1,638,018,598
| 5,668
|
Support for downloading only provided split
|
open
| 2023-03-23T17:53:39
| 2023-03-24T06:43:14
| null |
https://github.com/huggingface/datasets/pull/5668
|
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"merged_at": null
}
|
polinaeterna
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5668). All of your documentation changes will be reflected on that endpoint.",
"My previous comment didn't create the retro-link in the PR. I write it here again.\r\n\r\nYou can check the context and the discussions we had about this feature enhancement in this PR:\r\n- #2249"
] |
1,637,789,361
| 5,667
|
Jax requires jaxlib
|
closed
| 2023-03-23T15:41:09
| 2023-03-23T16:23:11
| 2023-03-23T16:14:52
|
https://github.com/huggingface/datasets/pull/5667
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5667",
"html_url": "https://github.com/huggingface/datasets/pull/5667",
"diff_url": "https://github.com/huggingface/datasets/pull/5667.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5667.patch",
"merged_at": "2023-03-23T16:14:52"
}
|
lhoestq
| true
|
[
"_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) | 0.008592 / 0.011353 (-0.002761) | 0.005182 / 0.011008 (-0.005826) | 0.097916 / 0.038508 (0.059408) | 0.034612 / 0.023109 (0.011503) | 0.313760 / 0.275898 (0.037862) | 0.353422 / 0.323480 (0.029942) | 0.005880 / 0.007986 (-0.002106) | 0.004123 / 0.004328 (-0.000205) | 0.073634 / 0.004250 (0.069384) | 0.049349 / 0.037052 (0.012297) | 0.317381 / 0.258489 (0.058892) | 0.365821 / 0.293841 (0.071980) | 0.036482 / 0.128546 (-0.092065) | 0.012126 / 0.075646 (-0.063521) | 0.334640 / 0.419271 (-0.084631) | 0.050551 / 0.043533 (0.007018) | 0.310472 / 0.255139 (0.055333) | 0.349049 / 0.283200 (0.065850) | 0.101343 / 0.141683 (-0.040340) | 1.447903 / 1.452155 (-0.004252) | 1.518793 / 1.492716 (0.026077) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210971 / 0.018006 (0.192965) | 0.449471 / 0.000490 (0.448982) | 0.003596 / 0.000200 (0.003396) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027386 / 0.037411 (-0.010025) | 0.112683 / 0.014526 (0.098157) | 0.117603 / 0.176557 (-0.058954) | 0.174186 / 0.737135 (-0.562949) | 0.123510 / 0.296338 (-0.172829) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422595 / 0.215209 (0.207386) | 4.224713 / 2.077655 (2.147058) | 2.006359 / 1.504120 (0.502240) | 1.823767 / 1.541195 (0.282572) | 1.898340 / 1.468490 (0.429849) | 0.721656 / 4.584777 (-3.863121) | 3.823498 / 3.745712 (0.077785) | 2.172380 / 5.269862 (-3.097481) | 1.469773 / 4.565676 (-3.095904) | 0.086978 / 0.424275 (-0.337297) | 0.012642 / 0.007607 (0.005035) | 0.517830 / 0.226044 (0.291785) | 5.171150 / 2.268929 (2.902221) | 2.495238 / 55.444624 (-52.949386) | 2.114380 / 6.876477 (-4.762097) | 2.274329 / 2.142072 (0.132257) | 0.863855 / 4.805227 (-3.941372) | 0.174127 / 6.500664 (-6.326537) | 0.065939 / 0.075469 (-0.009530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208831 / 1.841788 (-0.632957) | 15.016704 / 8.074308 (6.942396) | 14.721231 / 10.191392 (4.529839) | 0.144140 / 0.680424 (-0.536284) | 0.017781 / 0.534201 (-0.516420) | 0.425679 / 0.579283 (-0.153604) | 0.416747 / 0.434364 (-0.017617) | 0.490160 / 0.540337 (-0.050177) | 0.583639 / 1.386936 (-0.803297) |\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) | 0.007670 / 0.011353 (-0.003683) | 0.005383 / 0.011008 (-0.005626) | 0.075756 / 0.038508 (0.037248) | 0.033373 / 0.023109 (0.010263) | 0.341017 / 0.275898 (0.065119) | 0.378890 / 0.323480 (0.055410) | 0.005945 / 0.007986 (-0.002040) | 0.004179 / 0.004328 (-0.000150) | 0.074588 / 0.004250 (0.070337) | 0.048564 / 0.037052 (0.011511) | 0.338774 / 0.258489 (0.080285) | 0.391081 / 0.293841 (0.097240) | 0.036659 / 0.128546 (-0.091887) | 0.012241 / 0.075646 (-0.063406) | 0.086910 / 0.419271 (-0.332361) | 0.049745 / 0.043533 (0.006212) | 0.332810 / 0.255139 (0.077671) | 0.360317 / 0.283200 (0.077117) | 0.103399 / 0.141683 (-0.038283) | 1.456754 / 1.452155 (0.004599) | 1.542644 / 1.492716 (0.049928) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207182 / 0.018006 (0.189176) | 0.455659 / 0.000490 (0.455169) | 0.003609 / 0.000200 (0.003409) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029556 / 0.037411 (-0.007856) | 0.114215 / 0.014526 (0.099690) | 0.127721 / 0.176557 (-0.048836) | 0.177070 / 0.737135 (-0.560065) | 0.128840 / 0.296338 (-0.167499) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428176 / 0.215209 (0.212967) | 4.274324 / 2.077655 (2.196669) | 2.020058 / 1.504120 (0.515938) | 1.823343 / 1.541195 (0.282148) | 1.924688 / 1.468490 (0.456198) | 0.719195 / 4.584777 (-3.865582) | 3.760445 / 3.745712 (0.014733) | 2.133813 / 5.269862 (-3.136049) | 1.364876 / 4.565676 (-3.200801) | 0.087523 / 0.424275 (-0.336752) | 0.013712 / 0.007607 (0.006105) | 0.528403 / 0.226044 (0.302359) | 5.307780 / 2.268929 (3.038851) | 2.496747 / 55.444624 (-52.947877) | 2.169136 / 6.876477 (-4.707341) | 2.235719 / 2.142072 (0.093646) | 0.875281 / 4.805227 (-3.929946) | 0.172369 / 6.500664 (-6.328295) | 0.064667 / 0.075469 (-0.010802) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262594 / 1.841788 (-0.579193) | 15.182681 / 8.074308 (7.108373) | 14.725663 / 10.191392 (4.534271) | 0.180961 / 0.680424 (-0.499462) | 0.017632 / 0.534201 (-0.516569) | 0.427531 / 0.579283 (-0.151752) | 0.431741 / 0.434364 (-0.002622) | 0.503251 / 0.540337 (-0.037087) | 0.597423 / 1.386936 (-0.789513) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4cf224dcb1043a272971ed331a214cf65c504be \"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) | 0.009761 / 0.011353 (-0.001592) | 0.006779 / 0.011008 (-0.004229) | 0.132786 / 0.038508 (0.094277) | 0.037721 / 0.023109 (0.014611) | 0.435685 / 0.275898 (0.159787) | 0.447488 / 0.323480 (0.124009) | 0.006848 / 0.007986 (-0.001137) | 0.005099 / 0.004328 (0.000771) | 0.097384 / 0.004250 (0.093133) | 0.056663 / 0.037052 (0.019610) | 0.463407 / 0.258489 (0.204918) | 0.502544 / 0.293841 (0.208703) | 0.053817 / 0.128546 (-0.074729) | 0.020253 / 0.075646 (-0.055393) | 0.446653 / 0.419271 (0.027382) | 0.064465 / 0.043533 (0.020932) | 0.455375 / 0.255139 (0.200236) | 0.458378 / 0.283200 (0.175178) | 0.109124 / 0.141683 (-0.032559) | 1.957338 / 1.452155 (0.505184) | 1.960391 / 1.492716 (0.467674) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219566 / 0.018006 (0.201560) | 0.558181 / 0.000490 (0.557691) | 0.004678 / 0.000200 (0.004478) | 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) | 0.032643 / 0.037411 (-0.004768) | 0.147375 / 0.014526 (0.132849) | 0.130821 / 0.176557 (-0.045736) | 0.203202 / 0.737135 (-0.533933) | 0.145186 / 0.296338 (-0.151153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665773 / 0.215209 (0.450564) | 6.674021 / 2.077655 (4.596366) | 2.662372 / 1.504120 (1.158253) | 2.333327 / 1.541195 (0.792132) | 2.221413 / 1.468490 (0.752923) | 1.287001 / 4.584777 (-3.297776) | 5.534326 / 3.745712 (1.788614) | 3.188809 / 5.269862 (-2.081052) | 2.261717 / 4.565676 (-2.303960) | 0.151910 / 0.424275 (-0.272366) | 0.020509 / 0.007607 (0.012902) | 0.863608 / 0.226044 (0.637564) | 8.442155 / 2.268929 (6.173227) | 3.438260 / 55.444624 (-52.006364) | 2.692503 / 6.876477 (-4.183974) | 2.810997 / 2.142072 (0.668925) | 1.477345 / 4.805227 (-3.327882) | 0.261942 / 6.500664 (-6.238722) | 0.086347 / 0.075469 (0.010878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.529072 / 1.841788 (-0.312716) | 17.213019 / 8.074308 (9.138711) | 21.887309 / 10.191392 (11.695917) | 0.259660 / 0.680424 (-0.420763) | 0.027916 / 0.534201 (-0.506285) | 0.554103 / 0.579283 (-0.025180) | 0.614566 / 0.434364 (0.180202) | 0.700456 / 0.540337 (0.160119) | 0.756860 / 1.386936 (-0.630077) |\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) | 0.009267 / 0.011353 (-0.002086) | 0.006414 / 0.011008 (-0.004594) | 0.102404 / 0.038508 (0.063896) | 0.034885 / 0.023109 (0.011776) | 0.413191 / 0.275898 (0.137293) | 0.483901 / 0.323480 (0.160422) | 0.006614 / 0.007986 (-0.001372) | 0.004608 / 0.004328 (0.000280) | 0.096717 / 0.004250 (0.092467) | 0.055123 / 0.037052 (0.018071) | 0.417786 / 0.258489 (0.159297) | 0.490886 / 0.293841 (0.197045) | 0.056951 / 0.128546 (-0.071595) | 0.021073 / 0.075646 (-0.054574) | 0.116576 / 0.419271 (-0.302695) | 0.063968 / 0.043533 (0.020435) | 0.420495 / 0.255139 (0.165356) | 0.449667 / 0.283200 (0.166467) | 0.115318 / 0.141683 (-0.026365) | 1.899398 / 1.452155 (0.447243) | 1.992175 / 1.492716 (0.499459) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233076 / 0.018006 (0.215070) | 0.518377 / 0.000490 (0.517887) | 0.000809 / 0.000200 (0.000609) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030951 / 0.037411 (-0.006460) | 0.134940 / 0.014526 (0.120414) | 0.147789 / 0.176557 (-0.028767) | 0.205854 / 0.737135 (-0.531281) | 0.146726 / 0.296338 (-0.149613) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648006 / 0.215209 (0.432797) | 6.416688 / 2.077655 (4.339033) | 2.696462 / 1.504120 (1.192342) | 2.293071 / 1.541195 (0.751877) | 2.319426 / 1.468490 (0.850935) | 1.332398 / 4.584777 (-3.252379) | 5.706956 / 3.745712 (1.961244) | 4.464473 / 5.269862 (-0.805388) | 2.817364 / 4.565676 (-1.748312) | 0.157595 / 0.424275 (-0.266680) | 0.015721 / 0.007607 (0.008114) | 0.806055 / 0.226044 (0.580010) | 7.927795 / 2.268929 (5.658866) | 3.461251 / 55.444624 (-51.983373) | 2.664466 / 6.876477 (-4.212010) | 2.660041 / 2.142072 (0.517968) | 1.531135 / 4.805227 (-3.274092) | 0.260293 / 6.500664 (-6.240371) | 0.077440 / 0.075469 (0.001971) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687325 / 1.841788 (-0.154463) | 17.905080 / 8.074308 (9.830772) | 21.046794 / 10.191392 (10.855402) | 0.245335 / 0.680424 (-0.435089) | 0.026830 / 0.534201 (-0.507371) | 0.510798 / 0.579283 (-0.068485) | 0.590041 / 0.434364 (0.155677) | 0.607440 / 0.540337 (0.067102) | 0.725030 / 1.386936 (-0.661906) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91dcb3636e410a249177f5e0508ed101ad7ee25b \"CML watermark\")\n",
"I self-assigned #5666 and I was working on it... without success: https://github.com/huggingface/datasets/tree/fix-5666\r\n\r\nI think your approach is the right one because installation of jax is not trivial...\r\n\r\nNext time it would be better that you self-assign an issue before working on it, so that we avoid duplicate work... :sweat_smile: ",
"Oh sorry I forgot to self assign this time",
"<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) | 0.008436 / 0.011353 (-0.002917) | 0.005702 / 0.011008 (-0.005306) | 0.113518 / 0.038508 (0.075010) | 0.039639 / 0.023109 (0.016530) | 0.353200 / 0.275898 (0.077302) | 0.382428 / 0.323480 (0.058948) | 0.007419 / 0.007986 (-0.000566) | 0.005640 / 0.004328 (0.001311) | 0.083905 / 0.004250 (0.079655) | 0.053258 / 0.037052 (0.016205) | 0.371069 / 0.258489 (0.112580) | 0.390439 / 0.293841 (0.096598) | 0.042679 / 0.128546 (-0.085867) | 0.013438 / 0.075646 (-0.062208) | 0.390116 / 0.419271 (-0.029155) | 0.068782 / 0.043533 (0.025249) | 0.352620 / 0.255139 (0.097481) | 0.371939 / 0.283200 (0.088739) | 0.126157 / 0.141683 (-0.015525) | 1.694638 / 1.452155 (0.242484) | 1.799211 / 1.492716 (0.306495) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260099 / 0.018006 (0.242092) | 0.489852 / 0.000490 (0.489362) | 0.012549 / 0.000200 (0.012349) | 0.000275 / 0.000054 (0.000221) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032235 / 0.037411 (-0.005177) | 0.125325 / 0.014526 (0.110799) | 0.137242 / 0.176557 (-0.039315) | 0.206566 / 0.737135 (-0.530570) | 0.143260 / 0.296338 (-0.153078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478510 / 0.215209 (0.263301) | 4.746439 / 2.077655 (2.668784) | 2.195072 / 1.504120 (0.690952) | 1.958163 / 1.541195 (0.416969) | 2.028566 / 1.468490 (0.560075) | 0.821289 / 4.584777 (-3.763488) | 4.765529 / 3.745712 (1.019817) | 2.378753 / 5.269862 (-2.891108) | 1.514776 / 4.565676 (-3.050900) | 0.100673 / 0.424275 (-0.323602) | 0.014720 / 0.007607 (0.007113) | 0.606388 / 0.226044 (0.380343) | 5.975285 / 2.268929 (3.706357) | 2.866762 / 55.444624 (-52.577862) | 2.392132 / 6.876477 (-4.484345) | 2.546487 / 2.142072 (0.404415) | 0.982394 / 4.805227 (-3.822833) | 0.201195 / 6.500664 (-6.299469) | 0.077781 / 0.075469 (0.002312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.420613 / 1.841788 (-0.421174) | 17.743030 / 8.074308 (9.668722) | 16.752344 / 10.191392 (6.560951) | 0.167464 / 0.680424 (-0.512960) | 0.020908 / 0.534201 (-0.513293) | 0.502919 / 0.579283 (-0.076364) | 0.506375 / 0.434364 (0.072011) | 0.602695 / 0.540337 (0.062358) | 0.689398 / 1.386936 (-0.697538) |\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) | 0.008713 / 0.011353 (-0.002640) | 0.006152 / 0.011008 (-0.004856) | 0.091264 / 0.038508 (0.052756) | 0.040284 / 0.023109 (0.017174) | 0.417598 / 0.275898 (0.141700) | 0.460141 / 0.323480 (0.136661) | 0.006589 / 0.007986 (-0.001397) | 0.004671 / 0.004328 (0.000343) | 0.089360 / 0.004250 (0.085110) | 0.055113 / 0.037052 (0.018061) | 0.415241 / 0.258489 (0.156752) | 0.470566 / 0.293841 (0.176725) | 0.042963 / 0.128546 (-0.085584) | 0.014421 / 0.075646 (-0.061225) | 0.106333 / 0.419271 (-0.312939) | 0.057810 / 0.043533 (0.014277) | 0.417889 / 0.255139 (0.162750) | 0.444236 / 0.283200 (0.161036) | 0.119508 / 0.141683 (-0.022175) | 1.736209 / 1.452155 (0.284055) | 1.790319 / 1.492716 (0.297602) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219184 / 0.018006 (0.201178) | 0.493931 / 0.000490 (0.493441) | 0.006727 / 0.000200 (0.006527) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034415 / 0.037411 (-0.002996) | 0.132165 / 0.014526 (0.117639) | 0.143138 / 0.176557 (-0.033418) | 0.200052 / 0.737135 (-0.537083) | 0.148906 / 0.296338 (-0.147433) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483686 / 0.215209 (0.268476) | 4.849874 / 2.077655 (2.772220) | 2.374276 / 1.504120 (0.870156) | 2.168334 / 1.541195 (0.627139) | 2.285983 / 1.468490 (0.817493) | 0.833041 / 4.584777 (-3.751735) | 4.665915 / 3.745712 (0.920203) | 4.543559 / 5.269862 (-0.726302) | 2.246926 / 4.565676 (-2.318750) | 0.098490 / 0.424275 (-0.325785) | 0.014934 / 0.007607 (0.007327) | 0.591878 / 0.226044 (0.365834) | 6.039852 / 2.268929 (3.770923) | 2.881244 / 55.444624 (-52.563381) | 2.486297 / 6.876477 (-4.390179) | 2.564642 / 2.142072 (0.422569) | 0.985684 / 4.805227 (-3.819543) | 0.199101 / 6.500664 (-6.301563) | 0.078138 / 0.075469 (0.002669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.647744 / 1.841788 (-0.194043) | 18.986464 / 8.074308 (10.912156) | 17.246575 / 10.191392 (7.055183) | 0.219151 / 0.680424 (-0.461273) | 0.022219 / 0.534201 (-0.511982) | 0.547207 / 0.579283 (-0.032076) | 0.525943 / 0.434364 (0.091579) | 0.616909 / 0.540337 (0.076572) | 0.757423 / 1.386936 (-0.629513) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f423b69cd4371bd03bb819c60450534f8850ad61 \"CML watermark\")\n"
] |
1,637,675,062
| 5,666
|
Support tensorflow 2.12.0 in CI
|
closed
| 2023-03-23T14:37:51
| 2023-03-23T16:14:54
| 2023-03-23T16:14:54
|
https://github.com/huggingface/datasets/issues/5666
| null |
albertvillanova
| false
|
[] |
1,637,193,648
| 5,665
|
Feature request: IterableDataset.push_to_hub
|
closed
| 2023-03-23T09:53:04
| 2025-06-06T16:13:22
| 2025-06-06T16:12:36
|
https://github.com/huggingface/datasets/issues/5665
| null |
NielsRogge
| false
|
[
"+1",
"+1",
"+1, should be possible now? :) https://huggingface.co/blog/xethub-joins-hf",
"Haha we're working hard to integrate Xet in the HF back-end, it will enable cool use cases :)\n\nAnyway about `IterableDataset.push_to_hub`, I'd be happy to to provide guidance and answer questions if anyone wants to start a first simple implementation of this",
"+1",
"+1",
"+1",
"+1\n\nCurrently running into this when filtering [Common Corpus](https://huggingface.co/datasets/PleIAs/common_corpus) for Dutch entries.\n\nExtra points for somehow making it resumable on error. 11 TB is a lot of data to stream on a home connection without encountering any sort of errors along the way.",
"If it can help, IterableDataset does implement `.state_dict()` and `.load_state_dict()` that you can use to resume a stream already",
"+1",
"+1",
"+1",
"Just added a first implementation for `IterableDataset.push_to_hub()` :)\n\nI'll do a new release soon, in the meantime feel free to install `datasets` from source to try it out !"
] |
1,637,192,684
| 5,664
|
Fix CI by temporarily pinning tensorflow < 2.12.0
|
closed
| 2023-03-23T09:52:26
| 2023-03-23T10:17:11
| 2023-03-23T10:09:54
|
https://github.com/huggingface/datasets/pull/5664
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5664",
"html_url": "https://github.com/huggingface/datasets/pull/5664",
"diff_url": "https://github.com/huggingface/datasets/pull/5664.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5664.patch",
"merged_at": "2023-03-23T10:09:53"
}
|
albertvillanova
| true
|
[
"_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) | 0.007500 / 0.011353 (-0.003853) | 0.005279 / 0.011008 (-0.005729) | 0.098848 / 0.038508 (0.060340) | 0.035290 / 0.023109 (0.012181) | 0.342676 / 0.275898 (0.066778) | 0.375310 / 0.323480 (0.051830) | 0.006037 / 0.007986 (-0.001948) | 0.004143 / 0.004328 (-0.000185) | 0.075757 / 0.004250 (0.071506) | 0.049436 / 0.037052 (0.012383) | 0.344734 / 0.258489 (0.086245) | 0.388111 / 0.293841 (0.094270) | 0.037079 / 0.128546 (-0.091467) | 0.011986 / 0.075646 (-0.063660) | 0.333911 / 0.419271 (-0.085361) | 0.050415 / 0.043533 (0.006882) | 0.341723 / 0.255139 (0.086584) | 0.364136 / 0.283200 (0.080936) | 0.099371 / 0.141683 (-0.042312) | 1.467030 / 1.452155 (0.014876) | 1.565472 / 1.492716 (0.072755) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212534 / 0.018006 (0.194528) | 0.435854 / 0.000490 (0.435364) | 0.000419 / 0.000200 (0.000219) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027957 / 0.037411 (-0.009454) | 0.106835 / 0.014526 (0.092309) | 0.115733 / 0.176557 (-0.060824) | 0.172374 / 0.737135 (-0.564761) | 0.121907 / 0.296338 (-0.174431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413195 / 0.215209 (0.197986) | 4.144775 / 2.077655 (2.067120) | 1.885647 / 1.504120 (0.381527) | 1.645525 / 1.541195 (0.104331) | 1.690117 / 1.468490 (0.221627) | 0.705787 / 4.584777 (-3.878989) | 3.763338 / 3.745712 (0.017626) | 2.163044 / 5.269862 (-3.106818) | 1.478619 / 4.565676 (-3.087057) | 0.086458 / 0.424275 (-0.337817) | 0.012711 / 0.007607 (0.005103) | 0.503592 / 0.226044 (0.277547) | 5.031176 / 2.268929 (2.762248) | 2.345348 / 55.444624 (-53.099276) | 2.064573 / 6.876477 (-4.811903) | 2.203937 / 2.142072 (0.061865) | 0.838761 / 4.805227 (-3.966466) | 0.170116 / 6.500664 (-6.330548) | 0.064012 / 0.075469 (-0.011457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190887 / 1.841788 (-0.650901) | 15.091466 / 8.074308 (7.017158) | 14.549112 / 10.191392 (4.357720) | 0.180603 / 0.680424 (-0.499820) | 0.017387 / 0.534201 (-0.516814) | 0.421372 / 0.579283 (-0.157911) | 0.434644 / 0.434364 (0.000281) | 0.496958 / 0.540337 (-0.043380) | 0.593995 / 1.386936 (-0.792941) |\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) | 0.007790 / 0.011353 (-0.003563) | 0.005307 / 0.011008 (-0.005701) | 0.074779 / 0.038508 (0.036271) | 0.034442 / 0.023109 (0.011332) | 0.337973 / 0.275898 (0.062075) | 0.371944 / 0.323480 (0.048464) | 0.006088 / 0.007986 (-0.001897) | 0.005619 / 0.004328 (0.001291) | 0.073757 / 0.004250 (0.069507) | 0.049385 / 0.037052 (0.012333) | 0.338326 / 0.258489 (0.079837) | 0.387916 / 0.293841 (0.094075) | 0.037197 / 0.128546 (-0.091350) | 0.012371 / 0.075646 (-0.063275) | 0.086938 / 0.419271 (-0.332334) | 0.051379 / 0.043533 (0.007846) | 0.331580 / 0.255139 (0.076441) | 0.355765 / 0.283200 (0.072565) | 0.103368 / 0.141683 (-0.038315) | 1.475963 / 1.452155 (0.023808) | 1.530579 / 1.492716 (0.037863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223037 / 0.018006 (0.205031) | 0.441795 / 0.000490 (0.441305) | 0.003937 / 0.000200 (0.003737) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030081 / 0.037411 (-0.007330) | 0.110366 / 0.014526 (0.095841) | 0.124097 / 0.176557 (-0.052459) | 0.176237 / 0.737135 (-0.560898) | 0.127045 / 0.296338 (-0.169293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420191 / 0.215209 (0.204982) | 4.186721 / 2.077655 (2.109066) | 1.992336 / 1.504120 (0.488216) | 1.800567 / 1.541195 (0.259373) | 1.917982 / 1.468490 (0.449491) | 0.700932 / 4.584777 (-3.883845) | 3.888631 / 3.745712 (0.142918) | 2.138168 / 5.269862 (-3.131693) | 1.364636 / 4.565676 (-3.201041) | 0.085404 / 0.424275 (-0.338871) | 0.012550 / 0.007607 (0.004943) | 0.526110 / 0.226044 (0.300066) | 5.258717 / 2.268929 (2.989789) | 2.454287 / 55.444624 (-52.990338) | 2.130539 / 6.876477 (-4.745937) | 2.207982 / 2.142072 (0.065909) | 0.839242 / 4.805227 (-3.965985) | 0.167611 / 6.500664 (-6.333053) | 0.065706 / 0.075469 (-0.009763) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266125 / 1.841788 (-0.575662) | 15.480513 / 8.074308 (7.406205) | 14.959376 / 10.191392 (4.767983) | 0.149195 / 0.680424 (-0.531229) | 0.017881 / 0.534201 (-0.516320) | 0.430863 / 0.579283 (-0.148420) | 0.432878 / 0.434364 (-0.001485) | 0.499605 / 0.540337 (-0.040733) | 0.605592 / 1.386936 (-0.781344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c20230f8d8762fb67523677093e95e773ce88786 \"CML watermark\")\n"
] |
1,637,173,248
| 5,663
|
CI is broken: ModuleNotFoundError: jax requires jaxlib to be installed
|
closed
| 2023-03-23T09:39:43
| 2023-03-23T10:09:55
| 2023-03-23T10:09:55
|
https://github.com/huggingface/datasets/issues/5663
| null |
albertvillanova
| false
|
[] |
1,637,140,813
| 5,662
|
Fix unnecessary dict comprehension
|
closed
| 2023-03-23T09:18:58
| 2023-03-23T09:46:59
| 2023-03-23T09:37:49
|
https://github.com/huggingface/datasets/pull/5662
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5662",
"html_url": "https://github.com/huggingface/datasets/pull/5662",
"diff_url": "https://github.com/huggingface/datasets/pull/5662.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5662.patch",
"merged_at": "2023-03-23T09:37:49"
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"I am merging because the CI error is unrelated.",
"<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) | 0.009448 / 0.011353 (-0.001905) | 0.006156 / 0.011008 (-0.004852) | 0.123656 / 0.038508 (0.085147) | 0.034998 / 0.023109 (0.011889) | 0.374722 / 0.275898 (0.098824) | 0.418912 / 0.323480 (0.095432) | 0.007348 / 0.007986 (-0.000637) | 0.004779 / 0.004328 (0.000450) | 0.097541 / 0.004250 (0.093291) | 0.052523 / 0.037052 (0.015471) | 0.380118 / 0.258489 (0.121628) | 0.429448 / 0.293841 (0.135607) | 0.055156 / 0.128546 (-0.073390) | 0.019884 / 0.075646 (-0.055763) | 0.429613 / 0.419271 (0.010341) | 0.067554 / 0.043533 (0.024021) | 0.373940 / 0.255139 (0.118801) | 0.408115 / 0.283200 (0.124916) | 0.111353 / 0.141683 (-0.030329) | 1.821013 / 1.452155 (0.368858) | 1.972882 / 1.492716 (0.480165) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236686 / 0.018006 (0.218679) | 0.516519 / 0.000490 (0.516029) | 0.009582 / 0.000200 (0.009383) | 0.000404 / 0.000054 (0.000349) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029425 / 0.037411 (-0.007986) | 0.123972 / 0.014526 (0.109446) | 0.133768 / 0.176557 (-0.042789) | 0.207562 / 0.737135 (-0.529573) | 0.142841 / 0.296338 (-0.153497) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618531 / 0.215209 (0.403322) | 6.216854 / 2.077655 (4.139199) | 2.480138 / 1.504120 (0.976018) | 2.139884 / 1.541195 (0.598689) | 2.122992 / 1.468490 (0.654502) | 1.233824 / 4.584777 (-3.350953) | 5.426142 / 3.745712 (1.680430) | 4.891039 / 5.269862 (-0.378822) | 2.767033 / 4.565676 (-1.798643) | 0.142224 / 0.424275 (-0.282051) | 0.015754 / 0.007607 (0.008147) | 0.772210 / 0.226044 (0.546166) | 7.620484 / 2.268929 (5.351556) | 3.141617 / 55.444624 (-52.303007) | 2.471406 / 6.876477 (-4.405070) | 2.648008 / 2.142072 (0.505935) | 1.429281 / 4.805227 (-3.375946) | 0.255981 / 6.500664 (-6.244683) | 0.077710 / 0.075469 (0.002241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.547714 / 1.841788 (-0.294073) | 17.859985 / 8.074308 (9.785677) | 21.791878 / 10.191392 (11.600486) | 0.238569 / 0.680424 (-0.441854) | 0.027520 / 0.534201 (-0.506681) | 0.553960 / 0.579283 (-0.025324) | 0.616165 / 0.434364 (0.181801) | 0.622492 / 0.540337 (0.082154) | 0.716345 / 1.386936 (-0.670591) |\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) | 0.009624 / 0.011353 (-0.001729) | 0.006091 / 0.011008 (-0.004917) | 0.096623 / 0.038508 (0.058115) | 0.034903 / 0.023109 (0.011793) | 0.421009 / 0.275898 (0.145111) | 0.459236 / 0.323480 (0.135756) | 0.007778 / 0.007986 (-0.000207) | 0.004726 / 0.004328 (0.000398) | 0.099603 / 0.004250 (0.095353) | 0.051426 / 0.037052 (0.014373) | 0.420461 / 0.258489 (0.161972) | 0.469747 / 0.293841 (0.175906) | 0.053769 / 0.128546 (-0.074777) | 0.020636 / 0.075646 (-0.055011) | 0.115785 / 0.419271 (-0.303486) | 0.062692 / 0.043533 (0.019160) | 0.419388 / 0.255139 (0.164249) | 0.448675 / 0.283200 (0.165475) | 0.112099 / 0.141683 (-0.029584) | 1.787982 / 1.452155 (0.335827) | 1.884581 / 1.492716 (0.391864) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208837 / 0.018006 (0.190831) | 0.515593 / 0.000490 (0.515103) | 0.000447 / 0.000200 (0.000247) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031025 / 0.037411 (-0.006386) | 0.125179 / 0.014526 (0.110653) | 0.137050 / 0.176557 (-0.039506) | 0.203582 / 0.737135 (-0.533553) | 0.139209 / 0.296338 (-0.157130) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.601507 / 0.215209 (0.386298) | 6.034778 / 2.077655 (3.957123) | 2.550277 / 1.504120 (1.046157) | 2.242277 / 1.541195 (0.701082) | 2.306378 / 1.468490 (0.837888) | 1.251219 / 4.584777 (-3.333558) | 5.448698 / 3.745712 (1.702986) | 3.044666 / 5.269862 (-2.225196) | 2.000684 / 4.565676 (-2.564992) | 0.148385 / 0.424275 (-0.275890) | 0.015175 / 0.007607 (0.007567) | 0.800839 / 0.226044 (0.574795) | 8.062099 / 2.268929 (5.793171) | 3.400980 / 55.444624 (-52.043644) | 2.639583 / 6.876477 (-4.236894) | 2.660691 / 2.142072 (0.518618) | 1.467715 / 4.805227 (-3.337512) | 0.266429 / 6.500664 (-6.234235) | 0.076981 / 0.075469 (0.001512) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621128 / 1.841788 (-0.220659) | 17.949989 / 8.074308 (9.875680) | 20.946426 / 10.191392 (10.755034) | 0.259357 / 0.680424 (-0.421067) | 0.026094 / 0.534201 (-0.508107) | 0.527840 / 0.579283 (-0.051443) | 0.629027 / 0.434364 (0.194663) | 0.603931 / 0.540337 (0.063594) | 0.711370 / 1.386936 (-0.675566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ccf01db81bb7b70f3ea97b185e345c2b1df0274 \"CML watermark\")\n"
] |
1,637,129,445
| 5,661
|
CI is broken: Unnecessary `dict` comprehension
|
closed
| 2023-03-23T09:13:01
| 2023-03-23T09:37:51
| 2023-03-23T09:37:51
|
https://github.com/huggingface/datasets/issues/5661
| null |
albertvillanova
| false
|
[] |
1,635,543,646
| 5,660
|
integration with imbalanced-learn
|
closed
| 2023-03-22T11:05:17
| 2023-07-06T18:10:15
| 2023-07-06T18:10:15
|
https://github.com/huggingface/datasets/issues/5660
| null |
tansaku
| false
|
[
"You can convert any dataset to pandas to be used with imbalanced-learn using `.to_pandas()`\r\n\r\nOtherwise if you want to keep a `Dataset` object and still use e.g. [make_imbalance](https://imbalanced-learn.org/stable/references/generated/imblearn.datasets.make_imbalance.html#imblearn.datasets.make_imbalance), you just need to pass the list of rows ids and labels:\r\n\r\n```python\r\nrow_indices = list(range(len(dataset)))\r\nresampled_row_indices, _ = make_imbalance(\r\n row_indices,\r\n dataset[\"label\"],\r\n sampling_strategy={0: 25, 1: 50, 2: 50},\r\n random_state=RANDOM_STATE,\r\n)\r\n\r\nresampled_dataset = dataset.select(resampled_row_indices)\r\n```"
] |
1,635,447,540
| 5,659
|
[Audio] Soundfile/libsndfile requirements too stringent for decoding mp3 files
|
closed
| 2023-03-22T10:07:33
| 2024-07-12T01:35:01
| 2023-04-07T08:51:28
|
https://github.com/huggingface/datasets/issues/5659
| null |
sanchit-gandhi
| false
|
[
"cc @polinaeterna @lhoestq ",
"@sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). \r\nRequired `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. \r\nThe only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n\r\n```bash\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\ncd libsndfile/\r\nautoreconf -vif\r\n./configure --enable-werror \r\nmake\r\nmake install\r\n```\r\nfor this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n```bash\r\napt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\nbut for other Linux distributions it might be different.\r\n\r\nWhen the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n\r\n```bash\r\ncp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\ncp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n```\r\n\r\nAnother solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`. ",
"Not sure if it may help, but you could also try updating `pip` before installing soundfile",
"@lhoestq @sanchit-gandhi. I encountered the same error (also on the TPU v4) when trying to run `datasets` from source.\r\n\r\nDowngrading soundfile with `pip install soundfile==0.12.0` seems to fix the issue for me.",
"Maybe let's open an issue at https://github.com/bastibe/python-soundfile/issues in case they might know why you get `OSError: cannot load library 'libsndfile.so'` ?",
"> @sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). Required `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. The only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n> \r\n> ```shell\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> cd libsndfile/\r\n> autoreconf -vif\r\n> ./configure --enable-werror \r\n> make\r\n> make install\r\n> ```\r\n\r\nThis fixed the issue for me. After installing libsndfile as described above, I had to uninstall soundfile and re-install it with this command. `pip install \"soundfile>=0.12.1\"`",
"Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n\r\n1. Grab source code:\r\n```\r\ngit clone https://github.com/libsndfile/libsndfile.git\r\n```\r\n\r\n2. Set up a build environment:\r\n```\r\nsudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n libmpg123-dev pkg-config python\r\n```\r\n\r\n3. Build and test `libsndfile`:\r\n\r\n```\r\nautoreconf -vif\r\n./configure --enable-werror\r\nsudo make\r\nsudo make check\r\n```\r\n\r\n4. Create `_soundfile_data` submodule (if it does not exist already):\r\n```\r\nsudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```\r\n\r\n5. Copy `libsndfile` files into submodule:\r\n```\r\nsudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n```",
"On a different machine, I also tried separately by first upgrading pip, then installing soundfile. This worked too! Thanks @lhoestq 🙌",
"> @sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). Required `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume it **might not** be the case for some non standard Linux distributions. The only solution for using `soundfile` here is to build [`libsndfile`](https://github.com/libsndfile/libsndfile) from source:\r\n> \r\n> ```shell\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> cd libsndfile/\r\n> autoreconf -vif\r\n> ./configure --enable-werror \r\n> make\r\n> make install\r\n> ```\r\n> \r\n> for this, some building libraries should be installed, for Debian/Ubuntu it's like:\r\n> \r\n> ```shell\r\n> apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> but for other Linux distributions it might be different.\r\n> \r\n> When the binary is compiled, it should be put into location where `soundfile` would search for it (the directory is named `_soundfile_data`), it depends on where`libsdfile` (from the previous step) and `soundfile` were installed, might be something like this:\r\n> \r\n> ```shell\r\n> cp /usr/local/lib/libsndfile.so /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n> cp /usr/local/lib/libsndfile.la /usr/local/lib/python3.7/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> Another solution is to not use `soundfile` and apply custom processing function with `torchaudio` while setting `decode=False` in `Audio` feature and passing custom function to `.map`.\r\n\r\nThanks, the solution solved my problem. \r\n\r\n1. Purge uninstall libsndfile, uninstall python-soundfile.\r\n2. Build libsndfile from source code and install.\r\n3. Build python-soundfile from source code and install\r\n4. Well done.",
"> Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n> \r\n> 1. Grab source code:\r\n> \r\n> ```\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> ```\r\n> \r\n> 2. Set up a build environment:\r\n> \r\n> ```\r\n> sudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> 3. Build and test `libsndfile`:\r\n> \r\n> ```\r\n> autoreconf -vif\r\n> ./configure --enable-werror\r\n> sudo make\r\n> sudo make check\r\n> ```\r\n> \r\n> 4. Create `_soundfile_data` submodule (if it does not exist already):\r\n> \r\n> ```\r\n> sudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> 5. Copy `libsndfile` files into submodule:\r\n> \r\n> ```\r\n> sudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n\r\nI had to run 'make install' or the `/usr/local/lib/libsndfile.*` files didn't exist.\r\n\r\nIt's working though!",
"I had the same issue but it is working now! Thanks for all of your comments!",
"I had the same issue on SageMaker but not on Colab;\r\nThe `soundfile` versioning was fine.\r\n\r\n my approach to solve it was to match {\"numpy\", \"numba\"} exact versions\r\n\r\n```\r\n! pip install \"numpy==1.23.5\"\r\n! pip install \"numpy==0.58.1\"\r\n\r\n```\r\nthe numbers are from Colab where successfully I could do the job.\r\n\r\n",
"> Thank you so much for the comprehensive instructions @polinaeterna! Also confirming that they worked for me 🤗 In my case, I had to run several of these commands under \"sudo\" for privileges, but otherwise this workaround gave a successful `libsndfile` install:\r\n> \r\n> 1. Grab source code:\r\n> \r\n> ```\r\n> git clone https://github.com/libsndfile/libsndfile.git\r\n> ```\r\n> \r\n> 2. Set up a build environment:\r\n> \r\n> ```\r\n> sudo apt install autoconf autogen automake build-essential libasound2-dev \\\r\n> libflac-dev libogg-dev libtool libvorbis-dev libopus-dev libmp3lame-dev \\\r\n> libmpg123-dev pkg-config python\r\n> ```\r\n> \r\n> 3. Build and test `libsndfile`:\r\n> \r\n> ```\r\n> autoreconf -vif\r\n> ./configure --enable-werror\r\n> sudo make\r\n> sudo make check\r\n> ```\r\n> \r\n> 4. Create `_soundfile_data` submodule (if it does not exist already):\r\n> \r\n> ```\r\n> sudo mkdir /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n> \r\n> 5. Copy `libsndfile` files into submodule:\r\n> \r\n> ```\r\n> sudo cp /usr/local/lib/libsndfile.* /usr/local/lib/python3.8/dist-packages/_soundfile_data/\r\n> ```\r\n\r\nIt works and don't forget to \"apt uninstall libsndfile1\" after installing it from source code."
] |
1,634,867,204
| 5,658
|
docs: Update num_shards docs to mention num_proc on Dataset and DatasetDict
|
closed
| 2023-03-22T00:12:18
| 2023-03-24T16:43:34
| 2023-03-24T16:36:21
|
https://github.com/huggingface/datasets/pull/5658
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5658",
"html_url": "https://github.com/huggingface/datasets/pull/5658",
"diff_url": "https://github.com/huggingface/datasets/pull/5658.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5658.patch",
"merged_at": "2023-03-24T16:36:21"
}
|
connor-henderson
| true
|
[
"_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) | 0.007351 / 0.011353 (-0.004002) | 0.005025 / 0.011008 (-0.005983) | 0.095978 / 0.038508 (0.057470) | 0.033486 / 0.023109 (0.010377) | 0.294427 / 0.275898 (0.018529) | 0.325157 / 0.323480 (0.001677) | 0.005671 / 0.007986 (-0.002315) | 0.005284 / 0.004328 (0.000955) | 0.073159 / 0.004250 (0.068909) | 0.045162 / 0.037052 (0.008110) | 0.294004 / 0.258489 (0.035515) | 0.343545 / 0.293841 (0.049704) | 0.036857 / 0.128546 (-0.091689) | 0.012245 / 0.075646 (-0.063401) | 0.332258 / 0.419271 (-0.087014) | 0.051909 / 0.043533 (0.008377) | 0.295701 / 0.255139 (0.040562) | 0.315247 / 0.283200 (0.032048) | 0.102363 / 0.141683 (-0.039320) | 1.441944 / 1.452155 (-0.010211) | 1.527161 / 1.492716 (0.034445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211769 / 0.018006 (0.193763) | 0.452015 / 0.000490 (0.451525) | 0.004041 / 0.000200 (0.003841) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027396 / 0.037411 (-0.010015) | 0.108318 / 0.014526 (0.093793) | 0.116851 / 0.176557 (-0.059706) | 0.172658 / 0.737135 (-0.564478) | 0.122876 / 0.296338 (-0.173462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406484 / 0.215209 (0.191275) | 4.053849 / 2.077655 (1.976194) | 1.842947 / 1.504120 (0.338827) | 1.649473 / 1.541195 (0.108278) | 1.728629 / 1.468490 (0.260139) | 0.699519 / 4.584777 (-3.885258) | 3.730823 / 3.745712 (-0.014889) | 2.139624 / 5.269862 (-3.130237) | 1.487839 / 4.565676 (-3.077837) | 0.086699 / 0.424275 (-0.337576) | 0.012815 / 0.007607 (0.005208) | 0.514014 / 0.226044 (0.287969) | 5.153315 / 2.268929 (2.884387) | 2.324431 / 55.444624 (-53.120193) | 1.971533 / 6.876477 (-4.904944) | 2.074480 / 2.142072 (-0.067592) | 0.842419 / 4.805227 (-3.962808) | 0.169140 / 6.500664 (-6.331524) | 0.065206 / 0.075469 (-0.010263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180887 / 1.841788 (-0.660901) | 14.627401 / 8.074308 (6.553093) | 14.382699 / 10.191392 (4.191307) | 0.143986 / 0.680424 (-0.536438) | 0.017460 / 0.534201 (-0.516741) | 0.422100 / 0.579283 (-0.157183) | 0.417474 / 0.434364 (-0.016890) | 0.493712 / 0.540337 (-0.046625) | 0.589744 / 1.386936 (-0.797193) |\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) | 0.007538 / 0.011353 (-0.003815) | 0.005122 / 0.011008 (-0.005887) | 0.073858 / 0.038508 (0.035350) | 0.034561 / 0.023109 (0.011451) | 0.341250 / 0.275898 (0.065352) | 0.373063 / 0.323480 (0.049583) | 0.005785 / 0.007986 (-0.002200) | 0.005393 / 0.004328 (0.001065) | 0.072354 / 0.004250 (0.068104) | 0.047005 / 0.037052 (0.009953) | 0.341179 / 0.258489 (0.082690) | 0.386299 / 0.293841 (0.092458) | 0.038315 / 0.128546 (-0.090231) | 0.012200 / 0.075646 (-0.063446) | 0.086132 / 0.419271 (-0.333140) | 0.049873 / 0.043533 (0.006340) | 0.337985 / 0.255139 (0.082846) | 0.354806 / 0.283200 (0.071607) | 0.103557 / 0.141683 (-0.038126) | 1.445682 / 1.452155 (-0.006473) | 1.551008 / 1.492716 (0.058291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235873 / 0.018006 (0.217867) | 0.448445 / 0.000490 (0.447955) | 0.001307 / 0.000200 (0.001108) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029809 / 0.037411 (-0.007603) | 0.108833 / 0.014526 (0.094307) | 0.123289 / 0.176557 (-0.053268) | 0.176516 / 0.737135 (-0.560620) | 0.127186 / 0.296338 (-0.169153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422037 / 0.215209 (0.206828) | 4.188073 / 2.077655 (2.110418) | 1.999295 / 1.504120 (0.495175) | 1.809229 / 1.541195 (0.268034) | 1.930798 / 1.468490 (0.462308) | 0.694371 / 4.584777 (-3.890406) | 3.833432 / 3.745712 (0.087719) | 3.235600 / 5.269862 (-2.034262) | 1.867822 / 4.565676 (-2.697854) | 0.085734 / 0.424275 (-0.338541) | 0.012727 / 0.007607 (0.005120) | 0.542261 / 0.226044 (0.316217) | 5.289366 / 2.268929 (3.020437) | 2.469636 / 55.444624 (-52.974988) | 2.139392 / 6.876477 (-4.737084) | 2.193305 / 2.142072 (0.051233) | 0.846747 / 4.805227 (-3.958481) | 0.168965 / 6.500664 (-6.331699) | 0.064463 / 0.075469 (-0.011006) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263818 / 1.841788 (-0.577970) | 15.254642 / 8.074308 (7.180334) | 14.428111 / 10.191392 (4.236719) | 0.164770 / 0.680424 (-0.515654) | 0.017476 / 0.534201 (-0.516725) | 0.420198 / 0.579283 (-0.159085) | 0.443250 / 0.434364 (0.008886) | 0.496904 / 0.540337 (-0.043434) | 0.596541 / 1.386936 (-0.790395) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4db8e33eb9cf6cd4453cdfa246c065e0eedf170c \"CML watermark\")\n"
] |
1,634,156,563
| 5,656
|
Fix `fsspec.open` when using an HTTP proxy
|
closed
| 2023-03-21T15:23:29
| 2023-03-23T14:14:50
| 2023-03-23T13:15:46
|
https://github.com/huggingface/datasets/pull/5656
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5656",
"html_url": "https://github.com/huggingface/datasets/pull/5656",
"diff_url": "https://github.com/huggingface/datasets/pull/5656.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5656.patch",
"merged_at": "2023-03-23T13:15:46"
}
|
bryant1410
| true
|
[
"_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) | 0.007980 / 0.011353 (-0.003373) | 0.005351 / 0.011008 (-0.005657) | 0.096325 / 0.038508 (0.057817) | 0.034204 / 0.023109 (0.011095) | 0.328080 / 0.275898 (0.052182) | 0.361519 / 0.323480 (0.038039) | 0.005954 / 0.007986 (-0.002032) | 0.004106 / 0.004328 (-0.000222) | 0.072827 / 0.004250 (0.068576) | 0.050522 / 0.037052 (0.013470) | 0.326975 / 0.258489 (0.068486) | 0.373180 / 0.293841 (0.079339) | 0.037024 / 0.128546 (-0.091522) | 0.012347 / 0.075646 (-0.063299) | 0.332341 / 0.419271 (-0.086931) | 0.050695 / 0.043533 (0.007162) | 0.328298 / 0.255139 (0.073159) | 0.352808 / 0.283200 (0.069608) | 0.101637 / 0.141683 (-0.040046) | 1.435172 / 1.452155 (-0.016982) | 1.529797 / 1.492716 (0.037080) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305727 / 0.018006 (0.287721) | 0.583951 / 0.000490 (0.583462) | 0.011699 / 0.000200 (0.011499) | 0.000345 / 0.000054 (0.000290) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027917 / 0.037411 (-0.009495) | 0.107698 / 0.014526 (0.093173) | 0.120572 / 0.176557 (-0.055985) | 0.176066 / 0.737135 (-0.561069) | 0.125348 / 0.296338 (-0.170991) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411980 / 0.215209 (0.196771) | 4.113135 / 2.077655 (2.035480) | 1.868725 / 1.504120 (0.364605) | 1.677422 / 1.541195 (0.136227) | 1.796759 / 1.468490 (0.328269) | 0.701957 / 4.584777 (-3.882820) | 3.830742 / 3.745712 (0.085030) | 2.170444 / 5.269862 (-3.099418) | 1.345097 / 4.565676 (-3.220580) | 0.086661 / 0.424275 (-0.337614) | 0.013073 / 0.007607 (0.005466) | 0.519150 / 0.226044 (0.293106) | 5.193447 / 2.268929 (2.924518) | 2.391155 / 55.444624 (-53.053470) | 2.076610 / 6.876477 (-4.799867) | 2.245557 / 2.142072 (0.103484) | 0.846496 / 4.805227 (-3.958731) | 0.169246 / 6.500664 (-6.331418) | 0.066360 / 0.075469 (-0.009109) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196344 / 1.841788 (-0.645444) | 15.640363 / 8.074308 (7.566055) | 14.936144 / 10.191392 (4.744752) | 0.163613 / 0.680424 (-0.516811) | 0.017900 / 0.534201 (-0.516301) | 0.425377 / 0.579283 (-0.153906) | 0.431119 / 0.434364 (-0.003245) | 0.513669 / 0.540337 (-0.026669) | 0.592970 / 1.386936 (-0.793966) |\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) | 0.007958 / 0.011353 (-0.003395) | 0.005707 / 0.011008 (-0.005301) | 0.075377 / 0.038508 (0.036869) | 0.037126 / 0.023109 (0.014016) | 0.344589 / 0.275898 (0.068691) | 0.381060 / 0.323480 (0.057580) | 0.006592 / 0.007986 (-0.001393) | 0.004479 / 0.004328 (0.000151) | 0.074456 / 0.004250 (0.070206) | 0.054087 / 0.037052 (0.017035) | 0.344942 / 0.258489 (0.086453) | 0.393174 / 0.293841 (0.099333) | 0.037926 / 0.128546 (-0.090620) | 0.012638 / 0.075646 (-0.063009) | 0.087743 / 0.419271 (-0.331529) | 0.050081 / 0.043533 (0.006548) | 0.340406 / 0.255139 (0.085267) | 0.361487 / 0.283200 (0.078287) | 0.108546 / 0.141683 (-0.033137) | 1.424626 / 1.452155 (-0.027529) | 1.553958 / 1.492716 (0.061242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329922 / 0.018006 (0.311916) | 0.523239 / 0.000490 (0.522749) | 0.012164 / 0.000200 (0.011964) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031935 / 0.037411 (-0.005477) | 0.115680 / 0.014526 (0.101154) | 0.130062 / 0.176557 (-0.046494) | 0.180679 / 0.737135 (-0.556457) | 0.135548 / 0.296338 (-0.160790) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429648 / 0.215209 (0.214439) | 4.303342 / 2.077655 (2.225687) | 1.999395 / 1.504120 (0.495275) | 1.810354 / 1.541195 (0.269160) | 1.963132 / 1.468490 (0.494642) | 0.701654 / 4.584777 (-3.883122) | 3.844687 / 3.745712 (0.098975) | 2.153425 / 5.269862 (-3.116436) | 1.351541 / 4.565676 (-3.214135) | 0.086292 / 0.424275 (-0.337983) | 0.012491 / 0.007607 (0.004883) | 0.523144 / 0.226044 (0.297099) | 5.243283 / 2.268929 (2.974355) | 2.465849 / 55.444624 (-52.978775) | 2.154505 / 6.876477 (-4.721972) | 2.245500 / 2.142072 (0.103428) | 0.838902 / 4.805227 (-3.966326) | 0.169441 / 6.500664 (-6.331223) | 0.065631 / 0.075469 (-0.009838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262175 / 1.841788 (-0.579612) | 15.424650 / 8.074308 (7.350342) | 15.000718 / 10.191392 (4.809326) | 0.186328 / 0.680424 (-0.494096) | 0.018076 / 0.534201 (-0.516125) | 0.433458 / 0.579283 (-0.145825) | 0.424213 / 0.434364 (-0.010151) | 0.546568 / 0.540337 (0.006231) | 0.643529 / 1.386936 (-0.743407) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea7298bf121d7ae8079f0a59deb67c2fa1d4df6a \"CML watermark\")\n"
] |
1,634,030,017
| 5,655
|
Improve features decoding in to_iterable_dataset
|
closed
| 2023-03-21T14:18:09
| 2023-03-23T13:19:27
| 2023-03-23T13:12:25
|
https://github.com/huggingface/datasets/pull/5655
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5655",
"html_url": "https://github.com/huggingface/datasets/pull/5655",
"diff_url": "https://github.com/huggingface/datasets/pull/5655.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5655.patch",
"merged_at": "2023-03-23T13:12:25"
}
|
lhoestq
| true
|
[
"_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) | 0.009691 / 0.011353 (-0.001662) | 0.006160 / 0.011008 (-0.004848) | 0.127528 / 0.038508 (0.089020) | 0.034445 / 0.023109 (0.011335) | 0.391483 / 0.275898 (0.115585) | 0.425922 / 0.323480 (0.102442) | 0.006621 / 0.007986 (-0.001365) | 0.004550 / 0.004328 (0.000221) | 0.099134 / 0.004250 (0.094884) | 0.051089 / 0.037052 (0.014037) | 0.398675 / 0.258489 (0.140186) | 0.456740 / 0.293841 (0.162899) | 0.052279 / 0.128546 (-0.076267) | 0.020878 / 0.075646 (-0.054768) | 0.414954 / 0.419271 (-0.004317) | 0.061903 / 0.043533 (0.018370) | 0.393088 / 0.255139 (0.137949) | 0.410289 / 0.283200 (0.127089) | 0.101684 / 0.141683 (-0.039998) | 1.747102 / 1.452155 (0.294947) | 1.896976 / 1.492716 (0.404260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203193 / 0.018006 (0.185187) | 0.495011 / 0.000490 (0.494521) | 0.006290 / 0.000200 (0.006090) | 0.000098 / 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) | 0.034840 / 0.037411 (-0.002571) | 0.122529 / 0.014526 (0.108003) | 0.133870 / 0.176557 (-0.042686) | 0.207771 / 0.737135 (-0.529364) | 0.141441 / 0.296338 (-0.154897) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604190 / 0.215209 (0.388981) | 6.040295 / 2.077655 (3.962641) | 2.405703 / 1.504120 (0.901583) | 2.062767 / 1.541195 (0.521572) | 2.079313 / 1.468490 (0.610823) | 1.240107 / 4.584777 (-3.344670) | 5.316583 / 3.745712 (1.570871) | 3.104758 / 5.269862 (-2.165103) | 2.056489 / 4.565676 (-2.509187) | 0.149060 / 0.424275 (-0.275215) | 0.014467 / 0.007607 (0.006860) | 0.736882 / 0.226044 (0.510838) | 7.324142 / 2.268929 (5.055213) | 3.048752 / 55.444624 (-52.395872) | 2.385013 / 6.876477 (-4.491463) | 2.457478 / 2.142072 (0.315405) | 1.459276 / 4.805227 (-3.345951) | 0.253882 / 6.500664 (-6.246782) | 0.076756 / 0.075469 (0.001287) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.499166 / 1.841788 (-0.342622) | 17.294165 / 8.074308 (9.219857) | 20.385668 / 10.191392 (10.194276) | 0.254633 / 0.680424 (-0.425791) | 0.026253 / 0.534201 (-0.507948) | 0.532928 / 0.579283 (-0.046355) | 0.606095 / 0.434364 (0.171731) | 0.615025 / 0.540337 (0.074687) | 0.728651 / 1.386936 (-0.658285) |\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) | 0.009376 / 0.011353 (-0.001977) | 0.005981 / 0.011008 (-0.005027) | 0.109898 / 0.038508 (0.071390) | 0.033746 / 0.023109 (0.010637) | 0.410226 / 0.275898 (0.134328) | 0.470606 / 0.323480 (0.147126) | 0.006706 / 0.007986 (-0.001279) | 0.004482 / 0.004328 (0.000153) | 0.092280 / 0.004250 (0.088030) | 0.047988 / 0.037052 (0.010935) | 0.430628 / 0.258489 (0.172139) | 0.480668 / 0.293841 (0.186827) | 0.052099 / 0.128546 (-0.076447) | 0.018743 / 0.075646 (-0.056903) | 0.112204 / 0.419271 (-0.307068) | 0.059838 / 0.043533 (0.016305) | 0.418230 / 0.255139 (0.163091) | 0.451568 / 0.283200 (0.168368) | 0.107026 / 0.141683 (-0.034657) | 1.708111 / 1.452155 (0.255956) | 1.839268 / 1.492716 (0.346552) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229558 / 0.018006 (0.211552) | 0.488099 / 0.000490 (0.487609) | 0.004643 / 0.000200 (0.004443) | 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) | 0.030461 / 0.037411 (-0.006951) | 0.120993 / 0.014526 (0.106467) | 0.130874 / 0.176557 (-0.045682) | 0.193550 / 0.737135 (-0.543585) | 0.138164 / 0.296338 (-0.158174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635709 / 0.215209 (0.420500) | 6.225112 / 2.077655 (4.147457) | 2.639584 / 1.504120 (1.135465) | 2.254487 / 1.541195 (0.713293) | 2.280478 / 1.468490 (0.811988) | 1.205712 / 4.584777 (-3.379065) | 5.367845 / 3.745712 (1.622133) | 3.020207 / 5.269862 (-2.249655) | 2.001897 / 4.565676 (-2.563779) | 0.149582 / 0.424275 (-0.274693) | 0.014867 / 0.007607 (0.007260) | 0.759050 / 0.226044 (0.533006) | 7.692969 / 2.268929 (5.424041) | 3.274009 / 55.444624 (-52.170615) | 2.635529 / 6.876477 (-4.240948) | 2.672960 / 2.142072 (0.530888) | 1.426487 / 4.805227 (-3.378740) | 0.253368 / 6.500664 (-6.247296) | 0.078650 / 0.075469 (0.003181) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620265 / 1.841788 (-0.221523) | 17.674168 / 8.074308 (9.599860) | 21.120528 / 10.191392 (10.929136) | 0.244205 / 0.680424 (-0.436218) | 0.029646 / 0.534201 (-0.504555) | 0.510948 / 0.579283 (-0.068335) | 0.586255 / 0.434364 (0.151891) | 0.589286 / 0.540337 (0.048949) | 0.736561 / 1.386936 (-0.650375) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de5fe9ae5df84c489e08dcbdc3d2d20272b312c3 \"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) | 0.007778 / 0.011353 (-0.003575) | 0.005432 / 0.011008 (-0.005577) | 0.098776 / 0.038508 (0.060268) | 0.035196 / 0.023109 (0.012087) | 0.305646 / 0.275898 (0.029748) | 0.342661 / 0.323480 (0.019181) | 0.006513 / 0.007986 (-0.001472) | 0.005897 / 0.004328 (0.001568) | 0.075797 / 0.004250 (0.071547) | 0.056060 / 0.037052 (0.019007) | 0.306645 / 0.258489 (0.048156) | 0.352447 / 0.293841 (0.058606) | 0.037304 / 0.128546 (-0.091242) | 0.012514 / 0.075646 (-0.063132) | 0.334949 / 0.419271 (-0.084323) | 0.051600 / 0.043533 (0.008067) | 0.302302 / 0.255139 (0.047163) | 0.322238 / 0.283200 (0.039038) | 0.106896 / 0.141683 (-0.034787) | 1.483163 / 1.452155 (0.031008) | 1.587483 / 1.492716 (0.094767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292318 / 0.018006 (0.274312) | 0.541541 / 0.000490 (0.541051) | 0.008342 / 0.000200 (0.008142) | 0.000339 / 0.000054 (0.000285) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028287 / 0.037411 (-0.009124) | 0.107775 / 0.014526 (0.093250) | 0.119112 / 0.176557 (-0.057445) | 0.174002 / 0.737135 (-0.563134) | 0.126531 / 0.296338 (-0.169808) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401684 / 0.215209 (0.186475) | 4.024708 / 2.077655 (1.947053) | 1.812763 / 1.504120 (0.308643) | 1.629540 / 1.541195 (0.088345) | 1.731733 / 1.468490 (0.263243) | 0.711066 / 4.584777 (-3.873711) | 3.867499 / 3.745712 (0.121786) | 3.615968 / 5.269862 (-1.653893) | 1.876077 / 4.565676 (-2.689600) | 0.087003 / 0.424275 (-0.337272) | 0.012445 / 0.007607 (0.004838) | 0.499106 / 0.226044 (0.273061) | 4.975920 / 2.268929 (2.706992) | 2.279074 / 55.444624 (-53.165550) | 1.952311 / 6.876477 (-4.924166) | 2.167480 / 2.142072 (0.025408) | 0.855882 / 4.805227 (-3.949346) | 0.171378 / 6.500664 (-6.329287) | 0.066731 / 0.075469 (-0.008738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.184226 / 1.841788 (-0.657561) | 15.383396 / 8.074308 (7.309088) | 15.069783 / 10.191392 (4.878391) | 0.161489 / 0.680424 (-0.518935) | 0.017763 / 0.534201 (-0.516438) | 0.427103 / 0.579283 (-0.152180) | 0.434295 / 0.434364 (-0.000069) | 0.496848 / 0.540337 (-0.043489) | 0.592572 / 1.386936 (-0.794364) |\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) | 0.008014 / 0.011353 (-0.003339) | 0.005607 / 0.011008 (-0.005401) | 0.076826 / 0.038508 (0.038318) | 0.035283 / 0.023109 (0.012174) | 0.347809 / 0.275898 (0.071911) | 0.382482 / 0.323480 (0.059003) | 0.006276 / 0.007986 (-0.001709) | 0.005978 / 0.004328 (0.001650) | 0.074938 / 0.004250 (0.070687) | 0.054323 / 0.037052 (0.017271) | 0.344027 / 0.258489 (0.085538) | 0.397623 / 0.293841 (0.103783) | 0.037851 / 0.128546 (-0.090695) | 0.012649 / 0.075646 (-0.062997) | 0.086169 / 0.419271 (-0.333103) | 0.051510 / 0.043533 (0.007977) | 0.341112 / 0.255139 (0.085973) | 0.357957 / 0.283200 (0.074757) | 0.110949 / 0.141683 (-0.030734) | 1.479573 / 1.452155 (0.027419) | 1.578572 / 1.492716 (0.085855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310678 / 0.018006 (0.292672) | 0.525504 / 0.000490 (0.525015) | 0.000447 / 0.000200 (0.000247) | 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) | 0.031262 / 0.037411 (-0.006149) | 0.113801 / 0.014526 (0.099275) | 0.124967 / 0.176557 (-0.051590) | 0.175226 / 0.737135 (-0.561909) | 0.129377 / 0.296338 (-0.166962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420672 / 0.215209 (0.205463) | 4.181337 / 2.077655 (2.103682) | 1.985524 / 1.504120 (0.481404) | 1.803468 / 1.541195 (0.262273) | 1.952915 / 1.468490 (0.484425) | 0.710928 / 4.584777 (-3.873849) | 3.886245 / 3.745712 (0.140533) | 3.737837 / 5.269862 (-1.532024) | 1.806859 / 4.565676 (-2.758818) | 0.088461 / 0.424275 (-0.335814) | 0.013125 / 0.007607 (0.005518) | 0.522410 / 0.226044 (0.296365) | 5.232591 / 2.268929 (2.963663) | 2.451188 / 55.444624 (-52.993437) | 2.127725 / 6.876477 (-4.748751) | 2.232859 / 2.142072 (0.090786) | 0.854257 / 4.805227 (-3.950970) | 0.171004 / 6.500664 (-6.329661) | 0.066724 / 0.075469 (-0.008746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257700 / 1.841788 (-0.584088) | 15.738605 / 8.074308 (7.664297) | 15.021698 / 10.191392 (4.830306) | 0.147422 / 0.680424 (-0.533002) | 0.017928 / 0.534201 (-0.516273) | 0.428121 / 0.579283 (-0.151162) | 0.432056 / 0.434364 (-0.002308) | 0.498318 / 0.540337 (-0.042020) | 0.591040 / 1.386936 (-0.795896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac74267032ef3608779a8c8c4361b95a83ecbcb \"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) | 0.007014 / 0.011353 (-0.004339) | 0.004792 / 0.011008 (-0.006216) | 0.099822 / 0.038508 (0.061314) | 0.029333 / 0.023109 (0.006224) | 0.306453 / 0.275898 (0.030555) | 0.344598 / 0.323480 (0.021118) | 0.005121 / 0.007986 (-0.002865) | 0.004850 / 0.004328 (0.000522) | 0.076668 / 0.004250 (0.072417) | 0.039980 / 0.037052 (0.002927) | 0.312276 / 0.258489 (0.053787) | 0.354722 / 0.293841 (0.060881) | 0.031653 / 0.128546 (-0.096893) | 0.011743 / 0.075646 (-0.063903) | 0.322998 / 0.419271 (-0.096274) | 0.042813 / 0.043533 (-0.000720) | 0.308855 / 0.255139 (0.053716) | 0.332650 / 0.283200 (0.049451) | 0.087155 / 0.141683 (-0.054528) | 1.454946 / 1.452155 (0.002791) | 1.550589 / 1.492716 (0.057873) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192921 / 0.018006 (0.174914) | 0.411155 / 0.000490 (0.410666) | 0.004779 / 0.000200 (0.004579) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024462 / 0.037411 (-0.012950) | 0.100320 / 0.014526 (0.085794) | 0.105509 / 0.176557 (-0.071048) | 0.168533 / 0.737135 (-0.568602) | 0.110018 / 0.296338 (-0.186321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415025 / 0.215209 (0.199816) | 4.144583 / 2.077655 (2.066928) | 1.871627 / 1.504120 (0.367507) | 1.671638 / 1.541195 (0.130443) | 1.734458 / 1.468490 (0.265968) | 0.693435 / 4.584777 (-3.891342) | 3.487999 / 3.745712 (-0.257713) | 3.196553 / 5.269862 (-2.073308) | 1.628499 / 4.565676 (-2.937178) | 0.082999 / 0.424275 (-0.341276) | 0.012822 / 0.007607 (0.005215) | 0.514904 / 0.226044 (0.288860) | 5.157525 / 2.268929 (2.888596) | 2.313093 / 55.444624 (-53.131531) | 1.968335 / 6.876477 (-4.908142) | 2.083462 / 2.142072 (-0.058610) | 0.804485 / 4.805227 (-4.000742) | 0.152290 / 6.500664 (-6.348374) | 0.066813 / 0.075469 (-0.008656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210370 / 1.841788 (-0.631418) | 14.261779 / 8.074308 (6.187471) | 14.268121 / 10.191392 (4.076729) | 0.149216 / 0.680424 (-0.531207) | 0.016529 / 0.534201 (-0.517672) | 0.378814 / 0.579283 (-0.200469) | 0.386304 / 0.434364 (-0.048060) | 0.439653 / 0.540337 (-0.100684) | 0.523658 / 1.386936 (-0.863278) |\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) | 0.006979 / 0.011353 (-0.004374) | 0.004718 / 0.011008 (-0.006290) | 0.077023 / 0.038508 (0.038514) | 0.029080 / 0.023109 (0.005971) | 0.343145 / 0.275898 (0.067247) | 0.380633 / 0.323480 (0.057153) | 0.006057 / 0.007986 (-0.001928) | 0.003541 / 0.004328 (-0.000788) | 0.075773 / 0.004250 (0.071523) | 0.039112 / 0.037052 (0.002060) | 0.342355 / 0.258489 (0.083866) | 0.386002 / 0.293841 (0.092161) | 0.033238 / 0.128546 (-0.095308) | 0.011696 / 0.075646 (-0.063950) | 0.086178 / 0.419271 (-0.333093) | 0.045219 / 0.043533 (0.001686) | 0.360710 / 0.255139 (0.105571) | 0.367490 / 0.283200 (0.084290) | 0.093041 / 0.141683 (-0.048642) | 1.523670 / 1.452155 (0.071516) | 1.595280 / 1.492716 (0.102564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235888 / 0.018006 (0.217882) | 0.410205 / 0.000490 (0.409715) | 0.000405 / 0.000200 (0.000205) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025752 / 0.037411 (-0.011659) | 0.103343 / 0.014526 (0.088818) | 0.108722 / 0.176557 (-0.067834) | 0.159241 / 0.737135 (-0.577894) | 0.113684 / 0.296338 (-0.182654) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441809 / 0.215209 (0.226600) | 4.410893 / 2.077655 (2.333238) | 2.104061 / 1.504120 (0.599941) | 1.854016 / 1.541195 (0.312821) | 1.947100 / 1.468490 (0.478610) | 0.697682 / 4.584777 (-3.887095) | 3.467513 / 3.745712 (-0.278199) | 1.911603 / 5.269862 (-3.358258) | 1.187479 / 4.565676 (-3.378197) | 0.083153 / 0.424275 (-0.341122) | 0.012651 / 0.007607 (0.005044) | 0.542081 / 0.226044 (0.316036) | 5.444622 / 2.268929 (3.175693) | 2.524236 / 55.444624 (-52.920388) | 2.190463 / 6.876477 (-4.686014) | 2.265764 / 2.142072 (0.123691) | 0.810778 / 4.805227 (-3.994450) | 0.152459 / 6.500664 (-6.348205) | 0.067815 / 0.075469 (-0.007654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334388 / 1.841788 (-0.507400) | 14.640459 / 8.074308 (6.566151) | 14.714874 / 10.191392 (4.523482) | 0.153479 / 0.680424 (-0.526945) | 0.016709 / 0.534201 (-0.517492) | 0.379427 / 0.579283 (-0.199856) | 0.391602 / 0.434364 (-0.042762) | 0.438297 / 0.540337 (-0.102041) | 0.524170 / 1.386936 (-0.862766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b277cef5cb56c0c506eda082fb69fddb839156a1 \"CML watermark\")\n"
] |
1,633,523,705
| 5,654
|
Offset overflow when executing Dataset.map
|
open
| 2023-03-21T09:33:27
| 2023-03-21T10:32:07
| null |
https://github.com/huggingface/datasets/issues/5654
| null |
jan-pair
| false
|
[
"Upd. the above code works if we replace `25` with `1`, but the result value at key \"hr\" is not a tensor but a list of lists of lists of uint8.\r\n\r\nAdding `train_data.set_format(\"torch\")` after map fixes this, but the original issue remains\r\n\r\n",
"As a workaround, one can replace\r\n`return {\"hr\": torch.stack([crop_transf(tensor) for _ in range(25)])}`\r\nwith\r\n`return {f\"hr_crop_{i}\": crop_transf(tensor) for i in range(25)}`\r\nand then choose appropriate crop randomly in further processing, but I still don't understand why the original approach doesn't work(\r\n"
] |
1,633,254,159
| 5,653
|
Doc: save_to_disk, `num_proc` will affect `num_shards`, but it's not documented
|
closed
| 2023-03-21T05:25:35
| 2023-03-24T16:36:23
| 2023-03-24T16:36:23
|
https://github.com/huggingface/datasets/issues/5653
| null |
RmZeta2718
| false
|
[
"I agree this should be documented"
] |
1,632,546,073
| 5,652
|
Copy features
|
closed
| 2023-03-20T17:17:23
| 2023-03-23T13:19:19
| 2023-03-23T13:12:08
|
https://github.com/huggingface/datasets/pull/5652
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5652",
"html_url": "https://github.com/huggingface/datasets/pull/5652",
"diff_url": "https://github.com/huggingface/datasets/pull/5652.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5652.patch",
"merged_at": "2023-03-23T13:12:08"
}
|
lhoestq
| true
|
[
"_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) | 0.007455 / 0.011353 (-0.003898) | 0.005278 / 0.011008 (-0.005731) | 0.098981 / 0.038508 (0.060473) | 0.029208 / 0.023109 (0.006099) | 0.304132 / 0.275898 (0.028234) | 0.340010 / 0.323480 (0.016530) | 0.005514 / 0.007986 (-0.002472) | 0.003636 / 0.004328 (-0.000692) | 0.076737 / 0.004250 (0.072486) | 0.041985 / 0.037052 (0.004933) | 0.314941 / 0.258489 (0.056452) | 0.346686 / 0.293841 (0.052845) | 0.032528 / 0.128546 (-0.096018) | 0.011795 / 0.075646 (-0.063851) | 0.322122 / 0.419271 (-0.097150) | 0.051548 / 0.043533 (0.008015) | 0.310561 / 0.255139 (0.055422) | 0.329443 / 0.283200 (0.046243) | 0.092820 / 0.141683 (-0.048863) | 1.495764 / 1.452155 (0.043609) | 1.586734 / 1.492716 (0.094018) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195830 / 0.018006 (0.177824) | 0.422075 / 0.000490 (0.421586) | 0.005483 / 0.000200 (0.005283) | 0.000133 / 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) | 0.023468 / 0.037411 (-0.013943) | 0.097713 / 0.014526 (0.083187) | 0.105331 / 0.176557 (-0.071225) | 0.166237 / 0.737135 (-0.570898) | 0.108924 / 0.296338 (-0.187415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671901 / 0.215209 (0.456692) | 6.745691 / 2.077655 (4.668036) | 2.132508 / 1.504120 (0.628388) | 1.693808 / 1.541195 (0.152614) | 1.715282 / 1.468490 (0.246792) | 0.955354 / 4.584777 (-3.629422) | 3.810296 / 3.745712 (0.064584) | 2.214891 / 5.269862 (-3.054970) | 1.461513 / 4.565676 (-3.104164) | 0.109846 / 0.424275 (-0.314430) | 0.013546 / 0.007607 (0.005939) | 0.780046 / 0.226044 (0.554001) | 7.789020 / 2.268929 (5.520091) | 2.602411 / 55.444624 (-52.842213) | 1.995096 / 6.876477 (-4.881380) | 2.009022 / 2.142072 (-0.133051) | 1.069215 / 4.805227 (-3.736012) | 0.179812 / 6.500664 (-6.320852) | 0.068125 / 0.075469 (-0.007344) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.201866 / 1.841788 (-0.639921) | 13.878814 / 8.074308 (5.804506) | 14.179264 / 10.191392 (3.987872) | 0.128908 / 0.680424 (-0.551515) | 0.017257 / 0.534201 (-0.516944) | 0.379500 / 0.579283 (-0.199783) | 0.393308 / 0.434364 (-0.041056) | 0.444700 / 0.540337 (-0.095638) | 0.531043 / 1.386936 (-0.855893) |\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) | 0.007413 / 0.011353 (-0.003940) | 0.005431 / 0.011008 (-0.005577) | 0.078158 / 0.038508 (0.039650) | 0.028837 / 0.023109 (0.005728) | 0.343635 / 0.275898 (0.067737) | 0.383041 / 0.323480 (0.059561) | 0.005283 / 0.007986 (-0.002703) | 0.003673 / 0.004328 (-0.000655) | 0.076461 / 0.004250 (0.072211) | 0.038625 / 0.037052 (0.001573) | 0.341109 / 0.258489 (0.082620) | 0.387027 / 0.293841 (0.093186) | 0.032512 / 0.128546 (-0.096034) | 0.011903 / 0.075646 (-0.063744) | 0.086340 / 0.419271 (-0.332931) | 0.043211 / 0.043533 (-0.000321) | 0.339994 / 0.255139 (0.084855) | 0.370868 / 0.283200 (0.087668) | 0.091679 / 0.141683 (-0.050004) | 1.547188 / 1.452155 (0.095033) | 1.578545 / 1.492716 (0.085829) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216981 / 0.018006 (0.198975) | 0.412206 / 0.000490 (0.411716) | 0.004243 / 0.000200 (0.004043) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025392 / 0.037411 (-0.012020) | 0.102577 / 0.014526 (0.088051) | 0.107672 / 0.176557 (-0.068884) | 0.160657 / 0.737135 (-0.576478) | 0.111646 / 0.296338 (-0.184692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.698815 / 0.215209 (0.483606) | 6.958931 / 2.077655 (4.881276) | 2.344216 / 1.504120 (0.840096) | 1.907752 / 1.541195 (0.366557) | 1.964251 / 1.468490 (0.495761) | 0.950754 / 4.584777 (-3.634023) | 3.829700 / 3.745712 (0.083988) | 3.055565 / 5.269862 (-2.214297) | 1.575851 / 4.565676 (-2.989825) | 0.109227 / 0.424275 (-0.315048) | 0.013163 / 0.007607 (0.005556) | 0.804613 / 0.226044 (0.578569) | 8.015035 / 2.268929 (5.746107) | 2.796358 / 55.444624 (-52.648266) | 2.212561 / 6.876477 (-4.663916) | 2.229918 / 2.142072 (0.087845) | 1.062041 / 4.805227 (-3.743186) | 0.181384 / 6.500664 (-6.319280) | 0.068564 / 0.075469 (-0.006905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.287904 / 1.841788 (-0.553884) | 14.539222 / 8.074308 (6.464914) | 14.232097 / 10.191392 (4.040705) | 0.130870 / 0.680424 (-0.549554) | 0.016710 / 0.534201 (-0.517491) | 0.384454 / 0.579283 (-0.194829) | 0.391750 / 0.434364 (-0.042614) | 0.443995 / 0.540337 (-0.096343) | 0.526255 / 1.386936 (-0.860681) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bd46874a580b888bdc82b53daace79884f04bc62 \"CML watermark\")\n",
"Arf I need to fix some tests first - sorry",
"<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) | 0.008393 / 0.011353 (-0.002959) | 0.005635 / 0.011008 (-0.005373) | 0.114840 / 0.038508 (0.076332) | 0.039272 / 0.023109 (0.016163) | 0.352116 / 0.275898 (0.076218) | 0.386614 / 0.323480 (0.063134) | 0.006348 / 0.007986 (-0.001638) | 0.005872 / 0.004328 (0.001544) | 0.086437 / 0.004250 (0.082187) | 0.054003 / 0.037052 (0.016951) | 0.350302 / 0.258489 (0.091813) | 0.400148 / 0.293841 (0.106308) | 0.042436 / 0.128546 (-0.086111) | 0.013987 / 0.075646 (-0.061660) | 0.399434 / 0.419271 (-0.019837) | 0.059223 / 0.043533 (0.015690) | 0.354511 / 0.255139 (0.099372) | 0.377764 / 0.283200 (0.094564) | 0.112297 / 0.141683 (-0.029386) | 1.677483 / 1.452155 (0.225328) | 1.784942 / 1.492716 (0.292226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233334 / 0.018006 (0.215328) | 0.450575 / 0.000490 (0.450085) | 0.000376 / 0.000200 (0.000176) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031995 / 0.037411 (-0.005416) | 0.126798 / 0.014526 (0.112272) | 0.138453 / 0.176557 (-0.038104) | 0.207360 / 0.737135 (-0.529775) | 0.147744 / 0.296338 (-0.148594) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.481496 / 0.215209 (0.266287) | 4.810495 / 2.077655 (2.732840) | 2.457917 / 1.504120 (0.953797) | 2.300073 / 1.541195 (0.758879) | 2.065595 / 1.468490 (0.597105) | 0.814589 / 4.584777 (-3.770188) | 4.566496 / 3.745712 (0.820784) | 2.386947 / 5.269862 (-2.882914) | 1.531639 / 4.565676 (-3.034037) | 0.099569 / 0.424275 (-0.324706) | 0.014971 / 0.007607 (0.007364) | 0.590359 / 0.226044 (0.364314) | 5.885250 / 2.268929 (3.616322) | 2.706799 / 55.444624 (-52.737826) | 2.324485 / 6.876477 (-4.551992) | 2.452751 / 2.142072 (0.310678) | 0.966955 / 4.805227 (-3.838272) | 0.198165 / 6.500664 (-6.302499) | 0.076877 / 0.075469 (0.001408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.499085 / 1.841788 (-0.342702) | 17.705516 / 8.074308 (9.631208) | 16.481174 / 10.191392 (6.289782) | 0.191832 / 0.680424 (-0.488592) | 0.021417 / 0.534201 (-0.512784) | 0.519647 / 0.579283 (-0.059636) | 0.498432 / 0.434364 (0.064068) | 0.598206 / 0.540337 (0.057868) | 0.700990 / 1.386936 (-0.685946) |\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) | 0.008746 / 0.011353 (-0.002607) | 0.006052 / 0.011008 (-0.004956) | 0.092938 / 0.038508 (0.054430) | 0.038932 / 0.023109 (0.015823) | 0.406919 / 0.275898 (0.131021) | 0.444325 / 0.323480 (0.120845) | 0.006735 / 0.007986 (-0.001251) | 0.005972 / 0.004328 (0.001643) | 0.088152 / 0.004250 (0.083902) | 0.051009 / 0.037052 (0.013957) | 0.407415 / 0.258489 (0.148926) | 0.481048 / 0.293841 (0.187207) | 0.043268 / 0.128546 (-0.085278) | 0.014574 / 0.075646 (-0.061072) | 0.103555 / 0.419271 (-0.315716) | 0.058251 / 0.043533 (0.014719) | 0.406294 / 0.255139 (0.151155) | 0.429229 / 0.283200 (0.146029) | 0.116977 / 0.141683 (-0.024705) | 1.765885 / 1.452155 (0.313730) | 1.885557 / 1.492716 (0.392841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284014 / 0.018006 (0.266008) | 0.458066 / 0.000490 (0.457576) | 0.022286 / 0.000200 (0.022086) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033971 / 0.037411 (-0.003440) | 0.132030 / 0.014526 (0.117504) | 0.141725 / 0.176557 (-0.034831) | 0.199818 / 0.737135 (-0.537318) | 0.149176 / 0.296338 (-0.147162) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.511463 / 0.215209 (0.296254) | 4.917921 / 2.077655 (2.840267) | 2.382377 / 1.504120 (0.878257) | 2.154599 / 1.541195 (0.613404) | 2.247858 / 1.468490 (0.779368) | 0.834524 / 4.584777 (-3.750253) | 4.560010 / 3.745712 (0.814297) | 2.403055 / 5.269862 (-2.866806) | 1.780784 / 4.565676 (-2.784893) | 0.101409 / 0.424275 (-0.322866) | 0.014657 / 0.007607 (0.007050) | 0.610137 / 0.226044 (0.384093) | 6.051011 / 2.268929 (3.782083) | 2.887357 / 55.444624 (-52.557267) | 2.518225 / 6.876477 (-4.358252) | 2.559654 / 2.142072 (0.417582) | 0.981226 / 4.805227 (-3.824001) | 0.197323 / 6.500664 (-6.303341) | 0.076851 / 0.075469 (0.001382) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.554662 / 1.841788 (-0.287126) | 18.038993 / 8.074308 (9.964685) | 16.719948 / 10.191392 (6.528556) | 0.195641 / 0.680424 (-0.484783) | 0.020699 / 0.534201 (-0.513502) | 0.498949 / 0.579283 (-0.080334) | 0.487775 / 0.434364 (0.053411) | 0.591413 / 0.540337 (0.051075) | 0.708520 / 1.386936 (-0.678416) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39de0d78224c070be33d0820ec9203018fb721d1 \"CML watermark\")\n",
"Ready for review @mariosasko :)",
"Yea it does behave as expected, but modifying a dataset's features dict is not recommended and can lead to unpredictable behaviors. By copying the features, we make sure users don't modify the dataset's features dict.\r\n\r\nSince the attribute is public, users expect to be able to do whatever they want with it, without checking if they have to copy it or not",
"<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) | 0.008069 / 0.011353 (-0.003284) | 0.005051 / 0.011008 (-0.005958) | 0.096587 / 0.038508 (0.058079) | 0.032954 / 0.023109 (0.009844) | 0.317877 / 0.275898 (0.041979) | 0.328677 / 0.323480 (0.005197) | 0.005524 / 0.007986 (-0.002462) | 0.003958 / 0.004328 (-0.000370) | 0.072692 / 0.004250 (0.068441) | 0.044554 / 0.037052 (0.007502) | 0.311121 / 0.258489 (0.052632) | 0.355912 / 0.293841 (0.062071) | 0.035934 / 0.128546 (-0.092612) | 0.012056 / 0.075646 (-0.063590) | 0.332575 / 0.419271 (-0.086696) | 0.049788 / 0.043533 (0.006255) | 0.307918 / 0.255139 (0.052779) | 0.326757 / 0.283200 (0.043557) | 0.098671 / 0.141683 (-0.043012) | 1.424625 / 1.452155 (-0.027530) | 1.507944 / 1.492716 (0.015228) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207976 / 0.018006 (0.189970) | 0.439604 / 0.000490 (0.439114) | 0.000435 / 0.000200 (0.000235) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026961 / 0.037411 (-0.010451) | 0.106627 / 0.014526 (0.092101) | 0.115292 / 0.176557 (-0.061264) | 0.171901 / 0.737135 (-0.565234) | 0.123276 / 0.296338 (-0.173062) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407679 / 0.215209 (0.192469) | 4.071958 / 2.077655 (1.994303) | 1.854270 / 1.504120 (0.350151) | 1.678406 / 1.541195 (0.137211) | 1.715890 / 1.468490 (0.247400) | 0.705536 / 4.584777 (-3.879241) | 3.774198 / 3.745712 (0.028486) | 2.096429 / 5.269862 (-3.173432) | 1.431810 / 4.565676 (-3.133866) | 0.085557 / 0.424275 (-0.338718) | 0.012191 / 0.007607 (0.004584) | 0.502937 / 0.226044 (0.276893) | 5.034391 / 2.268929 (2.765463) | 2.393826 / 55.444624 (-53.050799) | 2.037383 / 6.876477 (-4.839094) | 2.192037 / 2.142072 (0.049964) | 0.829298 / 4.805227 (-3.975929) | 0.167781 / 6.500664 (-6.332883) | 0.063405 / 0.075469 (-0.012064) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.179189 / 1.841788 (-0.662599) | 14.464132 / 8.074308 (6.389824) | 14.869024 / 10.191392 (4.677632) | 0.172864 / 0.680424 (-0.507560) | 0.017817 / 0.534201 (-0.516384) | 0.427849 / 0.579283 (-0.151434) | 0.434447 / 0.434364 (0.000083) | 0.502077 / 0.540337 (-0.038260) | 0.599587 / 1.386936 (-0.787349) |\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) | 0.007366 / 0.011353 (-0.003987) | 0.004939 / 0.011008 (-0.006069) | 0.074982 / 0.038508 (0.036474) | 0.032611 / 0.023109 (0.009501) | 0.340670 / 0.275898 (0.064772) | 0.372471 / 0.323480 (0.048991) | 0.005567 / 0.007986 (-0.002418) | 0.003956 / 0.004328 (-0.000372) | 0.074550 / 0.004250 (0.070300) | 0.047097 / 0.037052 (0.010045) | 0.337049 / 0.258489 (0.078560) | 0.391512 / 0.293841 (0.097671) | 0.035712 / 0.128546 (-0.092835) | 0.012040 / 0.075646 (-0.063606) | 0.087126 / 0.419271 (-0.332146) | 0.048290 / 0.043533 (0.004757) | 0.335069 / 0.255139 (0.079930) | 0.362080 / 0.283200 (0.078881) | 0.098606 / 0.141683 (-0.043077) | 1.456802 / 1.452155 (0.004647) | 1.554652 / 1.492716 (0.061936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200015 / 0.018006 (0.182009) | 0.442772 / 0.000490 (0.442283) | 0.004594 / 0.000200 (0.004394) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028510 / 0.037411 (-0.008901) | 0.109654 / 0.014526 (0.095128) | 0.119921 / 0.176557 (-0.056636) | 0.170289 / 0.737135 (-0.566846) | 0.125288 / 0.296338 (-0.171051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430919 / 0.215209 (0.215710) | 4.274132 / 2.077655 (2.196478) | 2.014385 / 1.504120 (0.510265) | 1.822094 / 1.541195 (0.280899) | 1.938188 / 1.468490 (0.469698) | 0.707812 / 4.584777 (-3.876965) | 3.925730 / 3.745712 (0.180018) | 2.117481 / 5.269862 (-3.152381) | 1.369521 / 4.565676 (-3.196155) | 0.088414 / 0.424275 (-0.335861) | 0.013101 / 0.007607 (0.005494) | 0.538468 / 0.226044 (0.312424) | 5.384614 / 2.268929 (3.115685) | 2.487709 / 55.444624 (-52.956915) | 2.152060 / 6.876477 (-4.724417) | 2.225777 / 2.142072 (0.083705) | 0.856749 / 4.805227 (-3.948479) | 0.173299 / 6.500664 (-6.327366) | 0.068872 / 0.075469 (-0.006597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268009 / 1.841788 (-0.573778) | 15.102648 / 8.074308 (7.028340) | 14.216810 / 10.191392 (4.025418) | 0.163661 / 0.680424 (-0.516763) | 0.017394 / 0.534201 (-0.516807) | 0.418030 / 0.579283 (-0.161253) | 0.413717 / 0.434364 (-0.020647) | 0.487526 / 0.540337 (-0.052811) | 0.581499 / 1.386936 (-0.805437) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46bb11e96d053c497035a2436702860de9960a65 \"CML watermark\")\n"
] |
1,631,967,509
| 5,651
|
expanduser in save_to_disk
|
closed
| 2023-03-20T12:02:18
| 2023-10-27T14:04:37
| 2023-10-27T14:04:37
|
https://github.com/huggingface/datasets/issues/5651
| null |
RmZeta2718
| false
|
[
"`save_to_disk` should indeed expand `~`. Marking it as a \"good first issue\".",
"#self-assign\r\n\r\nFile path to code: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L1364\r\n\r\n@RmZeta2718 I created a pull request for this issue. ",
"Hello, \r\nIt says `save_to_disk` is deprecated in 2.8.0, so the alternative to this will be `storage_options`? \r\n\r\nhttps://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.save_to_disk",
"@ashikshafi08 I think you misunderstood the warning. The method `save_to_disk` is not deprecated only the optional parameter `fs`.\r\nAlso @benjaminbrown038 as I cannot find your PR I would like to work on this if you don't mind.",
"@mariosasko It's been several months and the PR is not reviewed. Could you please take a look? I assume this is not complicated and could be merged fairly soon."
] |
1,630,336,919
| 5,650
|
load_dataset can't work correct with my image data
|
closed
| 2023-03-18T13:59:13
| 2023-07-24T14:13:02
| 2023-07-24T14:13:01
|
https://github.com/huggingface/datasets/issues/5650
| null |
WiNE-iNEFF
| false
|
[
"Can you post a reproducible code snippet of what you tried to do?\r\n\r\n",
"> Can you post a reproducible code snippet of what you tried to do?\n> \n> \n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"my_folder_name\", split=\"train\")\n```",
"hi @WiNE-iNEFF ! can you please also tell a bit more about how your data is structured (directory structure and filenames patterns)?",
"> hi @WiNE-iNEFF ! can you please also tell a bit more about how your data is structured (directory structure and filenames patterns)?\n\nAll file have format .png converted in RGBA. \nIn main folder \"MyData\" contain 4 folder with images. In function load_dataset i use folder \"MyData\"",
"@WiNE-iNEFF I'm sorry there is still not enough information to answer your question :( For now I can only assume that your [filenames contain split names](https://huggingface.co/docs/datasets/repository_structure#splits-and-file-names) which are somehow incorrectly parsed. \r\nWhat would be the output if you omit `split` while loading? Like just\r\n```python\r\nds = load_dataset(\"MyData\")\r\nprint(ds)\r\n```\r\n\r\n",
"> @WiNE-iNEFF I'm sorry there is still not enough information to answer your question :( For now I can only assume that your [filenames contain split names](https://huggingface.co/docs/datasets/repository_structure#splits-and-file-names) which are somehow incorrectly parsed. \n> What would be the output if you omit `split` while loading? Like just\n> ```python\n> ds = load_dataset(\"MyData\")\n> print(ds)\n> ```\n> \n> \n\n```python\nDataset({\n features: ['image', 'label'],\n num_rows: 4\n})\n```",
"@WiNE-iNEFF My only guess is that 4 images in your data have `\"train\"` string in their names (something like `\"train_image_0.png\"`) and others do not and the loader ignores all the files that do not contain split name in filename. If it's true, please try to remove \"train\" from filenames. Or maybe they are inside a directory named \"train\", then the directory should be renamed (unless you want to put only these 4 specific images to the train but apparently you do not).\r\n\r\nIf there is a bug I cannot investigate it unfortunately because I cannot reproduce your case without some data samples. ",
"> @WiNE-iNEFF My only guess is that 4 images in your data have `\"train\"` string in their names (something like `\"train_image_0.png\"`) and others do not and the loader ignores all the files that do not contain split name in filename. If it's true, please try to remove \"train\" from filenames. Or maybe they are inside a directory named \"train\", then the directory should be renamed (unless you want to put only these 4 specific images to the train but apparently you do not).\n> \n> If there is a bug I cannot investigate it unfortunately because I cannot reproduce your case without some data samples. \n\nI checked my files and some of them do have the words train, valid and test in their names, but the number of such images is more than 500, not 4.",
"@WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)):\r\n```\r\n Fuaimeanna2/\r\n├─ test\r\n│ ├─ label_0\r\n│ │ ├── filename_0.jpg\r\n│ │ └── filename_1.jpg\r\n│ │ └── ...\r\n│ ├─ label_1\r\n│ │ └── ...\r\n│ ├─ label_2\r\n│ │ └── ...\r\n│ └─ label_3\r\n│ └── ...\r\n├─ train\r\n│ ├─ label_0\r\n│ │ └── ...\r\n│ ├─ label_1\r\n│ │ └── ...\r\n│ ├─ label_2\r\n│ │ └── ...\r\n│ └─ label_3\r\n│ └── ...\r\n└── validation\r\n ├─ label_0\r\n │ └── ...\r\n ├─ label_1\r\n │ └── ...\r\n ├─ label_2\r\n │ └── ...\r\n └─ label_3\r\n └── ...\r\n```",
"> @WiNE-iNEFF Probably they are named inconsistently so that the correct pattern for which files should correspond to which split cannot be inferred. You can make it clearer to the loader by removing split names from filenames and putting files in separate folder for each split (you can take a look at the [documentation for imagefolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder)):\n> ```\n> Fuaimeanna2/\n> ├─ test\n> │ ├─ label_0\n> │ │ ├── filename_0.jpg\n> │ │ └── filename_1.jpg\n> │ │ └── ...\n> │ ├─ label_1\n> │ │ └── ...\n> │ ├─ label_2\n> │ │ └── ...\n> │ └─ label_3\n> │ └── ...\n> ├─ train\n> │ ├─ label_0\n> │ │ └── ...\n> │ ├─ label_1\n> │ │ └── ...\n> │ ├─ label_2\n> │ │ └── ...\n> │ └─ label_3\n> │ └── ...\n> └── validation\n> ├─ label_0\n> │ └── ...\n> ├─ label_1\n> │ └── ...\n> ├─ label_2\n> │ └── ...\n> └─ label_3\n> └── ...\n> ```\n\nI have read this documentation more than once. It just wasn't a problem before.",
"Hi,\r\n\r\nYou need to use:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imagefolder\", split=\"train\", data_dir=\"path_to_your_folder\")\r\n```\r\ninstead of \r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"my_folder_name\", split=\"train\")\r\n```\r\nTo create an image dataset from your local folders.",
"> Hi,\r\n> \r\n> You need to use:\r\n> \r\n> ```\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"imagefolder\", split=\"train\", data_dir=\"path_to_your_folder\")\r\n> ```\r\n> \r\n> instead of\r\n> \r\n> ```\r\n> from datasets import load_dataset\r\n> \r\n> dataset = load_dataset(\"my_folder_name\", split=\"train\")\r\n> ```\r\n> \r\n> To create an image dataset from your local folders.\r\n\r\nThank you, but even using the method that you wrote above absolutely nothing changes, especially without using data_dir on my other data everything works fine",
"@WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? \r\n\r\n\r\n> even using the method that you wrote above absolutely nothing changes\r\n\r\nfyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset(\"my_folder_name\"`), not as `data_dir` (`load_dataset(\"imagefolder\", data_dir=\"my_folder_name\"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions.",
"Oh I didn't know that! OK but in any case, not sure why the image builder isn't working for @WiNE-iNEFF. But it's hard for us to help if we can't reproduce. I'd just check the structure of the folders, see if the splits are correctly set up, etc.",
"> @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? \n> \n> \n> > even using the method that you wrote above absolutely nothing changes\n> \n> fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset(\"my_folder_name\"`), not as `data_dir` (`load_dataset(\"imagefolder\", data_dir=\"my_folder_name\"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions.\n\nI'll try to try your method over the next few days, then I'll write it turned out ",
"> @WiNE-iNEFF have you tried the suggestion I posted above? with removing split names from filenames and structuring files in folders? \n> \n> \n> > even using the method that you wrote above absolutely nothing changes\n> \n> fyi - nothing changed because these two approaches are basically the same. it's just that when you pass your data directory as a dataset name (`load_dataset(\"my_folder_name\"`), not as `data_dir` (`load_dataset(\"imagefolder\", data_dir=\"my_folder_name\"`), `datasets` infers what module to use (`imagefolder` in your case) automatically, by file extensions.\n\nI tried creating a `train` folder and put my image folders in it. As a result, all 18,000 images were loaded. ",
"@WiNE-iNEFF great! So to explain what happened according to my assumptions:\r\n\r\nWhen you use a standard packaged loader (like `imagefolder`, `csv`, `jsonl`, and so on) and load your data like `load_dataset(\"my_folder_name\")` or `load_dataset(\"imagefolder\", data_dir=\"my_folder_name\"`, the library searches for patterns to divide files into splits. This is described a bit in [this doc](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names). And the order to search for patterns is the following:\r\n1. first it checks for [pattern like `data/<split_name>-xxxxx-of-xxxxx`](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#custom-split-names) (which allows to pass custom split names)\r\n2. then for directories named as splits (if you have directories named `train`, `test` etc.)\r\n3. then for [splits in filenames](https://huggingface.co/docs/datasets/v2.10.0/en/repository_structure#splits-and-file-names) (like if you have files named `train-image.jpg`, `test_0.jpg`, ...)\r\n4. then if no pattern was found, it treats all files as belonging to a single `train` split\r\n\r\nThe code is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L215).\r\nSo I assume that in your case, since you didn't have directories for splits (pattern 2), some files that included split keywords (pattern 3) were included and others were ignored as not matching the pattern. And when you added `train` directory, the pattern for directories (pattern 2) was triggered first and everything worked as expected. Everything worked in your previous cases probably because you didn't have split names keywords in filenames, so all the files ended up being a part of a single train split (pattern 4).\r\n\r\nAnother way to mitigate this apart from structuring your data according to the patterns is to explicitly state with files belong to which splits by passing them with `data_files` parameter:\r\n```python\r\nload_dataset(\"my_folder_name\", data_files={\"train\": \"**\"}) # to tell that all files should be included \r\n```\r\n\r\nNow I see that this order should be explained in documentation and also referenced in sections for packaged modules like `imagefolder`, thank you for pointing this out. \r\n\r\n \r\n",
"@NielsRogge @polinaeterna I have a similar problem when reading my dataset. I want to use DETR for object detection, but my data is in YOLO format. With a dataset of 10k images, yolo format involves having 10k labels. As far as I read regarding [COCO format](https://auto.gluon.ai/stable/tutorials/multimodal/object_detection/data_preparation/convert_data_to_coco_format.html), there must be one JSON per split. However, as I post in the [Hugging Face forum](https://discuss.huggingface.co/t/prepare-dataset-from-yolo-format-to-coco-for-detr/34894), when it is read, the number of rows is 1, which does not make sense. \r\nThe instruction to read the train-val-test splits are: \r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n\t\"train\": './train_labels.json',\r\n\t\"validation\": './val_labels.json',\r\n\t\"test\": './test_labels.json'\r\n}\r\ndataset = load_dataset(\"json\", data_files=data_files)\r\n```\r\nAn example of the short version of the json file I read, to reproduce my error, is the following: \r\n\r\n``` json\r\n{\r\n \"info\": {},\r\n \"licenses\": [],\r\n \"images\": [\r\n {\r\n \"id\": 1,\r\n \"file_name\": \"aceca_100.mp4frame21.png\",\r\n \"width\": 1280,\r\n \"height\": 720,\r\n \"pixel_values\": null,\r\n \"pixel_mask\": null\r\n },\r\n {\r\n \"id\": 2,\r\n \"file_name\": \"aceca_100.mp4frame24.png\",\r\n \"width\": 1280,\r\n \"height\": 720,\r\n \"pixel_values\": null,\r\n \"pixel_mask\": null\r\n },\r\n {\r\n \"id\": 3,\r\n \"file_name\": \"aceca_100.mp4frame25.png\",\r\n \"width\": 1280,\r\n \"height\": 720,\r\n \"pixel_values\": null,\r\n \"pixel_mask\": null}],\r\n \"annotations\": [\r\n {\r\n \"id\": 1,\r\n \"image_id\": 1,\r\n \"category_id\": 0,\r\n \"bbox\": [0.0, 278.21896388398557, 86.94096523844935, 156.0293445072134],\r\n \"area\": 13565.341816979679,\r\n \"iscrowd\": 0\r\n },\r\n {\r\n \"id\": 2,\r\n \"image_id\": 2,\r\n \"category_id\": 0,\r\n \"bbox\": [149.28851295721816, 297.6359759754418, 34.76802347007475, 98.03908698442889],\r\n \"area\": 3408.625277259324,\r\n \"iscrowd\": 0\r\n },\r\n {\r\n \"id\": 3,\r\n \"image_id\": 3,\r\n \"category_id\": 0,\r\n \"bbox\": [153.3817197549372, 300.168969412891, 31.787555842913775, 89.69583163436312],\r\n \"area\": 2851.2112569539095,\r\n \"iscrowd\": 0\r\n }\r\n ],\r\n \"categories\": [\r\n {\r\n \"id\": 0, \"name\": \"person\"\r\n }\r\n ]\r\n }\r\n```\r\nIf full files required, my email is aruigui98@gmail.com",
"Hi @Alberto1404, to load an object detection dataset it's recommended to make use of the metadata feature as explained [here](https://huggingface.co/docs/datasets/image_dataset#object-detection). ",
"Thank you @NielsRogge! It works!!!",
"You can now refer to https://huggingface.co/docs/datasets/repository_structure to learn about the `datasets`' data files inference, so I'm closing this issue."
] |
1,630,173,460
| 5,649
|
The index column created with .to_sql() is dependent on the batch_size when writing
|
closed
| 2023-03-18T05:25:17
| 2023-06-17T07:01:57
| 2023-06-17T07:01:57
|
https://github.com/huggingface/datasets/issues/5649
| null |
lsb
| false
|
[
"Thanks for reporting, @lsb. \r\n\r\nWe are investigating it.\r\n\r\nOn the other hand, please note that in the next `datasets` release, the index will not be created by default (see #5583). If you would like to have it, you will need to explicitly pass `index=True`. ",
"I think this is low enough priority for me to close this as Won't Fix. If I need any primary keys I can generate them beforehand. Feel free to reopen."
] |
1,629,253,719
| 5,648
|
flatten_indices doesn't work with pandas format
|
open
| 2023-03-17T12:44:25
| 2023-03-21T13:12:03
| null |
https://github.com/huggingface/datasets/issues/5648
| null |
alialamiidrissi
| false
|
[
"Thanks for reporting! This can be fixed by setting the format to `arrow` in `flatten_indices` and restoring the original format after the flattening. I'm working on a PR that reduces the number of the `flatten_indices` calls in our codebase and makes `flatten_indices` a no-op when a dataset does not have an indices mapping, so I'll incorporate the fix in that PR."
] |
1,628,225,544
| 5,647
|
Make all print statements optional
|
closed
| 2023-03-16T20:30:07
| 2023-07-21T14:20:25
| 2023-07-21T14:20:24
|
https://github.com/huggingface/datasets/issues/5647
| null |
gagan3012
| false
|
[
"related to #5444 ",
"We now log these messages instead of printing them (addressed in #6019), so I'm closing this issue."
] |
1,627,838,762
| 5,646
|
Allow self as key in `Features`
|
closed
| 2023-03-16T16:17:03
| 2023-03-16T17:21:58
| 2023-03-16T17:14:50
|
https://github.com/huggingface/datasets/pull/5646
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5646",
"html_url": "https://github.com/huggingface/datasets/pull/5646",
"diff_url": "https://github.com/huggingface/datasets/pull/5646.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5646.patch",
"merged_at": "2023-03-16T17:14:50"
}
|
mariosasko
| true
|
[
"_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) | 0.009980 / 0.011353 (-0.001373) | 0.006643 / 0.011008 (-0.004366) | 0.140722 / 0.038508 (0.102214) | 0.036693 / 0.023109 (0.013584) | 0.430019 / 0.275898 (0.154121) | 0.463218 / 0.323480 (0.139738) | 0.006977 / 0.007986 (-0.001008) | 0.006488 / 0.004328 (0.002160) | 0.099385 / 0.004250 (0.095134) | 0.047160 / 0.037052 (0.010108) | 0.431440 / 0.258489 (0.172951) | 0.500232 / 0.293841 (0.206391) | 0.057968 / 0.128546 (-0.070578) | 0.020197 / 0.075646 (-0.055449) | 0.438269 / 0.419271 (0.018998) | 0.071149 / 0.043533 (0.027617) | 0.428502 / 0.255139 (0.173363) | 0.486861 / 0.283200 (0.203661) | 0.119855 / 0.141683 (-0.021828) | 1.875372 / 1.452155 (0.423218) | 1.955055 / 1.492716 (0.462339) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243468 / 0.018006 (0.225462) | 0.547842 / 0.000490 (0.547352) | 0.004885 / 0.000200 (0.004685) | 0.000144 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031555 / 0.037411 (-0.005856) | 0.125869 / 0.014526 (0.111343) | 0.137816 / 0.176557 (-0.038741) | 0.206581 / 0.737135 (-0.530555) | 0.142976 / 0.296338 (-0.153362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624773 / 0.215209 (0.409564) | 6.154861 / 2.077655 (4.077206) | 2.504586 / 1.504120 (1.000466) | 1.989118 / 1.541195 (0.447923) | 2.092280 / 1.468490 (0.623790) | 1.240108 / 4.584777 (-3.344669) | 5.584893 / 3.745712 (1.839181) | 3.075369 / 5.269862 (-2.194492) | 2.174285 / 4.565676 (-2.391391) | 0.141555 / 0.424275 (-0.282720) | 0.016099 / 0.007607 (0.008492) | 0.720543 / 0.226044 (0.494498) | 7.489000 / 2.268929 (5.220071) | 3.239189 / 55.444624 (-52.205435) | 2.525772 / 6.876477 (-4.350704) | 2.773514 / 2.142072 (0.631441) | 1.410084 / 4.805227 (-3.395143) | 0.259252 / 6.500664 (-6.241412) | 0.082573 / 0.075469 (0.007104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.458186 / 1.841788 (-0.383602) | 17.503738 / 8.074308 (9.429430) | 20.817682 / 10.191392 (10.626290) | 0.231221 / 0.680424 (-0.449203) | 0.032550 / 0.534201 (-0.501651) | 0.559020 / 0.579283 (-0.020263) | 0.592987 / 0.434364 (0.158623) | 0.602661 / 0.540337 (0.062324) | 0.731912 / 1.386936 (-0.655024) |\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) | 0.009543 / 0.011353 (-0.001810) | 0.006953 / 0.011008 (-0.004055) | 0.087651 / 0.038508 (0.049143) | 0.031717 / 0.023109 (0.008608) | 0.437813 / 0.275898 (0.161915) | 0.468448 / 0.323480 (0.144968) | 0.007378 / 0.007986 (-0.000607) | 0.005170 / 0.004328 (0.000842) | 0.102286 / 0.004250 (0.098035) | 0.043643 / 0.037052 (0.006591) | 0.458788 / 0.258489 (0.200299) | 0.519891 / 0.293841 (0.226050) | 0.052875 / 0.128546 (-0.075671) | 0.020518 / 0.075646 (-0.055128) | 0.112675 / 0.419271 (-0.306597) | 0.066390 / 0.043533 (0.022858) | 0.423037 / 0.255139 (0.167898) | 0.420345 / 0.283200 (0.137146) | 0.119221 / 0.141683 (-0.022462) | 1.632244 / 1.452155 (0.180090) | 1.829585 / 1.492716 (0.336869) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242312 / 0.018006 (0.224305) | 0.547592 / 0.000490 (0.547102) | 0.006520 / 0.000200 (0.006320) | 0.000185 / 0.000054 (0.000131) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032204 / 0.037411 (-0.005207) | 0.113320 / 0.014526 (0.098794) | 0.135667 / 0.176557 (-0.040889) | 0.194360 / 0.737135 (-0.542775) | 0.127934 / 0.296338 (-0.168404) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648134 / 0.215209 (0.432925) | 6.470574 / 2.077655 (4.392920) | 2.799121 / 1.504120 (1.295001) | 2.160450 / 1.541195 (0.619255) | 2.261648 / 1.468490 (0.793158) | 1.244660 / 4.584777 (-3.340117) | 5.694636 / 3.745712 (1.948923) | 5.316191 / 5.269862 (0.046329) | 2.764551 / 4.565676 (-1.801126) | 0.152225 / 0.424275 (-0.272051) | 0.015959 / 0.007607 (0.008351) | 0.833606 / 0.226044 (0.607562) | 8.099765 / 2.268929 (5.830836) | 3.523005 / 55.444624 (-51.921620) | 2.855126 / 6.876477 (-4.021351) | 2.730849 / 2.142072 (0.588776) | 1.434351 / 4.805227 (-3.370876) | 0.251963 / 6.500664 (-6.248701) | 0.085718 / 0.075469 (0.010249) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.722466 / 1.841788 (-0.119322) | 17.846981 / 8.074308 (9.772673) | 21.578684 / 10.191392 (11.387292) | 0.239987 / 0.680424 (-0.440437) | 0.029189 / 0.534201 (-0.505012) | 0.543181 / 0.579283 (-0.036102) | 0.626527 / 0.434364 (0.192163) | 0.614334 / 0.540337 (0.073997) | 0.745934 / 1.386936 (-0.641002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c506ad7cd22668f37ec51ff01b7c7f7235b9212 \"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) | 0.007395 / 0.011353 (-0.003958) | 0.004965 / 0.011008 (-0.006043) | 0.096376 / 0.038508 (0.057868) | 0.033243 / 0.023109 (0.010134) | 0.299990 / 0.275898 (0.024092) | 0.336287 / 0.323480 (0.012807) | 0.005528 / 0.007986 (-0.002458) | 0.004003 / 0.004328 (-0.000326) | 0.072820 / 0.004250 (0.068569) | 0.042867 / 0.037052 (0.005815) | 0.296719 / 0.258489 (0.038230) | 0.337313 / 0.293841 (0.043472) | 0.036809 / 0.128546 (-0.091738) | 0.012239 / 0.075646 (-0.063407) | 0.332351 / 0.419271 (-0.086921) | 0.050449 / 0.043533 (0.006916) | 0.301483 / 0.255139 (0.046344) | 0.316673 / 0.283200 (0.033474) | 0.102526 / 0.141683 (-0.039157) | 1.415429 / 1.452155 (-0.036726) | 1.544381 / 1.492716 (0.051665) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211158 / 0.018006 (0.193152) | 0.434718 / 0.000490 (0.434228) | 0.003386 / 0.000200 (0.003186) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027945 / 0.037411 (-0.009466) | 0.108743 / 0.014526 (0.094217) | 0.119771 / 0.176557 (-0.056785) | 0.178667 / 0.737135 (-0.558468) | 0.123718 / 0.296338 (-0.172620) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413908 / 0.215209 (0.198699) | 4.136828 / 2.077655 (2.059174) | 1.932547 / 1.504120 (0.428427) | 1.715389 / 1.541195 (0.174194) | 1.791679 / 1.468490 (0.323189) | 0.692715 / 4.584777 (-3.892062) | 3.741807 / 3.745712 (-0.003905) | 2.066274 / 5.269862 (-3.203587) | 1.314106 / 4.565676 (-3.251570) | 0.087191 / 0.424275 (-0.337084) | 0.012866 / 0.007607 (0.005259) | 0.510012 / 0.226044 (0.283968) | 5.116419 / 2.268929 (2.847490) | 2.408562 / 55.444624 (-53.036063) | 2.002044 / 6.876477 (-4.874433) | 2.121868 / 2.142072 (-0.020204) | 0.837141 / 4.805227 (-3.968086) | 0.166596 / 6.500664 (-6.334068) | 0.063190 / 0.075469 (-0.012279) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204152 / 1.841788 (-0.637636) | 14.739793 / 8.074308 (6.665485) | 14.403469 / 10.191392 (4.212077) | 0.165781 / 0.680424 (-0.514642) | 0.017826 / 0.534201 (-0.516375) | 0.423527 / 0.579283 (-0.155756) | 0.431410 / 0.434364 (-0.002954) | 0.499422 / 0.540337 (-0.040915) | 0.596116 / 1.386936 (-0.790820) |\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) | 0.007365 / 0.011353 (-0.003988) | 0.005165 / 0.011008 (-0.005844) | 0.073403 / 0.038508 (0.034895) | 0.032542 / 0.023109 (0.009433) | 0.339304 / 0.275898 (0.063406) | 0.371892 / 0.323480 (0.048412) | 0.005544 / 0.007986 (-0.002442) | 0.004108 / 0.004328 (-0.000221) | 0.073750 / 0.004250 (0.069500) | 0.045613 / 0.037052 (0.008561) | 0.366159 / 0.258489 (0.107670) | 0.389864 / 0.293841 (0.096023) | 0.036006 / 0.128546 (-0.092540) | 0.012402 / 0.075646 (-0.063244) | 0.085137 / 0.419271 (-0.334135) | 0.048485 / 0.043533 (0.004952) | 0.334172 / 0.255139 (0.079033) | 0.353168 / 0.283200 (0.069969) | 0.099393 / 0.141683 (-0.042290) | 1.460584 / 1.452155 (0.008429) | 1.518601 / 1.492716 (0.025885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227352 / 0.018006 (0.209346) | 0.444211 / 0.000490 (0.443721) | 0.000410 / 0.000200 (0.000210) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029517 / 0.037411 (-0.007894) | 0.115557 / 0.014526 (0.101031) | 0.125855 / 0.176557 (-0.050701) | 0.175214 / 0.737135 (-0.561922) | 0.129324 / 0.296338 (-0.167014) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429783 / 0.215209 (0.214574) | 4.301159 / 2.077655 (2.223504) | 2.084939 / 1.504120 (0.580819) | 1.887781 / 1.541195 (0.346586) | 2.045712 / 1.468490 (0.577222) | 0.693319 / 4.584777 (-3.891458) | 3.788595 / 3.745712 (0.042883) | 2.087080 / 5.269862 (-3.182781) | 1.325247 / 4.565676 (-3.240429) | 0.085919 / 0.424275 (-0.338356) | 0.012710 / 0.007607 (0.005103) | 0.533432 / 0.226044 (0.307387) | 5.339468 / 2.268929 (3.070540) | 2.578351 / 55.444624 (-52.866273) | 2.224905 / 6.876477 (-4.651572) | 2.301064 / 2.142072 (0.158992) | 0.839622 / 4.805227 (-3.965605) | 0.166523 / 6.500664 (-6.334141) | 0.065254 / 0.075469 (-0.010215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262223 / 1.841788 (-0.579565) | 15.042523 / 8.074308 (6.968215) | 14.542719 / 10.191392 (4.351327) | 0.142230 / 0.680424 (-0.538194) | 0.017610 / 0.534201 (-0.516591) | 0.422357 / 0.579283 (-0.156926) | 0.417785 / 0.434364 (-0.016579) | 0.491990 / 0.540337 (-0.048348) | 0.585835 / 1.386936 (-0.801101) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2fcedd2a561fe6f5b6972ad18bfef722e1d2c77 \"CML watermark\")\n"
] |
1,627,108,278
| 5,645
|
Datasets map and select(range()) is giving dill error
|
closed
| 2023-03-16T10:01:28
| 2023-03-17T04:24:51
| 2023-03-17T04:24:51
|
https://github.com/huggingface/datasets/issues/5645
| null |
Tanya-11
| false
|
[
"It looks like an error that we observed once in https://github.com/huggingface/datasets/pull/5166\r\n\r\nCan you try to update `datasets` ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nif it doesn't work, can you make sure you don't have packages installed that may modify `dill`'s behavior, such as `apache-beam` ?",
"@lhoestq That fixed the problem, Thanks :)"
] |
1,626,204,046
| 5,644
|
Allow direct cast from binary to Audio/Image
|
closed
| 2023-03-15T20:02:54
| 2023-03-16T14:20:44
| 2023-03-16T14:12:55
|
https://github.com/huggingface/datasets/pull/5644
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5644",
"html_url": "https://github.com/huggingface/datasets/pull/5644",
"diff_url": "https://github.com/huggingface/datasets/pull/5644.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5644.patch",
"merged_at": "2023-03-16T14:12:55"
}
|
mariosasko
| true
|
[
"_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) | 0.008337 / 0.011353 (-0.003016) | 0.005588 / 0.011008 (-0.005421) | 0.110259 / 0.038508 (0.071751) | 0.038928 / 0.023109 (0.015819) | 0.350441 / 0.275898 (0.074543) | 0.378473 / 0.323480 (0.054993) | 0.006369 / 0.007986 (-0.001616) | 0.005730 / 0.004328 (0.001401) | 0.083042 / 0.004250 (0.078792) | 0.048686 / 0.037052 (0.011634) | 0.367561 / 0.258489 (0.109072) | 0.398073 / 0.293841 (0.104232) | 0.043247 / 0.128546 (-0.085299) | 0.013862 / 0.075646 (-0.061785) | 0.386745 / 0.419271 (-0.032527) | 0.060107 / 0.043533 (0.016574) | 0.345450 / 0.255139 (0.090311) | 0.371269 / 0.283200 (0.088069) | 0.117508 / 0.141683 (-0.024175) | 1.689345 / 1.452155 (0.237191) | 1.777119 / 1.492716 (0.284402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248248 / 0.018006 (0.230242) | 0.505200 / 0.000490 (0.504710) | 0.015354 / 0.000200 (0.015155) | 0.000794 / 0.000054 (0.000740) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.118583 / 0.014526 (0.104057) | 0.131546 / 0.176557 (-0.045010) | 0.196173 / 0.737135 (-0.540962) | 0.140532 / 0.296338 (-0.155807) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470733 / 0.215209 (0.255524) | 4.758868 / 2.077655 (2.681213) | 2.246731 / 1.504120 (0.742611) | 1.995232 / 1.541195 (0.454037) | 2.057596 / 1.468490 (0.589106) | 0.819227 / 4.584777 (-3.765550) | 4.472093 / 3.745712 (0.726381) | 2.428154 / 5.269862 (-2.841708) | 1.748023 / 4.565676 (-2.817654) | 0.101965 / 0.424275 (-0.322310) | 0.014706 / 0.007607 (0.007098) | 0.600593 / 0.226044 (0.374548) | 5.869565 / 2.268929 (3.600637) | 2.764890 / 55.444624 (-52.679735) | 2.332112 / 6.876477 (-4.544364) | 2.486190 / 2.142072 (0.344118) | 0.979123 / 4.805227 (-3.826104) | 0.199543 / 6.500664 (-6.301121) | 0.075906 / 0.075469 (0.000436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.397694 / 1.841788 (-0.444094) | 16.910500 / 8.074308 (8.836192) | 16.174131 / 10.191392 (5.982739) | 0.173975 / 0.680424 (-0.506449) | 0.021403 / 0.534201 (-0.512798) | 0.496187 / 0.579283 (-0.083096) | 0.487369 / 0.434364 (0.053005) | 0.565924 / 0.540337 (0.025587) | 0.684965 / 1.386936 (-0.701971) |\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) | 0.008253 / 0.011353 (-0.003100) | 0.005745 / 0.011008 (-0.005263) | 0.085848 / 0.038508 (0.047340) | 0.038753 / 0.023109 (0.015644) | 0.401278 / 0.275898 (0.125379) | 0.433132 / 0.323480 (0.109652) | 0.006112 / 0.007986 (-0.001874) | 0.005973 / 0.004328 (0.001644) | 0.085339 / 0.004250 (0.081088) | 0.053297 / 0.037052 (0.016244) | 0.400265 / 0.258489 (0.141776) | 0.455155 / 0.293841 (0.161314) | 0.043116 / 0.128546 (-0.085430) | 0.013957 / 0.075646 (-0.061689) | 0.099507 / 0.419271 (-0.319764) | 0.058858 / 0.043533 (0.015325) | 0.398030 / 0.255139 (0.142891) | 0.418171 / 0.283200 (0.134971) | 0.114392 / 0.141683 (-0.027291) | 1.683102 / 1.452155 (0.230947) | 1.801427 / 1.492716 (0.308711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242271 / 0.018006 (0.224265) | 0.494920 / 0.000490 (0.494430) | 0.007328 / 0.000200 (0.007128) | 0.000144 / 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) | 0.034061 / 0.037411 (-0.003351) | 0.146417 / 0.014526 (0.131891) | 0.161079 / 0.176557 (-0.015477) | 0.213999 / 0.737135 (-0.523137) | 0.166704 / 0.296338 (-0.129634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491214 / 0.215209 (0.276005) | 4.846946 / 2.077655 (2.769291) | 2.352595 / 1.504120 (0.848475) | 2.114055 / 1.541195 (0.572860) | 2.213537 / 1.468490 (0.745047) | 0.799625 / 4.584777 (-3.785152) | 4.440519 / 3.745712 (0.694807) | 4.476103 / 5.269862 (-0.793758) | 2.249384 / 4.565676 (-2.316292) | 0.098807 / 0.424275 (-0.325468) | 0.014463 / 0.007607 (0.006856) | 0.611793 / 0.226044 (0.385748) | 6.045710 / 2.268929 (3.776782) | 2.865957 / 55.444624 (-52.578667) | 2.454052 / 6.876477 (-4.422425) | 2.606153 / 2.142072 (0.464080) | 0.969057 / 4.805227 (-3.836170) | 0.198499 / 6.500664 (-6.302166) | 0.077012 / 0.075469 (0.001543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.497020 / 1.841788 (-0.344767) | 17.834277 / 8.074308 (9.759969) | 16.413792 / 10.191392 (6.222400) | 0.201979 / 0.680424 (-0.478445) | 0.020627 / 0.534201 (-0.513574) | 0.499767 / 0.579283 (-0.079516) | 0.496982 / 0.434364 (0.062618) | 0.579554 / 0.540337 (0.039216) | 0.693287 / 1.386936 (-0.693649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a3fee942ae159ff6cfe6a23b343605e7e12f55 \"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) | 0.007461 / 0.011353 (-0.003892) | 0.005341 / 0.011008 (-0.005668) | 0.099252 / 0.038508 (0.060744) | 0.034723 / 0.023109 (0.011614) | 0.300980 / 0.275898 (0.025082) | 0.353860 / 0.323480 (0.030380) | 0.006100 / 0.007986 (-0.001885) | 0.004149 / 0.004328 (-0.000180) | 0.074765 / 0.004250 (0.070514) | 0.052226 / 0.037052 (0.015174) | 0.305098 / 0.258489 (0.046609) | 0.357445 / 0.293841 (0.063604) | 0.036129 / 0.128546 (-0.092417) | 0.012482 / 0.075646 (-0.063165) | 0.333321 / 0.419271 (-0.085951) | 0.050489 / 0.043533 (0.006956) | 0.294728 / 0.255139 (0.039589) | 0.322722 / 0.283200 (0.039523) | 0.101226 / 0.141683 (-0.040456) | 1.436787 / 1.452155 (-0.015367) | 1.515784 / 1.492716 (0.023068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291836 / 0.018006 (0.273830) | 0.550735 / 0.000490 (0.550245) | 0.003828 / 0.000200 (0.003628) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028490 / 0.037411 (-0.008922) | 0.109543 / 0.014526 (0.095017) | 0.119451 / 0.176557 (-0.057105) | 0.176721 / 0.737135 (-0.560415) | 0.126711 / 0.296338 (-0.169628) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418863 / 0.215209 (0.203654) | 4.179167 / 2.077655 (2.101512) | 1.965126 / 1.504120 (0.461006) | 1.775544 / 1.541195 (0.234349) | 1.882667 / 1.468490 (0.414177) | 0.709201 / 4.584777 (-3.875576) | 3.754780 / 3.745712 (0.009068) | 2.175324 / 5.269862 (-3.094538) | 1.477454 / 4.565676 (-3.088223) | 0.085527 / 0.424275 (-0.338748) | 0.012685 / 0.007607 (0.005078) | 0.514276 / 0.226044 (0.288231) | 5.140518 / 2.268929 (2.871589) | 2.436011 / 55.444624 (-53.008614) | 2.114355 / 6.876477 (-4.762122) | 2.278893 / 2.142072 (0.136821) | 0.847825 / 4.805227 (-3.957402) | 0.169579 / 6.500664 (-6.331086) | 0.065306 / 0.075469 (-0.010163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190376 / 1.841788 (-0.651411) | 14.756581 / 8.074308 (6.682272) | 14.622610 / 10.191392 (4.431218) | 0.168186 / 0.680424 (-0.512238) | 0.017527 / 0.534201 (-0.516674) | 0.427808 / 0.579283 (-0.151475) | 0.437278 / 0.434364 (0.002914) | 0.509242 / 0.540337 (-0.031095) | 0.602500 / 1.386936 (-0.784436) |\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) | 0.007331 / 0.011353 (-0.004022) | 0.005703 / 0.011008 (-0.005305) | 0.074992 / 0.038508 (0.036484) | 0.034069 / 0.023109 (0.010960) | 0.343513 / 0.275898 (0.067615) | 0.369061 / 0.323480 (0.045582) | 0.006034 / 0.007986 (-0.001951) | 0.004344 / 0.004328 (0.000016) | 0.074678 / 0.004250 (0.070428) | 0.052262 / 0.037052 (0.015210) | 0.364758 / 0.258489 (0.106269) | 0.401130 / 0.293841 (0.107289) | 0.037635 / 0.128546 (-0.090912) | 0.012599 / 0.075646 (-0.063047) | 0.086935 / 0.419271 (-0.332337) | 0.058161 / 0.043533 (0.014628) | 0.338727 / 0.255139 (0.083589) | 0.355957 / 0.283200 (0.072757) | 0.111607 / 0.141683 (-0.030076) | 1.454357 / 1.452155 (0.002202) | 1.591529 / 1.492716 (0.098813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284379 / 0.018006 (0.266373) | 0.550720 / 0.000490 (0.550230) | 0.002868 / 0.000200 (0.002668) | 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) | 0.028876 / 0.037411 (-0.008535) | 0.110892 / 0.014526 (0.096366) | 0.122519 / 0.176557 (-0.054038) | 0.169774 / 0.737135 (-0.567361) | 0.129381 / 0.296338 (-0.166957) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429181 / 0.215209 (0.213972) | 4.251016 / 2.077655 (2.173361) | 2.056778 / 1.504120 (0.552658) | 1.860458 / 1.541195 (0.319264) | 1.958923 / 1.468490 (0.490432) | 0.712667 / 4.584777 (-3.872110) | 3.856910 / 3.745712 (0.111198) | 3.374535 / 5.269862 (-1.895327) | 1.846744 / 4.565676 (-2.718932) | 0.087238 / 0.424275 (-0.337037) | 0.012718 / 0.007607 (0.005111) | 0.524654 / 0.226044 (0.298609) | 5.209756 / 2.268929 (2.940827) | 2.494882 / 55.444624 (-52.949743) | 2.201150 / 6.876477 (-4.675327) | 2.274189 / 2.142072 (0.132117) | 0.844728 / 4.805227 (-3.960499) | 0.167467 / 6.500664 (-6.333197) | 0.064018 / 0.075469 (-0.011451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273284 / 1.841788 (-0.568503) | 15.104413 / 8.074308 (7.030105) | 15.134025 / 10.191392 (4.942633) | 0.147568 / 0.680424 (-0.532856) | 0.017429 / 0.534201 (-0.516772) | 0.422052 / 0.579283 (-0.157231) | 0.425786 / 0.434364 (-0.008578) | 0.491753 / 0.540337 (-0.048584) | 0.585091 / 1.386936 (-0.801845) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3d26e74898e0a9dc0d78490104e2e173269ef5b \"CML watermark\")\n"
] |
1,626,160,220
| 5,643
|
Support PyArrow arrays as column values in `from_dict`
|
closed
| 2023-03-15T19:32:40
| 2023-03-16T17:23:06
| 2023-03-16T17:15:40
|
https://github.com/huggingface/datasets/pull/5643
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5643",
"html_url": "https://github.com/huggingface/datasets/pull/5643",
"diff_url": "https://github.com/huggingface/datasets/pull/5643.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5643.patch",
"merged_at": "2023-03-16T17:15:39"
}
|
mariosasko
| true
|
[
"_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"
] |
1,626,043,177
| 5,642
|
Bump hfh to 0.11.0
|
closed
| 2023-03-15T18:26:07
| 2023-03-20T12:34:09
| 2023-03-20T12:26:58
|
https://github.com/huggingface/datasets/pull/5642
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5642",
"html_url": "https://github.com/huggingface/datasets/pull/5642",
"diff_url": "https://github.com/huggingface/datasets/pull/5642.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5642.patch",
"merged_at": "2023-03-20T12:26:58"
}
|
lhoestq
| true
|
[
"_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) | 0.006334 / 0.011353 (-0.005018) | 0.004447 / 0.011008 (-0.006561) | 0.099287 / 0.038508 (0.060779) | 0.027426 / 0.023109 (0.004317) | 0.322638 / 0.275898 (0.046740) | 0.370501 / 0.323480 (0.047021) | 0.004775 / 0.007986 (-0.003210) | 0.003289 / 0.004328 (-0.001040) | 0.076531 / 0.004250 (0.072280) | 0.037485 / 0.037052 (0.000432) | 0.335634 / 0.258489 (0.077145) | 0.384031 / 0.293841 (0.090190) | 0.031258 / 0.128546 (-0.097288) | 0.011619 / 0.075646 (-0.064027) | 0.326309 / 0.419271 (-0.092963) | 0.042513 / 0.043533 (-0.001020) | 0.340817 / 0.255139 (0.085678) | 0.369846 / 0.283200 (0.086646) | 0.084904 / 0.141683 (-0.056779) | 1.481739 / 1.452155 (0.029584) | 1.566593 / 1.492716 (0.073877) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186424 / 0.018006 (0.168418) | 0.400879 / 0.000490 (0.400389) | 0.003520 / 0.000200 (0.003320) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023287 / 0.037411 (-0.014124) | 0.097767 / 0.014526 (0.083241) | 0.103271 / 0.176557 (-0.073286) | 0.165414 / 0.737135 (-0.571722) | 0.106437 / 0.296338 (-0.189901) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422711 / 0.215209 (0.207502) | 4.221382 / 2.077655 (2.143727) | 1.906807 / 1.504120 (0.402687) | 1.709595 / 1.541195 (0.168400) | 1.720452 / 1.468490 (0.251962) | 0.699477 / 4.584777 (-3.885300) | 3.415840 / 3.745712 (-0.329873) | 2.835669 / 5.269862 (-2.434192) | 1.501775 / 4.565676 (-3.063901) | 0.082896 / 0.424275 (-0.341379) | 0.012855 / 0.007607 (0.005248) | 0.514373 / 0.226044 (0.288329) | 5.190000 / 2.268929 (2.921071) | 2.302539 / 55.444624 (-53.142086) | 1.963410 / 6.876477 (-4.913067) | 2.020944 / 2.142072 (-0.121128) | 0.805919 / 4.805227 (-3.999308) | 0.150604 / 6.500664 (-6.350060) | 0.065977 / 0.075469 (-0.009492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206487 / 1.841788 (-0.635300) | 13.631513 / 8.074308 (5.557205) | 13.800258 / 10.191392 (3.608866) | 0.146914 / 0.680424 (-0.533509) | 0.016454 / 0.534201 (-0.517747) | 0.377752 / 0.579283 (-0.201532) | 0.384312 / 0.434364 (-0.050052) | 0.434912 / 0.540337 (-0.105425) | 0.522507 / 1.386936 (-0.864429) |\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) | 0.006328 / 0.011353 (-0.005025) | 0.004406 / 0.011008 (-0.006602) | 0.077951 / 0.038508 (0.039443) | 0.026716 / 0.023109 (0.003607) | 0.337303 / 0.275898 (0.061405) | 0.372036 / 0.323480 (0.048556) | 0.004800 / 0.007986 (-0.003185) | 0.003153 / 0.004328 (-0.001175) | 0.076823 / 0.004250 (0.072573) | 0.035873 / 0.037052 (-0.001179) | 0.340243 / 0.258489 (0.081754) | 0.380183 / 0.293841 (0.086342) | 0.032185 / 0.128546 (-0.096361) | 0.011545 / 0.075646 (-0.064101) | 0.086887 / 0.419271 (-0.332384) | 0.041560 / 0.043533 (-0.001973) | 0.338716 / 0.255139 (0.083577) | 0.363080 / 0.283200 (0.079881) | 0.088375 / 0.141683 (-0.053308) | 1.499004 / 1.452155 (0.046850) | 1.585904 / 1.492716 (0.093188) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211645 / 0.018006 (0.193639) | 0.403707 / 0.000490 (0.403218) | 0.000415 / 0.000200 (0.000215) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024972 / 0.037411 (-0.012440) | 0.097996 / 0.014526 (0.083470) | 0.105941 / 0.176557 (-0.070616) | 0.155521 / 0.737135 (-0.581615) | 0.108246 / 0.296338 (-0.188092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442316 / 0.215209 (0.227107) | 4.417977 / 2.077655 (2.340322) | 2.078324 / 1.504120 (0.574205) | 1.863678 / 1.541195 (0.322483) | 1.917149 / 1.468490 (0.448659) | 0.697628 / 4.584777 (-3.887149) | 3.412810 / 3.745712 (-0.332902) | 1.866473 / 5.269862 (-3.403389) | 1.155923 / 4.565676 (-3.409754) | 0.082831 / 0.424275 (-0.341444) | 0.012367 / 0.007607 (0.004760) | 0.540018 / 0.226044 (0.313974) | 5.420472 / 2.268929 (3.151544) | 2.508540 / 55.444624 (-52.936084) | 2.166397 / 6.876477 (-4.710080) | 2.153486 / 2.142072 (0.011414) | 0.804860 / 4.805227 (-4.000367) | 0.151178 / 6.500664 (-6.349486) | 0.067870 / 0.075469 (-0.007599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310387 / 1.841788 (-0.531400) | 13.908916 / 8.074308 (5.834608) | 14.136895 / 10.191392 (3.945503) | 0.139389 / 0.680424 (-0.541035) | 0.016687 / 0.534201 (-0.517514) | 0.379624 / 0.579283 (-0.199659) | 0.382634 / 0.434364 (-0.051730) | 0.439632 / 0.540337 (-0.100706) | 0.524913 / 1.386936 (-0.862023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8f2143b4ed39b58ed415029e7838d767662da91 \"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) | 0.006365 / 0.011353 (-0.004988) | 0.004457 / 0.011008 (-0.006551) | 0.097989 / 0.038508 (0.059481) | 0.027686 / 0.023109 (0.004577) | 0.357412 / 0.275898 (0.081514) | 0.368573 / 0.323480 (0.045093) | 0.004859 / 0.007986 (-0.003127) | 0.003262 / 0.004328 (-0.001066) | 0.076487 / 0.004250 (0.072237) | 0.035526 / 0.037052 (-0.001527) | 0.332862 / 0.258489 (0.074373) | 0.369334 / 0.293841 (0.075493) | 0.030750 / 0.128546 (-0.097796) | 0.011503 / 0.075646 (-0.064143) | 0.323289 / 0.419271 (-0.095982) | 0.042302 / 0.043533 (-0.001231) | 0.334009 / 0.255139 (0.078870) | 0.354150 / 0.283200 (0.070951) | 0.082895 / 0.141683 (-0.058788) | 1.499727 / 1.452155 (0.047572) | 1.574123 / 1.492716 (0.081407) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192583 / 0.018006 (0.174577) | 0.408136 / 0.000490 (0.407646) | 0.001272 / 0.000200 (0.001072) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022883 / 0.037411 (-0.014528) | 0.095710 / 0.014526 (0.081185) | 0.106545 / 0.176557 (-0.070011) | 0.165784 / 0.737135 (-0.571352) | 0.108594 / 0.296338 (-0.187744) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429483 / 0.215209 (0.214274) | 4.292338 / 2.077655 (2.214683) | 1.917759 / 1.504120 (0.413639) | 1.711489 / 1.541195 (0.170294) | 1.735668 / 1.468490 (0.267178) | 0.707602 / 4.584777 (-3.877175) | 3.369643 / 3.745712 (-0.376070) | 1.874517 / 5.269862 (-3.395344) | 1.248560 / 4.565676 (-3.317117) | 0.083247 / 0.424275 (-0.341028) | 0.012606 / 0.007607 (0.004999) | 0.519342 / 0.226044 (0.293297) | 5.225462 / 2.268929 (2.956533) | 2.433230 / 55.444624 (-53.011394) | 2.006005 / 6.876477 (-4.870471) | 2.093156 / 2.142072 (-0.048916) | 0.809372 / 4.805227 (-3.995855) | 0.151691 / 6.500664 (-6.348973) | 0.066680 / 0.075469 (-0.008789) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226283 / 1.841788 (-0.615505) | 13.604338 / 8.074308 (5.530030) | 13.953245 / 10.191392 (3.761853) | 0.132904 / 0.680424 (-0.547520) | 0.016420 / 0.534201 (-0.517781) | 0.395316 / 0.579283 (-0.183967) | 0.385003 / 0.434364 (-0.049361) | 0.483303 / 0.540337 (-0.057034) | 0.578459 / 1.386936 (-0.808477) |\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) | 0.006218 / 0.011353 (-0.005135) | 0.004451 / 0.011008 (-0.006557) | 0.076892 / 0.038508 (0.038384) | 0.027017 / 0.023109 (0.003908) | 0.356976 / 0.275898 (0.081078) | 0.396083 / 0.323480 (0.072603) | 0.005510 / 0.007986 (-0.002476) | 0.003265 / 0.004328 (-0.001063) | 0.075771 / 0.004250 (0.071521) | 0.037117 / 0.037052 (0.000064) | 0.362181 / 0.258489 (0.103692) | 0.401771 / 0.293841 (0.107931) | 0.032062 / 0.128546 (-0.096484) | 0.011453 / 0.075646 (-0.064194) | 0.085773 / 0.419271 (-0.333498) | 0.041679 / 0.043533 (-0.001854) | 0.355120 / 0.255139 (0.099981) | 0.390170 / 0.283200 (0.106970) | 0.088210 / 0.141683 (-0.053473) | 1.526434 / 1.452155 (0.074279) | 1.586019 / 1.492716 (0.093302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196836 / 0.018006 (0.178830) | 0.401161 / 0.000490 (0.400671) | 0.002880 / 0.000200 (0.002680) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024445 / 0.037411 (-0.012966) | 0.100187 / 0.014526 (0.085661) | 0.106391 / 0.176557 (-0.070165) | 0.159764 / 0.737135 (-0.577372) | 0.109828 / 0.296338 (-0.186511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444228 / 0.215209 (0.229018) | 4.420769 / 2.077655 (2.343114) | 2.069437 / 1.504120 (0.565318) | 1.862587 / 1.541195 (0.321392) | 1.934627 / 1.468490 (0.466137) | 0.699681 / 4.584777 (-3.885095) | 3.352540 / 3.745712 (-0.393172) | 2.613172 / 5.269862 (-2.656689) | 1.445116 / 4.565676 (-3.120561) | 0.083086 / 0.424275 (-0.341189) | 0.012715 / 0.007607 (0.005108) | 0.537450 / 0.226044 (0.311405) | 5.403052 / 2.268929 (3.134123) | 2.506703 / 55.444624 (-52.937921) | 2.170198 / 6.876477 (-4.706279) | 2.201909 / 2.142072 (0.059837) | 0.799555 / 4.805227 (-4.005672) | 0.150825 / 6.500664 (-6.349839) | 0.067234 / 0.075469 (-0.008235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293097 / 1.841788 (-0.548691) | 13.817133 / 8.074308 (5.742825) | 14.247231 / 10.191392 (4.055839) | 0.128422 / 0.680424 (-0.552002) | 0.016541 / 0.534201 (-0.517660) | 0.382466 / 0.579283 (-0.196817) | 0.380560 / 0.434364 (-0.053804) | 0.439061 / 0.540337 (-0.101276) | 0.521865 / 1.386936 (-0.865071) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69e60be438c334919f590512fd664436bd6b3667 \"CML watermark\")\n",
"I also took the liberty of removing `_hf_hub_fixes.py` completely :)\r\n\r\n> Do you think this is really necessary and convenient? I would naively say that 5% of the users is not a negligible number...\r\n\r\nI think it's ok. Most of them are using old versions of `datasets` anyway.\r\n\r\n",
"merging, but lmk if you have other concerns",
"<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) | 0.006810 / 0.011353 (-0.004543) | 0.004683 / 0.011008 (-0.006325) | 0.100889 / 0.038508 (0.062381) | 0.030135 / 0.023109 (0.007026) | 0.356407 / 0.275898 (0.080509) | 0.389175 / 0.323480 (0.065695) | 0.005358 / 0.007986 (-0.002627) | 0.004760 / 0.004328 (0.000432) | 0.075904 / 0.004250 (0.071654) | 0.040341 / 0.037052 (0.003288) | 0.357363 / 0.258489 (0.098874) | 0.394185 / 0.293841 (0.100344) | 0.031322 / 0.128546 (-0.097224) | 0.011636 / 0.075646 (-0.064010) | 0.327327 / 0.419271 (-0.091944) | 0.042494 / 0.043533 (-0.001039) | 0.338079 / 0.255139 (0.082940) | 0.363388 / 0.283200 (0.080189) | 0.087102 / 0.141683 (-0.054581) | 1.505686 / 1.452155 (0.053531) | 1.562112 / 1.492716 (0.069396) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203630 / 0.018006 (0.185624) | 0.425986 / 0.000490 (0.425496) | 0.003786 / 0.000200 (0.003586) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024138 / 0.037411 (-0.013274) | 0.101752 / 0.014526 (0.087226) | 0.105436 / 0.176557 (-0.071121) | 0.165385 / 0.737135 (-0.571750) | 0.114510 / 0.296338 (-0.181828) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447561 / 0.215209 (0.232352) | 4.449212 / 2.077655 (2.371557) | 2.169472 / 1.504120 (0.665352) | 1.989025 / 1.541195 (0.447831) | 2.036267 / 1.468490 (0.567776) | 0.698647 / 4.584777 (-3.886130) | 3.483281 / 3.745712 (-0.262431) | 1.949306 / 5.269862 (-3.320555) | 1.290313 / 4.565676 (-3.275363) | 0.083079 / 0.424275 (-0.341196) | 0.012759 / 0.007607 (0.005152) | 0.540944 / 0.226044 (0.314899) | 5.473391 / 2.268929 (3.204463) | 2.632037 / 55.444624 (-52.812587) | 2.327396 / 6.876477 (-4.549081) | 2.428880 / 2.142072 (0.286808) | 0.808918 / 4.805227 (-3.996309) | 0.153283 / 6.500664 (-6.347381) | 0.068325 / 0.075469 (-0.007145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212527 / 1.841788 (-0.629260) | 14.306444 / 8.074308 (6.232136) | 14.904980 / 10.191392 (4.713588) | 0.142796 / 0.680424 (-0.537628) | 0.016829 / 0.534201 (-0.517372) | 0.384806 / 0.579283 (-0.194477) | 0.390505 / 0.434364 (-0.043859) | 0.441734 / 0.540337 (-0.098603) | 0.526159 / 1.386936 (-0.860777) |\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) | 0.006950 / 0.011353 (-0.004403) | 0.004647 / 0.011008 (-0.006362) | 0.078925 / 0.038508 (0.040417) | 0.028081 / 0.023109 (0.004971) | 0.343420 / 0.275898 (0.067522) | 0.380567 / 0.323480 (0.057087) | 0.005286 / 0.007986 (-0.002700) | 0.004816 / 0.004328 (0.000487) | 0.077332 / 0.004250 (0.073081) | 0.042131 / 0.037052 (0.005078) | 0.345371 / 0.258489 (0.086882) | 0.390232 / 0.293841 (0.096392) | 0.032395 / 0.128546 (-0.096152) | 0.011669 / 0.075646 (-0.063978) | 0.087649 / 0.419271 (-0.331622) | 0.042465 / 0.043533 (-0.001068) | 0.342863 / 0.255139 (0.087724) | 0.368947 / 0.283200 (0.085748) | 0.091725 / 0.141683 (-0.049958) | 1.477435 / 1.452155 (0.025280) | 1.563449 / 1.492716 (0.070733) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208016 / 0.018006 (0.190010) | 0.428387 / 0.000490 (0.427898) | 0.000443 / 0.000200 (0.000243) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026963 / 0.037411 (-0.010449) | 0.103854 / 0.014526 (0.089328) | 0.109068 / 0.176557 (-0.067488) | 0.160107 / 0.737135 (-0.577028) | 0.112843 / 0.296338 (-0.183496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437161 / 0.215209 (0.221952) | 4.396178 / 2.077655 (2.318523) | 2.067597 / 1.504120 (0.563477) | 1.875247 / 1.541195 (0.334053) | 1.962451 / 1.468490 (0.493961) | 0.701427 / 4.584777 (-3.883350) | 3.459564 / 3.745712 (-0.286148) | 1.959482 / 5.269862 (-3.310380) | 1.191866 / 4.565676 (-3.373810) | 0.083243 / 0.424275 (-0.341032) | 0.012740 / 0.007607 (0.005133) | 0.535236 / 0.226044 (0.309191) | 5.351715 / 2.268929 (3.082786) | 2.490868 / 55.444624 (-52.953756) | 2.195680 / 6.876477 (-4.680797) | 2.233854 / 2.142072 (0.091781) | 0.809041 / 4.805227 (-3.996187) | 0.151498 / 6.500664 (-6.349166) | 0.068297 / 0.075469 (-0.007172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303596 / 1.841788 (-0.538192) | 14.712746 / 8.074308 (6.638438) | 14.778412 / 10.191392 (4.587020) | 0.147093 / 0.680424 (-0.533331) | 0.017105 / 0.534201 (-0.517096) | 0.381687 / 0.579283 (-0.197596) | 0.402435 / 0.434364 (-0.031929) | 0.453538 / 0.540337 (-0.086800) | 0.538866 / 1.386936 (-0.848070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10f637c3a598c8042865b31f779e315a3da5337e \"CML watermark\")\n"
] |
1,625,942,730
| 5,641
|
Features cannot be named "self"
|
closed
| 2023-03-15T17:16:40
| 2023-03-16T17:14:51
| 2023-03-16T17:14:51
|
https://github.com/huggingface/datasets/issues/5641
| null |
alialamiidrissi
| false
|
[] |
1,625,896,057
| 5,640
|
Less zip false positives
|
closed
| 2023-03-15T16:48:59
| 2023-03-16T13:47:37
| 2023-03-16T13:40:12
|
https://github.com/huggingface/datasets/pull/5640
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5640",
"html_url": "https://github.com/huggingface/datasets/pull/5640",
"diff_url": "https://github.com/huggingface/datasets/pull/5640.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5640.patch",
"merged_at": "2023-03-16T13:40:12"
}
|
lhoestq
| true
|
[
"<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) | 0.006998 / 0.011353 (-0.004355) | 0.005093 / 0.011008 (-0.005916) | 0.100490 / 0.038508 (0.061982) | 0.032736 / 0.023109 (0.009627) | 0.297738 / 0.275898 (0.021840) | 0.322255 / 0.323480 (-0.001225) | 0.005583 / 0.007986 (-0.002402) | 0.004007 / 0.004328 (-0.000321) | 0.075863 / 0.004250 (0.071613) | 0.044212 / 0.037052 (0.007159) | 0.300033 / 0.258489 (0.041544) | 0.341997 / 0.293841 (0.048156) | 0.036172 / 0.128546 (-0.092374) | 0.012176 / 0.075646 (-0.063471) | 0.356052 / 0.419271 (-0.063220) | 0.050438 / 0.043533 (0.006905) | 0.294677 / 0.255139 (0.039538) | 0.318050 / 0.283200 (0.034850) | 0.104733 / 0.141683 (-0.036950) | 1.435681 / 1.452155 (-0.016474) | 1.534793 / 1.492716 (0.042076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242815 / 0.018006 (0.224809) | 0.565983 / 0.000490 (0.565494) | 0.006800 / 0.000200 (0.006600) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026548 / 0.037411 (-0.010863) | 0.104816 / 0.014526 (0.090290) | 0.116222 / 0.176557 (-0.060335) | 0.172143 / 0.737135 (-0.564992) | 0.121631 / 0.296338 (-0.174707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400126 / 0.215209 (0.184917) | 4.004538 / 2.077655 (1.926883) | 1.798822 / 1.504120 (0.294702) | 1.595191 / 1.541195 (0.053996) | 1.645777 / 1.468490 (0.177287) | 0.705643 / 4.584777 (-3.879134) | 3.750887 / 3.745712 (0.005175) | 2.136547 / 5.269862 (-3.133315) | 1.475881 / 4.565676 (-3.089795) | 0.086921 / 0.424275 (-0.337354) | 0.012379 / 0.007607 (0.004771) | 0.505824 / 0.226044 (0.279779) | 5.052364 / 2.268929 (2.783435) | 2.279983 / 55.444624 (-53.164641) | 1.932253 / 6.876477 (-4.944224) | 2.051359 / 2.142072 (-0.090714) | 0.851906 / 4.805227 (-3.953321) | 0.169566 / 6.500664 (-6.331098) | 0.064600 / 0.075469 (-0.010869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.165859 / 1.841788 (-0.675929) | 15.049950 / 8.074308 (6.975642) | 14.095981 / 10.191392 (3.904589) | 0.151779 / 0.680424 (-0.528645) | 0.017537 / 0.534201 (-0.516664) | 0.420164 / 0.579283 (-0.159119) | 0.418932 / 0.434364 (-0.015432) | 0.488749 / 0.540337 (-0.051588) | 0.582359 / 1.386936 (-0.804577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007426 / 0.011353 (-0.003927) | 0.005248 / 0.011008 (-0.005761) | 0.074118 / 0.038508 (0.035610) | 0.034223 / 0.023109 (0.011114) | 0.337780 / 0.275898 (0.061882) | 0.376300 / 0.323480 (0.052820) | 0.006142 / 0.007986 (-0.001843) | 0.004246 / 0.004328 (-0.000083) | 0.074177 / 0.004250 (0.069926) | 0.052698 / 0.037052 (0.015646) | 0.340229 / 0.258489 (0.081740) | 0.396172 / 0.293841 (0.102331) | 0.037293 / 0.128546 (-0.091253) | 0.012514 / 0.075646 (-0.063132) | 0.087144 / 0.419271 (-0.332128) | 0.051922 / 0.043533 (0.008390) | 0.333188 / 0.255139 (0.078049) | 0.355420 / 0.283200 (0.072220) | 0.110273 / 0.141683 (-0.031410) | 1.447826 / 1.452155 (-0.004329) | 1.561135 / 1.492716 (0.068419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269203 / 0.018006 (0.251197) | 0.551997 / 0.000490 (0.551508) | 0.001558 / 0.000200 (0.001359) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029511 / 0.037411 (-0.007900) | 0.108614 / 0.014526 (0.094089) | 0.123438 / 0.176557 (-0.053118) | 0.171596 / 0.737135 (-0.565539) | 0.126828 / 0.296338 (-0.169511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420520 / 0.215209 (0.205310) | 4.175672 / 2.077655 (2.098017) | 1.982220 / 1.504120 (0.478101) | 1.788575 / 1.541195 (0.247381) | 1.860840 / 1.468490 (0.392349) | 0.706730 / 4.584777 (-3.878047) | 3.858718 / 3.745712 (0.113005) | 3.069389 / 5.269862 (-2.200472) | 1.827603 / 4.565676 (-2.738073) | 0.087893 / 0.424275 (-0.336382) | 0.012613 / 0.007607 (0.005006) | 0.524177 / 0.226044 (0.298132) | 5.177077 / 2.268929 (2.908148) | 2.494397 / 55.444624 (-52.950227) | 2.189484 / 6.876477 (-4.686992) | 2.217626 / 2.142072 (0.075554) | 0.846326 / 4.805227 (-3.958901) | 0.176558 / 6.500664 (-6.324106) | 0.065018 / 0.075469 (-0.010451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268618 / 1.841788 (-0.573170) | 15.132711 / 8.074308 (7.058403) | 14.585530 / 10.191392 (4.394138) | 0.163454 / 0.680424 (-0.516970) | 0.017442 / 0.534201 (-0.516759) | 0.421746 / 0.579283 (-0.157537) | 0.425412 / 0.434364 (-0.008952) | 0.499178 / 0.540337 (-0.041159) | 0.595458 / 1.386936 (-0.791478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab77e58cd32413f4ef4828134a2470ebd53bb542 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007980 / 0.011353 (-0.003373) | 0.005414 / 0.011008 (-0.005594) | 0.099226 / 0.038508 (0.060718) | 0.035442 / 0.023109 (0.012332) | 0.304851 / 0.275898 (0.028952) | 0.337144 / 0.323480 (0.013664) | 0.006162 / 0.007986 (-0.001823) | 0.004151 / 0.004328 (-0.000177) | 0.074708 / 0.004250 (0.070458) | 0.049690 / 0.037052 (0.012638) | 0.307658 / 0.258489 (0.049168) | 0.358472 / 0.293841 (0.064631) | 0.037181 / 0.128546 (-0.091365) | 0.012259 / 0.075646 (-0.063387) | 0.335426 / 0.419271 (-0.083846) | 0.050790 / 0.043533 (0.007257) | 0.301715 / 0.255139 (0.046576) | 0.320834 / 0.283200 (0.037634) | 0.102357 / 0.141683 (-0.039326) | 1.454750 / 1.452155 (0.002596) | 1.571994 / 1.492716 (0.079278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218708 / 0.018006 (0.200702) | 0.444391 / 0.000490 (0.443901) | 0.005717 / 0.000200 (0.005517) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028017 / 0.037411 (-0.009395) | 0.112753 / 0.014526 (0.098227) | 0.121003 / 0.176557 (-0.055554) | 0.181085 / 0.737135 (-0.556050) | 0.127211 / 0.296338 (-0.169127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400803 / 0.215209 (0.185594) | 4.007315 / 2.077655 (1.929660) | 1.826911 / 1.504120 (0.322791) | 1.637799 / 1.541195 (0.096605) | 1.699754 / 1.468490 (0.231264) | 0.709413 / 4.584777 (-3.875364) | 4.008904 / 3.745712 (0.263192) | 3.916540 / 5.269862 (-1.353322) | 1.902102 / 4.565676 (-2.663575) | 0.089048 / 0.424275 (-0.335227) | 0.012763 / 0.007607 (0.005155) | 0.498957 / 0.226044 (0.272913) | 4.979865 / 2.268929 (2.710937) | 2.301987 / 55.444624 (-53.142637) | 1.929404 / 6.876477 (-4.947073) | 2.107839 / 2.142072 (-0.034233) | 0.857253 / 4.805227 (-3.947974) | 0.171935 / 6.500664 (-6.328729) | 0.066753 / 0.075469 (-0.008716) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186811 / 1.841788 (-0.654977) | 15.866319 / 8.074308 (7.792011) | 14.738555 / 10.191392 (4.547163) | 0.142879 / 0.680424 (-0.537544) | 0.017679 / 0.534201 (-0.516522) | 0.422840 / 0.579283 (-0.156443) | 0.450307 / 0.434364 (0.015943) | 0.491802 / 0.540337 (-0.048536) | 0.588837 / 1.386936 (-0.798099) |\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) | 0.007659 / 0.011353 (-0.003694) | 0.005331 / 0.011008 (-0.005678) | 0.075360 / 0.038508 (0.036852) | 0.034011 / 0.023109 (0.010902) | 0.354488 / 0.275898 (0.078590) | 0.401781 / 0.323480 (0.078301) | 0.005806 / 0.007986 (-0.002179) | 0.004029 / 0.004328 (-0.000300) | 0.073822 / 0.004250 (0.069572) | 0.049067 / 0.037052 (0.012015) | 0.364483 / 0.258489 (0.105994) | 0.405637 / 0.293841 (0.111796) | 0.037166 / 0.128546 (-0.091380) | 0.012397 / 0.075646 (-0.063249) | 0.087346 / 0.419271 (-0.331926) | 0.050888 / 0.043533 (0.007355) | 0.334796 / 0.255139 (0.079657) | 0.387681 / 0.283200 (0.104481) | 0.105056 / 0.141683 (-0.036627) | 1.471630 / 1.452155 (0.019475) | 1.554764 / 1.492716 (0.062047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231825 / 0.018006 (0.213819) | 0.449746 / 0.000490 (0.449256) | 0.000888 / 0.000200 (0.000688) | 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) | 0.030363 / 0.037411 (-0.007049) | 0.115234 / 0.014526 (0.100708) | 0.123005 / 0.176557 (-0.053551) | 0.172772 / 0.737135 (-0.564363) | 0.127818 / 0.296338 (-0.168520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425761 / 0.215209 (0.210552) | 4.237950 / 2.077655 (2.160295) | 1.992045 / 1.504120 (0.487925) | 1.801622 / 1.541195 (0.260427) | 1.918477 / 1.468490 (0.449987) | 0.722730 / 4.584777 (-3.862047) | 4.015968 / 3.745712 (0.270256) | 3.720412 / 5.269862 (-1.549450) | 1.763111 / 4.565676 (-2.802566) | 0.089041 / 0.424275 (-0.335234) | 0.012608 / 0.007607 (0.005001) | 0.522645 / 0.226044 (0.296601) | 5.227108 / 2.268929 (2.958180) | 2.444714 / 55.444624 (-52.999910) | 2.109745 / 6.876477 (-4.766732) | 2.194042 / 2.142072 (0.051969) | 0.871781 / 4.805227 (-3.933447) | 0.173149 / 6.500664 (-6.327515) | 0.066192 / 0.075469 (-0.009277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312051 / 1.841788 (-0.529737) | 16.024315 / 8.074308 (7.950007) | 15.123823 / 10.191392 (4.932431) | 0.163997 / 0.680424 (-0.516427) | 0.017595 / 0.534201 (-0.516606) | 0.426379 / 0.579283 (-0.152904) | 0.467709 / 0.434364 (0.033345) | 0.498308 / 0.540337 (-0.042030) | 0.591426 / 1.386936 (-0.795510) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13488cc110b67090289794f48d5c84a4fd0c063a \"CML watermark\")\n",
"CI is failing due to unrelated issues, hopefully https://github.com/huggingface/datasets/pull/5642 fixes 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) | 0.006478 / 0.011353 (-0.004875) | 0.004347 / 0.011008 (-0.006661) | 0.097103 / 0.038508 (0.058595) | 0.027650 / 0.023109 (0.004541) | 0.372355 / 0.275898 (0.096457) | 0.408794 / 0.323480 (0.085314) | 0.005034 / 0.007986 (-0.002952) | 0.003252 / 0.004328 (-0.001076) | 0.074068 / 0.004250 (0.069818) | 0.035542 / 0.037052 (-0.001510) | 0.367392 / 0.258489 (0.108903) | 0.409644 / 0.293841 (0.115803) | 0.031745 / 0.128546 (-0.096801) | 0.011501 / 0.075646 (-0.064145) | 0.323355 / 0.419271 (-0.095917) | 0.043065 / 0.043533 (-0.000467) | 0.377313 / 0.255139 (0.122174) | 0.395326 / 0.283200 (0.112127) | 0.087101 / 0.141683 (-0.054582) | 1.461228 / 1.452155 (0.009073) | 1.529413 / 1.492716 (0.036696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199245 / 0.018006 (0.181239) | 0.409978 / 0.000490 (0.409488) | 0.002655 / 0.000200 (0.002455) | 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) | 0.023903 / 0.037411 (-0.013508) | 0.097855 / 0.014526 (0.083330) | 0.106405 / 0.176557 (-0.070152) | 0.166889 / 0.737135 (-0.570247) | 0.110256 / 0.296338 (-0.186082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440351 / 0.215209 (0.225142) | 4.382848 / 2.077655 (2.305194) | 2.049602 / 1.504120 (0.545482) | 1.824638 / 1.541195 (0.283443) | 1.850519 / 1.468490 (0.382029) | 0.702652 / 4.584777 (-3.882125) | 3.394571 / 3.745712 (-0.351141) | 1.940608 / 5.269862 (-3.329254) | 1.263961 / 4.565676 (-3.301716) | 0.083985 / 0.424275 (-0.340290) | 0.013046 / 0.007607 (0.005439) | 0.538272 / 0.226044 (0.312228) | 5.407563 / 2.268929 (3.138634) | 2.519207 / 55.444624 (-52.925418) | 2.153379 / 6.876477 (-4.723098) | 2.394512 / 2.142072 (0.252439) | 0.812840 / 4.805227 (-3.992387) | 0.152868 / 6.500664 (-6.347796) | 0.067823 / 0.075469 (-0.007646) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220031 / 1.841788 (-0.621757) | 13.781237 / 8.074308 (5.706929) | 14.203975 / 10.191392 (4.012583) | 0.141077 / 0.680424 (-0.539347) | 0.016518 / 0.534201 (-0.517682) | 0.379079 / 0.579283 (-0.200204) | 0.378916 / 0.434364 (-0.055448) | 0.434589 / 0.540337 (-0.105749) | 0.521129 / 1.386936 (-0.865807) |\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) | 0.006997 / 0.011353 (-0.004356) | 0.004599 / 0.011008 (-0.006410) | 0.078700 / 0.038508 (0.040192) | 0.027902 / 0.023109 (0.004793) | 0.344406 / 0.275898 (0.068508) | 0.392918 / 0.323480 (0.069438) | 0.005175 / 0.007986 (-0.002811) | 0.004755 / 0.004328 (0.000427) | 0.077707 / 0.004250 (0.073457) | 0.039409 / 0.037052 (0.002357) | 0.343250 / 0.258489 (0.084761) | 0.405544 / 0.293841 (0.111703) | 0.032286 / 0.128546 (-0.096260) | 0.011674 / 0.075646 (-0.063972) | 0.087633 / 0.419271 (-0.331639) | 0.043346 / 0.043533 (-0.000186) | 0.355076 / 0.255139 (0.099937) | 0.382155 / 0.283200 (0.098955) | 0.090914 / 0.141683 (-0.050769) | 1.518369 / 1.452155 (0.066215) | 1.583530 / 1.492716 (0.090813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.160369 / 0.018006 (0.142362) | 0.406844 / 0.000490 (0.406354) | 0.002651 / 0.000200 (0.002451) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025295 / 0.037411 (-0.012116) | 0.101490 / 0.014526 (0.086964) | 0.108825 / 0.176557 (-0.067732) | 0.161673 / 0.737135 (-0.575462) | 0.113610 / 0.296338 (-0.182729) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443514 / 0.215209 (0.228305) | 4.436722 / 2.077655 (2.359067) | 2.144008 / 1.504120 (0.639888) | 2.005324 / 1.541195 (0.464129) | 2.123356 / 1.468490 (0.654866) | 0.697217 / 4.584777 (-3.887560) | 3.401105 / 3.745712 (-0.344607) | 1.874621 / 5.269862 (-3.395240) | 1.165069 / 4.565676 (-3.400608) | 0.082799 / 0.424275 (-0.341476) | 0.012806 / 0.007607 (0.005199) | 0.542688 / 0.226044 (0.316644) | 5.420963 / 2.268929 (3.152034) | 2.579034 / 55.444624 (-52.865590) | 2.240201 / 6.876477 (-4.636276) | 2.261309 / 2.142072 (0.119237) | 0.800246 / 4.805227 (-4.004981) | 0.150380 / 6.500664 (-6.350285) | 0.066880 / 0.075469 (-0.008589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281721 / 1.841788 (-0.560067) | 13.906361 / 8.074308 (5.832053) | 14.135336 / 10.191392 (3.943944) | 0.128865 / 0.680424 (-0.551559) | 0.016452 / 0.534201 (-0.517749) | 0.373563 / 0.579283 (-0.205720) | 0.385321 / 0.434364 (-0.049043) | 0.437198 / 0.540337 (-0.103139) | 0.530720 / 1.386936 (-0.856216) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2f8e17f3c8f8d0cb77a4c566a78e31fab47108c \"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) | 0.008099 / 0.011353 (-0.003254) | 0.005093 / 0.011008 (-0.005916) | 0.106258 / 0.038508 (0.067750) | 0.037051 / 0.023109 (0.013942) | 0.347960 / 0.275898 (0.072062) | 0.370849 / 0.323480 (0.047369) | 0.006122 / 0.007986 (-0.001863) | 0.004094 / 0.004328 (-0.000235) | 0.079549 / 0.004250 (0.075299) | 0.046563 / 0.037052 (0.009510) | 0.332735 / 0.258489 (0.074246) | 0.417061 / 0.293841 (0.123220) | 0.038105 / 0.128546 (-0.090441) | 0.011886 / 0.075646 (-0.063760) | 0.342103 / 0.419271 (-0.077169) | 0.053233 / 0.043533 (0.009700) | 0.344754 / 0.255139 (0.089615) | 0.355354 / 0.283200 (0.072155) | 0.101059 / 0.141683 (-0.040624) | 1.518561 / 1.452155 (0.066406) | 1.558652 / 1.492716 (0.065935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225919 / 0.018006 (0.207913) | 0.518539 / 0.000490 (0.518049) | 0.006230 / 0.000200 (0.006030) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026782 / 0.037411 (-0.010629) | 0.108457 / 0.014526 (0.093931) | 0.125203 / 0.176557 (-0.051353) | 0.175726 / 0.737135 (-0.561409) | 0.127051 / 0.296338 (-0.169287) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416427 / 0.215209 (0.201217) | 4.168851 / 2.077655 (2.091196) | 1.962238 / 1.504120 (0.458118) | 1.825224 / 1.541195 (0.284029) | 1.831200 / 1.468490 (0.362710) | 0.765526 / 4.584777 (-3.819250) | 4.303957 / 3.745712 (0.558245) | 2.193467 / 5.269862 (-3.076395) | 1.654605 / 4.565676 (-2.911071) | 0.096709 / 0.424275 (-0.327566) | 0.013792 / 0.007607 (0.006185) | 0.537862 / 0.226044 (0.311818) | 5.152230 / 2.268929 (2.883302) | 2.520938 / 55.444624 (-52.923686) | 2.108422 / 6.876477 (-4.768054) | 2.214220 / 2.142072 (0.072147) | 0.834320 / 4.805227 (-3.970907) | 0.170635 / 6.500664 (-6.330029) | 0.063131 / 0.075469 (-0.012338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215767 / 1.841788 (-0.626020) | 15.254781 / 8.074308 (7.180473) | 14.360764 / 10.191392 (4.169372) | 0.172511 / 0.680424 (-0.507913) | 0.020161 / 0.534201 (-0.514040) | 0.426936 / 0.579283 (-0.152347) | 0.438771 / 0.434364 (0.004407) | 0.486973 / 0.540337 (-0.053364) | 0.584238 / 1.386936 (-0.802698) |\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) | 0.006777 / 0.011353 (-0.004576) | 0.005304 / 0.011008 (-0.005704) | 0.073717 / 0.038508 (0.035209) | 0.033604 / 0.023109 (0.010494) | 0.340448 / 0.275898 (0.064550) | 0.351861 / 0.323480 (0.028381) | 0.005786 / 0.007986 (-0.002199) | 0.005013 / 0.004328 (0.000685) | 0.071263 / 0.004250 (0.067012) | 0.048189 / 0.037052 (0.011137) | 0.339457 / 0.258489 (0.080968) | 0.384383 / 0.293841 (0.090542) | 0.035563 / 0.128546 (-0.092983) | 0.011509 / 0.075646 (-0.064137) | 0.083722 / 0.419271 (-0.335550) | 0.048886 / 0.043533 (0.005353) | 0.350184 / 0.255139 (0.095045) | 0.361037 / 0.283200 (0.077837) | 0.105191 / 0.141683 (-0.036492) | 1.503247 / 1.452155 (0.051093) | 1.582298 / 1.492716 (0.089581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221687 / 0.018006 (0.203681) | 0.466489 / 0.000490 (0.465999) | 0.000484 / 0.000200 (0.000284) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027978 / 0.037411 (-0.009434) | 0.119572 / 0.014526 (0.105047) | 0.133530 / 0.176557 (-0.043026) | 0.177892 / 0.737135 (-0.559243) | 0.127045 / 0.296338 (-0.169294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430198 / 0.215209 (0.214989) | 4.435512 / 2.077655 (2.357858) | 2.007183 / 1.504120 (0.503063) | 1.799230 / 1.541195 (0.258036) | 1.884750 / 1.468490 (0.416260) | 0.745232 / 4.584777 (-3.839545) | 4.088069 / 3.745712 (0.342357) | 4.114669 / 5.269862 (-1.155193) | 2.374086 / 4.565676 (-2.191590) | 0.089154 / 0.424275 (-0.335121) | 0.012938 / 0.007607 (0.005331) | 0.505954 / 0.226044 (0.279909) | 5.194226 / 2.268929 (2.925298) | 2.487230 / 55.444624 (-52.957394) | 2.163353 / 6.876477 (-4.713124) | 2.177879 / 2.142072 (0.035807) | 0.828728 / 4.805227 (-3.976499) | 0.171157 / 6.500664 (-6.329507) | 0.062883 / 0.075469 (-0.012586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275906 / 1.841788 (-0.565882) | 15.235484 / 8.074308 (7.161176) | 14.467396 / 10.191392 (4.276004) | 0.198994 / 0.680424 (-0.481430) | 0.020203 / 0.534201 (-0.513998) | 0.447904 / 0.579283 (-0.131380) | 0.454210 / 0.434364 (0.019846) | 0.528062 / 0.540337 (-0.012275) | 0.619311 / 1.386936 (-0.767625) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11cd0f73acbce1d16174f2555e56fda511d5a08b \"CML watermark\")\n"
] |
1,625,737,098
| 5,639
|
Parquet file wrongly recognized as zip prevents loading a dataset
|
closed
| 2023-03-15T15:20:45
| 2023-03-16T13:40:14
| 2023-03-16T13:40:14
|
https://github.com/huggingface/datasets/issues/5639
| null |
clefourrier
| false
|
[] |
1,625,564,471
| 5,638
|
xPath to implement all operations for Path
|
closed
| 2023-03-15T13:47:11
| 2023-03-17T13:21:12
| 2023-03-17T13:21:12
|
https://github.com/huggingface/datasets/issues/5638
| null |
thomasw21
| false
|
[
" I think https://github.com/fsspec/universal_pathlib is the project you are looking for.\r\n\r\n`xPath` has the methods often used in dataset scripts, and `mkdir` is not one of them (`dl_manager`'s role is to \"interact\" with the file system, so using `mkdir` is discouraged).",
"Right is there a difference between UPath and xPath? Typically is xPath less well implemented compared to Upath, ie missing some implementations of some methods? Or are there methods in xPath that are not implemented with UPath?",
"`xPath` is an internal component (it doesn't have a leading underscore in the name, but it should) not meant to be used outside of `datasets`, and it's only tested on HTTP URLs, not S3.\r\n\r\n",
"Okay I understand that xPath won't support my usecase. What I was perhaps getting to is why not use UPath in `datasets` instead of `xPath` if UPath seems to have strictly more robust implementations.",
"It seems like `universal_pathlib` does not support `fsspec` URL chaining (`::` is the chaining symbol) and \"compression\" filesystems (e.g., `zip`), but this is what we need to access and stream files from within an archive (e.g., we want to stream URLs such as this one: `zip://data.parquet::https://www.dummyurl.com/archive.zip`)"
] |
1,625,295,691
| 5,637
|
IterableDataset with_format does not support 'device' keyword for jax
|
open
| 2023-03-15T11:04:12
| 2025-01-07T06:59:33
| null |
https://github.com/huggingface/datasets/issues/5637
| null |
Lime-Cakes
| false
|
[
"Hi! Yes, only `torch` is currently supported. Unlike `Dataset`, `IterableDataset` is not PyArrow-backed, so we cannot simply call `to_numpy` on the underlying subtables to format them numerically. Instead, we must manually convert examples to (numeric) arrays while preserving consistency with `Dataset`, which is not trivial, so this is still a to-do.",
"Any plans to support it in the future? Or would streaming dataset be left without support for jax and tensorflow?",
"@mariosasko \r\n`IterableDataset` and `with_format('torch', device=device)` also does not work (using `datasets==3.2.0`)"
] |
1,623,721,577
| 5,636
|
Fix CI: ignore C901 ("some_func" is to complex) in `ruff`
|
closed
| 2023-03-14T15:29:11
| 2023-03-14T16:37:06
| 2023-03-14T16:29:52
|
https://github.com/huggingface/datasets/pull/5636
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5636",
"html_url": "https://github.com/huggingface/datasets/pull/5636",
"diff_url": "https://github.com/huggingface/datasets/pull/5636.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5636.patch",
"merged_at": "2023-03-14T16:29:52"
}
|
polinaeterna
| true
|
[
"_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) | 0.006529 / 0.011353 (-0.004824) | 0.004527 / 0.011008 (-0.006481) | 0.098051 / 0.038508 (0.059543) | 0.028058 / 0.023109 (0.004949) | 0.368543 / 0.275898 (0.092645) | 0.397126 / 0.323480 (0.073646) | 0.005072 / 0.007986 (-0.002913) | 0.003377 / 0.004328 (-0.000952) | 0.076867 / 0.004250 (0.072617) | 0.040121 / 0.037052 (0.003069) | 0.373422 / 0.258489 (0.114933) | 0.403969 / 0.293841 (0.110128) | 0.031485 / 0.128546 (-0.097061) | 0.011673 / 0.075646 (-0.063973) | 0.321837 / 0.419271 (-0.097434) | 0.042828 / 0.043533 (-0.000704) | 0.370391 / 0.255139 (0.115252) | 0.391737 / 0.283200 (0.108538) | 0.084764 / 0.141683 (-0.056919) | 1.463114 / 1.452155 (0.010959) | 1.527042 / 1.492716 (0.034325) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200964 / 0.018006 (0.182958) | 0.403967 / 0.000490 (0.403477) | 0.002439 / 0.000200 (0.002239) | 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) | 0.023531 / 0.037411 (-0.013880) | 0.097424 / 0.014526 (0.082899) | 0.104854 / 0.176557 (-0.071703) | 0.165682 / 0.737135 (-0.571453) | 0.109416 / 0.296338 (-0.186922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431041 / 0.215209 (0.215832) | 4.326039 / 2.077655 (2.248384) | 2.085123 / 1.504120 (0.581003) | 1.922720 / 1.541195 (0.381525) | 2.006608 / 1.468490 (0.538118) | 0.703348 / 4.584777 (-3.881428) | 3.441516 / 3.745712 (-0.304196) | 1.875244 / 5.269862 (-3.394618) | 1.181341 / 4.565676 (-3.384336) | 0.083442 / 0.424275 (-0.340833) | 0.012966 / 0.007607 (0.005359) | 0.536047 / 0.226044 (0.310002) | 5.354856 / 2.268929 (3.085927) | 2.451064 / 55.444624 (-52.993560) | 2.076110 / 6.876477 (-4.800367) | 2.196507 / 2.142072 (0.054435) | 0.811196 / 4.805227 (-3.994032) | 0.152547 / 6.500664 (-6.348118) | 0.067978 / 0.075469 (-0.007491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196169 / 1.841788 (-0.645618) | 13.697234 / 8.074308 (5.622926) | 13.966652 / 10.191392 (3.775260) | 0.143735 / 0.680424 (-0.536688) | 0.016484 / 0.534201 (-0.517717) | 0.382349 / 0.579283 (-0.196934) | 0.401507 / 0.434364 (-0.032857) | 0.447297 / 0.540337 (-0.093041) | 0.529779 / 1.386936 (-0.857157) |\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) | 0.006698 / 0.011353 (-0.004655) | 0.004608 / 0.011008 (-0.006400) | 0.076220 / 0.038508 (0.037712) | 0.027340 / 0.023109 (0.004231) | 0.344095 / 0.275898 (0.068197) | 0.374715 / 0.323480 (0.051235) | 0.004883 / 0.007986 (-0.003102) | 0.004658 / 0.004328 (0.000330) | 0.075381 / 0.004250 (0.071130) | 0.036099 / 0.037052 (-0.000953) | 0.340382 / 0.258489 (0.081893) | 0.383488 / 0.293841 (0.089647) | 0.031534 / 0.128546 (-0.097012) | 0.011735 / 0.075646 (-0.063912) | 0.085895 / 0.419271 (-0.333377) | 0.042226 / 0.043533 (-0.001306) | 0.340301 / 0.255139 (0.085162) | 0.366079 / 0.283200 (0.082879) | 0.088828 / 0.141683 (-0.052854) | 1.487880 / 1.452155 (0.035725) | 1.561318 / 1.492716 (0.068601) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226366 / 0.018006 (0.208360) | 0.408934 / 0.000490 (0.408444) | 0.000396 / 0.000200 (0.000196) | 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) | 0.024521 / 0.037411 (-0.012891) | 0.100167 / 0.014526 (0.085641) | 0.106480 / 0.176557 (-0.070077) | 0.156377 / 0.737135 (-0.580758) | 0.111709 / 0.296338 (-0.184630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436138 / 0.215209 (0.220928) | 4.370919 / 2.077655 (2.293265) | 2.066402 / 1.504120 (0.562282) | 1.862157 / 1.541195 (0.320962) | 1.920701 / 1.468490 (0.452211) | 0.695517 / 4.584777 (-3.889260) | 3.435558 / 3.745712 (-0.310154) | 1.864000 / 5.269862 (-3.405861) | 1.164134 / 4.565676 (-3.401543) | 0.083006 / 0.424275 (-0.341269) | 0.012751 / 0.007607 (0.005144) | 0.535405 / 0.226044 (0.309360) | 5.368530 / 2.268929 (3.099602) | 2.494197 / 55.444624 (-52.950427) | 2.161370 / 6.876477 (-4.715107) | 2.180345 / 2.142072 (0.038272) | 0.808076 / 4.805227 (-3.997151) | 0.151891 / 6.500664 (-6.348773) | 0.067643 / 0.075469 (-0.007826) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334245 / 1.841788 (-0.507543) | 14.112805 / 8.074308 (6.038497) | 14.152303 / 10.191392 (3.960911) | 0.153492 / 0.680424 (-0.526932) | 0.016542 / 0.534201 (-0.517659) | 0.376013 / 0.579283 (-0.203270) | 0.386528 / 0.434364 (-0.047836) | 0.436461 / 0.540337 (-0.103876) | 0.519278 / 1.386936 (-0.867658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce1d1076fc55ac49277398304e551f0b56c3c9e2 \"CML watermark\")\n"
] |
1,623,682,558
| 5,635
|
Pass custom metadata filename to Image/Audio folders
|
open
| 2023-03-14T15:08:16
| 2023-03-22T17:50:31
| null |
https://github.com/huggingface/datasets/pull/5635
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5635",
"html_url": "https://github.com/huggingface/datasets/pull/5635",
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"patch_url": "https://github.com/huggingface/datasets/pull/5635.patch",
"merged_at": null
}
|
polinaeterna
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5635). All of your documentation changes will be reflected on that endpoint.",
"I'm not a big fan of this new param - I find assigning metadata files to splits via the `data_files` param cleaner. Also, assuming that the metadata filename is `metadata.json`/`metadata.csv` (I don't think we should allow other names), a user can do `load_dataset(\"imagefolder\", data_dir=\"data\")` to load a dataset with that structure.",
"@mariosasko I don't really like this change in it's current state either but passing specific files with `data_files` also looks not quite user-friendly to me. The idea of providing specific parameter for metadata filename seems natural to me but I don't see a way for implementing it without some ugly changes in `load.py` (passing the param to factories and creating metadata patterns on the fly). Why don't you like this parameter?\r\n\r\nFor context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with `data_files` approach.\r\n\r\nedit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)",
">For context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with data_files approach.\r\n>\r\n>edit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)\r\n\r\nSeems low prio, but one way to address this would be by allowing to pass \"exclude patterns\" to `data_files`"
] |
1,622,424,174
| 5,634
|
Not all progress bars are showing up when they should for downloading dataset
|
closed
| 2023-03-13T23:04:18
| 2023-10-11T16:30:16
| 2023-10-11T16:30:16
|
https://github.com/huggingface/datasets/issues/5634
| null |
garlandz-db
| false
|
[
"Hi! \r\n\r\nBy default, tqdm has `leave=True` to \"keep all traces of the progress bar upon the termination of iteration\". However, we use `leave=False` in some places (as of recently), which removes the bar once the iteration is over.\r\n\r\nI feel like our TQDM bars are noisy, so I think we should always set `leave=False` and also use the `delay` parameter to display progress bars only for tasks that take time (e.g., more than 3s). What do you think about this? Do you find these bars useful (after the dataset generation is over)?\r\n",
"Hi sorry for the late update. I think the problem still exists despite the `leave` flag\r\n\r\n<img width=\"1105\" alt=\"image\" src=\"https://user-images.githubusercontent.com/110427462/226501615-5b02fb02-fd5f-4eda-b1f7-a7ed6570892d.png\">\r\n\r\n\r\n```\r\nPackage Version\r\n------------------------ ---------\r\naiofiles 22.1.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\naiosqlite 0.18.0\r\nanyio 3.6.2\r\nappnope 0.1.3\r\nargon2-cffi 21.3.0\r\nargon2-cffi-bindings 21.2.0\r\narrow 1.2.3\r\nasttokens 2.2.1\r\nasync-generator 1.10\r\nasync-timeout 4.0.2\r\nattrs 22.2.0\r\nBabel 2.12.1\r\nbackcall 0.2.0\r\nbeautifulsoup4 4.11.2\r\nbleach 6.0.0\r\nbrotlipy 0.7.0\r\ncertifi 2022.12.7\r\ncffi 1.15.1\r\ncfgv 3.3.1\r\ncharset-normalizer 2.1.1\r\ncomm 0.1.2\r\nconda 22.9.0\r\nconda-package-handling 2.0.2\r\nconda_package_streaming 0.7.0\r\ncoverage 7.2.1\r\ncryptography 38.0.4\r\ndatasets 2.8.0\r\ndebugpy 1.6.6\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndill 0.3.6\r\ndistlib 0.3.6\r\ndistro 1.4.0\r\nentrypoints 0.4\r\nexceptiongroup 1.1.0\r\nexecuting 1.2.0\r\nfastjsonschema 2.16.3\r\nfilelock 3.9.0\r\nflaky 3.7.0\r\nfqdn 1.5.1\r\nfrozenlist 1.3.3\r\nfsspec 2023.3.0\r\nhuggingface-hub 0.10.1\r\nidentify 2.5.18\r\nidna 3.4\r\niniconfig 2.0.0\r\nipykernel 6.12.1\r\nipyparallel 8.4.1\r\nipython 7.32.0\r\nipython-genutils 0.2.0\r\nipywidgets 8.0.4\r\nisoduration 20.11.0\r\njedi 0.18.2\r\nJinja2 3.1.2\r\njson5 0.9.11\r\njsonpointer 2.3\r\njsonschema 4.17.3\r\njupyter_client 8.0.3\r\njupyter_core 5.2.0\r\njupyter-events 0.6.3\r\njupyter_server 2.4.0\r\njupyter_server_fileid 0.8.0\r\njupyter_server_terminals 0.4.4\r\njupyter_server_ydoc 0.6.1\r\njupyter-ydoc 0.2.2\r\njupyterlab 3.6.1\r\njupyterlab-pygments 0.2.2\r\njupyterlab_server 2.20.0\r\njupyterlab-widgets 3.0.5\r\nlibmambapy 1.1.0\r\nmamba 1.1.0\r\nMarkupSafe 2.1.2\r\nmatplotlib-inline 0.1.6\r\nmistune 2.0.5\r\nmultidict 6.0.4\r\nmultiprocess 0.70.14\r\nnbclassic 0.5.3\r\nnbclient 0.7.2\r\nnbconvert 7.2.9\r\nnbformat 5.7.3\r\nnest-asyncio 1.5.6\r\nnodeenv 1.7.0\r\nnotebook 6.5.3\r\nnotebook_shim 0.2.2\r\nnumpy 1.24.2\r\noutcome 1.2.0\r\npackaging 23.0\r\npandas 1.5.3\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\npip 22.3.1\r\nplatformdirs 3.0.0\r\nplotly 5.13.1\r\npluggy 1.0.0\r\npre-commit 3.1.0\r\nprometheus-client 0.16.0\r\nprompt-toolkit 3.0.38\r\npsutil 5.9.4\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npyarrow 11.0.0\r\npycosat 0.6.4\r\npycparser 2.21\r\nPygments 2.14.0\r\npyOpenSSL 22.1.0\r\npyrsistent 0.19.3\r\nPySocks 1.7.1\r\npytest 7.2.1\r\npytest-asyncio 0.20.3\r\npytest-cov 4.0.0\r\npytest-timeout 2.1.0\r\npython-dateutil 2.8.2\r\npython-json-logger 2.0.7\r\npytz 2022.7.1\r\nPyYAML 6.0\r\npyzmq 25.0.0\r\nrequests 2.28.1\r\nresponses 0.18.0\r\nrfc3339-validator 0.1.4\r\nrfc3986-validator 0.1.1\r\nruamel-yaml-conda 0.15.80\r\nSend2Trash 1.8.0\r\nsetuptools 65.6.3\r\nsimplegeneric 0.8.1\r\nsix 1.16.0\r\nsniffio 1.3.0\r\nsortedcontainers 2.4.0\r\nsoupsieve 2.4\r\nstack-data 0.6.2\r\ntenacity 8.2.2\r\nterminado 0.17.1\r\ntinycss2 1.2.1\r\ntomli 2.0.1\r\ntoolz 0.12.0\r\ntornado 6.2\r\ntqdm 4.65.0\r\ntraitlets 5.8.1\r\ntrio 0.22.0\r\ntyping_extensions 4.5.0\r\nuri-template 1.2.0\r\nurllib3 1.26.13\r\nvirtualenv 20.19.0\r\nwcwidth 0.2.6\r\nwebcolors 1.12\r\nwebencodings 0.5.1\r\nwebsocket-client 1.5.1\r\nwheel 0.38.4\r\nwidgetsnbextension 4.0.5\r\nxxhash 3.2.0\r\ny-py 0.5.9\r\nyarl 1.8.2\r\nypy-websocket 0.8.2\r\nzstandard 0.19.0\r\n```\r\n\r\nAny idea why this is happening? I debugged this to know the tqdm.pbar value is not being updated properly and its not the kernel not sending the comm messages to the IProgress bar"
] |
1,621,469,970
| 5,633
|
Cannot import datasets
|
closed
| 2023-03-13T13:14:44
| 2023-03-13T17:54:19
| 2023-03-13T17:54:19
|
https://github.com/huggingface/datasets/issues/5633
| null |
ruplet
| false
|
[
"Okay, the issue was likely caused by mixing `conda` and `pip` usage - I forgot that I have already used `pip` in this environment previously and that it was 'spoiled' because of it. Creating another environment and installing `datasets` by pip with other packages from the `requirements.txt` file solved the problem."
] |
1,621,177,391
| 5,632
|
Dataset cannot convert too large dictionnary
|
open
| 2023-03-13T10:14:40
| 2023-03-16T15:28:57
| null |
https://github.com/huggingface/datasets/issues/5632
| null |
MaraLac
| false
|
[
"Answered on the forum:\r\n\r\n> To fix the overflow error, we need to merge [support LargeListArray in pyarrow by xwwwwww · Pull Request #4800 · huggingface/datasets · GitHub](https://github.com/huggingface/datasets/pull/4800), which adds support for the large lists. However, before merging it, we need to come up with a cleaner API for large lists. I hope to find some time to address this before Datasets 3.0."
] |
1,620,442,854
| 5,631
|
Custom split names
|
closed
| 2023-03-12T17:21:43
| 2023-03-24T14:13:00
| 2023-03-24T14:13:00
|
https://github.com/huggingface/datasets/issues/5631
| null |
ErfanMoosaviMonazzah
| false
|
[
"Hi!\r\n\r\nYou can also use names other than \"train\", \"validation\" and \"test\". As an example, check the [script](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/e095840f23f3dffc1056c078c2f9320dad9ca74d/common_voice_11_0.py#L139) of the Common Voice 11 dataset. "
] |
1,620,327,510
| 5,630
|
adds early exit if url is `PathLike`
|
open
| 2023-03-12T11:23:28
| 2023-03-15T11:58:38
| null |
https://github.com/huggingface/datasets/pull/5630
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5630",
"html_url": "https://github.com/huggingface/datasets/pull/5630",
"diff_url": "https://github.com/huggingface/datasets/pull/5630.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5630.patch",
"merged_at": null
}
|
vvvm23
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5630). All of your documentation changes will be reflected on that endpoint."
] |
1,619,921,247
| 5,629
|
load_dataset gives "403" error when using Financial phrasebank
|
open
| 2023-03-11T07:46:39
| 2023-03-13T18:27:26
| null |
https://github.com/huggingface/datasets/issues/5629
| null |
Jimchoo91
| false
|
[
"Hi! You seem to be using an outdated version of `datasets` that downloads the older script version. To avoid the error, you can either pass `revision=\"main\"` to `load_dataset` (this can fail if a script uses newer features of the lib) or update your installation with `pip install -U datasets` (better solution)."
] |
1,619,641,810
| 5,628
|
add kwargs to index search
|
closed
| 2023-03-10T21:24:58
| 2023-03-15T14:48:47
| 2023-03-15T14:46:04
|
https://github.com/huggingface/datasets/pull/5628
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5628",
"html_url": "https://github.com/huggingface/datasets/pull/5628",
"diff_url": "https://github.com/huggingface/datasets/pull/5628.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5628.patch",
"merged_at": "2023-03-15T14:46:04"
}
|
SaulLu
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._"
] |
1,619,336,609
| 5,627
|
Unable to load AutoTrain-generated dataset from the hub
|
open
| 2023-03-10T17:25:58
| 2023-03-11T15:44:42
| null |
https://github.com/huggingface/datasets/issues/5627
| null |
ijmiller2
| false
|
[
"The AutoTrain format is not supported right now. I think it would require a dedicated dataset builder",
"Okay, good to know. Thanks for the reply. For now I will just have to\nmanage the split manually before training, because I can’t find any way of\npulling out file indices or file names from the autogenerated split. The\nfile names field of the image dataset (loaded directly from arrow file) is\nmissing, just fyi (for anyone else this might be relevant too).\n\nOn Fri, Mar 10, 2023 at 7:02 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> The AutoTrain format is not supported right now. I think it would require\n> a dedicated dataset builder\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5627#issuecomment-1464734308>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACBJ4F5A353MCZ76OGRJ6CTW3PFI7ANCNFSM6AAAAAAVWXNUTE>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] |
1,619,252,984
| 5,626
|
Support streaming datasets with numpy.load
|
closed
| 2023-03-10T16:33:39
| 2023-03-21T06:36:05
| 2023-03-21T06:28:54
|
https://github.com/huggingface/datasets/pull/5626
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5626",
"html_url": "https://github.com/huggingface/datasets/pull/5626",
"diff_url": "https://github.com/huggingface/datasets/pull/5626.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5626.patch",
"merged_at": "2023-03-21T06:28:54"
}
|
albertvillanova
| true
|
[
"_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) | 0.006607 / 0.011353 (-0.004746) | 0.004610 / 0.011008 (-0.006398) | 0.100673 / 0.038508 (0.062165) | 0.027739 / 0.023109 (0.004630) | 0.326290 / 0.275898 (0.050392) | 0.344296 / 0.323480 (0.020816) | 0.005021 / 0.007986 (-0.002964) | 0.003327 / 0.004328 (-0.001002) | 0.077779 / 0.004250 (0.073529) | 0.040237 / 0.037052 (0.003185) | 0.308992 / 0.258489 (0.050503) | 0.355017 / 0.293841 (0.061176) | 0.031203 / 0.128546 (-0.097343) | 0.011749 / 0.075646 (-0.063898) | 0.327431 / 0.419271 (-0.091840) | 0.043033 / 0.043533 (-0.000500) | 0.309713 / 0.255139 (0.054574) | 0.336550 / 0.283200 (0.053351) | 0.084891 / 0.141683 (-0.056792) | 1.555641 / 1.452155 (0.103487) | 1.613214 / 1.492716 (0.120497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216269 / 0.018006 (0.198262) | 0.422066 / 0.000490 (0.421576) | 0.004055 / 0.000200 (0.003855) | 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) | 0.023759 / 0.037411 (-0.013652) | 0.096937 / 0.014526 (0.082411) | 0.105312 / 0.176557 (-0.071244) | 0.167840 / 0.737135 (-0.569295) | 0.107998 / 0.296338 (-0.188340) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458315 / 0.215209 (0.243106) | 4.584803 / 2.077655 (2.507148) | 2.193641 / 1.504120 (0.689521) | 1.981494 / 1.541195 (0.440299) | 2.020358 / 1.468490 (0.551868) | 0.696763 / 4.584777 (-3.888014) | 3.388432 / 3.745712 (-0.357280) | 3.335038 / 5.269862 (-1.934823) | 1.648551 / 4.565676 (-2.917126) | 0.083753 / 0.424275 (-0.340522) | 0.012855 / 0.007607 (0.005248) | 0.562331 / 0.226044 (0.336286) | 5.649259 / 2.268929 (3.380330) | 2.680309 / 55.444624 (-52.764315) | 2.319297 / 6.876477 (-4.557180) | 2.444016 / 2.142072 (0.301943) | 0.809821 / 4.805227 (-3.995407) | 0.152855 / 6.500664 (-6.347809) | 0.067756 / 0.075469 (-0.007713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.213318 / 1.841788 (-0.628470) | 13.887822 / 8.074308 (5.813514) | 14.276325 / 10.191392 (4.084933) | 0.156227 / 0.680424 (-0.524197) | 0.016377 / 0.534201 (-0.517824) | 0.377080 / 0.579283 (-0.202203) | 0.386561 / 0.434364 (-0.047803) | 0.435631 / 0.540337 (-0.104707) | 0.520863 / 1.386936 (-0.866073) |\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) | 0.006740 / 0.011353 (-0.004613) | 0.004704 / 0.011008 (-0.006304) | 0.076840 / 0.038508 (0.038331) | 0.027519 / 0.023109 (0.004409) | 0.343219 / 0.275898 (0.067321) | 0.376810 / 0.323480 (0.053330) | 0.005048 / 0.007986 (-0.002938) | 0.003356 / 0.004328 (-0.000972) | 0.077098 / 0.004250 (0.072848) | 0.038601 / 0.037052 (0.001548) | 0.345723 / 0.258489 (0.087233) | 0.388635 / 0.293841 (0.094794) | 0.033612 / 0.128546 (-0.094934) | 0.011689 / 0.075646 (-0.063957) | 0.086446 / 0.419271 (-0.332825) | 0.044390 / 0.043533 (0.000857) | 0.343763 / 0.255139 (0.088624) | 0.368591 / 0.283200 (0.085392) | 0.091605 / 0.141683 (-0.050078) | 1.478615 / 1.452155 (0.026461) | 1.580858 / 1.492716 (0.088142) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223547 / 0.018006 (0.205541) | 0.411243 / 0.000490 (0.410753) | 0.000916 / 0.000200 (0.000716) | 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) | 0.025223 / 0.037411 (-0.012189) | 0.100970 / 0.014526 (0.086445) | 0.108178 / 0.176557 (-0.068378) | 0.156827 / 0.737135 (-0.580308) | 0.111431 / 0.296338 (-0.184907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434168 / 0.215209 (0.218959) | 4.361874 / 2.077655 (2.284219) | 2.060735 / 1.504120 (0.556615) | 1.861100 / 1.541195 (0.319906) | 1.920692 / 1.468490 (0.452202) | 0.697909 / 4.584777 (-3.886868) | 3.477036 / 3.745712 (-0.268676) | 3.002469 / 5.269862 (-2.267392) | 1.449325 / 4.565676 (-3.116351) | 0.083034 / 0.424275 (-0.341241) | 0.012805 / 0.007607 (0.005198) | 0.531391 / 0.226044 (0.305347) | 5.323015 / 2.268929 (3.054086) | 2.488605 / 55.444624 (-52.956020) | 2.158254 / 6.876477 (-4.718222) | 2.189633 / 2.142072 (0.047560) | 0.805972 / 4.805227 (-3.999256) | 0.153105 / 6.500664 (-6.347559) | 0.068909 / 0.075469 (-0.006561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276851 / 1.841788 (-0.564937) | 14.431510 / 8.074308 (6.357202) | 14.544788 / 10.191392 (4.353396) | 0.146589 / 0.680424 (-0.533835) | 0.016890 / 0.534201 (-0.517311) | 0.379897 / 0.579283 (-0.199387) | 0.389153 / 0.434364 (-0.045211) | 0.440097 / 0.540337 (-0.100241) | 0.524191 / 1.386936 (-0.862745) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e1af108015e43f9df8734a1faeeaeb9eafce3971 \"CML watermark\")\n"
] |
1,618,971,855
| 5,625
|
Allow "jsonl" data type signifier
|
open
| 2023-03-10T13:21:48
| 2023-03-11T10:35:39
| null |
https://github.com/huggingface/datasets/issues/5625
| null |
BramVanroy
| false
|
[
"You can use \"json\" instead. It doesn't work by extension names, but rather by dataset builder names, e.g. \"text\", \"imagefolder\", etc. I don't think the example in `transformers` is correct because of that",
"Yes, I understand the reasoning but this issue is to propose that the example in transformers (while incorrect) \"makes sense\" in terms of user expectation. So the question is whether it would be possible to add \"aliases\" for common types (like \"json\" and \"text\") based on common extensions (like jsonl and txt)?"
] |
1,617,400,192
| 5,624
|
glue datasets returning -1 for test split
|
closed
| 2023-03-09T14:47:18
| 2023-03-09T16:49:29
| 2023-03-09T16:49:29
|
https://github.com/huggingface/datasets/issues/5624
| null |
lithafnium
| false
|
[
"Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31\r\n> The test labels are not public.\r\n>\r\n> Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...\r\n"
] |
1,616,712,665
| 5,623
|
Remove set_access_token usage + fail tests if FutureWarning
|
closed
| 2023-03-09T08:46:01
| 2023-03-09T15:39:00
| 2023-03-09T15:31:59
|
https://github.com/huggingface/datasets/pull/5623
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5623",
"html_url": "https://github.com/huggingface/datasets/pull/5623",
"diff_url": "https://github.com/huggingface/datasets/pull/5623.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5623.patch",
"merged_at": "2023-03-09T15:31:58"
}
|
Wauplin
| true
|
[
"<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) | 0.008505 / 0.011353 (-0.002848) | 0.004445 / 0.011008 (-0.006563) | 0.102197 / 0.038508 (0.063689) | 0.029886 / 0.023109 (0.006776) | 0.305387 / 0.275898 (0.029489) | 0.355986 / 0.323480 (0.032507) | 0.006814 / 0.007986 (-0.001172) | 0.003298 / 0.004328 (-0.001030) | 0.079204 / 0.004250 (0.074954) | 0.035618 / 0.037052 (-0.001434) | 0.320430 / 0.258489 (0.061941) | 0.353330 / 0.293841 (0.059490) | 0.033280 / 0.128546 (-0.095266) | 0.011300 / 0.075646 (-0.064347) | 0.324627 / 0.419271 (-0.094644) | 0.040405 / 0.043533 (-0.003128) | 0.308760 / 0.255139 (0.053621) | 0.331885 / 0.283200 (0.048685) | 0.084605 / 0.141683 (-0.057077) | 1.576598 / 1.452155 (0.124443) | 1.530694 / 1.492716 (0.037977) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191142 / 0.018006 (0.173136) | 0.404042 / 0.000490 (0.403552) | 0.001185 / 0.000200 (0.000985) | 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) | 0.022889 / 0.037411 (-0.014523) | 0.095862 / 0.014526 (0.081336) | 0.104382 / 0.176557 (-0.072175) | 0.139407 / 0.737135 (-0.597728) | 0.106813 / 0.296338 (-0.189525) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419083 / 0.215209 (0.203874) | 4.188702 / 2.077655 (2.111047) | 1.897854 / 1.504120 (0.393734) | 1.689544 / 1.541195 (0.148350) | 1.714032 / 1.468490 (0.245542) | 0.695541 / 4.584777 (-3.889236) | 3.370584 / 3.745712 (-0.375128) | 3.205549 / 5.269862 (-2.064313) | 1.641202 / 4.565676 (-2.924474) | 0.081849 / 0.424275 (-0.342426) | 0.012043 / 0.007607 (0.004436) | 0.529618 / 0.226044 (0.303574) | 5.314167 / 2.268929 (3.045238) | 2.357271 / 55.444624 (-53.087353) | 1.979684 / 6.876477 (-4.896793) | 2.030057 / 2.142072 (-0.112015) | 0.813013 / 4.805227 (-3.992214) | 0.150165 / 6.500664 (-6.350499) | 0.064595 / 0.075469 (-0.010874) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237824 / 1.841788 (-0.603964) | 13.552178 / 8.074308 (5.477870) | 14.089433 / 10.191392 (3.898041) | 0.149325 / 0.680424 (-0.531099) | 0.028543 / 0.534201 (-0.505658) | 0.396848 / 0.579283 (-0.182435) | 0.396230 / 0.434364 (-0.038134) | 0.466317 / 0.540337 (-0.074021) | 0.539579 / 1.386936 (-0.847357) |\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) | 0.006224 / 0.011353 (-0.005128) | 0.004429 / 0.011008 (-0.006579) | 0.075740 / 0.038508 (0.037232) | 0.026717 / 0.023109 (0.003608) | 0.341685 / 0.275898 (0.065787) | 0.383671 / 0.323480 (0.060191) | 0.004682 / 0.007986 (-0.003304) | 0.004681 / 0.004328 (0.000352) | 0.076638 / 0.004250 (0.072387) | 0.034577 / 0.037052 (-0.002476) | 0.341160 / 0.258489 (0.082671) | 0.407590 / 0.293841 (0.113749) | 0.031121 / 0.128546 (-0.097425) | 0.011479 / 0.075646 (-0.064167) | 0.085299 / 0.419271 (-0.333973) | 0.042005 / 0.043533 (-0.001528) | 0.339682 / 0.255139 (0.084543) | 0.377669 / 0.283200 (0.094469) | 0.087751 / 0.141683 (-0.053932) | 1.523910 / 1.452155 (0.071756) | 1.607487 / 1.492716 (0.114771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225605 / 0.018006 (0.207599) | 0.395851 / 0.000490 (0.395361) | 0.004404 / 0.000200 (0.004204) | 0.000082 / 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) | 0.024489 / 0.037411 (-0.012922) | 0.099813 / 0.014526 (0.085287) | 0.107392 / 0.176557 (-0.069165) | 0.139567 / 0.737135 (-0.597568) | 0.110080 / 0.296338 (-0.186258) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449051 / 0.215209 (0.233841) | 4.463098 / 2.077655 (2.385443) | 2.122548 / 1.504120 (0.618428) | 1.913863 / 1.541195 (0.372669) | 1.963988 / 1.468490 (0.495498) | 0.698442 / 4.584777 (-3.886335) | 3.330425 / 3.745712 (-0.415287) | 1.867843 / 5.269862 (-3.402019) | 1.163740 / 4.565676 (-3.401937) | 0.083209 / 0.424275 (-0.341066) | 0.012594 / 0.007607 (0.004987) | 0.547074 / 0.226044 (0.321030) | 5.474779 / 2.268929 (3.205851) | 2.548025 / 55.444624 (-52.896599) | 2.202435 / 6.876477 (-4.674041) | 2.220330 / 2.142072 (0.078257) | 0.810104 / 4.805227 (-3.995124) | 0.151141 / 6.500664 (-6.349523) | 0.066204 / 0.075469 (-0.009265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272075 / 1.841788 (-0.569712) | 13.749523 / 8.074308 (5.675215) | 14.270974 / 10.191392 (4.079582) | 0.141285 / 0.680424 (-0.539139) | 0.016526 / 0.534201 (-0.517675) | 0.393175 / 0.579283 (-0.186109) | 0.391577 / 0.434364 (-0.042787) | 0.492824 / 0.540337 (-0.047513) | 0.580069 / 1.386936 (-0.806867) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cda14136c9f79c763c17d49b77eabfb233fbb35 \"CML watermark\")\n",
"_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) | 0.008901 / 0.011353 (-0.002452) | 0.005017 / 0.011008 (-0.005991) | 0.099340 / 0.038508 (0.060832) | 0.034218 / 0.023109 (0.011109) | 0.295927 / 0.275898 (0.020029) | 0.330087 / 0.323480 (0.006607) | 0.008041 / 0.007986 (0.000056) | 0.005013 / 0.004328 (0.000685) | 0.074255 / 0.004250 (0.070004) | 0.049634 / 0.037052 (0.012582) | 0.299972 / 0.258489 (0.041483) | 0.349879 / 0.293841 (0.056038) | 0.038500 / 0.128546 (-0.090047) | 0.011980 / 0.075646 (-0.063666) | 0.332408 / 0.419271 (-0.086863) | 0.048385 / 0.043533 (0.004852) | 0.300393 / 0.255139 (0.045254) | 0.316972 / 0.283200 (0.033772) | 0.101674 / 0.141683 (-0.040009) | 1.424300 / 1.452155 (-0.027854) | 1.520658 / 1.492716 (0.027942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270084 / 0.018006 (0.252078) | 0.538612 / 0.000490 (0.538123) | 0.004439 / 0.000200 (0.004240) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026841 / 0.037411 (-0.010570) | 0.106454 / 0.014526 (0.091928) | 0.118371 / 0.176557 (-0.058186) | 0.155545 / 0.737135 (-0.581590) | 0.125119 / 0.296338 (-0.171220) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395794 / 0.215209 (0.180585) | 3.958195 / 2.077655 (1.880540) | 1.789010 / 1.504120 (0.284890) | 1.601380 / 1.541195 (0.060186) | 1.641062 / 1.468490 (0.172572) | 0.679547 / 4.584777 (-3.905230) | 3.778018 / 3.745712 (0.032306) | 2.101232 / 5.269862 (-3.168630) | 1.463932 / 4.565676 (-3.101745) | 0.083639 / 0.424275 (-0.340636) | 0.012339 / 0.007607 (0.004732) | 0.498708 / 0.226044 (0.272663) | 4.995178 / 2.268929 (2.726249) | 2.272650 / 55.444624 (-53.171975) | 1.907879 / 6.876477 (-4.968598) | 2.012666 / 2.142072 (-0.129407) | 0.829564 / 4.805227 (-3.975663) | 0.165049 / 6.500664 (-6.335615) | 0.062291 / 0.075469 (-0.013178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193977 / 1.841788 (-0.647811) | 14.816939 / 8.074308 (6.742631) | 14.369729 / 10.191392 (4.178337) | 0.156339 / 0.680424 (-0.524084) | 0.029151 / 0.534201 (-0.505050) | 0.449362 / 0.579283 (-0.129921) | 0.451895 / 0.434364 (0.017531) | 0.520324 / 0.540337 (-0.020013) | 0.610716 / 1.386936 (-0.776220) |\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) | 0.007145 / 0.011353 (-0.004207) | 0.005299 / 0.011008 (-0.005710) | 0.074216 / 0.038508 (0.035708) | 0.033015 / 0.023109 (0.009906) | 0.337117 / 0.275898 (0.061219) | 0.367161 / 0.323480 (0.043682) | 0.005898 / 0.007986 (-0.002088) | 0.005283 / 0.004328 (0.000955) | 0.073795 / 0.004250 (0.069544) | 0.049253 / 0.037052 (0.012201) | 0.343327 / 0.258489 (0.084838) | 0.396417 / 0.293841 (0.102576) | 0.037162 / 0.128546 (-0.091384) | 0.012456 / 0.075646 (-0.063191) | 0.086668 / 0.419271 (-0.332604) | 0.049937 / 0.043533 (0.006404) | 0.335138 / 0.255139 (0.079999) | 0.358111 / 0.283200 (0.074912) | 0.107328 / 0.141683 (-0.034355) | 1.482290 / 1.452155 (0.030135) | 1.557872 / 1.492716 (0.065156) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343759 / 0.018006 (0.325752) | 0.542697 / 0.000490 (0.542207) | 0.025943 / 0.000200 (0.025743) | 0.000264 / 0.000054 (0.000209) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028469 / 0.037411 (-0.008943) | 0.108620 / 0.014526 (0.094094) | 0.123667 / 0.176557 (-0.052890) | 0.168829 / 0.737135 (-0.568306) | 0.125875 / 0.296338 (-0.170464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424640 / 0.215209 (0.209431) | 4.227611 / 2.077655 (2.149956) | 2.003605 / 1.504120 (0.499486) | 1.810696 / 1.541195 (0.269501) | 1.882700 / 1.468490 (0.414210) | 0.701361 / 4.584777 (-3.883416) | 3.808054 / 3.745712 (0.062342) | 3.234896 / 5.269862 (-2.034966) | 1.872195 / 4.565676 (-2.693482) | 0.088102 / 0.424275 (-0.336173) | 0.012810 / 0.007607 (0.005203) | 0.551855 / 0.226044 (0.325810) | 5.245654 / 2.268929 (2.976725) | 2.557123 / 55.444624 (-52.887502) | 2.238897 / 6.876477 (-4.637580) | 2.256260 / 2.142072 (0.114187) | 0.849804 / 4.805227 (-3.955424) | 0.170557 / 6.500664 (-6.330107) | 0.064718 / 0.075469 (-0.010751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271701 / 1.841788 (-0.570087) | 14.925010 / 8.074308 (6.850702) | 14.966948 / 10.191392 (4.775556) | 0.162966 / 0.680424 (-0.517458) | 0.017618 / 0.534201 (-0.516583) | 0.433484 / 0.579283 (-0.145799) | 0.430047 / 0.434364 (-0.004316) | 0.537356 / 0.540337 (-0.002981) | 0.639237 / 1.386936 (-0.747699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aba888cb4d225b1a05596f52258a079bda98df70 \"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) | 0.012054 / 0.011353 (0.000702) | 0.005923 / 0.011008 (-0.005085) | 0.129531 / 0.038508 (0.091023) | 0.036283 / 0.023109 (0.013173) | 0.374406 / 0.275898 (0.098508) | 0.452538 / 0.323480 (0.129058) | 0.009419 / 0.007986 (0.001434) | 0.004783 / 0.004328 (0.000454) | 0.095292 / 0.004250 (0.091042) | 0.041290 / 0.037052 (0.004238) | 0.403940 / 0.258489 (0.145451) | 0.443091 / 0.293841 (0.149250) | 0.054635 / 0.128546 (-0.073911) | 0.019062 / 0.075646 (-0.056584) | 0.417053 / 0.419271 (-0.002218) | 0.060865 / 0.043533 (0.017332) | 0.378535 / 0.255139 (0.123396) | 0.401036 / 0.283200 (0.117836) | 0.122959 / 0.141683 (-0.018724) | 1.768517 / 1.452155 (0.316362) | 1.794700 / 1.492716 (0.301984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246529 / 0.018006 (0.228523) | 0.576887 / 0.000490 (0.576397) | 0.005031 / 0.000200 (0.004831) | 0.000125 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027363 / 0.037411 (-0.010049) | 0.119037 / 0.014526 (0.104511) | 0.148109 / 0.176557 (-0.028447) | 0.179370 / 0.737135 (-0.557765) | 0.145105 / 0.296338 (-0.151234) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.588748 / 0.215209 (0.373539) | 5.934433 / 2.077655 (3.856778) | 2.549811 / 1.504120 (1.045691) | 2.234616 / 1.541195 (0.693421) | 2.268002 / 1.468490 (0.799512) | 1.154643 / 4.584777 (-3.430134) | 5.333935 / 3.745712 (1.588223) | 2.971065 / 5.269862 (-2.298796) | 2.131427 / 4.565676 (-2.434250) | 0.127737 / 0.424275 (-0.296538) | 0.014699 / 0.007607 (0.007091) | 0.735160 / 0.226044 (0.509115) | 7.403838 / 2.268929 (5.134909) | 3.298169 / 55.444624 (-52.146455) | 2.661285 / 6.876477 (-4.215192) | 2.688877 / 2.142072 (0.546805) | 1.344110 / 4.805227 (-3.461118) | 0.242016 / 6.500664 (-6.258648) | 0.077418 / 0.075469 (0.001948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.566426 / 1.841788 (-0.275362) | 17.144308 / 8.074308 (9.070000) | 19.360598 / 10.191392 (9.169206) | 0.238554 / 0.680424 (-0.441870) | 0.044946 / 0.534201 (-0.489255) | 0.554183 / 0.579283 (-0.025100) | 0.630175 / 0.434364 (0.195811) | 0.630319 / 0.540337 (0.089982) | 0.745060 / 1.386936 (-0.641876) |\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) | 0.009255 / 0.011353 (-0.002098) | 0.006951 / 0.011008 (-0.004057) | 0.092021 / 0.038508 (0.053513) | 0.035588 / 0.023109 (0.012479) | 0.415564 / 0.275898 (0.139666) | 0.446393 / 0.323480 (0.122913) | 0.006532 / 0.007986 (-0.001453) | 0.005099 / 0.004328 (0.000771) | 0.094801 / 0.004250 (0.090550) | 0.044926 / 0.037052 (0.007874) | 0.439125 / 0.258489 (0.180636) | 0.473004 / 0.293841 (0.179163) | 0.057025 / 0.128546 (-0.071522) | 0.018711 / 0.075646 (-0.056935) | 0.110844 / 0.419271 (-0.308427) | 0.058347 / 0.043533 (0.014814) | 0.435721 / 0.255139 (0.180583) | 0.434624 / 0.283200 (0.151424) | 0.114505 / 0.141683 (-0.027178) | 1.722379 / 1.452155 (0.270225) | 1.775836 / 1.492716 (0.283120) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275893 / 0.018006 (0.257887) | 0.552590 / 0.000490 (0.552100) | 0.007919 / 0.000200 (0.007719) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030003 / 0.037411 (-0.007408) | 0.130145 / 0.014526 (0.115619) | 0.131878 / 0.176557 (-0.044678) | 0.194693 / 0.737135 (-0.542442) | 0.137689 / 0.296338 (-0.158650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.619591 / 0.215209 (0.404382) | 6.324095 / 2.077655 (4.246441) | 2.756563 / 1.504120 (1.252444) | 2.384744 / 1.541195 (0.843549) | 2.450407 / 1.468490 (0.981917) | 1.235391 / 4.584777 (-3.349386) | 5.535383 / 3.745712 (1.789671) | 4.831927 / 5.269862 (-0.437934) | 2.757158 / 4.565676 (-1.808519) | 0.133980 / 0.424275 (-0.290295) | 0.014965 / 0.007607 (0.007358) | 0.731423 / 0.226044 (0.505379) | 7.401850 / 2.268929 (5.132921) | 3.346585 / 55.444624 (-52.098039) | 2.705523 / 6.876477 (-4.170953) | 2.637397 / 2.142072 (0.495324) | 1.347745 / 4.805227 (-3.457482) | 0.248658 / 6.500664 (-6.252006) | 0.077427 / 0.075469 (0.001958) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.520860 / 1.841788 (-0.320928) | 17.153000 / 8.074308 (9.078692) | 19.051393 / 10.191392 (8.860001) | 0.236840 / 0.680424 (-0.443584) | 0.026638 / 0.534201 (-0.507563) | 0.518417 / 0.579283 (-0.060866) | 0.607555 / 0.434364 (0.173191) | 0.637381 / 0.540337 (0.097044) | 0.767109 / 1.386936 (-0.619827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ee291f2c5e68a782c82f916e250d470a7e285e7 \"CML watermark\")\n",
"Great, I merged it. Thanks for the 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) | 0.006711 / 0.011353 (-0.004641) | 0.004472 / 0.011008 (-0.006536) | 0.099581 / 0.038508 (0.061073) | 0.028036 / 0.023109 (0.004927) | 0.301197 / 0.275898 (0.025298) | 0.339341 / 0.323480 (0.015861) | 0.005107 / 0.007986 (-0.002879) | 0.003312 / 0.004328 (-0.001017) | 0.075823 / 0.004250 (0.071573) | 0.040861 / 0.037052 (0.003809) | 0.303407 / 0.258489 (0.044918) | 0.350717 / 0.293841 (0.056876) | 0.031657 / 0.128546 (-0.096889) | 0.011627 / 0.075646 (-0.064020) | 0.325465 / 0.419271 (-0.093806) | 0.052671 / 0.043533 (0.009138) | 0.301953 / 0.255139 (0.046814) | 0.327164 / 0.283200 (0.043964) | 0.091264 / 0.141683 (-0.050419) | 1.508947 / 1.452155 (0.056792) | 1.605685 / 1.492716 (0.112968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202977 / 0.018006 (0.184971) | 0.400602 / 0.000490 (0.400112) | 0.003253 / 0.000200 (0.003053) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022453 / 0.037411 (-0.014958) | 0.098633 / 0.014526 (0.084107) | 0.105996 / 0.176557 (-0.070561) | 0.162428 / 0.737135 (-0.574707) | 0.107139 / 0.296338 (-0.189199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453061 / 0.215209 (0.237852) | 4.530844 / 2.077655 (2.453190) | 2.286394 / 1.504120 (0.782274) | 2.076479 / 1.541195 (0.535284) | 2.143730 / 1.468490 (0.675240) | 0.702540 / 4.584777 (-3.882237) | 3.442688 / 3.745712 (-0.303024) | 1.874429 / 5.269862 (-3.395433) | 1.172331 / 4.565676 (-3.393346) | 0.083643 / 0.424275 (-0.340632) | 0.012519 / 0.007607 (0.004911) | 0.556859 / 0.226044 (0.330814) | 5.582843 / 2.268929 (3.313915) | 2.753734 / 55.444624 (-52.690890) | 2.415771 / 6.876477 (-4.460705) | 2.531428 / 2.142072 (0.389356) | 0.813005 / 4.805227 (-3.992222) | 0.153322 / 6.500664 (-6.347343) | 0.068061 / 0.075469 (-0.007408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180481 / 1.841788 (-0.661306) | 13.623933 / 8.074308 (5.549625) | 14.431288 / 10.191392 (4.239896) | 0.127580 / 0.680424 (-0.552844) | 0.016714 / 0.534201 (-0.517487) | 0.394236 / 0.579283 (-0.185047) | 0.381718 / 0.434364 (-0.052646) | 0.486749 / 0.540337 (-0.053589) | 0.565939 / 1.386936 (-0.820997) |\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) | 0.006720 / 0.011353 (-0.004633) | 0.004518 / 0.011008 (-0.006491) | 0.076819 / 0.038508 (0.038311) | 0.027272 / 0.023109 (0.004163) | 0.340890 / 0.275898 (0.064992) | 0.381435 / 0.323480 (0.057955) | 0.004980 / 0.007986 (-0.003005) | 0.003382 / 0.004328 (-0.000947) | 0.076368 / 0.004250 (0.072117) | 0.037365 / 0.037052 (0.000313) | 0.341484 / 0.258489 (0.082995) | 0.388917 / 0.293841 (0.095076) | 0.032004 / 0.128546 (-0.096543) | 0.011612 / 0.075646 (-0.064034) | 0.084929 / 0.419271 (-0.334342) | 0.041861 / 0.043533 (-0.001671) | 0.350392 / 0.255139 (0.095253) | 0.369745 / 0.283200 (0.086546) | 0.088301 / 0.141683 (-0.053382) | 1.587296 / 1.452155 (0.135141) | 1.629761 / 1.492716 (0.137045) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174825 / 0.018006 (0.156818) | 0.414371 / 0.000490 (0.413881) | 0.001595 / 0.000200 (0.001395) | 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) | 0.025403 / 0.037411 (-0.012009) | 0.099593 / 0.014526 (0.085067) | 0.108819 / 0.176557 (-0.067738) | 0.161613 / 0.737135 (-0.575523) | 0.112302 / 0.296338 (-0.184037) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439234 / 0.215209 (0.224024) | 4.389073 / 2.077655 (2.311418) | 2.063215 / 1.504120 (0.559095) | 1.852550 / 1.541195 (0.311356) | 1.920014 / 1.468490 (0.451524) | 0.710255 / 4.584777 (-3.874522) | 3.430549 / 3.745712 (-0.315164) | 1.886072 / 5.269862 (-3.383790) | 1.177490 / 4.565676 (-3.388186) | 0.084877 / 0.424275 (-0.339398) | 0.012894 / 0.007607 (0.005287) | 0.544950 / 0.226044 (0.318906) | 5.467347 / 2.268929 (3.198419) | 2.508169 / 55.444624 (-52.936455) | 2.167756 / 6.876477 (-4.708721) | 2.212817 / 2.142072 (0.070744) | 0.824762 / 4.805227 (-3.980465) | 0.154387 / 6.500664 (-6.346277) | 0.068535 / 0.075469 (-0.006934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284165 / 1.841788 (-0.557623) | 14.153006 / 8.074308 (6.078697) | 14.152569 / 10.191392 (3.961177) | 0.130083 / 0.680424 (-0.550341) | 0.016556 / 0.534201 (-0.517645) | 0.383828 / 0.579283 (-0.195455) | 0.388241 / 0.434364 (-0.046123) | 0.477982 / 0.540337 (-0.062355) | 0.565583 / 1.386936 (-0.821353) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e7442d34a059ff377437381542cc762feab057 \"CML watermark\")\n"
] |
1,615,190,942
| 5,622
|
Update README template to better template
|
closed
| 2023-03-08T12:30:23
| 2023-03-11T05:07:38
| 2023-03-11T05:07:38
|
https://github.com/huggingface/datasets/pull/5622
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5622",
"html_url": "https://github.com/huggingface/datasets/pull/5622",
"diff_url": "https://github.com/huggingface/datasets/pull/5622.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5622.patch",
"merged_at": null
}
|
emiltj
| true
|
[
"IMO this template should stay generic.\r\n\r\nAlso, we now use [the card template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md) from `hugginface_hub` as the source of truth on the Hub (you now have the option to import it into the dataset card/README.md), so I think the next step would be deleting this template rather than updating it.",
"Agreed, the PR was a mistake and meant for my own repo. My bad",
"Feel free to close the PR then."
] |
1,615,029,615
| 5,621
|
Adding Oracle Cloud to docs
|
closed
| 2023-03-08T10:22:50
| 2023-03-11T00:57:18
| 2023-03-11T00:49:56
|
https://github.com/huggingface/datasets/pull/5621
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5621",
"html_url": "https://github.com/huggingface/datasets/pull/5621",
"diff_url": "https://github.com/huggingface/datasets/pull/5621.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5621.patch",
"merged_at": "2023-03-11T00:49:56"
}
|
ahosler
| true
|
[
"_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) | 0.006183 / 0.011353 (-0.005170) | 0.004377 / 0.011008 (-0.006631) | 0.096898 / 0.038508 (0.058390) | 0.027729 / 0.023109 (0.004620) | 0.336582 / 0.275898 (0.060684) | 0.353792 / 0.323480 (0.030312) | 0.004541 / 0.007986 (-0.003445) | 0.004349 / 0.004328 (0.000020) | 0.074403 / 0.004250 (0.070153) | 0.033918 / 0.037052 (-0.003134) | 0.341505 / 0.258489 (0.083016) | 0.380192 / 0.293841 (0.086351) | 0.031703 / 0.128546 (-0.096843) | 0.011561 / 0.075646 (-0.064086) | 0.321848 / 0.419271 (-0.097423) | 0.043407 / 0.043533 (-0.000126) | 0.330365 / 0.255139 (0.075226) | 0.364630 / 0.283200 (0.081430) | 0.084798 / 0.141683 (-0.056885) | 1.450908 / 1.452155 (-0.001246) | 1.522235 / 1.492716 (0.029519) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198267 / 0.018006 (0.180261) | 0.409554 / 0.000490 (0.409065) | 0.002501 / 0.000200 (0.002301) | 0.000270 / 0.000054 (0.000215) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021801 / 0.037411 (-0.015610) | 0.097429 / 0.014526 (0.082904) | 0.103259 / 0.176557 (-0.073298) | 0.161483 / 0.737135 (-0.575652) | 0.107843 / 0.296338 (-0.188496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427057 / 0.215209 (0.211848) | 4.259477 / 2.077655 (2.181823) | 1.945819 / 1.504120 (0.441699) | 1.733013 / 1.541195 (0.191819) | 1.748486 / 1.468490 (0.279996) | 0.702231 / 4.584777 (-3.882546) | 3.387608 / 3.745712 (-0.358104) | 1.890187 / 5.269862 (-3.379675) | 1.300465 / 4.565676 (-3.265211) | 0.083702 / 0.424275 (-0.340573) | 0.012674 / 0.007607 (0.005067) | 0.527978 / 0.226044 (0.301934) | 5.259610 / 2.268929 (2.990681) | 2.366512 / 55.444624 (-53.078113) | 2.013811 / 6.876477 (-4.862666) | 2.058175 / 2.142072 (-0.083898) | 0.815042 / 4.805227 (-3.990185) | 0.153496 / 6.500664 (-6.347168) | 0.065442 / 0.075469 (-0.010027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227494 / 1.841788 (-0.614294) | 13.812921 / 8.074308 (5.738613) | 14.430149 / 10.191392 (4.238757) | 0.145422 / 0.680424 (-0.535002) | 0.016672 / 0.534201 (-0.517529) | 0.382126 / 0.579283 (-0.197157) | 0.388369 / 0.434364 (-0.045995) | 0.446133 / 0.540337 (-0.094204) | 0.531044 / 1.386936 (-0.855892) |\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) | 0.006273 / 0.011353 (-0.005080) | 0.004557 / 0.011008 (-0.006452) | 0.077398 / 0.038508 (0.038890) | 0.027295 / 0.023109 (0.004185) | 0.340866 / 0.275898 (0.064968) | 0.373918 / 0.323480 (0.050438) | 0.004967 / 0.007986 (-0.003018) | 0.003337 / 0.004328 (-0.000991) | 0.076041 / 0.004250 (0.071791) | 0.036708 / 0.037052 (-0.000344) | 0.346126 / 0.258489 (0.087637) | 0.385177 / 0.293841 (0.091336) | 0.032272 / 0.128546 (-0.096275) | 0.011756 / 0.075646 (-0.063890) | 0.086512 / 0.419271 (-0.332759) | 0.049310 / 0.043533 (0.005777) | 0.339352 / 0.255139 (0.084213) | 0.372058 / 0.283200 (0.088859) | 0.089712 / 0.141683 (-0.051971) | 1.501964 / 1.452155 (0.049809) | 1.573753 / 1.492716 (0.081037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.162075 / 0.018006 (0.144069) | 0.391462 / 0.000490 (0.390973) | 0.002868 / 0.000200 (0.002668) | 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) | 0.024176 / 0.037411 (-0.013235) | 0.099631 / 0.014526 (0.085105) | 0.107544 / 0.176557 (-0.069013) | 0.157659 / 0.737135 (-0.579477) | 0.111130 / 0.296338 (-0.185209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442086 / 0.215209 (0.226877) | 4.426311 / 2.077655 (2.348657) | 2.086133 / 1.504120 (0.582013) | 1.860415 / 1.541195 (0.319220) | 1.892306 / 1.468490 (0.423816) | 0.702752 / 4.584777 (-3.882025) | 3.394358 / 3.745712 (-0.351354) | 1.857396 / 5.269862 (-3.412466) | 1.167168 / 4.565676 (-3.398509) | 0.083549 / 0.424275 (-0.340726) | 0.012780 / 0.007607 (0.005173) | 0.547075 / 0.226044 (0.321031) | 5.466619 / 2.268929 (3.197691) | 2.548893 / 55.444624 (-52.895731) | 2.185574 / 6.876477 (-4.690903) | 2.188000 / 2.142072 (0.045928) | 0.810370 / 4.805227 (-3.994857) | 0.153320 / 6.500664 (-6.347344) | 0.068409 / 0.075469 (-0.007060) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330431 / 1.841788 (-0.511356) | 14.178916 / 8.074308 (6.104608) | 14.409594 / 10.191392 (4.218202) | 0.156270 / 0.680424 (-0.524154) | 0.016452 / 0.534201 (-0.517749) | 0.379837 / 0.579283 (-0.199447) | 0.389896 / 0.434364 (-0.044468) | 0.443892 / 0.540337 (-0.096446) | 0.531392 / 1.386936 (-0.855544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e502117cafd92fd9c25d1d6dd047cc650c691629 \"CML watermark\")\n"
] |
1,613,460,520
| 5,620
|
Bump pyarrow to 8.0.0
|
closed
| 2023-03-07T13:31:53
| 2023-03-08T14:01:27
| 2023-03-08T13:54:22
|
https://github.com/huggingface/datasets/pull/5620
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5620",
"html_url": "https://github.com/huggingface/datasets/pull/5620",
"diff_url": "https://github.com/huggingface/datasets/pull/5620.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5620.patch",
"merged_at": "2023-03-08T13:54:21"
}
|
lhoestq
| true
|
[
"_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"
] |
1,613,439,709
| 5,619
|
unpin fsspec
|
closed
| 2023-03-07T13:22:41
| 2023-03-07T13:47:01
| 2023-03-07T13:39:02
|
https://github.com/huggingface/datasets/pull/5619
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5619",
"html_url": "https://github.com/huggingface/datasets/pull/5619",
"diff_url": "https://github.com/huggingface/datasets/pull/5619.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5619.patch",
"merged_at": "2023-03-07T13:39:02"
}
|
lhoestq
| true
|
[
"_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) | 0.009954 / 0.011353 (-0.001398) | 0.005468 / 0.011008 (-0.005541) | 0.101228 / 0.038508 (0.062720) | 0.037878 / 0.023109 (0.014769) | 0.305635 / 0.275898 (0.029737) | 0.391672 / 0.323480 (0.068192) | 0.008893 / 0.007986 (0.000908) | 0.005861 / 0.004328 (0.001533) | 0.076940 / 0.004250 (0.072689) | 0.046242 / 0.037052 (0.009190) | 0.324033 / 0.258489 (0.065544) | 0.383306 / 0.293841 (0.089465) | 0.039298 / 0.128546 (-0.089249) | 0.012187 / 0.075646 (-0.063459) | 0.336774 / 0.419271 (-0.082498) | 0.053493 / 0.043533 (0.009960) | 0.303381 / 0.255139 (0.048242) | 0.323494 / 0.283200 (0.040295) | 0.118613 / 0.141683 (-0.023070) | 1.463430 / 1.452155 (0.011275) | 1.549856 / 1.492716 (0.057139) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289264 / 0.018006 (0.271258) | 0.520348 / 0.000490 (0.519858) | 0.004543 / 0.000200 (0.004343) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028183 / 0.037411 (-0.009229) | 0.107869 / 0.014526 (0.093343) | 0.124019 / 0.176557 (-0.052537) | 0.167769 / 0.737135 (-0.569367) | 0.130304 / 0.296338 (-0.166034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402296 / 0.215209 (0.187087) | 4.018884 / 2.077655 (1.941229) | 1.834050 / 1.504120 (0.329930) | 1.649974 / 1.541195 (0.108779) | 1.741697 / 1.468490 (0.273207) | 0.684354 / 4.584777 (-3.900423) | 3.778213 / 3.745712 (0.032501) | 2.158086 / 5.269862 (-3.111775) | 1.472671 / 4.565676 (-3.093006) | 0.083912 / 0.424275 (-0.340363) | 0.012285 / 0.007607 (0.004678) | 0.501689 / 0.226044 (0.275645) | 5.014722 / 2.268929 (2.745794) | 2.310722 / 55.444624 (-53.133902) | 1.983214 / 6.876477 (-4.893262) | 2.154518 / 2.142072 (0.012446) | 0.821277 / 4.805227 (-3.983950) | 0.164434 / 6.500664 (-6.336231) | 0.062568 / 0.075469 (-0.012901) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224338 / 1.841788 (-0.617450) | 14.981623 / 8.074308 (6.907315) | 14.296356 / 10.191392 (4.104964) | 0.193554 / 0.680424 (-0.486870) | 0.028511 / 0.534201 (-0.505690) | 0.437649 / 0.579283 (-0.141634) | 0.448934 / 0.434364 (0.014570) | 0.552624 / 0.540337 (0.012287) | 0.654268 / 1.386936 (-0.732668) |\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) | 0.007772 / 0.011353 (-0.003581) | 0.005534 / 0.011008 (-0.005474) | 0.074347 / 0.038508 (0.035839) | 0.034486 / 0.023109 (0.011376) | 0.343430 / 0.275898 (0.067532) | 0.385778 / 0.323480 (0.062298) | 0.006424 / 0.007986 (-0.001562) | 0.004241 / 0.004328 (-0.000087) | 0.072839 / 0.004250 (0.068589) | 0.055523 / 0.037052 (0.018471) | 0.342778 / 0.258489 (0.084289) | 0.389961 / 0.293841 (0.096120) | 0.037238 / 0.128546 (-0.091308) | 0.012450 / 0.075646 (-0.063197) | 0.085282 / 0.419271 (-0.333990) | 0.049678 / 0.043533 (0.006146) | 0.345300 / 0.255139 (0.090161) | 0.365220 / 0.283200 (0.082020) | 0.109257 / 0.141683 (-0.032426) | 1.480284 / 1.452155 (0.028129) | 1.627881 / 1.492716 (0.135165) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.323330 / 0.018006 (0.305324) | 0.530824 / 0.000490 (0.530334) | 0.000463 / 0.000200 (0.000263) | 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) | 0.032398 / 0.037411 (-0.005013) | 0.115889 / 0.014526 (0.101363) | 0.131093 / 0.176557 (-0.045464) | 0.180757 / 0.737135 (-0.556379) | 0.134395 / 0.296338 (-0.161943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423931 / 0.215209 (0.208722) | 4.238207 / 2.077655 (2.160553) | 2.075721 / 1.504120 (0.571602) | 1.887752 / 1.541195 (0.346557) | 2.055054 / 1.468490 (0.586564) | 0.703145 / 4.584777 (-3.881632) | 3.937120 / 3.745712 (0.191408) | 3.748550 / 5.269862 (-1.521311) | 1.562849 / 4.565676 (-3.002827) | 0.087695 / 0.424275 (-0.336580) | 0.012614 / 0.007607 (0.005007) | 0.523901 / 0.226044 (0.297856) | 5.230210 / 2.268929 (2.961282) | 2.592667 / 55.444624 (-52.851958) | 2.345662 / 6.876477 (-4.530815) | 2.475388 / 2.142072 (0.333316) | 0.836443 / 4.805227 (-3.968784) | 0.170304 / 6.500664 (-6.330360) | 0.067741 / 0.075469 (-0.007729) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255171 / 1.841788 (-0.586617) | 16.312856 / 8.074308 (8.238548) | 13.184770 / 10.191392 (2.993378) | 0.145557 / 0.680424 (-0.534867) | 0.017723 / 0.534201 (-0.516478) | 0.423447 / 0.579283 (-0.155836) | 0.423063 / 0.434364 (-0.011301) | 0.494159 / 0.540337 (-0.046179) | 0.589590 / 1.386936 (-0.797346) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ea6f1db3f80eb3bb7ac6f252c2cd5bd97537c01 \"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) | 0.012068 / 0.011353 (0.000715) | 0.006127 / 0.011008 (-0.004881) | 0.112550 / 0.038508 (0.074042) | 0.043201 / 0.023109 (0.020092) | 0.346666 / 0.275898 (0.070768) | 0.413852 / 0.323480 (0.090372) | 0.009342 / 0.007986 (0.001356) | 0.006302 / 0.004328 (0.001974) | 0.086901 / 0.004250 (0.082650) | 0.053992 / 0.037052 (0.016940) | 0.362192 / 0.258489 (0.103703) | 0.409867 / 0.293841 (0.116026) | 0.046124 / 0.128546 (-0.082422) | 0.014139 / 0.075646 (-0.061507) | 0.386386 / 0.419271 (-0.032886) | 0.058465 / 0.043533 (0.014932) | 0.344832 / 0.255139 (0.089693) | 0.370684 / 0.283200 (0.087485) | 0.122886 / 0.141683 (-0.018796) | 1.724013 / 1.452155 (0.271858) | 1.775756 / 1.492716 (0.283039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220289 / 0.018006 (0.202283) | 0.493585 / 0.000490 (0.493096) | 0.001970 / 0.000200 (0.001770) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030763 / 0.037411 (-0.006649) | 0.128237 / 0.014526 (0.113711) | 0.138364 / 0.176557 (-0.038192) | 0.188115 / 0.737135 (-0.549021) | 0.145367 / 0.296338 (-0.150972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452487 / 0.215209 (0.237277) | 4.592728 / 2.077655 (2.515074) | 2.075712 / 1.504120 (0.571592) | 1.845424 / 1.541195 (0.304229) | 1.956400 / 1.468490 (0.487910) | 0.808387 / 4.584777 (-3.776390) | 4.483678 / 3.745712 (0.737966) | 3.870287 / 5.269862 (-1.399574) | 2.151205 / 4.565676 (-2.414471) | 0.098123 / 0.424275 (-0.326152) | 0.014139 / 0.007607 (0.006531) | 0.577775 / 0.226044 (0.351730) | 5.785545 / 2.268929 (3.516616) | 2.614418 / 55.444624 (-52.830206) | 2.312136 / 6.876477 (-4.564341) | 2.364189 / 2.142072 (0.222117) | 0.970028 / 4.805227 (-3.835199) | 0.189592 / 6.500664 (-6.311072) | 0.072883 / 0.075469 (-0.002586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.414252 / 1.841788 (-0.427535) | 17.518307 / 8.074308 (9.443999) | 16.053748 / 10.191392 (5.862356) | 0.215297 / 0.680424 (-0.465127) | 0.033947 / 0.534201 (-0.500253) | 0.525794 / 0.579283 (-0.053489) | 0.514676 / 0.434364 (0.080312) | 0.595066 / 0.540337 (0.054728) | 0.689404 / 1.386936 (-0.697532) |\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) | 0.008185 / 0.011353 (-0.003168) | 0.005776 / 0.011008 (-0.005232) | 0.084919 / 0.038508 (0.046411) | 0.037575 / 0.023109 (0.014466) | 0.401192 / 0.275898 (0.125294) | 0.443920 / 0.323480 (0.120440) | 0.006446 / 0.007986 (-0.001540) | 0.004428 / 0.004328 (0.000099) | 0.084013 / 0.004250 (0.079763) | 0.052013 / 0.037052 (0.014961) | 0.398429 / 0.258489 (0.139940) | 0.455676 / 0.293841 (0.161836) | 0.041568 / 0.128546 (-0.086978) | 0.013631 / 0.075646 (-0.062015) | 0.098709 / 0.419271 (-0.320563) | 0.055889 / 0.043533 (0.012356) | 0.402002 / 0.255139 (0.146863) | 0.424248 / 0.283200 (0.141049) | 0.113288 / 0.141683 (-0.028395) | 1.672214 / 1.452155 (0.220059) | 1.792940 / 1.492716 (0.300223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211847 / 0.018006 (0.193841) | 0.486711 / 0.000490 (0.486221) | 0.002907 / 0.000200 (0.002707) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032931 / 0.037411 (-0.004480) | 0.142073 / 0.014526 (0.127547) | 0.142872 / 0.176557 (-0.033685) | 0.202612 / 0.737135 (-0.534523) | 0.154390 / 0.296338 (-0.141949) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488682 / 0.215209 (0.273473) | 4.755805 / 2.077655 (2.678150) | 2.348778 / 1.504120 (0.844658) | 2.144992 / 1.541195 (0.603797) | 2.245654 / 1.468490 (0.777164) | 0.792690 / 4.584777 (-3.792087) | 4.569190 / 3.745712 (0.823478) | 3.919317 / 5.269862 (-1.350545) | 2.140302 / 4.565676 (-2.425374) | 0.096430 / 0.424275 (-0.327845) | 0.014551 / 0.007607 (0.006944) | 0.605138 / 0.226044 (0.379094) | 5.989470 / 2.268929 (3.720542) | 2.915525 / 55.444624 (-52.529099) | 2.516243 / 6.876477 (-4.360234) | 2.673114 / 2.142072 (0.531041) | 0.932330 / 4.805227 (-3.872897) | 0.191456 / 6.500664 (-6.309209) | 0.073887 / 0.075469 (-0.001582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455552 / 1.841788 (-0.386236) | 17.824864 / 8.074308 (9.750556) | 15.764150 / 10.191392 (5.572758) | 0.184935 / 0.680424 (-0.495489) | 0.020552 / 0.534201 (-0.513649) | 0.486816 / 0.579283 (-0.092467) | 0.489006 / 0.434364 (0.054642) | 0.609826 / 0.540337 (0.069488) | 0.721313 / 1.386936 (-0.665623) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a0a35c5fa84a8a7df656c1f5b0a7266126fa9b75 \"CML watermark\")\n"
] |
1,612,977,934
| 5,618
|
Unpin fsspec < 2023.3.0 once issue fixed
|
closed
| 2023-03-07T08:41:51
| 2023-03-07T13:39:03
| 2023-03-07T13:39:03
|
https://github.com/huggingface/datasets/issues/5618
| null |
albertvillanova
| false
|
[] |
1,612,947,422
| 5,617
|
Fix CI by temporarily pinning fsspec < 2023.3.0
|
closed
| 2023-03-07T08:18:20
| 2023-03-07T08:44:55
| 2023-03-07T08:37:28
|
https://github.com/huggingface/datasets/pull/5617
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5617",
"html_url": "https://github.com/huggingface/datasets/pull/5617",
"diff_url": "https://github.com/huggingface/datasets/pull/5617.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5617.patch",
"merged_at": "2023-03-07T08:37:28"
}
|
albertvillanova
| true
|
[
"_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) | 0.008771 / 0.011353 (-0.002582) | 0.004665 / 0.011008 (-0.006343) | 0.101645 / 0.038508 (0.063137) | 0.030190 / 0.023109 (0.007081) | 0.298581 / 0.275898 (0.022683) | 0.371206 / 0.323480 (0.047727) | 0.007272 / 0.007986 (-0.000714) | 0.003432 / 0.004328 (-0.000896) | 0.078645 / 0.004250 (0.074395) | 0.037640 / 0.037052 (0.000588) | 0.314014 / 0.258489 (0.055525) | 0.345682 / 0.293841 (0.051841) | 0.033675 / 0.128546 (-0.094871) | 0.011513 / 0.075646 (-0.064134) | 0.320683 / 0.419271 (-0.098589) | 0.041633 / 0.043533 (-0.001900) | 0.302697 / 0.255139 (0.047558) | 0.323560 / 0.283200 (0.040361) | 0.089309 / 0.141683 (-0.052374) | 1.477570 / 1.452155 (0.025415) | 1.528004 / 1.492716 (0.035287) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184710 / 0.018006 (0.166704) | 0.412794 / 0.000490 (0.412305) | 0.001421 / 0.000200 (0.001221) | 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) | 0.023133 / 0.037411 (-0.014278) | 0.099492 / 0.014526 (0.084967) | 0.104806 / 0.176557 (-0.071751) | 0.150765 / 0.737135 (-0.586370) | 0.110127 / 0.296338 (-0.186211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438642 / 0.215209 (0.223433) | 4.349753 / 2.077655 (2.272098) | 2.178754 / 1.504120 (0.674634) | 1.952839 / 1.541195 (0.411645) | 1.840574 / 1.468490 (0.372084) | 0.694016 / 4.584777 (-3.890761) | 3.375186 / 3.745712 (-0.370526) | 1.892391 / 5.269862 (-3.377470) | 1.177643 / 4.565676 (-3.388033) | 0.082328 / 0.424275 (-0.341947) | 0.012280 / 0.007607 (0.004673) | 0.534478 / 0.226044 (0.308434) | 5.377043 / 2.268929 (3.108114) | 2.645273 / 55.444624 (-52.799351) | 2.336391 / 6.876477 (-4.540086) | 2.387917 / 2.142072 (0.245845) | 0.814399 / 4.805227 (-3.990828) | 0.149226 / 6.500664 (-6.351438) | 0.066614 / 0.075469 (-0.008855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205467 / 1.841788 (-0.636321) | 13.857481 / 8.074308 (5.783173) | 14.269958 / 10.191392 (4.078566) | 0.152199 / 0.680424 (-0.528225) | 0.029083 / 0.534201 (-0.505118) | 0.397590 / 0.579283 (-0.181693) | 0.410587 / 0.434364 (-0.023777) | 0.480479 / 0.540337 (-0.059858) | 0.576014 / 1.386936 (-0.810922) |\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) | 0.006956 / 0.011353 (-0.004397) | 0.004914 / 0.011008 (-0.006094) | 0.077571 / 0.038508 (0.039063) | 0.028309 / 0.023109 (0.005200) | 0.344523 / 0.275898 (0.068625) | 0.383039 / 0.323480 (0.059560) | 0.005202 / 0.007986 (-0.002783) | 0.003513 / 0.004328 (-0.000816) | 0.076393 / 0.004250 (0.072142) | 0.042035 / 0.037052 (0.004982) | 0.342950 / 0.258489 (0.084461) | 0.387432 / 0.293841 (0.093591) | 0.032267 / 0.128546 (-0.096280) | 0.011914 / 0.075646 (-0.063732) | 0.087140 / 0.419271 (-0.332131) | 0.042624 / 0.043533 (-0.000909) | 0.342391 / 0.255139 (0.087253) | 0.367016 / 0.283200 (0.083817) | 0.091757 / 0.141683 (-0.049926) | 1.515845 / 1.452155 (0.063690) | 1.607929 / 1.492716 (0.115213) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234461 / 0.018006 (0.216455) | 0.420430 / 0.000490 (0.419941) | 0.000403 / 0.000200 (0.000203) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026639 / 0.037411 (-0.010772) | 0.101860 / 0.014526 (0.087334) | 0.109696 / 0.176557 (-0.066860) | 0.160902 / 0.737135 (-0.576233) | 0.112431 / 0.296338 (-0.183907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438444 / 0.215209 (0.223235) | 4.378881 / 2.077655 (2.301226) | 2.063975 / 1.504120 (0.559855) | 1.863069 / 1.541195 (0.321874) | 1.955684 / 1.468490 (0.487193) | 0.694106 / 4.584777 (-3.890671) | 3.467683 / 3.745712 (-0.278029) | 2.882441 / 5.269862 (-2.387421) | 1.484533 / 4.565676 (-3.081143) | 0.082682 / 0.424275 (-0.341593) | 0.012597 / 0.007607 (0.004990) | 0.539219 / 0.226044 (0.313174) | 5.384838 / 2.268929 (3.115909) | 2.528273 / 55.444624 (-52.916351) | 2.190332 / 6.876477 (-4.686145) | 2.252573 / 2.142072 (0.110500) | 0.801047 / 4.805227 (-4.004180) | 0.151082 / 6.500664 (-6.349582) | 0.067564 / 0.075469 (-0.007905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306469 / 1.841788 (-0.535319) | 14.220154 / 8.074308 (6.145846) | 13.300979 / 10.191392 (3.109586) | 0.153827 / 0.680424 (-0.526597) | 0.016818 / 0.534201 (-0.517383) | 0.383528 / 0.579283 (-0.195755) | 0.393970 / 0.434364 (-0.040394) | 0.468395 / 0.540337 (-0.071943) | 0.558748 / 1.386936 (-0.828188) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824860ca204a3bd84a7d63f71df5df4c56c2432f \"CML watermark\")\n"
] |
1,612,932,508
| 5,616
|
CI is broken after fsspec-2023.3.0 release
|
closed
| 2023-03-07T08:06:39
| 2023-03-07T08:37:29
| 2023-03-07T08:37:29
|
https://github.com/huggingface/datasets/issues/5616
| null |
albertvillanova
| false
|
[] |
1,612,552,653
| 5,615
|
IterableDataset.add_column is unable to accept another IterableDataset as a parameter.
|
closed
| 2023-03-07T01:52:00
| 2023-03-09T15:24:05
| 2023-03-09T15:23:54
|
https://github.com/huggingface/datasets/issues/5615
| null |
zsaladin
| false
|
[
"Hi! You can use `concatenate_datasets([ids1, ids2], axis=1)` to do this."
] |
1,611,896,357
| 5,614
|
Fix archive fs test
|
closed
| 2023-03-06T17:28:09
| 2023-03-07T13:27:50
| 2023-03-07T13:20:57
|
https://github.com/huggingface/datasets/pull/5614
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5614",
"html_url": "https://github.com/huggingface/datasets/pull/5614",
"diff_url": "https://github.com/huggingface/datasets/pull/5614.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5614.patch",
"merged_at": "2023-03-07T13:20:57"
}
|
lhoestq
| true
|
[
"_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) | 0.008664 / 0.011353 (-0.002689) | 0.004622 / 0.011008 (-0.006387) | 0.101716 / 0.038508 (0.063208) | 0.030044 / 0.023109 (0.006935) | 0.298476 / 0.275898 (0.022578) | 0.360873 / 0.323480 (0.037393) | 0.007012 / 0.007986 (-0.000974) | 0.003409 / 0.004328 (-0.000919) | 0.077731 / 0.004250 (0.073480) | 0.035493 / 0.037052 (-0.001560) | 0.311474 / 0.258489 (0.052985) | 0.357276 / 0.293841 (0.063435) | 0.033909 / 0.128546 (-0.094638) | 0.011315 / 0.075646 (-0.064332) | 0.323149 / 0.419271 (-0.096122) | 0.040678 / 0.043533 (-0.002855) | 0.298487 / 0.255139 (0.043348) | 0.323107 / 0.283200 (0.039907) | 0.086641 / 0.141683 (-0.055042) | 1.452905 / 1.452155 (0.000750) | 1.510953 / 1.492716 (0.018237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190607 / 0.018006 (0.172601) | 0.409786 / 0.000490 (0.409297) | 0.000818 / 0.000200 (0.000618) | 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) | 0.023267 / 0.037411 (-0.014144) | 0.095390 / 0.014526 (0.080864) | 0.104381 / 0.176557 (-0.072175) | 0.150735 / 0.737135 (-0.586401) | 0.106876 / 0.296338 (-0.189462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434259 / 0.215209 (0.219050) | 4.326978 / 2.077655 (2.249323) | 2.036690 / 1.504120 (0.532570) | 1.836459 / 1.541195 (0.295264) | 1.904003 / 1.468490 (0.435513) | 0.697265 / 4.584777 (-3.887512) | 3.435911 / 3.745712 (-0.309802) | 3.240918 / 5.269862 (-2.028944) | 1.629220 / 4.565676 (-2.936456) | 0.083158 / 0.424275 (-0.341117) | 0.012604 / 0.007607 (0.004997) | 0.539818 / 0.226044 (0.313773) | 5.397860 / 2.268929 (3.128932) | 2.483890 / 55.444624 (-52.960735) | 2.132404 / 6.876477 (-4.744072) | 2.162583 / 2.142072 (0.020510) | 0.817773 / 4.805227 (-3.987454) | 0.151677 / 6.500664 (-6.348987) | 0.066569 / 0.075469 (-0.008900) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243449 / 1.841788 (-0.598339) | 13.699854 / 8.074308 (5.625546) | 13.930979 / 10.191392 (3.739587) | 0.165344 / 0.680424 (-0.515079) | 0.028910 / 0.534201 (-0.505291) | 0.396201 / 0.579283 (-0.183082) | 0.404448 / 0.434364 (-0.029916) | 0.482031 / 0.540337 (-0.058306) | 0.570023 / 1.386936 (-0.816913) |\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) | 0.006785 / 0.011353 (-0.004568) | 0.004643 / 0.011008 (-0.006365) | 0.076755 / 0.038508 (0.038247) | 0.027893 / 0.023109 (0.004783) | 0.342539 / 0.275898 (0.066641) | 0.379103 / 0.323480 (0.055623) | 0.005107 / 0.007986 (-0.002879) | 0.003413 / 0.004328 (-0.000915) | 0.075779 / 0.004250 (0.071528) | 0.039251 / 0.037052 (0.002199) | 0.343425 / 0.258489 (0.084935) | 0.385292 / 0.293841 (0.091451) | 0.032229 / 0.128546 (-0.096317) | 0.011666 / 0.075646 (-0.063980) | 0.086452 / 0.419271 (-0.332819) | 0.042918 / 0.043533 (-0.000615) | 0.343145 / 0.255139 (0.088006) | 0.367916 / 0.283200 (0.084717) | 0.090810 / 0.141683 (-0.050873) | 1.471679 / 1.452155 (0.019524) | 1.566683 / 1.492716 (0.073966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220343 / 0.018006 (0.202336) | 0.396155 / 0.000490 (0.395665) | 0.003831 / 0.000200 (0.003631) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024990 / 0.037411 (-0.012421) | 0.101270 / 0.014526 (0.086744) | 0.110115 / 0.176557 (-0.066442) | 0.161770 / 0.737135 (-0.575365) | 0.112187 / 0.296338 (-0.184151) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436199 / 0.215209 (0.220989) | 4.329084 / 2.077655 (2.251429) | 2.043335 / 1.504120 (0.539215) | 1.836799 / 1.541195 (0.295604) | 1.908362 / 1.468490 (0.439872) | 0.700518 / 4.584777 (-3.884259) | 3.418003 / 3.745712 (-0.327710) | 1.860621 / 5.269862 (-3.409241) | 1.171343 / 4.565676 (-3.394334) | 0.083150 / 0.424275 (-0.341125) | 0.012543 / 0.007607 (0.004936) | 0.533528 / 0.226044 (0.307483) | 5.339660 / 2.268929 (3.070732) | 2.499494 / 55.444624 (-52.945131) | 2.154773 / 6.876477 (-4.721704) | 2.198734 / 2.142072 (0.056661) | 0.803383 / 4.805227 (-4.001844) | 0.150980 / 6.500664 (-6.349684) | 0.068050 / 0.075469 (-0.007419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309487 / 1.841788 (-0.532301) | 14.177068 / 8.074308 (6.102760) | 13.218912 / 10.191392 (3.027520) | 0.156857 / 0.680424 (-0.523567) | 0.016534 / 0.534201 (-0.517667) | 0.383986 / 0.579283 (-0.195297) | 0.395264 / 0.434364 (-0.039100) | 0.442310 / 0.540337 (-0.098027) | 0.535535 / 1.386936 (-0.851401) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64e24bca88be711f4fdcb9c18edaddc1db0bbe2e \"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) | 0.009446 / 0.011353 (-0.001907) | 0.005061 / 0.011008 (-0.005948) | 0.099783 / 0.038508 (0.061275) | 0.036379 / 0.023109 (0.013270) | 0.296769 / 0.275898 (0.020871) | 0.368990 / 0.323480 (0.045510) | 0.007891 / 0.007986 (-0.000094) | 0.003940 / 0.004328 (-0.000389) | 0.076284 / 0.004250 (0.072034) | 0.044390 / 0.037052 (0.007337) | 0.313373 / 0.258489 (0.054884) | 0.361118 / 0.293841 (0.067277) | 0.039058 / 0.128546 (-0.089488) | 0.012016 / 0.075646 (-0.063631) | 0.334239 / 0.419271 (-0.085033) | 0.047028 / 0.043533 (0.003495) | 0.297766 / 0.255139 (0.042627) | 0.312853 / 0.283200 (0.029653) | 0.099117 / 0.141683 (-0.042566) | 1.475487 / 1.452155 (0.023332) | 1.557487 / 1.492716 (0.064771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206243 / 0.018006 (0.188237) | 0.443920 / 0.000490 (0.443430) | 0.001404 / 0.000200 (0.001205) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026347 / 0.037411 (-0.011065) | 0.105880 / 0.014526 (0.091354) | 0.116227 / 0.176557 (-0.060330) | 0.157404 / 0.737135 (-0.579732) | 0.121668 / 0.296338 (-0.174671) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398614 / 0.215209 (0.183405) | 3.970657 / 2.077655 (1.893002) | 1.778899 / 1.504120 (0.274779) | 1.591806 / 1.541195 (0.050611) | 1.687717 / 1.468490 (0.219227) | 0.695399 / 4.584777 (-3.889378) | 3.829281 / 3.745712 (0.083569) | 2.140856 / 5.269862 (-3.129006) | 1.355027 / 4.565676 (-3.210650) | 0.085714 / 0.424275 (-0.338561) | 0.012130 / 0.007607 (0.004523) | 0.505807 / 0.226044 (0.279762) | 5.053098 / 2.268929 (2.784170) | 2.321694 / 55.444624 (-53.122931) | 2.015909 / 6.876477 (-4.860568) | 2.100862 / 2.142072 (-0.041210) | 0.855689 / 4.805227 (-3.949539) | 0.167192 / 6.500664 (-6.333472) | 0.062376 / 0.075469 (-0.013093) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196647 / 1.841788 (-0.645141) | 14.971356 / 8.074308 (6.897048) | 13.897184 / 10.191392 (3.705792) | 0.193267 / 0.680424 (-0.487157) | 0.029252 / 0.534201 (-0.504949) | 0.444885 / 0.579283 (-0.134398) | 0.452792 / 0.434364 (0.018429) | 0.550157 / 0.540337 (0.009819) | 0.658524 / 1.386936 (-0.728412) |\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) | 0.007774 / 0.011353 (-0.003579) | 0.005304 / 0.011008 (-0.005704) | 0.075530 / 0.038508 (0.037022) | 0.034930 / 0.023109 (0.011821) | 0.343879 / 0.275898 (0.067981) | 0.386487 / 0.323480 (0.063008) | 0.005998 / 0.007986 (-0.001987) | 0.005619 / 0.004328 (0.001291) | 0.075865 / 0.004250 (0.071614) | 0.050499 / 0.037052 (0.013446) | 0.345503 / 0.258489 (0.087014) | 0.392081 / 0.293841 (0.098240) | 0.037118 / 0.128546 (-0.091429) | 0.012540 / 0.075646 (-0.063107) | 0.086202 / 0.419271 (-0.333069) | 0.050672 / 0.043533 (0.007139) | 0.343622 / 0.255139 (0.088483) | 0.353853 / 0.283200 (0.070653) | 0.105408 / 0.141683 (-0.036274) | 1.460695 / 1.452155 (0.008540) | 1.524270 / 1.492716 (0.031554) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219356 / 0.018006 (0.201350) | 0.440740 / 0.000490 (0.440251) | 0.014313 / 0.000200 (0.014114) | 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) | 0.030297 / 0.037411 (-0.007115) | 0.108723 / 0.014526 (0.094197) | 0.125085 / 0.176557 (-0.051471) | 0.176664 / 0.737135 (-0.560471) | 0.126659 / 0.296338 (-0.169680) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445790 / 0.215209 (0.230581) | 4.241046 / 2.077655 (2.163391) | 2.027381 / 1.504120 (0.523261) | 1.821070 / 1.541195 (0.279876) | 1.934417 / 1.468490 (0.465927) | 0.710897 / 4.584777 (-3.873880) | 3.840397 / 3.745712 (0.094685) | 3.959196 / 5.269862 (-1.310666) | 1.646069 / 4.565676 (-2.919608) | 0.088615 / 0.424275 (-0.335660) | 0.012321 / 0.007607 (0.004714) | 0.523463 / 0.226044 (0.297418) | 5.240147 / 2.268929 (2.971218) | 2.521639 / 55.444624 (-52.922986) | 2.246535 / 6.876477 (-4.629942) | 2.365913 / 2.142072 (0.223841) | 0.851288 / 4.805227 (-3.953939) | 0.170179 / 6.500664 (-6.330485) | 0.064732 / 0.075469 (-0.010737) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255505 / 1.841788 (-0.586283) | 15.305457 / 8.074308 (7.231148) | 13.214186 / 10.191392 (3.022794) | 0.188971 / 0.680424 (-0.491453) | 0.018972 / 0.534201 (-0.515229) | 0.429621 / 0.579283 (-0.149662) | 0.428738 / 0.434364 (-0.005626) | 0.536241 / 0.540337 (-0.004096) | 0.632998 / 1.386936 (-0.753938) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b64fae9509f6e9da9cabf0ce677966598fc61e38 \"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) | 0.008435 / 0.011353 (-0.002918) | 0.004454 / 0.011008 (-0.006554) | 0.099091 / 0.038508 (0.060583) | 0.028890 / 0.023109 (0.005781) | 0.297450 / 0.275898 (0.021551) | 0.329025 / 0.323480 (0.005545) | 0.006584 / 0.007986 (-0.001401) | 0.004669 / 0.004328 (0.000340) | 0.077387 / 0.004250 (0.073137) | 0.033701 / 0.037052 (-0.003352) | 0.301272 / 0.258489 (0.042783) | 0.345401 / 0.293841 (0.051560) | 0.033473 / 0.128546 (-0.095073) | 0.011244 / 0.075646 (-0.064402) | 0.321941 / 0.419271 (-0.097330) | 0.040646 / 0.043533 (-0.002887) | 0.306686 / 0.255139 (0.051547) | 0.321868 / 0.283200 (0.038668) | 0.084281 / 0.141683 (-0.057401) | 1.491414 / 1.452155 (0.039259) | 1.542799 / 1.492716 (0.050083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188368 / 0.018006 (0.170362) | 0.398595 / 0.000490 (0.398105) | 0.000805 / 0.000200 (0.000605) | 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) | 0.022690 / 0.037411 (-0.014721) | 0.096795 / 0.014526 (0.082269) | 0.104037 / 0.176557 (-0.072520) | 0.149409 / 0.737135 (-0.587727) | 0.108022 / 0.296338 (-0.188317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419316 / 0.215209 (0.204107) | 4.186850 / 2.077655 (2.109196) | 1.920182 / 1.504120 (0.416062) | 1.715493 / 1.541195 (0.174298) | 1.757767 / 1.468490 (0.289277) | 0.692296 / 4.584777 (-3.892480) | 3.342330 / 3.745712 (-0.403382) | 1.842063 / 5.269862 (-3.427798) | 1.150190 / 4.565676 (-3.415487) | 0.082792 / 0.424275 (-0.341483) | 0.012540 / 0.007607 (0.004933) | 0.528867 / 0.226044 (0.302822) | 5.297818 / 2.268929 (3.028890) | 2.313173 / 55.444624 (-53.131451) | 1.941723 / 6.876477 (-4.934754) | 1.982948 / 2.142072 (-0.159125) | 0.808951 / 4.805227 (-3.996276) | 0.149338 / 6.500664 (-6.351326) | 0.064838 / 0.075469 (-0.010631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187865 / 1.841788 (-0.653923) | 13.381918 / 8.074308 (5.307610) | 13.730627 / 10.191392 (3.539234) | 0.149976 / 0.680424 (-0.530447) | 0.028249 / 0.534201 (-0.505952) | 0.392591 / 0.579283 (-0.186692) | 0.403451 / 0.434364 (-0.030912) | 0.467484 / 0.540337 (-0.072853) | 0.560296 / 1.386936 (-0.826640) |\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) | 0.006440 / 0.011353 (-0.004913) | 0.004488 / 0.011008 (-0.006521) | 0.077875 / 0.038508 (0.039367) | 0.027284 / 0.023109 (0.004174) | 0.341625 / 0.275898 (0.065727) | 0.374960 / 0.323480 (0.051480) | 0.005581 / 0.007986 (-0.002405) | 0.003326 / 0.004328 (-0.001003) | 0.076928 / 0.004250 (0.072677) | 0.038205 / 0.037052 (0.001153) | 0.345933 / 0.258489 (0.087444) | 0.383675 / 0.293841 (0.089834) | 0.031908 / 0.128546 (-0.096638) | 0.011724 / 0.075646 (-0.063922) | 0.086974 / 0.419271 (-0.332298) | 0.043084 / 0.043533 (-0.000449) | 0.339663 / 0.255139 (0.084524) | 0.363782 / 0.283200 (0.080582) | 0.090934 / 0.141683 (-0.050749) | 1.459718 / 1.452155 (0.007563) | 1.541104 / 1.492716 (0.048388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224005 / 0.018006 (0.205998) | 0.400727 / 0.000490 (0.400238) | 0.000427 / 0.000200 (0.000227) | 0.000061 / 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) | 0.024604 / 0.037411 (-0.012807) | 0.099813 / 0.014526 (0.085287) | 0.104034 / 0.176557 (-0.072523) | 0.156245 / 0.737135 (-0.580890) | 0.108739 / 0.296338 (-0.187600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440500 / 0.215209 (0.225291) | 4.379934 / 2.077655 (2.302279) | 2.075826 / 1.504120 (0.571706) | 1.867635 / 1.541195 (0.326441) | 1.919035 / 1.468490 (0.450545) | 0.696613 / 4.584777 (-3.888164) | 3.334993 / 3.745712 (-0.410720) | 1.857139 / 5.269862 (-3.412723) | 1.160598 / 4.565676 (-3.405079) | 0.083120 / 0.424275 (-0.341155) | 0.012475 / 0.007607 (0.004868) | 0.544607 / 0.226044 (0.318563) | 5.436808 / 2.268929 (3.167879) | 2.518562 / 55.444624 (-52.926063) | 2.158434 / 6.876477 (-4.718042) | 2.170691 / 2.142072 (0.028618) | 0.811297 / 4.805227 (-3.993930) | 0.150675 / 6.500664 (-6.349990) | 0.065655 / 0.075469 (-0.009814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277627 / 1.841788 (-0.564160) | 13.833501 / 8.074308 (5.759193) | 13.038718 / 10.191392 (2.847325) | 0.148837 / 0.680424 (-0.531587) | 0.016440 / 0.534201 (-0.517761) | 0.379147 / 0.579283 (-0.200136) | 0.379753 / 0.434364 (-0.054611) | 0.460197 / 0.540337 (-0.080141) | 0.544152 / 1.386936 (-0.842784) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e2a235cbab1c91dc5eca0cb123f9c9d9f743461 \"CML watermark\")\n"
] |
1,611,875,473
| 5,613
|
Version mismatch with multiprocess and dill on Python 3.10
|
open
| 2023-03-06T17:14:41
| 2024-04-05T20:13:52
| null |
https://github.com/huggingface/datasets/issues/5613
| null |
adampauls
| false
|
[
"Sorry, I just found https://github.com/apache/beam/issues/24458. It seems this issue is being worked on. ",
"Reopening, since I think the docs should inform the user of this problem. For example, [this page](https://huggingface.co/docs/datasets/installation) says \r\n> Datasets is tested on Python 3.7+.\r\n\r\nbut it should probably say that Beam Datasets do not work with Python 3.10 (or link to a known issues page). ",
"Same problem on Colab using a vanilla setup running :\r\nPython 3.10.11 \r\napache-beam 2.47.0\r\ndatasets 2.12.0",
"Same problem, \r\npy 3.10.11\r\napache-beam==2.47.0\r\ndatasets==2.12.0",
"I have made a workaround by forcing an install of the version of `multiprocess` version `0.70.15` (after installing `datasets` and `apache-beam`). I can confirm that (on Python 3.10 in [this colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing)) `datasets` can download pre-processed Wikipedia dumps and can download non-pre-processed dumps using `beam_runner=\"DirectRunner\"`. I don't know if/how other `beam_runner`s can be made compatible.",
"Same problem.\r\n\r\n```\r\npython = \"^3.10\"\r\napache-beam = { extras = [\"gcp\"], version = \"2.54.0\" }\r\ndatasets = \"^2.18.0\"\r\n```"
] |
1,611,262,510
| 5,612
|
Arrow map type in parquet files unsupported
|
open
| 2023-03-06T12:03:24
| 2024-03-15T18:56:12
| null |
https://github.com/huggingface/datasets/issues/5612
| null |
TevenLeScao
| false
|
[
"I'm attaching a minimal reproducible example:\r\n```python\r\nfrom datasets import load_dataset\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\ntable_with_map = pa.Table.from_pydict(\r\n {\"a\": [1, 2], \"b\": [[(\"a\", 2)], [(\"b\", 4)]]},\r\n schema=pa.schema({\"a\": pa.int32(), \"b\": pa.map_(pa.string(), pa.int32())})\r\n)\r\npq.write_table(table_with_map, \"parquet_with_map.parquet\")\r\ndset = load_dataset(\"parquet\", data_files=\"parquet_with_map.parquet\", split=\"train\") # error unless streaming=True\r\n``` \r\n\r\nFor a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay \"unresolved\" (resolving the features with `_resolve_features` will lead to an error).",
"I've also been wondering about datasets support for Arrow Map datatypes. I had a situation where I had a pandas series of dict[str, float] with hundreds of different possible key values (ie. not bounded), and this got converted to a sequence of structs where every single struct had the entire set of keys.\r\n\r\nI worked around it, by explicitly creating a sequence of [str, float], but given that pyarrow has an explicit Map datatype, it would be good to be able to explicitly cast/force this data type combination.",
"(feel free to ignore) polars will not support this type: https://github.com/pola-rs/polars/issues/3942#issuecomment-1202331210\r\n\r\n> Polars will not add the map dtype. It's benefit do not outweigh the extra complexity. Maybe we can investigate conversion of maps to struct. But I will have to explore that.",
"Looks like they chose to convert every instance with https://github.com/pola-rs/polars/pull/4226"
] |
1,611,197,906
| 5,611
|
add Dataset.to_list
|
closed
| 2023-03-06T11:21:57
| 2023-03-27T13:34:19
| 2023-03-27T13:26:38
|
https://github.com/huggingface/datasets/pull/5611
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5611",
"html_url": "https://github.com/huggingface/datasets/pull/5611",
"diff_url": "https://github.com/huggingface/datasets/pull/5611.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5611.patch",
"merged_at": "2023-03-27T13:26:38"
}
|
kyoto7250
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! `Table.to_pylist` requires PyArrow 7.0+, and our minimal version requirement is 6.0, so we need to bump the version requirement to avoid CI failure. I'll do this in a separate PR.",
"<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) | 0.006857 / 0.011353 (-0.004496) | 0.004711 / 0.011008 (-0.006297) | 0.098332 / 0.038508 (0.059824) | 0.028547 / 0.023109 (0.005438) | 0.307647 / 0.275898 (0.031749) | 0.334891 / 0.323480 (0.011411) | 0.005252 / 0.007986 (-0.002734) | 0.003495 / 0.004328 (-0.000833) | 0.075529 / 0.004250 (0.071279) | 0.042167 / 0.037052 (0.005114) | 0.308509 / 0.258489 (0.050020) | 0.348294 / 0.293841 (0.054453) | 0.032042 / 0.128546 (-0.096504) | 0.011684 / 0.075646 (-0.063962) | 0.321740 / 0.419271 (-0.097531) | 0.057725 / 0.043533 (0.014193) | 0.309431 / 0.255139 (0.054292) | 0.326818 / 0.283200 (0.043618) | 0.093261 / 0.141683 (-0.048422) | 1.475344 / 1.452155 (0.023190) | 1.563952 / 1.492716 (0.071236) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205056 / 0.018006 (0.187050) | 0.421656 / 0.000490 (0.421166) | 0.004167 / 0.000200 (0.003967) | 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) | 0.023935 / 0.037411 (-0.013476) | 0.097220 / 0.014526 (0.082695) | 0.104942 / 0.176557 (-0.071615) | 0.170339 / 0.737135 (-0.566796) | 0.107556 / 0.296338 (-0.188782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424509 / 0.215209 (0.209300) | 4.223637 / 2.077655 (2.145982) | 2.090700 / 1.504120 (0.586580) | 1.902537 / 1.541195 (0.361343) | 1.981192 / 1.468490 (0.512701) | 0.695272 / 4.584777 (-3.889505) | 3.570169 / 3.745712 (-0.175544) | 1.885007 / 5.269862 (-3.384854) | 1.162828 / 4.565676 (-3.402848) | 0.084956 / 0.424275 (-0.339319) | 0.012818 / 0.007607 (0.005210) | 0.534395 / 0.226044 (0.308351) | 5.354318 / 2.268929 (3.085389) | 2.436875 / 55.444624 (-53.007749) | 2.111365 / 6.876477 (-4.765112) | 2.232874 / 2.142072 (0.090802) | 0.804703 / 4.805227 (-4.000524) | 0.152406 / 6.500664 (-6.348258) | 0.066926 / 0.075469 (-0.008543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198621 / 1.841788 (-0.643166) | 13.907491 / 8.074308 (5.833183) | 14.356286 / 10.191392 (4.164894) | 0.140714 / 0.680424 (-0.539710) | 0.016440 / 0.534201 (-0.517761) | 0.380868 / 0.579283 (-0.198415) | 0.396004 / 0.434364 (-0.038360) | 0.448275 / 0.540337 (-0.092062) | 0.537818 / 1.386936 (-0.849118) |\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) | 0.006789 / 0.011353 (-0.004564) | 0.004652 / 0.011008 (-0.006356) | 0.076449 / 0.038508 (0.037941) | 0.028389 / 0.023109 (0.005280) | 0.378644 / 0.275898 (0.102746) | 0.423870 / 0.323480 (0.100391) | 0.005824 / 0.007986 (-0.002162) | 0.003398 / 0.004328 (-0.000931) | 0.075575 / 0.004250 (0.071324) | 0.039656 / 0.037052 (0.002604) | 0.370072 / 0.258489 (0.111583) | 0.441812 / 0.293841 (0.147971) | 0.031817 / 0.128546 (-0.096729) | 0.011701 / 0.075646 (-0.063946) | 0.085759 / 0.419271 (-0.333513) | 0.042328 / 0.043533 (-0.001205) | 0.364103 / 0.255139 (0.108964) | 0.413910 / 0.283200 (0.130711) | 0.090871 / 0.141683 (-0.050812) | 1.505749 / 1.452155 (0.053594) | 1.608555 / 1.492716 (0.115839) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212533 / 0.018006 (0.194527) | 0.404519 / 0.000490 (0.404030) | 0.000373 / 0.000200 (0.000174) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024849 / 0.037411 (-0.012562) | 0.100769 / 0.014526 (0.086243) | 0.110450 / 0.176557 (-0.066107) | 0.161715 / 0.737135 (-0.575420) | 0.113599 / 0.296338 (-0.182739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436780 / 0.215209 (0.221571) | 4.387103 / 2.077655 (2.309448) | 2.081942 / 1.504120 (0.577822) | 1.873661 / 1.541195 (0.332466) | 1.947718 / 1.468490 (0.479228) | 0.696434 / 4.584777 (-3.888343) | 3.405300 / 3.745712 (-0.340412) | 1.897388 / 5.269862 (-3.372474) | 1.169969 / 4.565676 (-3.395707) | 0.083085 / 0.424275 (-0.341190) | 0.012480 / 0.007607 (0.004873) | 0.535635 / 0.226044 (0.309591) | 5.364462 / 2.268929 (3.095533) | 2.531168 / 55.444624 (-52.913457) | 2.184324 / 6.876477 (-4.692153) | 2.228613 / 2.142072 (0.086541) | 0.807127 / 4.805227 (-3.998100) | 0.151971 / 6.500664 (-6.348693) | 0.068430 / 0.075469 (-0.007039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306401 / 1.841788 (-0.535387) | 14.479552 / 8.074308 (6.405244) | 14.428398 / 10.191392 (4.237006) | 0.159505 / 0.680424 (-0.520919) | 0.016856 / 0.534201 (-0.517344) | 0.375197 / 0.579283 (-0.204086) | 0.384328 / 0.434364 (-0.050036) | 0.440688 / 0.540337 (-0.099650) | 0.524998 / 1.386936 (-0.861938) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50b887b840cf3cab86b0394b41050b579c4b79ba \"CML watermark\")\n"
] |
1,610,698,006
| 5,610
|
use datasets streaming mode in trainer ddp mode cause memory leak
|
open
| 2023-03-06T05:26:49
| 2024-03-07T01:11:32
| null |
https://github.com/huggingface/datasets/issues/5610
| null |
gromzhu
| false
|
[
"Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n",
"found an article described a problem, may be helpful for somebody:\r\nhttps://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/\r\nI confirm, it`s not memory leak, after some time memory growing has stopped",
"\"After some time\" - from your description, it sounds like memory growth can happen for 12 hours+, even days, before it stops? That seems very scary."
] |
1,610,062,862
| 5,609
|
`load_from_disk` vs `load_dataset` performance.
|
open
| 2023-03-05T05:27:15
| 2023-07-13T18:48:05
| null |
https://github.com/huggingface/datasets/issues/5609
| null |
davidgilbertson
| false
|
[
"Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the \"benchmark\".",
"Great to hear! I'll give it a try when I've got a moment.",
"@mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths):\r\n```\r\ndataset = load_dataset(\"csv\", cache_dir=\"cache\", data_files=[\"/STORAGE/DATA/mijam/vit/code/list_filtered.csv\"], num_proc=16, split=\"train\").cast_column(\"image\", Image())\r\ndataset = dataset.class_encode_column(\"label\")\r\n```\r\nquite fast. \r\n\r\nThen I do `save_to_disk()` and some time later:\r\n```\r\ndataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big')\r\n```\r\nreally slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.\r\n",
"@mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load)."
] |
1,609,996,563
| 5,608
|
audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.
|
closed
| 2023-03-05T00:14:45
| 2023-03-12T00:02:57
| 2023-03-12T00:02:57
|
https://github.com/huggingface/datasets/issues/5608
| null |
jcho19
| false
|
[
"Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?",
"Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│ ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│ ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│ ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│ └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3"
] |
1,609,166,035
| 5,607
|
Fix outdated `verification_mode` values
|
closed
| 2023-03-03T19:50:29
| 2023-03-09T17:34:13
| 2023-03-09T17:27:07
|
https://github.com/huggingface/datasets/pull/5607
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5607",
"html_url": "https://github.com/huggingface/datasets/pull/5607",
"diff_url": "https://github.com/huggingface/datasets/pull/5607.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5607.patch",
"merged_at": "2023-03-09T17:27:07"
}
|
polinaeterna
| true
|
[
"_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) | 0.006142 / 0.011353 (-0.005211) | 0.004506 / 0.011008 (-0.006502) | 0.100224 / 0.038508 (0.061715) | 0.026988 / 0.023109 (0.003879) | 0.301625 / 0.275898 (0.025727) | 0.346337 / 0.323480 (0.022857) | 0.004642 / 0.007986 (-0.003343) | 0.003481 / 0.004328 (-0.000847) | 0.075847 / 0.004250 (0.071597) | 0.036959 / 0.037052 (-0.000094) | 0.302697 / 0.258489 (0.044208) | 0.351917 / 0.293841 (0.058076) | 0.030719 / 0.128546 (-0.097828) | 0.011591 / 0.075646 (-0.064056) | 0.319709 / 0.419271 (-0.099563) | 0.042000 / 0.043533 (-0.001532) | 0.306854 / 0.255139 (0.051715) | 0.326903 / 0.283200 (0.043703) | 0.082711 / 0.141683 (-0.058972) | 1.486616 / 1.452155 (0.034461) | 1.603229 / 1.492716 (0.110513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198990 / 0.018006 (0.180983) | 0.427733 / 0.000490 (0.427243) | 0.003612 / 0.000200 (0.003412) | 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) | 0.022932 / 0.037411 (-0.014480) | 0.096969 / 0.014526 (0.082443) | 0.105749 / 0.176557 (-0.070807) | 0.166101 / 0.737135 (-0.571034) | 0.108646 / 0.296338 (-0.187692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428174 / 0.215209 (0.212965) | 4.271452 / 2.077655 (2.193797) | 1.907588 / 1.504120 (0.403468) | 1.680870 / 1.541195 (0.139675) | 1.761336 / 1.468490 (0.292846) | 0.700380 / 4.584777 (-3.884396) | 3.415168 / 3.745712 (-0.330544) | 1.886122 / 5.269862 (-3.383740) | 1.276814 / 4.565676 (-3.288863) | 0.083429 / 0.424275 (-0.340846) | 0.012988 / 0.007607 (0.005381) | 0.518821 / 0.226044 (0.292776) | 5.188284 / 2.268929 (2.919356) | 2.433084 / 55.444624 (-53.011540) | 1.988034 / 6.876477 (-4.888443) | 2.100275 / 2.142072 (-0.041797) | 0.808252 / 4.805227 (-3.996976) | 0.158102 / 6.500664 (-6.342562) | 0.067686 / 0.075469 (-0.007783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204171 / 1.841788 (-0.637616) | 13.548756 / 8.074308 (5.474448) | 14.339805 / 10.191392 (4.148413) | 0.142853 / 0.680424 (-0.537571) | 0.016529 / 0.534201 (-0.517672) | 0.383800 / 0.579283 (-0.195483) | 0.380362 / 0.434364 (-0.054002) | 0.437716 / 0.540337 (-0.102621) | 0.524306 / 1.386936 (-0.862630) |\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) | 0.006730 / 0.011353 (-0.004623) | 0.004652 / 0.011008 (-0.006356) | 0.077476 / 0.038508 (0.038968) | 0.027584 / 0.023109 (0.004475) | 0.340907 / 0.275898 (0.065009) | 0.377950 / 0.323480 (0.054470) | 0.005946 / 0.007986 (-0.002040) | 0.003548 / 0.004328 (-0.000780) | 0.076270 / 0.004250 (0.072019) | 0.037483 / 0.037052 (0.000431) | 0.346390 / 0.258489 (0.087901) | 0.384739 / 0.293841 (0.090898) | 0.031744 / 0.128546 (-0.096802) | 0.011598 / 0.075646 (-0.064049) | 0.085651 / 0.419271 (-0.333620) | 0.047308 / 0.043533 (0.003775) | 0.344704 / 0.255139 (0.089565) | 0.363410 / 0.283200 (0.080211) | 0.095009 / 0.141683 (-0.046674) | 1.478307 / 1.452155 (0.026152) | 1.576808 / 1.492716 (0.084092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197545 / 0.018006 (0.179539) | 0.431984 / 0.000490 (0.431494) | 0.001529 / 0.000200 (0.001329) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025452 / 0.037411 (-0.011959) | 0.100176 / 0.014526 (0.085651) | 0.108222 / 0.176557 (-0.068335) | 0.160556 / 0.737135 (-0.576580) | 0.112748 / 0.296338 (-0.183591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436326 / 0.215209 (0.221117) | 4.378443 / 2.077655 (2.300788) | 2.056001 / 1.504120 (0.551881) | 1.853406 / 1.541195 (0.312211) | 1.931645 / 1.468490 (0.463155) | 0.698340 / 4.584777 (-3.886437) | 3.368961 / 3.745712 (-0.376751) | 2.583622 / 5.269862 (-2.686239) | 1.501274 / 4.565676 (-3.064402) | 0.083034 / 0.424275 (-0.341241) | 0.012725 / 0.007607 (0.005117) | 0.539991 / 0.226044 (0.313947) | 5.418413 / 2.268929 (3.149485) | 2.517205 / 55.444624 (-52.927420) | 2.179332 / 6.876477 (-4.697144) | 2.215376 / 2.142072 (0.073304) | 0.806133 / 4.805227 (-3.999094) | 0.151499 / 6.500664 (-6.349165) | 0.067270 / 0.075469 (-0.008199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308324 / 1.841788 (-0.533464) | 14.357361 / 8.074308 (6.283053) | 14.684768 / 10.191392 (4.493376) | 0.139575 / 0.680424 (-0.540849) | 0.016409 / 0.534201 (-0.517792) | 0.374087 / 0.579283 (-0.205196) | 0.390628 / 0.434364 (-0.043735) | 0.443102 / 0.540337 (-0.097235) | 0.536089 / 1.386936 (-0.850847) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#778d4e1c13ece980e706f8c7cb06e8473fd61315 \"CML watermark\")\n"
] |
1,608,911,632
| 5,606
|
Add `Dataset.to_list` to the API
|
closed
| 2023-03-03T16:17:10
| 2023-03-27T13:26:40
| 2023-03-27T13:26:40
|
https://github.com/huggingface/datasets/issues/5606
| null |
mariosasko
| false
|
[
"Hello, I have an interest in this issue.\r\nIs the `Dataset.to_dict` you are describing correct in the code here?\r\n\r\nhttps://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667",
"Yes, this is where `Dataset.to_dict` is defined.",
"#self-assign"
] |
1,608,865,460
| 5,605
|
Update README logo
|
closed
| 2023-03-03T15:46:31
| 2023-03-03T21:57:18
| 2023-03-03T21:50:17
|
https://github.com/huggingface/datasets/pull/5605
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5605",
"html_url": "https://github.com/huggingface/datasets/pull/5605",
"diff_url": "https://github.com/huggingface/datasets/pull/5605.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5605.patch",
"merged_at": "2023-03-03T21:50:17"
}
|
gary149
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"Are you sure it's safe to remove? https://github.com/huggingface/datasets/pull/3866",
"<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) | 0.009520 / 0.011353 (-0.001833) | 0.005319 / 0.011008 (-0.005690) | 0.099372 / 0.038508 (0.060863) | 0.036173 / 0.023109 (0.013064) | 0.295752 / 0.275898 (0.019853) | 0.362882 / 0.323480 (0.039402) | 0.008442 / 0.007986 (0.000456) | 0.004225 / 0.004328 (-0.000103) | 0.076645 / 0.004250 (0.072394) | 0.044198 / 0.037052 (0.007146) | 0.311948 / 0.258489 (0.053459) | 0.342963 / 0.293841 (0.049122) | 0.038613 / 0.128546 (-0.089933) | 0.012127 / 0.075646 (-0.063519) | 0.334427 / 0.419271 (-0.084844) | 0.048309 / 0.043533 (0.004776) | 0.297046 / 0.255139 (0.041907) | 0.314562 / 0.283200 (0.031363) | 0.105797 / 0.141683 (-0.035886) | 1.460967 / 1.452155 (0.008812) | 1.500907 / 1.492716 (0.008190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216185 / 0.018006 (0.198179) | 0.438924 / 0.000490 (0.438435) | 0.001210 / 0.000200 (0.001011) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026193 / 0.037411 (-0.011219) | 0.105888 / 0.014526 (0.091363) | 0.115812 / 0.176557 (-0.060744) | 0.158748 / 0.737135 (-0.578387) | 0.121514 / 0.296338 (-0.174824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399837 / 0.215209 (0.184628) | 3.996992 / 2.077655 (1.919338) | 1.784964 / 1.504120 (0.280844) | 1.591078 / 1.541195 (0.049883) | 1.666424 / 1.468490 (0.197934) | 0.711450 / 4.584777 (-3.873327) | 3.787814 / 3.745712 (0.042102) | 2.056776 / 5.269862 (-3.213085) | 1.332163 / 4.565676 (-3.233514) | 0.085755 / 0.424275 (-0.338520) | 0.012033 / 0.007607 (0.004426) | 0.511500 / 0.226044 (0.285455) | 5.098999 / 2.268929 (2.830071) | 2.288261 / 55.444624 (-53.156364) | 1.947483 / 6.876477 (-4.928994) | 1.987838 / 2.142072 (-0.154234) | 0.852241 / 4.805227 (-3.952986) | 0.164781 / 6.500664 (-6.335883) | 0.061825 / 0.075469 (-0.013644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202253 / 1.841788 (-0.639534) | 14.632608 / 8.074308 (6.558300) | 13.331320 / 10.191392 (3.139928) | 0.157944 / 0.680424 (-0.522480) | 0.029284 / 0.534201 (-0.504917) | 0.446636 / 0.579283 (-0.132647) | 0.437009 / 0.434364 (0.002645) | 0.521883 / 0.540337 (-0.018455) | 0.606687 / 1.386936 (-0.780249) |\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) | 0.007528 / 0.011353 (-0.003825) | 0.005274 / 0.011008 (-0.005734) | 0.073524 / 0.038508 (0.035016) | 0.033893 / 0.023109 (0.010784) | 0.335432 / 0.275898 (0.059534) | 0.379981 / 0.323480 (0.056501) | 0.005954 / 0.007986 (-0.002031) | 0.004126 / 0.004328 (-0.000203) | 0.072891 / 0.004250 (0.068641) | 0.046517 / 0.037052 (0.009465) | 0.337241 / 0.258489 (0.078752) | 0.385562 / 0.293841 (0.091721) | 0.036410 / 0.128546 (-0.092136) | 0.012246 / 0.075646 (-0.063401) | 0.085974 / 0.419271 (-0.333298) | 0.049665 / 0.043533 (0.006133) | 0.330919 / 0.255139 (0.075780) | 0.352041 / 0.283200 (0.068841) | 0.103751 / 0.141683 (-0.037931) | 1.468851 / 1.452155 (0.016696) | 1.565380 / 1.492716 (0.072663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260431 / 0.018006 (0.242425) | 0.444554 / 0.000490 (0.444064) | 0.016055 / 0.000200 (0.015855) | 0.000283 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029130 / 0.037411 (-0.008281) | 0.112002 / 0.014526 (0.097476) | 0.120769 / 0.176557 (-0.055788) | 0.169345 / 0.737135 (-0.567790) | 0.129609 / 0.296338 (-0.166730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432211 / 0.215209 (0.217002) | 4.293008 / 2.077655 (2.215353) | 2.071291 / 1.504120 (0.567171) | 1.859322 / 1.541195 (0.318127) | 1.971434 / 1.468490 (0.502943) | 0.704042 / 4.584777 (-3.880735) | 3.791696 / 3.745712 (0.045983) | 3.142632 / 5.269862 (-2.127230) | 1.735151 / 4.565676 (-2.830525) | 0.086203 / 0.424275 (-0.338072) | 0.012542 / 0.007607 (0.004935) | 0.534870 / 0.226044 (0.308826) | 5.326042 / 2.268929 (3.057113) | 2.547960 / 55.444624 (-52.896664) | 2.212730 / 6.876477 (-4.663747) | 2.296177 / 2.142072 (0.154105) | 0.840311 / 4.805227 (-3.964917) | 0.168353 / 6.500664 (-6.332311) | 0.065949 / 0.075469 (-0.009520) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255589 / 1.841788 (-0.586199) | 14.947344 / 8.074308 (6.873036) | 13.253721 / 10.191392 (3.062329) | 0.162349 / 0.680424 (-0.518075) | 0.017579 / 0.534201 (-0.516622) | 0.420758 / 0.579283 (-0.158525) | 0.430030 / 0.434364 (-0.004334) | 0.524669 / 0.540337 (-0.015669) | 0.623920 / 1.386936 (-0.763016) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35b789e8f6826b6b5a6b48fcc2416c890a1f326a \"CML watermark\")\n"
] |
1,608,304,775
| 5,604
|
Problems with downloading The Pile
|
closed
| 2023-03-03T09:52:08
| 2023-10-14T02:15:52
| 2023-03-24T12:44:25
|
https://github.com/huggingface/datasets/issues/5604
| null |
sentialx
| false
|
[
"Hi! \r\n\r\n\r\nYou can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error:\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\ndataset = load_dataset('the_pile', split='train', cache_dir='F:\\datasets', download_config=DownloadConfig(resume_download=True))\r\n```\r\n\r\n",
"@mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point .\r\nbelow is the script to download and save on disk .\r\n\r\n```\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n\r\n#load the Pile dataset from Hugging Face Datasets\r\n#dataset = load_dataset('the_pile')\r\ndataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n\r\n\r\n# save each file in the dataset to disk\r\nfor i, example in enumerate(dataset['train']):\r\n filename = f'pile_file_{i}.json'\r\n with open(filename, 'w') as f:\r\n f.write(str(example))\r\n\r\nprint(\"Finished saving Pile dataset files to disk.\")\r\n```\r\n",
"@mariosasko , it shows nothing in dataset folder\r\n\r\n```\r\n du -sh /mnt/nlp/hugging_face/*\r\n20K /mnt/nlp/hugging_face/datasets\r\n4.0K /mnt/nlp/hugging_face/download_pile.py\r\n```\r\n",
"@mariosasko \r\n\r\n```\r\nroot@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py\r\nNo config specified, defaulting to: the_pile/all\r\nDownloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349...\r\nDownloading data files: 0%| | 0/3 [00:00<?, ?it/s]\r\n\r\n\r\n\r\n\r\n\r\nDownloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s]\r\nDownloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s]\r\nTraceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s]\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 444, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 567, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 525, in _fp_read\r\n data = self._fp.read(chunk_amt)\r\n File \"/usr/lib/python3.8/http/client.py\", line 459, in read\r\n n = self.readinto(b)\r\n File \"/usr/lib/python3.8/http/client.py\", line 503, in readinto\r\n n = self.fp.readinto(b)\r\n File \"/usr/lib/python3.8/socket.py\", line 669, in readinto\r\n return self._sock.recv_into(b)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1241, in recv_into\r\n return self.read(nbytes, buffer)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1099, in read\r\n return self._sslobj.read(len, buffer)\r\nConnectionResetError: [Errno 104] Connection reset by peer\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 816, in generate\r\n yield from self.raw.stream(chunk_size, decode_content=True)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 628, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 593, in read\r\n raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n File \"/usr/lib/python3.8/contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 461, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\nurllib3.exceptions.ProtocolError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_pile.py\", line 6, in <module>\r\n dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 945, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py\", line 192, in _split_generators\r\n data_dir = dl_manager.download(_DATA_URLS[self.config.name])\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 427, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 443, in map_nested\r\n mapped = [\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 444, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in _single_map_nested\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in <listcomp>\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return function(data_struct)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 453, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 182, in cached_path\r\n output_path = get_from_cache(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 575, in get_from_cache\r\n http_get(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 379, in http_get\r\n for chunk in response.iter_content(chunk_size=1024):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 818, in generate\r\n raise ChunkedEncodingError(e)\r\nrequests.exceptions.ChunkedEncodingError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n```\r\n",
"Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s.\n\nAlso, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?",
"@sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)",
"@sentialx probably worth noting that `resume_download=True` doesn't directly save the dataset to disk, but instead just helps in resuming the dataset resume on interruption as @mariosasko mentions. resolving resumptions after a crash is [an open issue](https://github.com/huggingface/datasets/issues/5380) at the moment."
] |
1,607,143,509
| 5,603
|
Don't compute checksums if not necessary in `datasets-cli test`
|
closed
| 2023-03-02T16:42:39
| 2023-03-03T15:45:32
| 2023-03-03T15:38:28
|
https://github.com/huggingface/datasets/pull/5603
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5603",
"html_url": "https://github.com/huggingface/datasets/pull/5603",
"diff_url": "https://github.com/huggingface/datasets/pull/5603.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5603.patch",
"merged_at": "2023-03-03T15:38:28"
}
|
lhoestq
| true
|
[
"_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) | 0.008550 / 0.011353 (-0.002803) | 0.004476 / 0.011008 (-0.006532) | 0.100902 / 0.038508 (0.062394) | 0.029684 / 0.023109 (0.006575) | 0.308081 / 0.275898 (0.032183) | 0.363435 / 0.323480 (0.039955) | 0.006987 / 0.007986 (-0.000999) | 0.003401 / 0.004328 (-0.000927) | 0.078218 / 0.004250 (0.073967) | 0.036657 / 0.037052 (-0.000395) | 0.319670 / 0.258489 (0.061181) | 0.349952 / 0.293841 (0.056111) | 0.033416 / 0.128546 (-0.095130) | 0.011511 / 0.075646 (-0.064135) | 0.323888 / 0.419271 (-0.095384) | 0.042429 / 0.043533 (-0.001104) | 0.307310 / 0.255139 (0.052171) | 0.329459 / 0.283200 (0.046259) | 0.085209 / 0.141683 (-0.056474) | 1.475893 / 1.452155 (0.023739) | 1.502782 / 1.492716 (0.010065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200137 / 0.018006 (0.182131) | 0.411269 / 0.000490 (0.410780) | 0.000415 / 0.000200 (0.000215) | 0.000061 / 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) | 0.022626 / 0.037411 (-0.014785) | 0.097045 / 0.014526 (0.082519) | 0.102955 / 0.176557 (-0.073602) | 0.148411 / 0.737135 (-0.588725) | 0.107238 / 0.296338 (-0.189100) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421683 / 0.215209 (0.206474) | 4.203031 / 2.077655 (2.125376) | 1.908232 / 1.504120 (0.404112) | 1.698867 / 1.541195 (0.157672) | 1.743561 / 1.468490 (0.275071) | 0.693199 / 4.584777 (-3.891578) | 3.361022 / 3.745712 (-0.384690) | 2.989610 / 5.269862 (-2.280251) | 1.533036 / 4.565676 (-3.032641) | 0.082675 / 0.424275 (-0.341601) | 0.012419 / 0.007607 (0.004812) | 0.531543 / 0.226044 (0.305499) | 5.330595 / 2.268929 (3.061666) | 2.347519 / 55.444624 (-53.097105) | 1.975672 / 6.876477 (-4.900804) | 2.039541 / 2.142072 (-0.102532) | 0.810281 / 4.805227 (-3.994946) | 0.148917 / 6.500664 (-6.351747) | 0.065441 / 0.075469 (-0.010028) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266213 / 1.841788 (-0.575574) | 13.628106 / 8.074308 (5.553798) | 13.852191 / 10.191392 (3.660799) | 0.149004 / 0.680424 (-0.531420) | 0.028549 / 0.534201 (-0.505652) | 0.399824 / 0.579283 (-0.179459) | 0.401231 / 0.434364 (-0.033133) | 0.473251 / 0.540337 (-0.067086) | 0.561094 / 1.386936 (-0.825842) |\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) | 0.006669 / 0.011353 (-0.004684) | 0.004477 / 0.011008 (-0.006532) | 0.077514 / 0.038508 (0.039006) | 0.027489 / 0.023109 (0.004380) | 0.341935 / 0.275898 (0.066037) | 0.377392 / 0.323480 (0.053912) | 0.004947 / 0.007986 (-0.003039) | 0.004600 / 0.004328 (0.000271) | 0.075938 / 0.004250 (0.071687) | 0.039586 / 0.037052 (0.002534) | 0.344966 / 0.258489 (0.086477) | 0.392181 / 0.293841 (0.098340) | 0.031838 / 0.128546 (-0.096708) | 0.011572 / 0.075646 (-0.064075) | 0.085811 / 0.419271 (-0.333461) | 0.042250 / 0.043533 (-0.001283) | 0.345605 / 0.255139 (0.090466) | 0.367814 / 0.283200 (0.084615) | 0.090683 / 0.141683 (-0.051000) | 1.483168 / 1.452155 (0.031014) | 1.559724 / 1.492716 (0.067008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235655 / 0.018006 (0.217649) | 0.399016 / 0.000490 (0.398527) | 0.003096 / 0.000200 (0.002896) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024454 / 0.037411 (-0.012957) | 0.100710 / 0.014526 (0.086185) | 0.107950 / 0.176557 (-0.068606) | 0.161560 / 0.737135 (-0.575576) | 0.111840 / 0.296338 (-0.184498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441362 / 0.215209 (0.226153) | 4.428105 / 2.077655 (2.350450) | 2.074501 / 1.504120 (0.570381) | 1.866672 / 1.541195 (0.325477) | 1.928266 / 1.468490 (0.459776) | 0.703561 / 4.584777 (-3.881216) | 3.396537 / 3.745712 (-0.349175) | 3.047369 / 5.269862 (-2.222492) | 1.595133 / 4.565676 (-2.970543) | 0.084028 / 0.424275 (-0.340247) | 0.012349 / 0.007607 (0.004741) | 0.539354 / 0.226044 (0.313310) | 5.401535 / 2.268929 (3.132606) | 2.499874 / 55.444624 (-52.944750) | 2.161406 / 6.876477 (-4.715071) | 2.197385 / 2.142072 (0.055313) | 0.810864 / 4.805227 (-3.994363) | 0.152277 / 6.500664 (-6.348387) | 0.067266 / 0.075469 (-0.008203) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280900 / 1.841788 (-0.560887) | 13.815731 / 8.074308 (5.741423) | 13.007438 / 10.191392 (2.816046) | 0.129711 / 0.680424 (-0.550713) | 0.016852 / 0.534201 (-0.517349) | 0.380775 / 0.579283 (-0.198508) | 0.384143 / 0.434364 (-0.050221) | 0.459954 / 0.540337 (-0.080383) | 0.549335 / 1.386936 (-0.837601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8805d67bd81ce48f481d5c1e56b84e6ebcaa2b2b \"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) | 0.009570 / 0.011353 (-0.001783) | 0.005219 / 0.011008 (-0.005789) | 0.098472 / 0.038508 (0.059964) | 0.035429 / 0.023109 (0.012320) | 0.303086 / 0.275898 (0.027188) | 0.365926 / 0.323480 (0.042446) | 0.008797 / 0.007986 (0.000811) | 0.004220 / 0.004328 (-0.000108) | 0.076670 / 0.004250 (0.072419) | 0.045596 / 0.037052 (0.008543) | 0.309476 / 0.258489 (0.050987) | 0.343958 / 0.293841 (0.050117) | 0.038741 / 0.128546 (-0.089805) | 0.011990 / 0.075646 (-0.063657) | 0.332326 / 0.419271 (-0.086945) | 0.048897 / 0.043533 (0.005364) | 0.296002 / 0.255139 (0.040863) | 0.322048 / 0.283200 (0.038849) | 0.104403 / 0.141683 (-0.037280) | 1.461777 / 1.452155 (0.009622) | 1.516362 / 1.492716 (0.023645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201565 / 0.018006 (0.183559) | 0.435781 / 0.000490 (0.435291) | 0.004215 / 0.000200 (0.004015) | 0.000282 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027272 / 0.037411 (-0.010139) | 0.106157 / 0.014526 (0.091631) | 0.116948 / 0.176557 (-0.059609) | 0.160404 / 0.737135 (-0.576731) | 0.122518 / 0.296338 (-0.173820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397721 / 0.215209 (0.182512) | 3.966433 / 2.077655 (1.888778) | 1.755410 / 1.504120 (0.251290) | 1.566480 / 1.541195 (0.025285) | 1.623684 / 1.468490 (0.155194) | 0.696820 / 4.584777 (-3.887957) | 3.750437 / 3.745712 (0.004725) | 2.105875 / 5.269862 (-3.163986) | 1.442026 / 4.565676 (-3.123650) | 0.085026 / 0.424275 (-0.339249) | 0.012239 / 0.007607 (0.004632) | 0.502613 / 0.226044 (0.276569) | 5.049016 / 2.268929 (2.780087) | 2.314499 / 55.444624 (-53.130126) | 1.967943 / 6.876477 (-4.908534) | 2.033507 / 2.142072 (-0.108565) | 0.861908 / 4.805227 (-3.943319) | 0.167784 / 6.500664 (-6.332880) | 0.063022 / 0.075469 (-0.012447) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210434 / 1.841788 (-0.631353) | 14.979319 / 8.074308 (6.905011) | 14.095263 / 10.191392 (3.903871) | 0.174203 / 0.680424 (-0.506221) | 0.028547 / 0.534201 (-0.505654) | 0.442509 / 0.579283 (-0.136774) | 0.445811 / 0.434364 (0.011447) | 0.531313 / 0.540337 (-0.009024) | 0.636541 / 1.386936 (-0.750395) |\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) | 0.007341 / 0.011353 (-0.004012) | 0.005197 / 0.011008 (-0.005811) | 0.075413 / 0.038508 (0.036905) | 0.033261 / 0.023109 (0.010152) | 0.339596 / 0.275898 (0.063698) | 0.376051 / 0.323480 (0.052571) | 0.005827 / 0.007986 (-0.002159) | 0.005473 / 0.004328 (0.001144) | 0.074851 / 0.004250 (0.070600) | 0.049059 / 0.037052 (0.012007) | 0.357182 / 0.258489 (0.098693) | 0.384589 / 0.293841 (0.090748) | 0.037122 / 0.128546 (-0.091424) | 0.012298 / 0.075646 (-0.063348) | 0.088191 / 0.419271 (-0.331081) | 0.052002 / 0.043533 (0.008469) | 0.343216 / 0.255139 (0.088077) | 0.364534 / 0.283200 (0.081334) | 0.105462 / 0.141683 (-0.036221) | 1.486717 / 1.452155 (0.034562) | 1.584725 / 1.492716 (0.092009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199210 / 0.018006 (0.181203) | 0.439069 / 0.000490 (0.438580) | 0.000436 / 0.000200 (0.000236) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029931 / 0.037411 (-0.007480) | 0.109564 / 0.014526 (0.095038) | 0.122284 / 0.176557 (-0.054273) | 0.170819 / 0.737135 (-0.566317) | 0.125886 / 0.296338 (-0.170452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422724 / 0.215209 (0.207515) | 4.210304 / 2.077655 (2.132650) | 2.001481 / 1.504120 (0.497361) | 1.810818 / 1.541195 (0.269623) | 1.901367 / 1.468490 (0.432877) | 0.686004 / 4.584777 (-3.898773) | 3.768850 / 3.745712 (0.023138) | 2.079501 / 5.269862 (-3.190360) | 1.326970 / 4.565676 (-3.238706) | 0.085991 / 0.424275 (-0.338284) | 0.012298 / 0.007607 (0.004690) | 0.526878 / 0.226044 (0.300833) | 5.267241 / 2.268929 (2.998312) | 2.451781 / 55.444624 (-52.992843) | 2.109143 / 6.876477 (-4.767333) | 2.185426 / 2.142072 (0.043353) | 0.830165 / 4.805227 (-3.975063) | 0.166167 / 6.500664 (-6.334497) | 0.064077 / 0.075469 (-0.011392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270430 / 1.841788 (-0.571358) | 14.844852 / 8.074308 (6.770544) | 13.196672 / 10.191392 (3.005280) | 0.162853 / 0.680424 (-0.517571) | 0.017727 / 0.534201 (-0.516474) | 0.424803 / 0.579283 (-0.154480) | 0.439970 / 0.434364 (0.005606) | 0.530691 / 0.540337 (-0.009647) | 0.630474 / 1.386936 (-0.756462) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24fb01b720ef4203d4ae6225f43cba912b1f6d55 \"CML watermark\")\n"
] |
1,607,054,110
| 5,602
|
Return dict structure if columns are lists - to_tf_dataset
|
open
| 2023-03-02T15:51:12
| 2023-04-12T15:54:53
| null |
https://github.com/huggingface/datasets/pull/5602
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5602",
"html_url": "https://github.com/huggingface/datasets/pull/5602",
"diff_url": "https://github.com/huggingface/datasets/pull/5602.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5602.patch",
"merged_at": null
}
|
amyeroberts
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.",
"This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was that if the user passed a single column name in either `columns` or `label_cols`, we converted it to a length-1 list. Then, later in the code, we convert output dicts with only one key to naked Tensors.\r\n\r\nWould it be easier if we removed the argument, but instead treated the cases differently? Passing a column name as a string could yield a single naked Tensor in the output as before, but passing a list of length 1 would yield a full dict? That way if you wanted dict output with a single key you could just say `columns=[col_name]`.\r\n\r\n(I'm not totally convinced this is a good idea yet, it just seems like it might be more intuitive)",
"@Rocketknight1 Happy to implement it that way - it's certainly cleaner to not have another arg. In this case, am I right in saying we'd effectively set `return_dict` [here](https://github.com/huggingface/datasets/blob/6569014a9948eab7d031a3587405e64ba92d6c59/src/datasets/arrow_dataset.py#L410) - where columns are made into a list if they were a string? \r\n\r\nThere only concern I have is this changes the default behaviour, which might break things for people who were happily using `columns=[\"my_col_str\"]` before. \r\n\r\n\r\n",
"@amyeroberts That's correct! Probably the simplest way to implement it would be to just add the flag there.\r\n\r\nAnd yeah, I'm aware this might be a slightly breaking change, but we've mostly tried to move users to `prepare_tf_dataset` in `transformers` at this point, so hopefully as long as that method doesn't break then most users won't be negatively affected by the change.",
"@lhoestq @Rocketknight1 - I've remove the `return_dict` argument and implemented @Rocketknight1 's suggestion. LMK what you think :) ",
"@lhoestq Of course :) I've opened a draft PR here for the updates needed in transformers examples and docs to keep the returned data structure consistent: https://github.com/huggingface/transformers/pull/21935. Note: even with the different structure, `model.fit` can still successfully be called. \r\n\r\nFor the [link you shared](https://github.com/huggingface/datasets/pull/url) - for me it returns a 404 error. Is there another link I could follow to see how to run the transformers CI with this branch? \r\n\r\nCurrently looking into the failing tests 😭 ",
"Oh sorry - I fixed the URL: https://github.com/huggingface/transformers/commit/4eb55bbd593adf2e49362613ee32a11ddc4a854d",
"The error shows `There appear to be 80 leaked shared_memory objects to clean up at shutdown`. IIRC to_tf_dataset does some shared memory stuff for multiprocessing - maybe @Rocketknight1 you know what's going on ?",
"@lhoestq That warning appears anytime you interrupt a process using Python `SharedMemory` objects - it's only a problem if you still get the error when the process finishes normally! Our implementation of `to_tf_dataset` should clean things up properly.",
"Ok, not sure why it fails then :/",
"Hmm, will investigate! Sorry, I misread - I thought that warning was coming up in the context of another error",
"IMO outputing different types based on nuances in the input could confuse users.\r\n\r\nAlso, in the ideal scenario,`to_tf_function` should return a `tf.data.Dataset` that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors, and all the model-specific code should live in Transformers (e.g., in `prepare_tf_dataset`). So the goal would be to make `to_tf_dataset` more user-friendly, not more complex :).",
"I think we agree @mariosasko :) \r\n\r\n> Also, in the ideal scenario,to_tf_function should return a tf.data.Dataset that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors\r\n\r\nThis I'll leave for another PR as it's outside the scope of this one and @Rocketknight1 will have far more knowledge and ideas about what is possible\r\n\r\n> all the model-specific code should live in Transformers (e.g., in prepare_tf_dataset\r\n\r\nAgreed! This PR isn't really a model specific change - although it was highlighted when trying to train a model. We definitely want to move model specific things out of datasets as much as possible. \r\n\r\n> IMO outputing different types based on nuances in the input could confuse users.\r\n> So the goal would be to make to_tf_dataset more user-friendly, not more complex :).\r\n\r\nThe aim was to move more towards being able to return the dict of TF tensors you suggest, whilst maintaining backwards compatibility. Personally, I found it surprising to be returned a tuple structure when I was using `to_tf_dataset`. The aim was to make `to_tf_dataset` more user friendly, but I agree that it has the potential to be confusing. \r\n\r\nFor context, the thought process behind this design was to: \r\n* Not add even more arguments to `to_tf_dataset`. \r\n* Have a feature selection -> return type logic in keeping with `datasets` e.g. `dataset['train'][:10]['feat1']` returns a list of values, whereas `dataset['train'][:10]['feat1', 'feat2']` returns a dictionary. \r\n\r\nVery happy to add any suggestions or changes you might have about how to make this design better! :) \r\n",
"Hi ! Anything blocking here ? I'b be happy to help",
"Hi @lhoestq - sorry this hasn't been very active for the past ~1.5 weeks. There's nothing specific blocking, other than not being able to replicate without running on CI, and still need to test a bit more to narrow down the issue. I should have time tomorrow to pick it up again :) ",
"@lhoestq @Rocketknight1 Friendly ping for a review :) ",
"Awesome ! What about showing a warning that this change is about to happen in the next version of `datasets`, and then apply this change in a subsequent major release ? This way folks at twitter won't hate us: https://github.com/twitter/the-algorithm/blob/138bb519975407d4ea0dc1478d897d451ef05dab/trust_and_safety_models/toxicity/data/mb_generator.py#L142-L148",
"@lhoestq Sounds good! How would you like this warning to happen? I could open a PR to add a warning message within `to_tf_dataset`?",
"Yup sounds good :)"
] |
1,606,685,976
| 5,601
|
Authorization error
|
closed
| 2023-03-02T12:08:39
| 2023-03-14T16:55:35
| 2023-03-14T16:55:34
|
https://github.com/huggingface/datasets/issues/5601
| null |
OleksandrKorovii
| false
|
[
"Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.",
"Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now."
] |
1,606,585,596
| 5,600
|
Dataloader getitem not working for DreamboothDatasets
|
closed
| 2023-03-02T11:00:27
| 2023-03-13T17:59:35
| 2023-03-13T17:59:35
|
https://github.com/huggingface/datasets/issues/5600
| null |
salahiguiliz
| false
|
[
"Hi! \r\n\r\n> (see example of DreamboothDatasets)\r\n\r\n\r\nCould you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data."
] |
1,605,018,478
| 5,598
|
Fix push_to_hub with no dataset_infos
|
closed
| 2023-03-01T13:54:06
| 2023-03-02T13:47:13
| 2023-03-02T13:40:17
|
https://github.com/huggingface/datasets/pull/5598
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5598",
"html_url": "https://github.com/huggingface/datasets/pull/5598",
"diff_url": "https://github.com/huggingface/datasets/pull/5598.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5598.patch",
"merged_at": "2023-03-02T13:40:17"
}
|
lhoestq
| true
|
[
"_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) | 0.008823 / 0.011353 (-0.002529) | 0.004738 / 0.011008 (-0.006270) | 0.102338 / 0.038508 (0.063830) | 0.030603 / 0.023109 (0.007494) | 0.302995 / 0.275898 (0.027097) | 0.362080 / 0.323480 (0.038600) | 0.007096 / 0.007986 (-0.000889) | 0.003493 / 0.004328 (-0.000835) | 0.079129 / 0.004250 (0.074878) | 0.037966 / 0.037052 (0.000914) | 0.310412 / 0.258489 (0.051923) | 0.346740 / 0.293841 (0.052899) | 0.033795 / 0.128546 (-0.094751) | 0.011595 / 0.075646 (-0.064051) | 0.325189 / 0.419271 (-0.094083) | 0.041679 / 0.043533 (-0.001854) | 0.302339 / 0.255139 (0.047200) | 0.322519 / 0.283200 (0.039319) | 0.089058 / 0.141683 (-0.052625) | 1.496223 / 1.452155 (0.044068) | 1.512562 / 1.492716 (0.019845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009298 / 0.018006 (-0.008709) | 0.406726 / 0.000490 (0.406236) | 0.003753 / 0.000200 (0.003553) | 0.000082 / 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) | 0.023327 / 0.037411 (-0.014084) | 0.098175 / 0.014526 (0.083649) | 0.106040 / 0.176557 (-0.070516) | 0.151934 / 0.737135 (-0.585201) | 0.108465 / 0.296338 (-0.187873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419073 / 0.215209 (0.203864) | 4.188012 / 2.077655 (2.110358) | 1.857667 / 1.504120 (0.353547) | 1.664124 / 1.541195 (0.122929) | 1.704341 / 1.468490 (0.235851) | 0.699671 / 4.584777 (-3.885106) | 3.391110 / 3.745712 (-0.354602) | 1.871136 / 5.269862 (-3.398725) | 1.176794 / 4.565676 (-3.388882) | 0.083322 / 0.424275 (-0.340953) | 0.012450 / 0.007607 (0.004843) | 0.525058 / 0.226044 (0.299014) | 5.265425 / 2.268929 (2.996497) | 2.320672 / 55.444624 (-53.123952) | 1.964806 / 6.876477 (-4.911671) | 2.027055 / 2.142072 (-0.115017) | 0.819768 / 4.805227 (-3.985459) | 0.149638 / 6.500664 (-6.351026) | 0.064774 / 0.075469 (-0.010695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204575 / 1.841788 (-0.637212) | 13.651878 / 8.074308 (5.577570) | 13.751973 / 10.191392 (3.560581) | 0.154781 / 0.680424 (-0.525643) | 0.028887 / 0.534201 (-0.505314) | 0.404905 / 0.579283 (-0.174379) | 0.411320 / 0.434364 (-0.023043) | 0.485026 / 0.540337 (-0.055311) | 0.579690 / 1.386936 (-0.807246) |\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) | 0.006615 / 0.011353 (-0.004737) | 0.004606 / 0.011008 (-0.006402) | 0.076099 / 0.038508 (0.037591) | 0.027247 / 0.023109 (0.004137) | 0.360731 / 0.275898 (0.084833) | 0.393688 / 0.323480 (0.070208) | 0.005079 / 0.007986 (-0.002906) | 0.003345 / 0.004328 (-0.000984) | 0.077184 / 0.004250 (0.072934) | 0.037850 / 0.037052 (0.000797) | 0.379738 / 0.258489 (0.121249) | 0.400474 / 0.293841 (0.106633) | 0.031581 / 0.128546 (-0.096966) | 0.011508 / 0.075646 (-0.064138) | 0.084966 / 0.419271 (-0.334306) | 0.041740 / 0.043533 (-0.001793) | 0.349887 / 0.255139 (0.094748) | 0.384405 / 0.283200 (0.101205) | 0.089022 / 0.141683 (-0.052661) | 1.503448 / 1.452155 (0.051293) | 1.564870 / 1.492716 (0.072154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233581 / 0.018006 (0.215574) | 0.413819 / 0.000490 (0.413330) | 0.000398 / 0.000200 (0.000198) | 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) | 0.024805 / 0.037411 (-0.012607) | 0.101348 / 0.014526 (0.086822) | 0.108701 / 0.176557 (-0.067856) | 0.160011 / 0.737135 (-0.577124) | 0.111696 / 0.296338 (-0.184642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436303 / 0.215209 (0.221094) | 4.368684 / 2.077655 (2.291029) | 2.082366 / 1.504120 (0.578247) | 1.888108 / 1.541195 (0.346913) | 1.958295 / 1.468490 (0.489804) | 0.700858 / 4.584777 (-3.883919) | 3.408321 / 3.745712 (-0.337391) | 1.872960 / 5.269862 (-3.396902) | 1.165116 / 4.565676 (-3.400560) | 0.083556 / 0.424275 (-0.340719) | 0.012348 / 0.007607 (0.004741) | 0.536551 / 0.226044 (0.310506) | 5.359974 / 2.268929 (3.091045) | 2.539043 / 55.444624 (-52.905581) | 2.200314 / 6.876477 (-4.676162) | 2.222051 / 2.142072 (0.079979) | 0.808567 / 4.805227 (-3.996661) | 0.151222 / 6.500664 (-6.349442) | 0.066351 / 0.075469 (-0.009118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265502 / 1.841788 (-0.576286) | 13.692066 / 8.074308 (5.617758) | 13.124507 / 10.191392 (2.933115) | 0.129545 / 0.680424 (-0.550879) | 0.016827 / 0.534201 (-0.517374) | 0.380326 / 0.579283 (-0.198957) | 0.387268 / 0.434364 (-0.047096) | 0.463722 / 0.540337 (-0.076616) | 0.553681 / 1.386936 (-0.833255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6569014a9948eab7d031a3587405e64ba92d6c59 \"CML watermark\")\n"
] |
1,604,928,721
| 5,597
|
in-place dataset update
|
closed
| 2023-03-01T12:58:18
| 2023-03-02T13:30:41
| 2023-03-02T03:47:00
|
https://github.com/huggingface/datasets/issues/5597
| null |
speedcell4
| false
|
[
"We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.",
"Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?",
"Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example."
] |
1,604,919,993
| 5,596
|
[TypeError: Couldn't cast array of type] Can only load a subset of the dataset
|
closed
| 2023-03-01T12:53:08
| 2023-12-05T03:22:00
| 2023-03-02T11:12:11
|
https://github.com/huggingface/datasets/issues/5596
| null |
loubnabnl
| false
|
[
"Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data",
"We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!",
"A similar error occurs in the Pile dataset (EleutherAI/the_pile)\r\n\r\nLoading the dataset produces the following error.\r\n\r\n```\r\nTypeError: Couldn't cast array of type\r\nstruct<file: string, id: string>\r\nto\r\n{'id': Value(dtype='string', id=None)}\r\n```\r\n",
"I think this was fixed in https://huggingface.co/datasets/EleutherAI/the_pile/discussions/11",
"i have the same problem ，how to solve :\r\n raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\nTypeError: Couldn't cast array of type\r\nlist<item: string>\r\nto\r\n{'content': Value(dtype='string', id=None), 'role': Value(dtype='string', id=None)}"
] |
1,604,070,629
| 5,595
|
Unpins sqlAlchemy
|
closed
| 2023-03-01T01:33:45
| 2023-04-04T08:20:19
| 2023-04-04T08:19:14
|
https://github.com/huggingface/datasets/pull/5595
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5595",
"html_url": "https://github.com/huggingface/datasets/pull/5595",
"diff_url": "https://github.com/huggingface/datasets/pull/5595.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5595.patch",
"merged_at": null
}
|
lazarust
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5595). All of your documentation changes will be reflected on that endpoint.",
"It looks like this issue hasn't been fixed yet, so let's wait a bit more.",
"@lazarust thanks for your work, but unfortunately we cannot merge it.\r\n\r\nSee my comment in: https://github.com/huggingface/datasets/issues/5477#issuecomment-1495512688\r\n\r\nThe fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:\r\n- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0\r\n\r\nbut it will not be back-ported to `pandas-1`:\r\n- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159\r\n\r\nAlso note that `pandas-2.0.0` dropped support for Python 3.7:\r\n- https://github.com/pandas-dev/pandas/issues/41678\r\n- https://github.com/pandas-dev/pandas/pull/41989\r\n\r\nTherefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`). See our latest CI checks below:\r\n- \"CI / test\" fails because it runs on Python 3.7\r\n- \"CI / test_py310\" succeeds because it runs on Python 3.10 "
] |
1,603,980,995
| 5,594
|
Error while downloading the xtreme udpos dataset
|
closed
| 2023-02-28T23:40:53
| 2023-11-04T20:45:56
| 2023-07-24T14:22:18
|
https://github.com/huggingface/datasets/issues/5594
| null |
simran-khanuja
| false
|
[
"Hi! I cannot reproduce this error on my machine.\r\n\r\nThe raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows:\r\n```python\r\ntrain_dataset = load_dataset('xtreme', 'udpos.English', split=\"train\", cache_dir=args.cache_dir, download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n```",
"Hi! Apologies for the delayed response! I tried the above and it doesn't solve the issue. Actually, the dataset gets downloaded most times, but sometimes this error occurs (at random afaik). Is it possible that there is a server issue for this particular dataset? I am able to download other datasets using the same code on the same machine with no issues :( I get this error now : \r\n```\r\nDownloading data: 16%|███████████████▌ | 55.9M/355M [04:45<25:25, 196kB/s]\r\nTraceback (most recent call last):\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 1107, in <module>\r\n main()\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 439, in main\r\n en_dataset = load_dataset(\"xtreme\", \"udpos.English\", split=\"train\", download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 949, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/utils/info_utils.py\", line 62, in verify_checksums\r\n raise NonMatchingChecksumError(\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz']\r\nSet `verification_mode='no_checks'` to skip checksums verification and ignore this error\r\n```",
"If this happens randomly, then this means the data file from the error message is not always downloaded correctly. \r\n\r\nThe only solution in this scenario is to download the dataset again by passing `download_mode=\"force_redownload\"` to the `load_dataset` call.",
"Wow. I effectively have to redownload a dataset of 1TB because of this now?\r\nBecause 3% of its parts are broken?\r\n\r\nWhy is this downloader library so sh*t and badly documented also? I found almost nothing on the net, at least finally this issue about the problem here.\r\nNo words to express how disappointed I am by that dataset tool provided by Huggingface here, which I sadly have to use because HF is the only place where the Dataset I plan to work with is hosted....\r\n\r\nI mean... checksum check after download... or hitting timeout of a part... and redownload if not matching... that's content of every junior developer training session.\r\n\r\nI added `verification_mode=\"all_checks\"`. And it really calculated checksums for 4096 parts of ~350 MB... But then did nothing and tried to extract still, hitting the error again. \r\n\r\nEDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads`",
"I'm getting it too, although just retrying fixed it. Nevertheless, the dataset is too large to have re-downloaded the whole thing, for it's probably just one file with an issue. It would be good to know if there's a way people could manually examine the files (first for sizes, then possibly checksums)... going to the web or elsewhere to compare and correct it by hand, if ever needed.",
"Okay, no, it got further but it is repeatedly giving me:\r\n```/home/jaggz/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py\", line 195, in _generate_examples\r\nresult[\"audio\"] = {\"path\": path, \"bytes\": file.read()}\r\n^^^^^^^^^^^\r\nFile \"/usr/lib/python3.11/tarfile.py\", line 687, in read\r\nraise ReadError(\"unexpected end of data\")\r\ntarfile.ReadError: unexpected end of data\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\nFile \"/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 625, in <module>\r\nmain()\r\nFile \"/home/jaggz/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 360, in main\r\nraw_datasets[\"train\"] = load_dataset(\r\n^^^^^^^^^^^^^\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/load.py\", line 2153, in load_dataset\r\nbuilder_instance.download_and_prepare(\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py\", line 954, in download_and_prepare\r\nself._download_and_prepare(\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py\", line 1717, in _download_and_prepare\r\nsuper()._download_and_prepare(\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py\", line 1049, in _download_and_prepare\r\nself._prepare_split(split_generator, **prepare_split_kwargs)\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py\", line 1555, in _prepare_split\r\nfor job_id, done, content in self._prepare_split_single(\r\nFile \"/home/jaggz/venvs/pynow/lib/python3.11/site-packages/datasets/builder.py\", line 1712, in _prepare_split_single\r\nraise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the datase\r\n",
"@RuntimeRacer \r\n> EDIT: Apparently it is able to fix it by getting a little help: Just delete the broken parts and associated files from `~/.cache/huggingface/datasets/downloads`\r\n\r\nHow do you know the broken parts?\r\nMine's consistently erroring and.. yeah, really this thing should be able to check the files (but where's that even done)...\r\n\r\n2023-11-02 00:14:09.846055: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py:299: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.\r\n warnings.warn(\r\n11/02/2023 00:14:37 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: True\r\n11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\n...\r\nlogging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys,\r\n...\r\nrun_name=./whisper-tiny-en,\r\n...\r\nweight_decay=0.0,\r\n)\r\n11/02/2023 00:14:37 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\n...\r\nlogging_dir=./whisper-tiny-en/runs/Nov02_00-14-28_jsys,\r\n...\r\nweight_decay=0.0,\r\n)\r\n\r\nDownloading data files: 0%| | 0/5 [00:00<?, ?it/s]\r\nDownloading data files: 100%|██████████| 5/5 [00:00<00:00, 2426.42it/s]\r\n\r\nExtracting data files: 0%| | 0/5 [00:00<?, ?it/s]\r\nExtracting data files: 100%|██████████| 5/5 [00:00<00:00, 421.16it/s]\r\n\r\nDownloading data files: 0%| | 0/5 [00:00<?, ?it/s]\r\nDownloading data files: 100%|██████████| 5/5 [00:00<00:00, 18707.87it/s]\r\n\r\nExtracting data files: 0%| | 0/5 [00:00<?, ?it/s]\r\nExtracting data files: 100%|██████████| 5/5 [00:00<00:00, 3754.97it/s]\r\n\r\nGenerating train split: 0 examples [00:00, ? examples/s]\r\n\r\nReading metadata...: 0it [00:00, ?it/s]\u001b[A\r\n...\r\nReading metadata...: 948736it [00:23, 40632.92it/s] \r\n\r\nGenerating train split: 1 examples [00:23, 23.37s/ examples]\r\n...\r\nGenerating train split: 948736 examples [08:28, 1866.15 examples/s]\r\n\r\nGenerating validation split: 0 examples [00:00, ? examples/s]\r\n\r\nReading metadata...: 0it [00:00, ?it/s]\u001b[A\r\n\r\nReading metadata...: 16089it [00:00, 157411.88it/s]\u001b[A\r\nReading metadata...: 16354it [00:00, 158233.27it/s]\r\n\r\nGenerating validation split: 1 examples [00:00, 7.60 examples/s]\r\nGenerating validation split: 16354 examples [00:14, 1154.77 examples/s]\r\n\r\nGenerating test split: 0 examples [00:00, ? examples/s]\r\n\r\nReading metadata...: 0it [00:00, ?it/s]\u001b[A\r\nReading metadata...: 16354it [00:00, 194855.03it/s]\r\n\r\nGenerating test split: 1 examples [00:00, 4.53 examples/s]\r\nGenerating test split: 16354 examples [00:07, 2105.43 examples/s]\r\n\r\nGenerating other split: 0 examples [00:00, ? examples/s]\r\n\r\nReading metadata...: 0it [00:00, ?it/s]\u001b[A\r\nReading metadata...: 290846it [00:01, 235823.90it/s]\r\n\r\nGenerating other split: 1 examples [00:01, 1.27s/ examples]\r\n...\r\nGenerating other split: 290846 examples [02:12, 2196.96 examples/s]\r\nGenerating invalidated split: 0 examples [00:00, ? examples/s]\r\nReading metadata...: 252599it [00:01, 241965.85it/s]\r\n\r\nGenerating invalidated split: 1 examples [00:01, 1.08s/ examples]\r\n...\r\nGenerating invalidated split: 60130 examples [00:34, 1764.14 examples/s]\r\nTraceback (most recent call last):\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 1676, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py\", line 195, in _generate_examples\r\n result[\"audio\"] = {\"path\": path, \"bytes\": file.read()}\r\n ^^^^^^^^^^^\r\n File \"/usr/lib/python3.11/tarfile.py\", line 687, in read\r\n raise ReadError(\"unexpected end of data\")\r\ntarfile.ReadError: unexpected end of 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 \"/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 625, in <module>\r\n main()\r\n File \"/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 360, in main\r\n raw_datasets[\"train\"] = load_dataset(\r\n ^^^^^^^^^^^^^\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/load.py\", line 2153, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 954, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 1717, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 1049, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 1555, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/j/venvs/pycur/lib/python3.11/site-packages/datasets/builder.py\", line 1712, 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",
"@jaggzh Hi, I actually came around with a fix for this, wasn't that easy to solve since there were a lot of hidden pitfalls in the code, and it's quite hacky, but I was able to download the full dataset.\r\n\r\nI just didn't create a PR for it yet since I was too lazy to create a fork and change my local repo's origin. 😅 \r\nLet me try to do this tonight, I'll give you a ping once it's up.\r\n\r\nEDIT: And no, what I wrote above about adding a param to the download config does NOT solve it apparently. A code fix is required here.",
"@jaggzh PR is up: https://github.com/huggingface/datasets/pull/6380\r\n\r\n🤞 on approval for merge to the main repo.",
"@mariosasko Can you re-open this? We really need some better diagnostics output, at the least, to locate which files are contributing, some checksum output, etc. I can't even tell if this is a mozilla...py issue or huggingface datasets or ....",
"@RuntimeRacer \r\nBeautiful, thank you so much. I patched with your PR and am re-running now.\r\n(I'm running this script: https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py)\r\nOkay, actually it failed; so now I'm running with verification_mode='all_checks' added to the load_data() call and it's re-running now. Wish me luck.\r\n(Note: It's generating checksums; I don't see an option that handles anything between basic_checks and all_checks -- Something checking dl'ed files' lengths would be a good common fix I'd think; corruption is more rare nowadays than a short file (although maybe your patch helps prevent that in the first place.) :}",
"@RuntimeRacer \r\nNo luck. Sigh.\r\n[Edit: My tmux copy didn't get some data. That was weird. I'm adding in the initial part of the output:]\r\n```\r\nDownloading data files: 100%|██████████| 5/5 [00:00<00:00, 2190.69it/s]\r\nComputing checksums: 100%|██████████| 41/41 [11:39<00:00, 17.05s/it] Extracting data files: 100%|██████████| 5/5 [00:00<00:00, 12.37it/s]\r\nDownloading data files: 100%|██████████| 5/5 [00:00<00:00, 107.64it/s]\r\nExtracting data files: 100%|██████████| 5/5 [00:00<00:00, 3149.82it/s]\r\nReading metadata...: 948736it [00:03, 243227.36it/s]s/s]\r\n...\r\n```\r\n```\r\n...\r\nReading metadata...: 252599it [00:01, 249267.71it/s]xamples/s]\r\nGenerating invalidated split: 60130 examples [00:31, 1916.33 examples/s]\r\nTraceback (most recent call last):\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 1676, in _prepare_split_single\r\nfor key, record in generator:\r\nFile \"/home/j/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/common_voice_11_0.py\", line 195, in _generate_examples\r\nresult[\"audio\"] = {\"path\": path, \"bytes\": file.read()}\r\n^^^^^^^^^^^\r\nFile \"/usr/lib/python3.11/tarfile.py\", line 687, in read\r\nraise ReadError(\"unexpected end of data\")\r\ntarfile.ReadError: unexpected end of data\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\nFile \"/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 627, in <module>\r\nmain()\r\nFile \"/home/j/src/transformers/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py\", line 360, in main\r\nraw_datasets[\"train\"] = load_dataset(\r\n^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/load.py\", line 2153, in load_dataset\r\nbuilder_instance.download_and_prepare(\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 954, in download_and_prepare\r\nself._download_and_prepare(\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 1717, in _download_and_prepare\r\nsuper()._download_and_prepare(\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 1049, in _download_and_prepare\r\nself._prepare_split(split_generator, **prepare_split_kwargs)\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 1555, in _prepare_split\r\nfor job_id, done, content in self._prepare_split_single(\r\nFile \"/home/j/src/py/datasets/src/datasets/builder.py\", line 1712\r\n```",
"I'm unable to reproduce this error. Based on https://github.com/psf/requests/issues/4956, newer releases of `urllib3` check the returned content length by default, so perhaps updating `requests` and `urllib3` to the latest versions (`pip install -U requests urllib3`) and loading the dataset with `datasets.load_dataset(\"xtreme\", \"udpos.English\", download_config=datasets.DownloadConfig(resume_download=True))` (re-run when it fails to resume the download) can fix the issue.",
"@jaggzh I think you will need to re-download the whole dataset with my patched code. Files which have already been downloaded and marked as complete by the broken downloader won't be detected even on re-run (I described that in the PR).\r\nI also had to download reazonspeech, which is over 1TB, twice. 🙈 \r\nFor re-download, you need to manually delete the dataset files from your local machine's huggingface download cache.\r\n\r\n@mariosasko Not sure how you tested it, but it's not an issue in `requests` or `urllib`. The problem is the huggingface downloader, which generates a nested download thread for the actual download I think.\r\nThe issue I had with the reazonspeech dataset (https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) basically was, that it started downloading a part, but sometimes the connection would 'starve' and only continue with a few kilobytes, and eventually stop receiving any data at all.\r\nSometimes it would even recover during the download and finish properly.\r\nHowever, if it did not recover, the request would hit the really generous default timeout (which is 100 seconds I think), however the exception thrown by the failure inside `urllib`, isn't captured or handled by the upper level downloader code of the `datasets` library.\r\n`datasets` even has a retry mechanism, which would continue interrupted downloads if they have the `.incomplete` suffix, which isn't cleared if, for example, a manual `CTRL+C` is sent by the user to the python process.\r\nBut: If it runs into that edge case I described above (TL;DR: connection starves after minutes + timeout exception which isn't captured), the cache downloader will consider the download as successful and remove the `.incomplete` suffix nevertheless, leaving the archive file in a corrupted state.\r\n\r\nHonestly, I spent hours on trying to figure out what was even going on and why the retry mechanics of the cache downloader didn't work at all.\r\nBut it is indeed an issue caused by the download process itself not receiving any info about actual content size and filesize size on disk of the archive to be downloaded, thus, having no direct control in case something fails on the request level.\r\n\r\nIMHO, this requires a major refactor of the way this part of the downloader works.\r\nYet I was able to quick-fix it by adding some synthetic Exception handling and explicit retry-handling in the code, als done in my PR.",
"@RuntimeRacer \r\nUgh. It took a day. I'm seeing if I can get some debug code in here to examine the files myself. (I'm not sure why checksum tests would fail, so, yeah, I think you're right -- this stuff needs some work. Going through ipdb right now to try to get some idea of what's going on in the code).",
"@RuntimeRacer Data can only be appended to the `.incomplete` files if `load_dataset` is called with `download_config=DownloadConfig(resume_download=True)`. \r\n\r\nWhere exactly does this exception happen (in the code)? The error stack trace would help a lot.",
"@mariosasko I do not have a trace of this exception nor do I know which type it is. I am honestly not even sure if an exception is thrown, or the process just aborts without error.\r\n\r\n> @RuntimeRacer Data can only be appended to the .incomplete files if load_dataset is called with download_config=DownloadConfig(resume_download=True).\r\n\r\nWell, I think I did a very clear explaination of the issue in the PR I shared, and the description above, but maybe I wasn't precise enough. Let me try to explain once more:\r\n\r\nWhat you mention here is the \"normal\" case, if the process is aborted. In this case, there will be files with `.incomplete` suffix, which the cache downloader can continue to download. That is correct.\r\n\r\nBUT: What I am talking about all the time is an edge case: if the download step crashes / timeouts internally, the cache downloader will NOT be aware of this, and REMOVES the `.incomplete` suffix.\r\nIt does NOT know that the file is incomplete when the `http_get` function returns and will remove the `.incomplete` suffix in any case once `http_get` returns.\r\nBut the problem is that `http_get` returns without failure, even if the download failed.\r\nAnd this is still a problem even with latest `urllib` and `requests` library.\r\n",
"@RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post. \r\n\r\nHowever, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to https://github.com/huggingface/huggingface_hub/pull/1766 when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right?\r\n\r\n@jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets.",
"(I don't have any .incomplete files, just the extraction errors.)\r\nI was going through the code to try to relate filenames to the hex/hash files, but realized I might not need to.\r\nSo instead I coded up a script in bash to examine the tar files for validity (had an issue with bash subshells not adding to my array so I had cgpt recode it in perl).\r\n\r\n```perl\r\n#!/usr/bin/perl\r\nuse strict;\r\nuse warnings;\r\n\r\n# Initialize the array to store tar files\r\nmy @tars;\r\n\r\n# Open the current directory\r\nopendir(my $dh, '.') or die \"Cannot open directory: $!\";\r\n\r\n# Read files in the current directory\r\nwhile (my $f = readdir($dh)) {\r\n # Skip files ending with lock, json, or py\r\n next if $f =~ /\\.(lock|json|py)$/;\r\n\r\n # Use the `file` command to determine the type of file\r\n my $ft = `file \"$f\"`;\r\n\r\n # If it's a tar archive, add it to the list\r\n if ($ft =~ /tar archive/) {\r\n push @tars, $f;\r\n }\r\n}\r\n\r\nclosedir($dh);\r\n\r\nprint \"Final Tars count: \" . scalar(@tars) . \"\\n\";\r\n\r\n# Iterate over the tar files and check them\r\nforeach my $i (0 .. $#tars) {\r\n my $f = $tars[$i];\r\n printf '%d/%d ', $i+1, scalar(@tars);\r\n \r\n # Use `ls -lgG` to list the files, similar to the original bash script\r\n system(\"ls -lgG '$f'\");\r\n\r\n # Check the integrity of the tar file\r\n my $errfn = \"/tmp/$f.tarerr\";\r\n if (system(\"tar tf '$f' > /dev/null 2> '$errfn'\") != 0) {\r\n print \" BAD $f\\n\";\r\n print \" ERR: \";\r\n system(\"cat '$errfn'\");\r\n }\r\n\r\n # Remove the error file if it exists\r\n unlink $errfn if -e $errfn;\r\n}\r\n```\r\n\r\nThis found one hash file that errored in the tar extraction, and one small tmp* file that also was supposedly a tar and was erroring. I removed those two and re-data loaded.. it grabbed just what it needed and I'm on my way. Yay!\r\n\r\nSo... is there a way for the datasets api to get file sizes? That would be a very easy and fast test, leaving checksum slowdowns for extra-messed-up situations.\r\n\r\n",
"> @RuntimeRacer Updating `urllib3` and `requests` to the latest versions fixes the issue explained in this [blog](https://blog.petrzemek.net/2018/04/22/on-incomplete-http-reads-and-the-requests-library-in-python/) post.\r\n> \r\n> However, the issue explained above seems more similar to [this](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) one. To address it, we can reduce the default timeout to 10 seconds (btw, this was the initial value, but it was causing problems for some users) and expose a config variable so that users can easily control it. Additionally, we can re-run `http_get` similarly to [huggingface/huggingface_hub#1766](https://github.com/huggingface/huggingface_hub/pull/1766) when the connection/timeout error happens to make the logic even more robust. Would this work for you? The last part is what you did in the PR, right?\r\n> \r\n> @jaggzh From all the datasets mentioned in this issue, `xtreme` is the only one that stores the data file checksums in the metadata. So, the checksum check has no effect when enabled for the rest of the datasets.\r\n\r\n@mariosasko Well if you look at my commit date, you will see that I run into this problem still in October. The blog post you mention and the update in the pull request for `urllib` was from July: https://github.com/psf/requests/issues/4956#issuecomment-1648632935\r\n\r\nBut yeah the [issue on StackOverflow](https://stackoverflow.com/questions/52731196/python-3-6-5-requests-with-streaming-getting-stuck-in-iter-content-even-if-chun) you mentioned seems like that's the source issue I was running into there.\r\nI experimented with timeouts, but changing them didn't help to resolve the issue of the starving connection unfortunately.\r\nHowever, https://github.com/huggingface/huggingface_hub/pull/1766 seems like that could be working; it's very similar to my change. So yeah I think this would fix it probably.\r\n\r\nAlso I can confirm the checksum option did not work for [reazonspeech](https://huggingface.co/datasets/reazon-research/reazonspeech/tree/main) as well. So maybe it's a double edge case that only occurs for some datasets. 🤷♂️ ",
"Also, the hf urls to files -- while I can't see a way of getting a listing from the hf site side -- do include the file size in the http header response. So we do have a quick way of just verifying lengths for resume. (This message may not be interesting to you all).\r\n\r\nFirst, a json clip (mozilla-foundation___common_voice_11_0/en/11.0.0/3f27acf10f303eac5b6fbbbe02495aeddb46ecffdb0a2fe3507fcfbf89094631/dataset_info.json):\r\n\r\n* I don't know how specific this .json is to mozilla common voice\r\n* Note that *dataset_size* is not the dataset size :) DatasetInfo class docs indicate it might be their \"combined size in bytes of the Arrow tables for all splits.\"\r\n* *num_bytes*: does match the individual file size though, and matches the http header (further down)\r\n```\r\n{\r\n \"builder_name\" : \"common_voice_11_0\",\r\n...\r\n \"config_name\" : \"en\",\r\n \"dataset_name\" : \"common_voice_11_0\",\r\n \"dataset_size\" : 1680793952,\r\n...\r\n \"download_checksums\" : {\r\n...\r\n \"https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar\" : {\r\n \"checksum\" : null,\r\n \"num_bytes\" : 2110853120\r\n },\r\n...\r\n```\r\n\r\n```bash\r\n~/.cache/huggingface/datasets/downloads$ ls -lgG b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40* | cut -c 14-\r\n```\r\n```\r\n2110853120 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40\r\n148 Nov 1 16:28 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.json\r\n0 Nov 1 16:07 b45f82cb87bab2c35361857fcd46042ab658b42c37dc9a455248c2866c9b8f40.lock\r\n```\r\n\r\n* Note the -L to follow redirects. Two headers are below:\r\n\r\n```bash\r\n$ curl -I -L https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/resolve/main/audio/en/invalidated/en_invalidated_3.tar\r\n```\r\n```\r\nHTTP/2 302 \r\ncontent-type: text/plain; charset=utf-8\r\ncontent-length: 1215\r\nlocation: https://cdn-lfs.huggingface.co/repos/00/ce/00ce867b4ae70bd23a10b60c32a8626d87b2666fc088ad03f86b94788faff554/984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27en_invalidated_3.tar%3B+filename%3D%22en_invalidated_3.tar%22%3B&response-content-type=application%2Fx-tar&Expires=1699389040&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5OTM4OTA0MH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8wMC9jZS8wMGNlODY3YjRhZTcwYmQyM2ExMGI2MGMzMmE4NjI2ZDg3YjI2NjZmYzA4OGFkMDNmODZiOTQ3ODhmYWZmNTU0Lzk4NDA4NmZjMjUwYmFkZWNlMjk5MmU4YmU0ZDdjNDQzMGY3YzEyMDhmYjhiZjM3ZGM3YzRhZWNkYzgwM2IyMjA%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=WYc32e75PqbKSAv3KTpG86ooFT6oOyDDQpCt1i2B8gVS10J3qvpZlDmxaBgnGlCCl7SRiAvhIQctgwooNtWbUeDqK3T4bAo0-OOrGCuVi-%7EKWUBcoHce7nHWpl%7Ex9ubHS%7EFoYcGB2SCEqh5fIgGjNV-VKRX6TSXkRto5bclQq4VCJKHufDsJ114A1V4Qu%7EYiRIWKG4Gi93Xv4OFhyWY0uqykvP5c0x02F%7ELX0m3WbW-eXBk6Fw2xnV1XLrEkdR-9Ax2vHqMYIIw6yV0wWEc1hxE393P9mMG1TNDj%7EXDuCoOaA7LbrwBCxai%7Ew2MopdPamTXyOia5-FnSqEdsV29v4Q__&Key-Pair-Id=KVTP0A1DKRTAX\r\ndate: Sat, 04 Nov 2023 20:30:40 GMT\r\nx-powered-by: huggingface-moon\r\nx-request-id: Root=1-6546a9f0-5e7f729d09bdb38e35649a7e\r\naccess-control-allow-origin: https://huggingface.co\r\nvary: Origin, Accept\r\naccess-control-expose-headers: X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,ETag,Link,Accept-Ranges,Content-Range\r\nx-repo-commit: 23b4059922516c140711b91831aa3393a22e9b80\r\naccept-ranges: bytes\r\nx-linked-size: 2110853120\r\nx-linked-etag: \"984086fc250badece2992e8be4d7c4430f7c1208fb8bf37dc7c4aecdc803b220\"\r\nx-cache: Miss from cloudfront\r\nvia: 1.1 f31a6426ebd75ce4393909b12f5cbdcc.cloudfront.net (CloudFront)\r\nx-amz-cf-pop: LAX53-P4\r\nx-amz-cf-id: BcYMFcHVcxPome2IjAvx0ZU90G41QlNI_HEHDGDqCQaEPvrOsnsGXw==\r\n\r\nHTTP/2 200 \r\ncontent-type: application/x-tar\r\ncontent-length: 2110853120\r\ndate: Sat, 04 Nov 2023 20:19:35 GMT\r\nlast-modified: Fri, 18 Nov 2022 15:08:22 GMT\r\netag: \"acac28988e2f7e73b68e865179fbd008\"\r\nx-amz-storage-class: INTELLIGENT_TIERING\r\nx-amz-version-id: LgTuOcd9FGN4JnAXp26O.1v2VW42GPtF\r\ncontent-disposition: attachment; filename*=UTF-8''en_invalidated_3.tar; filename=\"en_invalidated_3.tar\";\r\naccept-ranges: bytes\r\nserver: AmazonS3\r\nx-cache: Hit from cloudfront\r\nvia: 1.1 d07c8167eda81d307ca96358727f505e.cloudfront.net (CloudFront)\r\nx-amz-cf-pop: LAX50-P5\r\nx-amz-cf-id: 6oNZg_V8U1M_JXsMHQAPuRmDfxbY2BnMUWcVH0nz3VnfEZCzF5lgkQ==\r\nage: 666\r\ncache-control: public, max-age=604800, immutable, s-maxage=604800\r\nvary: Origin\r\n\r\n```\r\n"
] |
1,603,619,124
| 5,592
|
Fix docstring example
|
closed
| 2023-02-28T18:42:37
| 2023-02-28T19:26:33
| 2023-02-28T19:19:15
|
https://github.com/huggingface/datasets/pull/5592
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5592",
"html_url": "https://github.com/huggingface/datasets/pull/5592",
"diff_url": "https://github.com/huggingface/datasets/pull/5592.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5592.patch",
"merged_at": "2023-02-28T19:19:15"
}
|
stevhliu
| true
|
[
"_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) | 0.009526 / 0.011353 (-0.001827) | 0.005132 / 0.011008 (-0.005876) | 0.101312 / 0.038508 (0.062804) | 0.035703 / 0.023109 (0.012594) | 0.301788 / 0.275898 (0.025890) | 0.368411 / 0.323480 (0.044932) | 0.008163 / 0.007986 (0.000177) | 0.005462 / 0.004328 (0.001134) | 0.077282 / 0.004250 (0.073031) | 0.044139 / 0.037052 (0.007086) | 0.312280 / 0.258489 (0.053791) | 0.351870 / 0.293841 (0.058029) | 0.038266 / 0.128546 (-0.090281) | 0.012051 / 0.075646 (-0.063595) | 0.335109 / 0.419271 (-0.084163) | 0.047596 / 0.043533 (0.004064) | 0.300931 / 0.255139 (0.045792) | 0.325705 / 0.283200 (0.042505) | 0.100472 / 0.141683 (-0.041211) | 1.475037 / 1.452155 (0.022882) | 1.520059 / 1.492716 (0.027343) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211096 / 0.018006 (0.193089) | 0.442988 / 0.000490 (0.442498) | 0.003644 / 0.000200 (0.003444) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027492 / 0.037411 (-0.009919) | 0.108981 / 0.014526 (0.094455) | 0.117836 / 0.176557 (-0.058720) | 0.161220 / 0.737135 (-0.575915) | 0.124765 / 0.296338 (-0.171574) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413480 / 0.215209 (0.198271) | 4.111355 / 2.077655 (2.033700) | 1.933024 / 1.504120 (0.428904) | 1.727467 / 1.541195 (0.186272) | 1.827106 / 1.468490 (0.358616) | 0.688209 / 4.584777 (-3.896568) | 3.759672 / 3.745712 (0.013960) | 2.163806 / 5.269862 (-3.106056) | 1.473521 / 4.565676 (-3.092155) | 0.082859 / 0.424275 (-0.341416) | 0.012320 / 0.007607 (0.004713) | 0.515321 / 0.226044 (0.289277) | 5.158651 / 2.268929 (2.889722) | 2.489123 / 55.444624 (-52.955501) | 2.218910 / 6.876477 (-4.657566) | 2.257306 / 2.142072 (0.115233) | 0.861477 / 4.805227 (-3.943750) | 0.165857 / 6.500664 (-6.334807) | 0.063723 / 0.075469 (-0.011746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195163 / 1.841788 (-0.646625) | 14.954518 / 8.074308 (6.880210) | 14.272289 / 10.191392 (4.080897) | 0.167420 / 0.680424 (-0.513004) | 0.028907 / 0.534201 (-0.505294) | 0.450117 / 0.579283 (-0.129166) | 0.448532 / 0.434364 (0.014168) | 0.534406 / 0.540337 (-0.005931) | 0.633468 / 1.386936 (-0.753468) |\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) | 0.007658 / 0.011353 (-0.003694) | 0.005266 / 0.011008 (-0.005742) | 0.075293 / 0.038508 (0.036785) | 0.034442 / 0.023109 (0.011333) | 0.346558 / 0.275898 (0.070660) | 0.391496 / 0.323480 (0.068017) | 0.005852 / 0.007986 (-0.002133) | 0.004121 / 0.004328 (-0.000207) | 0.074254 / 0.004250 (0.070004) | 0.048361 / 0.037052 (0.011309) | 0.344613 / 0.258489 (0.086124) | 0.401497 / 0.293841 (0.107656) | 0.037243 / 0.128546 (-0.091303) | 0.012505 / 0.075646 (-0.063142) | 0.087188 / 0.419271 (-0.332084) | 0.050114 / 0.043533 (0.006581) | 0.340454 / 0.255139 (0.085315) | 0.361087 / 0.283200 (0.077887) | 0.104692 / 0.141683 (-0.036991) | 1.419432 / 1.452155 (-0.032722) | 1.524709 / 1.492716 (0.031993) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231820 / 0.018006 (0.213814) | 0.445791 / 0.000490 (0.445301) | 0.000442 / 0.000200 (0.000242) | 0.000061 / 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) | 0.030445 / 0.037411 (-0.006967) | 0.111183 / 0.014526 (0.096657) | 0.123494 / 0.176557 (-0.053063) | 0.173121 / 0.737135 (-0.564014) | 0.124968 / 0.296338 (-0.171371) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428854 / 0.215209 (0.213645) | 4.270262 / 2.077655 (2.192608) | 2.012075 / 1.504120 (0.507955) | 1.826564 / 1.541195 (0.285370) | 1.931699 / 1.468490 (0.463209) | 0.728762 / 4.584777 (-3.856015) | 3.879640 / 3.745712 (0.133928) | 3.325715 / 5.269862 (-1.944147) | 1.818573 / 4.565676 (-2.747104) | 0.087879 / 0.424275 (-0.336396) | 0.012530 / 0.007607 (0.004923) | 0.530249 / 0.226044 (0.304204) | 5.286110 / 2.268929 (3.017181) | 2.566649 / 55.444624 (-52.877975) | 2.210162 / 6.876477 (-4.666315) | 2.297562 / 2.142072 (0.155490) | 0.906161 / 4.805227 (-3.899066) | 0.171914 / 6.500664 (-6.328750) | 0.064182 / 0.075469 (-0.011287) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285781 / 1.841788 (-0.556006) | 16.159072 / 8.074308 (8.084763) | 14.087492 / 10.191392 (3.896100) | 0.148789 / 0.680424 (-0.531635) | 0.018078 / 0.534201 (-0.516123) | 0.427748 / 0.579283 (-0.151535) | 0.447079 / 0.434364 (0.012715) | 0.535917 / 0.540337 (-0.004421) | 0.627491 / 1.386936 (-0.759445) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88fa043d08c12923709c0492e037130c99c029fb \"CML watermark\")\n"
] |
1,603,571,407
| 5,591
|
set dev version
|
closed
| 2023-02-28T18:09:05
| 2023-02-28T18:16:31
| 2023-02-28T18:09:15
|
https://github.com/huggingface/datasets/pull/5591
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5591",
"html_url": "https://github.com/huggingface/datasets/pull/5591",
"diff_url": "https://github.com/huggingface/datasets/pull/5591.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5591.patch",
"merged_at": "2023-02-28T18:09:15"
}
|
lhoestq
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5591). All of your documentation changes will be reflected on that endpoint.",
"<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) | 0.008826 / 0.011353 (-0.002527) | 0.004595 / 0.011008 (-0.006413) | 0.103387 / 0.038508 (0.064879) | 0.030241 / 0.023109 (0.007132) | 0.351202 / 0.275898 (0.075303) | 0.417601 / 0.323480 (0.094121) | 0.007121 / 0.007986 (-0.000865) | 0.003497 / 0.004328 (-0.000831) | 0.079256 / 0.004250 (0.075006) | 0.037617 / 0.037052 (0.000564) | 0.380542 / 0.258489 (0.122053) | 0.397863 / 0.293841 (0.104022) | 0.034291 / 0.128546 (-0.094255) | 0.011767 / 0.075646 (-0.063879) | 0.323737 / 0.419271 (-0.095534) | 0.041502 / 0.043533 (-0.002031) | 0.352982 / 0.255139 (0.097843) | 0.378618 / 0.283200 (0.095418) | 0.091671 / 0.141683 (-0.050012) | 1.499278 / 1.452155 (0.047123) | 1.517489 / 1.492716 (0.024773) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190108 / 0.018006 (0.172102) | 0.414404 / 0.000490 (0.413915) | 0.001064 / 0.000200 (0.000864) | 0.000066 / 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) | 0.023214 / 0.037411 (-0.014198) | 0.099351 / 0.014526 (0.084825) | 0.105227 / 0.176557 (-0.071330) | 0.150620 / 0.737135 (-0.586516) | 0.109323 / 0.296338 (-0.187015) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412463 / 0.215209 (0.197254) | 4.138123 / 2.077655 (2.060469) | 1.845163 / 1.504120 (0.341043) | 1.641108 / 1.541195 (0.099913) | 1.715471 / 1.468490 (0.246981) | 0.697397 / 4.584777 (-3.887380) | 3.449829 / 3.745712 (-0.295883) | 1.959309 / 5.269862 (-3.310553) | 1.285754 / 4.565676 (-3.279923) | 0.082746 / 0.424275 (-0.341529) | 0.012523 / 0.007607 (0.004916) | 0.524745 / 0.226044 (0.298700) | 5.257085 / 2.268929 (2.988156) | 2.293163 / 55.444624 (-53.151461) | 1.958309 / 6.876477 (-4.918168) | 2.016106 / 2.142072 (-0.125966) | 0.814359 / 4.805227 (-3.990869) | 0.149443 / 6.500664 (-6.351221) | 0.066013 / 0.075469 (-0.009456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248495 / 1.841788 (-0.593292) | 14.303301 / 8.074308 (6.228993) | 14.238533 / 10.191392 (4.047141) | 0.161421 / 0.680424 (-0.519003) | 0.028779 / 0.534201 (-0.505422) | 0.396511 / 0.579283 (-0.182772) | 0.412784 / 0.434364 (-0.021580) | 0.473984 / 0.540337 (-0.066353) | 0.569610 / 1.386936 (-0.817327) |\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) | 0.007003 / 0.011353 (-0.004350) | 0.004621 / 0.011008 (-0.006387) | 0.079418 / 0.038508 (0.040910) | 0.028659 / 0.023109 (0.005550) | 0.340594 / 0.275898 (0.064696) | 0.377972 / 0.323480 (0.054492) | 0.005421 / 0.007986 (-0.002565) | 0.004852 / 0.004328 (0.000523) | 0.077579 / 0.004250 (0.073329) | 0.042662 / 0.037052 (0.005610) | 0.342264 / 0.258489 (0.083775) | 0.387255 / 0.293841 (0.093414) | 0.032574 / 0.128546 (-0.095972) | 0.011820 / 0.075646 (-0.063826) | 0.087960 / 0.419271 (-0.331312) | 0.045199 / 0.043533 (0.001667) | 0.341785 / 0.255139 (0.086646) | 0.365014 / 0.283200 (0.081814) | 0.096129 / 0.141683 (-0.045554) | 1.498962 / 1.452155 (0.046807) | 1.557331 / 1.492716 (0.064615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236216 / 0.018006 (0.218210) | 0.440189 / 0.000490 (0.439699) | 0.000399 / 0.000200 (0.000199) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026357 / 0.037411 (-0.011055) | 0.104485 / 0.014526 (0.089959) | 0.109616 / 0.176557 (-0.066941) | 0.163005 / 0.737135 (-0.574130) | 0.113859 / 0.296338 (-0.182479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437452 / 0.215209 (0.222243) | 4.371854 / 2.077655 (2.294199) | 2.056845 / 1.504120 (0.552725) | 1.856071 / 1.541195 (0.314876) | 1.957978 / 1.468490 (0.489488) | 0.703171 / 4.584777 (-3.881606) | 3.433889 / 3.745712 (-0.311823) | 1.968321 / 5.269862 (-3.301541) | 1.204947 / 4.565676 (-3.360729) | 0.084499 / 0.424275 (-0.339777) | 0.012729 / 0.007607 (0.005122) | 0.537534 / 0.226044 (0.311490) | 5.383346 / 2.268929 (3.114417) | 2.522136 / 55.444624 (-52.922488) | 2.192715 / 6.876477 (-4.683762) | 2.243579 / 2.142072 (0.101507) | 0.811136 / 4.805227 (-3.994091) | 0.154015 / 6.500664 (-6.346649) | 0.069324 / 0.075469 (-0.006145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294232 / 1.841788 (-0.547556) | 14.809448 / 8.074308 (6.735140) | 13.510074 / 10.191392 (3.318682) | 0.158033 / 0.680424 (-0.522391) | 0.016703 / 0.534201 (-0.517498) | 0.393976 / 0.579283 (-0.185307) | 0.385983 / 0.434364 (-0.048381) | 0.476691 / 0.540337 (-0.063646) | 0.565694 / 1.386936 (-0.821242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0dd3126196e8fcd9ba81a6602b46623b4e77e6e \"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) | 0.009155 / 0.011353 (-0.002198) | 0.005227 / 0.011008 (-0.005781) | 0.099767 / 0.038508 (0.061259) | 0.035338 / 0.023109 (0.012229) | 0.293913 / 0.275898 (0.018015) | 0.366976 / 0.323480 (0.043496) | 0.007802 / 0.007986 (-0.000184) | 0.005286 / 0.004328 (0.000958) | 0.075117 / 0.004250 (0.070867) | 0.042336 / 0.037052 (0.005284) | 0.304690 / 0.258489 (0.046201) | 0.343496 / 0.293841 (0.049655) | 0.038745 / 0.128546 (-0.089802) | 0.012275 / 0.075646 (-0.063371) | 0.334455 / 0.419271 (-0.084817) | 0.052611 / 0.043533 (0.009078) | 0.293229 / 0.255139 (0.038090) | 0.314340 / 0.283200 (0.031140) | 0.108676 / 0.141683 (-0.033007) | 1.444495 / 1.452155 (-0.007659) | 1.492244 / 1.492716 (-0.000472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204852 / 0.018006 (0.186846) | 0.438202 / 0.000490 (0.437712) | 0.005043 / 0.000200 (0.004843) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027268 / 0.037411 (-0.010143) | 0.109497 / 0.014526 (0.094972) | 0.117187 / 0.176557 (-0.059369) | 0.162551 / 0.737135 (-0.574584) | 0.124175 / 0.296338 (-0.172164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401667 / 0.215209 (0.186458) | 4.010274 / 2.077655 (1.932619) | 1.882617 / 1.504120 (0.378497) | 1.721960 / 1.541195 (0.180765) | 1.806874 / 1.468490 (0.338384) | 0.711253 / 4.584777 (-3.873524) | 3.806585 / 3.745712 (0.060873) | 3.713011 / 5.269862 (-1.556851) | 1.896558 / 4.565676 (-2.669119) | 0.086092 / 0.424275 (-0.338184) | 0.012129 / 0.007607 (0.004522) | 0.504905 / 0.226044 (0.278861) | 5.050794 / 2.268929 (2.781865) | 2.324331 / 55.444624 (-53.120293) | 2.020170 / 6.876477 (-4.856307) | 2.079685 / 2.142072 (-0.062388) | 0.854782 / 4.805227 (-3.950445) | 0.166754 / 6.500664 (-6.333910) | 0.062434 / 0.075469 (-0.013035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187897 / 1.841788 (-0.653891) | 14.618517 / 8.074308 (6.544209) | 13.205760 / 10.191392 (3.014368) | 0.154322 / 0.680424 (-0.526102) | 0.029243 / 0.534201 (-0.504958) | 0.442390 / 0.579283 (-0.136893) | 0.434651 / 0.434364 (0.000287) | 0.523082 / 0.540337 (-0.017256) | 0.602675 / 1.386936 (-0.784261) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.005225 / 0.011008 (-0.005783) | 0.076497 / 0.038508 (0.037989) | 0.032761 / 0.023109 (0.009652) | 0.336005 / 0.275898 (0.060107) | 0.373547 / 0.323480 (0.050067) | 0.005460 / 0.007986 (-0.002526) | 0.003933 / 0.004328 (-0.000395) | 0.074540 / 0.004250 (0.070289) | 0.047785 / 0.037052 (0.010733) | 0.341917 / 0.258489 (0.083428) | 0.396978 / 0.293841 (0.103137) | 0.036763 / 0.128546 (-0.091783) | 0.012043 / 0.075646 (-0.063603) | 0.087632 / 0.419271 (-0.331640) | 0.049376 / 0.043533 (0.005843) | 0.335169 / 0.255139 (0.080030) | 0.354852 / 0.283200 (0.071652) | 0.100180 / 0.141683 (-0.041503) | 1.443422 / 1.452155 (-0.008733) | 1.518618 / 1.492716 (0.025901) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209593 / 0.018006 (0.191587) | 0.444028 / 0.000490 (0.443538) | 0.004545 / 0.000200 (0.004345) | 0.000100 / 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) | 0.029676 / 0.037411 (-0.007735) | 0.115444 / 0.014526 (0.100918) | 0.121765 / 0.176557 (-0.054791) | 0.171037 / 0.737135 (-0.566098) | 0.128592 / 0.296338 (-0.167746) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428556 / 0.215209 (0.213347) | 4.228531 / 2.077655 (2.150877) | 2.039190 / 1.504120 (0.535070) | 1.836518 / 1.541195 (0.295324) | 1.897040 / 1.468490 (0.428550) | 0.698893 / 4.584777 (-3.885884) | 3.753998 / 3.745712 (0.008286) | 2.097731 / 5.269862 (-3.172131) | 1.338315 / 4.565676 (-3.227361) | 0.087119 / 0.424275 (-0.337156) | 0.012149 / 0.007607 (0.004542) | 0.520774 / 0.226044 (0.294730) | 5.227420 / 2.268929 (2.958492) | 2.522235 / 55.444624 (-52.922389) | 2.194213 / 6.876477 (-4.682264) | 2.241406 / 2.142072 (0.099333) | 0.843119 / 4.805227 (-3.962109) | 0.169128 / 6.500664 (-6.331536) | 0.065071 / 0.075469 (-0.010398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254490 / 1.841788 (-0.587298) | 15.037137 / 8.074308 (6.962829) | 13.115333 / 10.191392 (2.923941) | 0.181743 / 0.680424 (-0.498681) | 0.017748 / 0.534201 (-0.516453) | 0.425758 / 0.579283 (-0.153525) | 0.429926 / 0.434364 (-0.004438) | 0.524386 / 0.540337 (-0.015951) | 0.643044 / 1.386936 (-0.743892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e820e79a3b879855b514e2a62d84b738013940 \"CML watermark\")\n"
] |
1,603,549,504
| 5,590
|
Release: 2.10.1
|
closed
| 2023-02-28T17:58:11
| 2023-02-28T18:16:27
| 2023-02-28T18:06:08
|
https://github.com/huggingface/datasets/pull/5590
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5590",
"html_url": "https://github.com/huggingface/datasets/pull/5590",
"diff_url": "https://github.com/huggingface/datasets/pull/5590.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5590.patch",
"merged_at": "2023-02-28T18:06:08"
}
|
lhoestq
| true
|
[
"<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) | 0.008717 / 0.011353 (-0.002636) | 0.004570 / 0.011008 (-0.006439) | 0.100228 / 0.038508 (0.061720) | 0.030076 / 0.023109 (0.006967) | 0.317919 / 0.275898 (0.042021) | 0.366360 / 0.323480 (0.042880) | 0.007008 / 0.007986 (-0.000978) | 0.003498 / 0.004328 (-0.000831) | 0.077607 / 0.004250 (0.073356) | 0.036106 / 0.037052 (-0.000946) | 0.314128 / 0.258489 (0.055639) | 0.351450 / 0.293841 (0.057609) | 0.033697 / 0.128546 (-0.094849) | 0.011424 / 0.075646 (-0.064222) | 0.323867 / 0.419271 (-0.095404) | 0.042073 / 0.043533 (-0.001460) | 0.304564 / 0.255139 (0.049425) | 0.334865 / 0.283200 (0.051665) | 0.087791 / 0.141683 (-0.053892) | 1.488075 / 1.452155 (0.035920) | 1.513676 / 1.492716 (0.020959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010936 / 0.018006 (-0.007070) | 0.409610 / 0.000490 (0.409121) | 0.004820 / 0.000200 (0.004620) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023931 / 0.037411 (-0.013481) | 0.096826 / 0.014526 (0.082300) | 0.105764 / 0.176557 (-0.070792) | 0.153241 / 0.737135 (-0.583895) | 0.108976 / 0.296338 (-0.187363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412833 / 0.215209 (0.197624) | 4.129735 / 2.077655 (2.052081) | 1.819049 / 1.504120 (0.314929) | 1.617411 / 1.541195 (0.076216) | 1.682353 / 1.468490 (0.213863) | 0.688987 / 4.584777 (-3.895790) | 3.388276 / 3.745712 (-0.357436) | 1.857452 / 5.269862 (-3.412410) | 1.158020 / 4.565676 (-3.407657) | 0.082161 / 0.424275 (-0.342114) | 0.012319 / 0.007607 (0.004712) | 0.523052 / 0.226044 (0.297008) | 5.237726 / 2.268929 (2.968797) | 2.275605 / 55.444624 (-53.169020) | 1.931664 / 6.876477 (-4.944813) | 1.970026 / 2.142072 (-0.172046) | 0.805240 / 4.805227 (-3.999988) | 0.148431 / 6.500664 (-6.352233) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196456 / 1.841788 (-0.645332) | 13.750113 / 8.074308 (5.675805) | 13.853543 / 10.191392 (3.662151) | 0.137892 / 0.680424 (-0.542532) | 0.028304 / 0.534201 (-0.505897) | 0.400128 / 0.579283 (-0.179155) | 0.410409 / 0.434364 (-0.023955) | 0.479165 / 0.540337 (-0.061172) | 0.575002 / 1.386936 (-0.811934) |\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) | 0.006587 / 0.011353 (-0.004766) | 0.004526 / 0.011008 (-0.006482) | 0.075673 / 0.038508 (0.037165) | 0.027429 / 0.023109 (0.004320) | 0.341808 / 0.275898 (0.065910) | 0.379520 / 0.323480 (0.056040) | 0.004972 / 0.007986 (-0.003014) | 0.003354 / 0.004328 (-0.000975) | 0.075373 / 0.004250 (0.071123) | 0.038347 / 0.037052 (0.001294) | 0.343671 / 0.258489 (0.085181) | 0.389632 / 0.293841 (0.095791) | 0.031694 / 0.128546 (-0.096853) | 0.011458 / 0.075646 (-0.064188) | 0.084210 / 0.419271 (-0.335062) | 0.042662 / 0.043533 (-0.000871) | 0.339436 / 0.255139 (0.084297) | 0.367493 / 0.283200 (0.084294) | 0.091604 / 0.141683 (-0.050079) | 1.526762 / 1.452155 (0.074607) | 1.569110 / 1.492716 (0.076394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211496 / 0.018006 (0.193489) | 0.404868 / 0.000490 (0.404379) | 0.004267 / 0.000200 (0.004067) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025189 / 0.037411 (-0.012222) | 0.099139 / 0.014526 (0.084613) | 0.105898 / 0.176557 (-0.070659) | 0.160997 / 0.737135 (-0.576138) | 0.110158 / 0.296338 (-0.186180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444286 / 0.215209 (0.229077) | 4.445479 / 2.077655 (2.367824) | 2.118920 / 1.504120 (0.614800) | 1.908296 / 1.541195 (0.367102) | 1.947211 / 1.468490 (0.478721) | 0.704850 / 4.584777 (-3.879927) | 3.395990 / 3.745712 (-0.349723) | 1.892529 / 5.269862 (-3.377332) | 1.172190 / 4.565676 (-3.393486) | 0.084235 / 0.424275 (-0.340040) | 0.012588 / 0.007607 (0.004981) | 0.546962 / 0.226044 (0.320918) | 5.475842 / 2.268929 (3.206913) | 2.575280 / 55.444624 (-52.869344) | 2.245658 / 6.876477 (-4.630818) | 2.274767 / 2.142072 (0.132695) | 0.813755 / 4.805227 (-3.991473) | 0.151927 / 6.500664 (-6.348737) | 0.067167 / 0.075469 (-0.008302) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267666 / 1.841788 (-0.574122) | 13.658905 / 8.074308 (5.584597) | 13.207249 / 10.191392 (3.015857) | 0.128590 / 0.680424 (-0.551833) | 0.016531 / 0.534201 (-0.517670) | 0.385050 / 0.579283 (-0.194233) | 0.388945 / 0.434364 (-0.045419) | 0.472378 / 0.540337 (-0.067959) | 0.568929 / 1.386936 (-0.818007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87cd5f7f7fda60d0f91f50424bcc3f327fe0d059 \"CML watermark\")\n",
"_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) | 0.009339 / 0.011353 (-0.002014) | 0.005197 / 0.011008 (-0.005811) | 0.100698 / 0.038508 (0.062190) | 0.035484 / 0.023109 (0.012375) | 0.299030 / 0.275898 (0.023132) | 0.366603 / 0.323480 (0.043124) | 0.007909 / 0.007986 (-0.000077) | 0.005683 / 0.004328 (0.001355) | 0.077719 / 0.004250 (0.073469) | 0.042147 / 0.037052 (0.005094) | 0.310174 / 0.258489 (0.051685) | 0.342720 / 0.293841 (0.048879) | 0.039679 / 0.128546 (-0.088867) | 0.012042 / 0.075646 (-0.063605) | 0.335663 / 0.419271 (-0.083609) | 0.051137 / 0.043533 (0.007604) | 0.298218 / 0.255139 (0.043079) | 0.316398 / 0.283200 (0.033198) | 0.108906 / 0.141683 (-0.032776) | 1.422823 / 1.452155 (-0.029331) | 1.472955 / 1.492716 (-0.019761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205845 / 0.018006 (0.187839) | 0.445942 / 0.000490 (0.445453) | 0.003553 / 0.000200 (0.003353) | 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) | 0.025506 / 0.037411 (-0.011906) | 0.107494 / 0.014526 (0.092969) | 0.116226 / 0.176557 (-0.060331) | 0.157313 / 0.737135 (-0.579822) | 0.123822 / 0.296338 (-0.172516) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400908 / 0.215209 (0.185699) | 3.980232 / 2.077655 (1.902578) | 1.805410 / 1.504120 (0.301290) | 1.615698 / 1.541195 (0.074503) | 1.677213 / 1.468490 (0.208723) | 0.697882 / 4.584777 (-3.886895) | 3.752781 / 3.745712 (0.007069) | 2.076062 / 5.269862 (-3.193800) | 1.446909 / 4.565676 (-3.118768) | 0.084572 / 0.424275 (-0.339703) | 0.011917 / 0.007607 (0.004310) | 0.511815 / 0.226044 (0.285771) | 5.121487 / 2.268929 (2.852558) | 2.277642 / 55.444624 (-53.166982) | 1.930393 / 6.876477 (-4.946084) | 1.965855 / 2.142072 (-0.176218) | 0.843391 / 4.805227 (-3.961837) | 0.163581 / 6.500664 (-6.337083) | 0.062547 / 0.075469 (-0.012922) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223930 / 1.841788 (-0.617858) | 14.354466 / 8.074308 (6.280158) | 14.015159 / 10.191392 (3.823767) | 0.148658 / 0.680424 (-0.531766) | 0.028469 / 0.534201 (-0.505732) | 0.437614 / 0.579283 (-0.141669) | 0.435452 / 0.434364 (0.001089) | 0.523623 / 0.540337 (-0.016715) | 0.625109 / 1.386936 (-0.761827) |\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) | 0.006917 / 0.011353 (-0.004436) | 0.005080 / 0.011008 (-0.005928) | 0.075806 / 0.038508 (0.037298) | 0.032402 / 0.023109 (0.009293) | 0.331105 / 0.275898 (0.055207) | 0.361226 / 0.323480 (0.037746) | 0.005694 / 0.007986 (-0.002292) | 0.003810 / 0.004328 (-0.000518) | 0.076886 / 0.004250 (0.072635) | 0.046158 / 0.037052 (0.009106) | 0.338791 / 0.258489 (0.080302) | 0.385733 / 0.293841 (0.091892) | 0.035590 / 0.128546 (-0.092956) | 0.011997 / 0.075646 (-0.063649) | 0.087854 / 0.419271 (-0.331417) | 0.048985 / 0.043533 (0.005452) | 0.331248 / 0.255139 (0.076109) | 0.354633 / 0.283200 (0.071434) | 0.101609 / 0.141683 (-0.040074) | 1.496899 / 1.452155 (0.044745) | 1.570469 / 1.492716 (0.077753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180871 / 0.018006 (0.162865) | 0.449417 / 0.000490 (0.448928) | 0.004300 / 0.000200 (0.004100) | 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) | 0.029054 / 0.037411 (-0.008358) | 0.110888 / 0.014526 (0.096362) | 0.121736 / 0.176557 (-0.054821) | 0.172563 / 0.737135 (-0.564572) | 0.126565 / 0.296338 (-0.169773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419545 / 0.215209 (0.204336) | 4.193685 / 2.077655 (2.116031) | 2.049967 / 1.504120 (0.545847) | 1.855038 / 1.541195 (0.313843) | 1.899822 / 1.468490 (0.431332) | 0.709123 / 4.584777 (-3.875654) | 3.795939 / 3.745712 (0.050227) | 2.076055 / 5.269862 (-3.193807) | 1.335864 / 4.565676 (-3.229812) | 0.085555 / 0.424275 (-0.338720) | 0.012197 / 0.007607 (0.004590) | 0.516164 / 0.226044 (0.290119) | 5.158983 / 2.268929 (2.890054) | 2.445581 / 55.444624 (-52.999044) | 2.122256 / 6.876477 (-4.754221) | 2.160011 / 2.142072 (0.017939) | 0.840251 / 4.805227 (-3.964976) | 0.165924 / 6.500664 (-6.334740) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285292 / 1.841788 (-0.556495) | 14.561084 / 8.074308 (6.486776) | 12.899269 / 10.191392 (2.707877) | 0.185657 / 0.680424 (-0.494767) | 0.017866 / 0.534201 (-0.516335) | 0.425365 / 0.579283 (-0.153918) | 0.427183 / 0.434364 (-0.007181) | 0.529773 / 0.540337 (-0.010564) | 0.642061 / 1.386936 (-0.744875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0628013d009dd5150e8a1c1a4ac9d93887b88a76 \"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) | 0.008995 / 0.011353 (-0.002357) | 0.004540 / 0.011008 (-0.006469) | 0.099675 / 0.038508 (0.061167) | 0.030338 / 0.023109 (0.007229) | 0.307167 / 0.275898 (0.031269) | 0.338789 / 0.323480 (0.015309) | 0.007293 / 0.007986 (-0.000692) | 0.004681 / 0.004328 (0.000352) | 0.077475 / 0.004250 (0.073225) | 0.036399 / 0.037052 (-0.000654) | 0.304615 / 0.258489 (0.046126) | 0.351611 / 0.293841 (0.057770) | 0.034449 / 0.128546 (-0.094097) | 0.011565 / 0.075646 (-0.064082) | 0.322765 / 0.419271 (-0.096506) | 0.041971 / 0.043533 (-0.001562) | 0.307492 / 0.255139 (0.052354) | 0.327240 / 0.283200 (0.044040) | 0.087110 / 0.141683 (-0.054573) | 1.484600 / 1.452155 (0.032445) | 1.536651 / 1.492716 (0.043934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185876 / 0.018006 (0.167869) | 0.404276 / 0.000490 (0.403787) | 0.001592 / 0.000200 (0.001392) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023272 / 0.037411 (-0.014139) | 0.096273 / 0.014526 (0.081747) | 0.105400 / 0.176557 (-0.071157) | 0.149720 / 0.737135 (-0.587416) | 0.107807 / 0.296338 (-0.188532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420072 / 0.215209 (0.204863) | 4.184108 / 2.077655 (2.106454) | 1.880690 / 1.504120 (0.376570) | 1.673103 / 1.541195 (0.131909) | 1.715792 / 1.468490 (0.247302) | 0.695771 / 4.584777 (-3.889006) | 3.450224 / 3.745712 (-0.295488) | 2.999218 / 5.269862 (-2.270644) | 1.585571 / 4.565676 (-2.980106) | 0.082105 / 0.424275 (-0.342170) | 0.012453 / 0.007607 (0.004846) | 0.528538 / 0.226044 (0.302494) | 5.287951 / 2.268929 (3.019023) | 2.289127 / 55.444624 (-53.155497) | 1.956503 / 6.876477 (-4.919974) | 2.004498 / 2.142072 (-0.137575) | 0.813547 / 4.805227 (-3.991681) | 0.151574 / 6.500664 (-6.349090) | 0.063763 / 0.075469 (-0.011706) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239125 / 1.841788 (-0.602662) | 13.627676 / 8.074308 (5.553368) | 13.747815 / 10.191392 (3.556423) | 0.157745 / 0.680424 (-0.522679) | 0.028590 / 0.534201 (-0.505611) | 0.397472 / 0.579283 (-0.181811) | 0.405925 / 0.434364 (-0.028439) | 0.477942 / 0.540337 (-0.062396) | 0.572379 / 1.386936 (-0.814557) |\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) | 0.006637 / 0.011353 (-0.004716) | 0.004657 / 0.011008 (-0.006351) | 0.082056 / 0.038508 (0.043548) | 0.027974 / 0.023109 (0.004865) | 0.342887 / 0.275898 (0.066989) | 0.375938 / 0.323480 (0.052458) | 0.004958 / 0.007986 (-0.003028) | 0.004738 / 0.004328 (0.000409) | 0.080449 / 0.004250 (0.076198) | 0.038138 / 0.037052 (0.001085) | 0.345636 / 0.258489 (0.087147) | 0.385992 / 0.293841 (0.092151) | 0.033265 / 0.128546 (-0.095281) | 0.011965 / 0.075646 (-0.063681) | 0.091441 / 0.419271 (-0.327830) | 0.051407 / 0.043533 (0.007874) | 0.353758 / 0.255139 (0.098619) | 0.372118 / 0.283200 (0.088919) | 0.093947 / 0.141683 (-0.047735) | 1.468197 / 1.452155 (0.016042) | 1.554677 / 1.492716 (0.061960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222034 / 0.018006 (0.204027) | 0.403658 / 0.000490 (0.403169) | 0.003242 / 0.000200 (0.003042) | 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) | 0.025335 / 0.037411 (-0.012076) | 0.100404 / 0.014526 (0.085878) | 0.107858 / 0.176557 (-0.068698) | 0.156115 / 0.737135 (-0.581021) | 0.113967 / 0.296338 (-0.182372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437567 / 0.215209 (0.222358) | 4.362486 / 2.077655 (2.284832) | 2.067315 / 1.504120 (0.563195) | 1.857669 / 1.541195 (0.316475) | 1.926380 / 1.468490 (0.457890) | 0.703905 / 4.584777 (-3.880872) | 3.437139 / 3.745712 (-0.308573) | 3.051931 / 5.269862 (-2.217930) | 1.356494 / 4.565676 (-3.209182) | 0.083679 / 0.424275 (-0.340596) | 0.012507 / 0.007607 (0.004900) | 0.539572 / 0.226044 (0.313528) | 5.405790 / 2.268929 (3.136861) | 2.532769 / 55.444624 (-52.911855) | 2.181950 / 6.876477 (-4.694527) | 2.212627 / 2.142072 (0.070554) | 0.807468 / 4.805227 (-3.997759) | 0.152146 / 6.500664 (-6.348518) | 0.068891 / 0.075469 (-0.006578) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286972 / 1.841788 (-0.554816) | 13.987186 / 8.074308 (5.912878) | 13.115065 / 10.191392 (2.923673) | 0.162143 / 0.680424 (-0.518281) | 0.016767 / 0.534201 (-0.517434) | 0.384766 / 0.579283 (-0.194517) | 0.397438 / 0.434364 (-0.036926) | 0.470850 / 0.540337 (-0.069487) | 0.562216 / 1.386936 (-0.824720) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"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) | 0.010877 / 0.011353 (-0.000476) | 0.005739 / 0.011008 (-0.005269) | 0.118542 / 0.038508 (0.080034) | 0.042266 / 0.023109 (0.019157) | 0.359317 / 0.275898 (0.083419) | 0.412995 / 0.323480 (0.089515) | 0.009158 / 0.007986 (0.001173) | 0.006343 / 0.004328 (0.002014) | 0.089587 / 0.004250 (0.085336) | 0.047899 / 0.037052 (0.010847) | 0.358745 / 0.258489 (0.100256) | 0.421316 / 0.293841 (0.127476) | 0.044540 / 0.128546 (-0.084006) | 0.013872 / 0.075646 (-0.061774) | 0.399856 / 0.419271 (-0.019415) | 0.056484 / 0.043533 (0.012951) | 0.356922 / 0.255139 (0.101783) | 0.385598 / 0.283200 (0.102398) | 0.116039 / 0.141683 (-0.025644) | 1.726095 / 1.452155 (0.273940) | 1.888643 / 1.492716 (0.395927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269517 / 0.018006 (0.251511) | 0.511204 / 0.000490 (0.510714) | 0.001906 / 0.000200 (0.001706) | 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) | 0.031133 / 0.037411 (-0.006278) | 0.128513 / 0.014526 (0.113987) | 0.139639 / 0.176557 (-0.036918) | 0.189778 / 0.737135 (-0.547358) | 0.145219 / 0.296338 (-0.151120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486693 / 0.215209 (0.271484) | 4.851999 / 2.077655 (2.774344) | 2.255334 / 1.504120 (0.751214) | 2.052271 / 1.541195 (0.511077) | 2.143262 / 1.468490 (0.674772) | 0.835765 / 4.584777 (-3.749012) | 4.451280 / 3.745712 (0.705568) | 2.534392 / 5.269862 (-2.735469) | 1.747817 / 4.565676 (-2.817859) | 0.101186 / 0.424275 (-0.323089) | 0.014281 / 0.007607 (0.006674) | 0.616164 / 0.226044 (0.390120) | 6.161789 / 2.268929 (3.892860) | 2.815347 / 55.444624 (-52.629277) | 2.408305 / 6.876477 (-4.468172) | 2.508240 / 2.142072 (0.366167) | 1.017709 / 4.805227 (-3.787519) | 0.198272 / 6.500664 (-6.302392) | 0.075663 / 0.075469 (0.000194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.435501 / 1.841788 (-0.406287) | 18.149581 / 8.074308 (10.075273) | 16.619011 / 10.191392 (6.427619) | 0.205080 / 0.680424 (-0.475344) | 0.033780 / 0.534201 (-0.500421) | 0.515768 / 0.579283 (-0.063515) | 0.542628 / 0.434364 (0.108264) | 0.634067 / 0.540337 (0.093730) | 0.757841 / 1.386936 (-0.629095) |\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) | 0.008541 / 0.011353 (-0.002812) | 0.005733 / 0.011008 (-0.005275) | 0.089859 / 0.038508 (0.051351) | 0.039379 / 0.023109 (0.016270) | 0.402037 / 0.275898 (0.126139) | 0.454046 / 0.323480 (0.130566) | 0.006652 / 0.007986 (-0.001334) | 0.004555 / 0.004328 (0.000227) | 0.087651 / 0.004250 (0.083401) | 0.054934 / 0.037052 (0.017881) | 0.404468 / 0.258489 (0.145979) | 0.467127 / 0.293841 (0.173286) | 0.042034 / 0.128546 (-0.086512) | 0.014225 / 0.075646 (-0.061421) | 0.103281 / 0.419271 (-0.315990) | 0.057767 / 0.043533 (0.014234) | 0.396391 / 0.255139 (0.141252) | 0.429364 / 0.283200 (0.146165) | 0.120193 / 0.141683 (-0.021489) | 1.794029 / 1.452155 (0.341875) | 1.875431 / 1.492716 (0.382714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325707 / 0.018006 (0.307701) | 0.503841 / 0.000490 (0.503351) | 0.010224 / 0.000200 (0.010024) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035289 / 0.037411 (-0.002123) | 0.139018 / 0.014526 (0.124492) | 0.145112 / 0.176557 (-0.031445) | 0.202616 / 0.737135 (-0.534519) | 0.152975 / 0.296338 (-0.143363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493110 / 0.215209 (0.277901) | 4.885713 / 2.077655 (2.808058) | 2.344417 / 1.504120 (0.840297) | 2.135734 / 1.541195 (0.594540) | 2.254118 / 1.468490 (0.785628) | 0.811516 / 4.584777 (-3.773261) | 4.484454 / 3.745712 (0.738742) | 2.459913 / 5.269862 (-2.809948) | 1.553106 / 4.565676 (-3.012570) | 0.100943 / 0.424275 (-0.323332) | 0.014848 / 0.007607 (0.007241) | 0.626214 / 0.226044 (0.400170) | 6.206925 / 2.268929 (3.937997) | 2.986549 / 55.444624 (-52.458076) | 2.521895 / 6.876477 (-4.354582) | 2.610917 / 2.142072 (0.468845) | 0.998496 / 4.805227 (-3.806731) | 0.199405 / 6.500664 (-6.301260) | 0.077355 / 0.075469 (0.001886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.525135 / 1.841788 (-0.316653) | 18.708407 / 8.074308 (10.634099) | 16.049482 / 10.191392 (5.858090) | 0.170986 / 0.680424 (-0.509437) | 0.021090 / 0.534201 (-0.513111) | 0.511734 / 0.579283 (-0.067549) | 0.495507 / 0.434364 (0.061143) | 0.628578 / 0.540337 (0.088241) | 0.749546 / 1.386936 (-0.637390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n"
] |
1,603,535,704
| 5,589
|
Revert "pass the dataset features to the IterableDataset.from_generator"
|
closed
| 2023-02-28T17:52:04
| 2023-09-24T10:07:33
| 2023-03-21T14:18:18
|
https://github.com/huggingface/datasets/pull/5589
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5589",
"html_url": "https://github.com/huggingface/datasets/pull/5589",
"diff_url": "https://github.com/huggingface/datasets/pull/5589.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5589.patch",
"merged_at": null
}
|
lhoestq
| true
|
[
"_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) | 0.008442 / 0.011353 (-0.002911) | 0.004567 / 0.011008 (-0.006441) | 0.100688 / 0.038508 (0.062180) | 0.029568 / 0.023109 (0.006459) | 0.306993 / 0.275898 (0.031095) | 0.362626 / 0.323480 (0.039146) | 0.006983 / 0.007986 (-0.001002) | 0.003424 / 0.004328 (-0.000905) | 0.079050 / 0.004250 (0.074799) | 0.036087 / 0.037052 (-0.000966) | 0.318205 / 0.258489 (0.059716) | 0.353882 / 0.293841 (0.060041) | 0.033091 / 0.128546 (-0.095455) | 0.011468 / 0.075646 (-0.064178) | 0.321125 / 0.419271 (-0.098146) | 0.040645 / 0.043533 (-0.002888) | 0.309827 / 0.255139 (0.054688) | 0.344848 / 0.283200 (0.061648) | 0.087100 / 0.141683 (-0.054583) | 1.465123 / 1.452155 (0.012968) | 1.499457 / 1.492716 (0.006741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.171619 / 0.018006 (0.153613) | 0.410198 / 0.000490 (0.409709) | 0.002391 / 0.000200 (0.002191) | 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) | 0.022913 / 0.037411 (-0.014499) | 0.097275 / 0.014526 (0.082749) | 0.103902 / 0.176557 (-0.072655) | 0.148855 / 0.737135 (-0.588281) | 0.107247 / 0.296338 (-0.189092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413139 / 0.215209 (0.197930) | 4.131760 / 2.077655 (2.054105) | 1.854491 / 1.504120 (0.350371) | 1.625524 / 1.541195 (0.084329) | 1.666665 / 1.468490 (0.198175) | 0.687105 / 4.584777 (-3.897672) | 3.327124 / 3.745712 (-0.418588) | 1.830820 / 5.269862 (-3.439042) | 1.147930 / 4.565676 (-3.417746) | 0.081586 / 0.424275 (-0.342689) | 0.012422 / 0.007607 (0.004815) | 0.523723 / 0.226044 (0.297678) | 5.246977 / 2.268929 (2.978049) | 2.288350 / 55.444624 (-53.156275) | 1.933740 / 6.876477 (-4.942737) | 1.954356 / 2.142072 (-0.187716) | 0.804434 / 4.805227 (-4.000793) | 0.147621 / 6.500664 (-6.353043) | 0.064835 / 0.075469 (-0.010634) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244841 / 1.841788 (-0.596947) | 13.758465 / 8.074308 (5.684157) | 13.984576 / 10.191392 (3.793184) | 0.144860 / 0.680424 (-0.535564) | 0.028616 / 0.534201 (-0.505584) | 0.401928 / 0.579283 (-0.177355) | 0.415294 / 0.434364 (-0.019069) | 0.476483 / 0.540337 (-0.063854) | 0.569257 / 1.386936 (-0.817679) |\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) | 0.006556 / 0.011353 (-0.004797) | 0.004502 / 0.011008 (-0.006507) | 0.074828 / 0.038508 (0.036319) | 0.027537 / 0.023109 (0.004427) | 0.339961 / 0.275898 (0.064063) | 0.372491 / 0.323480 (0.049011) | 0.005010 / 0.007986 (-0.002976) | 0.004624 / 0.004328 (0.000295) | 0.074459 / 0.004250 (0.070208) | 0.037539 / 0.037052 (0.000486) | 0.341031 / 0.258489 (0.082542) | 0.383397 / 0.293841 (0.089556) | 0.031706 / 0.128546 (-0.096840) | 0.011542 / 0.075646 (-0.064104) | 0.084882 / 0.419271 (-0.334389) | 0.041860 / 0.043533 (-0.001673) | 0.338699 / 0.255139 (0.083560) | 0.365666 / 0.283200 (0.082467) | 0.088966 / 0.141683 (-0.052717) | 1.502493 / 1.452155 (0.050339) | 1.570746 / 1.492716 (0.078030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217547 / 0.018006 (0.199541) | 0.392407 / 0.000490 (0.391918) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099259 / 0.014526 (0.084734) | 0.107850 / 0.176557 (-0.068707) | 0.157686 / 0.737135 (-0.579449) | 0.109761 / 0.296338 (-0.186578) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434791 / 0.215209 (0.219582) | 4.323099 / 2.077655 (2.245444) | 2.063610 / 1.504120 (0.559490) | 1.866136 / 1.541195 (0.324941) | 1.910185 / 1.468490 (0.441695) | 0.696584 / 4.584777 (-3.888193) | 3.398017 / 3.745712 (-0.347695) | 1.848473 / 5.269862 (-3.421388) | 1.168238 / 4.565676 (-3.397438) | 0.083222 / 0.424275 (-0.341053) | 0.012332 / 0.007607 (0.004725) | 0.538953 / 0.226044 (0.312909) | 5.421273 / 2.268929 (3.152344) | 2.499877 / 55.444624 (-52.944747) | 2.161853 / 6.876477 (-4.714624) | 2.183941 / 2.142072 (0.041868) | 0.803916 / 4.805227 (-4.001311) | 0.150266 / 6.500664 (-6.350398) | 0.067399 / 0.075469 (-0.008070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280479 / 1.841788 (-0.561309) | 13.728074 / 8.074308 (5.653766) | 12.946098 / 10.191392 (2.754706) | 0.128459 / 0.680424 (-0.551965) | 0.016567 / 0.534201 (-0.517634) | 0.374461 / 0.579283 (-0.204822) | 0.386973 / 0.434364 (-0.047391) | 0.459754 / 0.540337 (-0.080583) | 0.543870 / 1.386936 (-0.843066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#595b3d47e1fc579f5db1cbc376f756edf32904dd \"CML watermark\")\n",
"Instead of reverting the change, maybe we can use the same conversion in `to_iterable_dataset` as in `ArrowBasedBuilder._as_streaming_dataset` to avoid decoding images twice?",
"True, let me take a look",
"Closing in favor of https://github.com/huggingface/datasets/pull/5655"
] |
1,603,304,766
| 5,588
|
Flatten dataset on the fly in `save_to_disk`
|
closed
| 2023-02-28T15:37:46
| 2023-02-28T17:28:35
| 2023-02-28T17:21:17
|
https://github.com/huggingface/datasets/pull/5588
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5588",
"html_url": "https://github.com/huggingface/datasets/pull/5588",
"diff_url": "https://github.com/huggingface/datasets/pull/5588.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5588.patch",
"merged_at": "2023-02-28T17:21:17"
}
|
mariosasko
| true
|
[
"<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) | 0.009866 / 0.011353 (-0.001487) | 0.005334 / 0.011008 (-0.005675) | 0.101771 / 0.038508 (0.063263) | 0.037722 / 0.023109 (0.014613) | 0.301026 / 0.275898 (0.025128) | 0.336618 / 0.323480 (0.013138) | 0.008679 / 0.007986 (0.000693) | 0.005640 / 0.004328 (0.001312) | 0.077076 / 0.004250 (0.072825) | 0.045068 / 0.037052 (0.008016) | 0.302570 / 0.258489 (0.044081) | 0.359093 / 0.293841 (0.065252) | 0.038865 / 0.128546 (-0.089681) | 0.012318 / 0.075646 (-0.063328) | 0.334819 / 0.419271 (-0.084452) | 0.047980 / 0.043533 (0.004447) | 0.296999 / 0.255139 (0.041860) | 0.318855 / 0.283200 (0.035656) | 0.110633 / 0.141683 (-0.031050) | 1.464326 / 1.452155 (0.012172) | 1.537386 / 1.492716 (0.044670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282906 / 0.018006 (0.264900) | 0.498418 / 0.000490 (0.497928) | 0.001507 / 0.000200 (0.001307) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.114385 / 0.014526 (0.099859) | 0.125783 / 0.176557 (-0.050774) | 0.193458 / 0.737135 (-0.543678) | 0.129725 / 0.296338 (-0.166614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403822 / 0.215209 (0.188613) | 4.034180 / 2.077655 (1.956525) | 1.768206 / 1.504120 (0.264086) | 1.579267 / 1.541195 (0.038072) | 1.725077 / 1.468490 (0.256587) | 0.698743 / 4.584777 (-3.886034) | 3.723481 / 3.745712 (-0.022231) | 2.302374 / 5.269862 (-2.967488) | 1.497954 / 4.565676 (-3.067723) | 0.087360 / 0.424275 (-0.336915) | 0.012453 / 0.007607 (0.004846) | 0.523374 / 0.226044 (0.297329) | 5.244962 / 2.268929 (2.976033) | 2.272874 / 55.444624 (-53.171750) | 1.935570 / 6.876477 (-4.940907) | 2.043151 / 2.142072 (-0.098921) | 0.866298 / 4.805227 (-3.938929) | 0.169376 / 6.500664 (-6.331288) | 0.064578 / 0.075469 (-0.010892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217372 / 1.841788 (-0.624416) | 15.896050 / 8.074308 (7.821742) | 15.165190 / 10.191392 (4.973798) | 0.171168 / 0.680424 (-0.509256) | 0.029770 / 0.534201 (-0.504431) | 0.449030 / 0.579283 (-0.130253) | 0.454704 / 0.434364 (0.020340) | 0.550689 / 0.540337 (0.010351) | 0.651182 / 1.386936 (-0.735754) |\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) | 0.008072 / 0.011353 (-0.003281) | 0.005533 / 0.011008 (-0.005475) | 0.076343 / 0.038508 (0.037835) | 0.037997 / 0.023109 (0.014888) | 0.350465 / 0.275898 (0.074567) | 0.391168 / 0.323480 (0.067688) | 0.006475 / 0.007986 (-0.001511) | 0.004299 / 0.004328 (-0.000029) | 0.074867 / 0.004250 (0.070617) | 0.055256 / 0.037052 (0.018204) | 0.363919 / 0.258489 (0.105430) | 0.396521 / 0.293841 (0.102680) | 0.037746 / 0.128546 (-0.090801) | 0.012556 / 0.075646 (-0.063091) | 0.087974 / 0.419271 (-0.331297) | 0.050850 / 0.043533 (0.007317) | 0.345857 / 0.255139 (0.090718) | 0.361019 / 0.283200 (0.077820) | 0.111007 / 0.141683 (-0.030676) | 1.444014 / 1.452155 (-0.008140) | 1.533154 / 1.492716 (0.040438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332114 / 0.018006 (0.314108) | 0.517232 / 0.000490 (0.516742) | 0.004459 / 0.000200 (0.004259) | 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) | 0.033147 / 0.037411 (-0.004264) | 0.119983 / 0.014526 (0.105457) | 0.125970 / 0.176557 (-0.050586) | 0.196375 / 0.737135 (-0.540760) | 0.133849 / 0.296338 (-0.162489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429477 / 0.215209 (0.214267) | 4.263750 / 2.077655 (2.186096) | 2.079409 / 1.504120 (0.575289) | 1.899831 / 1.541195 (0.358636) | 2.048472 / 1.468490 (0.579982) | 0.720945 / 4.584777 (-3.863832) | 3.813195 / 3.745712 (0.067483) | 2.250353 / 5.269862 (-3.019508) | 1.401496 / 4.565676 (-3.164181) | 0.090052 / 0.424275 (-0.334223) | 0.012552 / 0.007607 (0.004945) | 0.536839 / 0.226044 (0.310794) | 5.361089 / 2.268929 (3.092161) | 2.559710 / 55.444624 (-52.884914) | 2.226963 / 6.876477 (-4.649513) | 2.341898 / 2.142072 (0.199825) | 0.872115 / 4.805227 (-3.933112) | 0.173776 / 6.500664 (-6.326888) | 0.068567 / 0.075469 (-0.006902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294583 / 1.841788 (-0.547205) | 16.624099 / 8.074308 (8.549791) | 13.698509 / 10.191392 (3.507117) | 0.161917 / 0.680424 (-0.518506) | 0.017744 / 0.534201 (-0.516457) | 0.428547 / 0.579283 (-0.150736) | 0.424687 / 0.434364 (-0.009677) | 0.525812 / 0.540337 (-0.014525) | 0.629075 / 1.386936 (-0.757861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33e4d6af919db17bf9a1eac544a0501b5972393b \"CML watermark\")\n",
"_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) | 0.008667 / 0.011353 (-0.002686) | 0.004921 / 0.011008 (-0.006087) | 0.098352 / 0.038508 (0.059844) | 0.033983 / 0.023109 (0.010873) | 0.291640 / 0.275898 (0.015742) | 0.323388 / 0.323480 (-0.000092) | 0.007943 / 0.007986 (-0.000043) | 0.003922 / 0.004328 (-0.000407) | 0.075861 / 0.004250 (0.071610) | 0.042606 / 0.037052 (0.005554) | 0.298571 / 0.258489 (0.040081) | 0.345496 / 0.293841 (0.051655) | 0.037443 / 0.128546 (-0.091103) | 0.012114 / 0.075646 (-0.063532) | 0.333269 / 0.419271 (-0.086003) | 0.047762 / 0.043533 (0.004229) | 0.295452 / 0.255139 (0.040313) | 0.319641 / 0.283200 (0.036441) | 0.101083 / 0.141683 (-0.040600) | 1.432179 / 1.452155 (-0.019976) | 1.523976 / 1.492716 (0.031260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241327 / 0.018006 (0.223321) | 0.538315 / 0.000490 (0.537825) | 0.003479 / 0.000200 (0.003279) | 0.000082 / 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) | 0.025857 / 0.037411 (-0.011554) | 0.104833 / 0.014526 (0.090307) | 0.116826 / 0.176557 (-0.059730) | 0.183460 / 0.737135 (-0.553675) | 0.119595 / 0.296338 (-0.176743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397533 / 0.215209 (0.182324) | 3.968664 / 2.077655 (1.891010) | 1.774025 / 1.504120 (0.269905) | 1.577424 / 1.541195 (0.036229) | 1.623049 / 1.468490 (0.154559) | 0.701008 / 4.584777 (-3.883769) | 3.753278 / 3.745712 (0.007565) | 2.078313 / 5.269862 (-3.191549) | 1.335639 / 4.565676 (-3.230037) | 0.085216 / 0.424275 (-0.339059) | 0.012087 / 0.007607 (0.004480) | 0.513219 / 0.226044 (0.287174) | 5.097693 / 2.268929 (2.828765) | 2.275030 / 55.444624 (-53.169594) | 1.928037 / 6.876477 (-4.948439) | 1.941216 / 2.142072 (-0.200856) | 0.856720 / 4.805227 (-3.948507) | 0.166723 / 6.500664 (-6.333941) | 0.062263 / 0.075469 (-0.013206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196054 / 1.841788 (-0.645734) | 14.190526 / 8.074308 (6.116218) | 14.053768 / 10.191392 (3.862376) | 0.179982 / 0.680424 (-0.500442) | 0.029024 / 0.534201 (-0.505177) | 0.440391 / 0.579283 (-0.138892) | 0.445627 / 0.434364 (0.011264) | 0.543098 / 0.540337 (0.002761) | 0.640577 / 1.386936 (-0.746359) |\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) | 0.007008 / 0.011353 (-0.004345) | 0.005015 / 0.011008 (-0.005993) | 0.073783 / 0.038508 (0.035274) | 0.032401 / 0.023109 (0.009292) | 0.343382 / 0.275898 (0.067484) | 0.358317 / 0.323480 (0.034837) | 0.005548 / 0.007986 (-0.002437) | 0.005188 / 0.004328 (0.000859) | 0.072867 / 0.004250 (0.068617) | 0.048555 / 0.037052 (0.011502) | 0.334516 / 0.258489 (0.076027) | 0.390263 / 0.293841 (0.096422) | 0.036343 / 0.128546 (-0.092203) | 0.012243 / 0.075646 (-0.063404) | 0.087067 / 0.419271 (-0.332205) | 0.049025 / 0.043533 (0.005492) | 0.333977 / 0.255139 (0.078838) | 0.354427 / 0.283200 (0.071227) | 0.104771 / 0.141683 (-0.036912) | 1.434588 / 1.452155 (-0.017567) | 1.519788 / 1.492716 (0.027072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264002 / 0.018006 (0.245996) | 0.547902 / 0.000490 (0.547412) | 0.000461 / 0.000200 (0.000261) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028916 / 0.037411 (-0.008496) | 0.110267 / 0.014526 (0.095741) | 0.119190 / 0.176557 (-0.057367) | 0.188599 / 0.737135 (-0.548537) | 0.126948 / 0.296338 (-0.169391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422777 / 0.215209 (0.207568) | 4.209813 / 2.077655 (2.132158) | 2.001360 / 1.504120 (0.497240) | 1.802651 / 1.541195 (0.261456) | 1.860357 / 1.468490 (0.391867) | 0.695006 / 4.584777 (-3.889771) | 3.741917 / 3.745712 (-0.003795) | 3.313071 / 5.269862 (-1.956791) | 1.726366 / 4.565676 (-2.839311) | 0.086185 / 0.424275 (-0.338090) | 0.012256 / 0.007607 (0.004649) | 0.536874 / 0.226044 (0.310830) | 5.253008 / 2.268929 (2.984079) | 2.457189 / 55.444624 (-52.987436) | 2.112199 / 6.876477 (-4.764278) | 2.117867 / 2.142072 (-0.024205) | 0.831914 / 4.805227 (-3.973314) | 0.168238 / 6.500664 (-6.332426) | 0.065075 / 0.075469 (-0.010394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280795 / 1.841788 (-0.560993) | 14.606608 / 8.074308 (6.532299) | 13.317597 / 10.191392 (3.126205) | 0.166590 / 0.680424 (-0.513834) | 0.017520 / 0.534201 (-0.516681) | 0.420978 / 0.579283 (-0.158305) | 0.415708 / 0.434364 (-0.018656) | 0.523619 / 0.540337 (-0.016718) | 0.625299 / 1.386936 (-0.761637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2a83a8ea4b3a87a925ef44b787e87b59bf68225 \"CML watermark\")\n"
] |
1,603,139,420
| 5,587
|
Fix `sort` with indices mapping
|
closed
| 2023-02-28T14:05:08
| 2023-02-28T17:28:57
| 2023-02-28T17:21:58
|
https://github.com/huggingface/datasets/pull/5587
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5587",
"html_url": "https://github.com/huggingface/datasets/pull/5587",
"diff_url": "https://github.com/huggingface/datasets/pull/5587.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5587.patch",
"merged_at": "2023-02-28T17:21:58"
}
|
mariosasko
| true
|
[
"<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) | 0.008740 / 0.011353 (-0.002613) | 0.004501 / 0.011008 (-0.006507) | 0.100045 / 0.038508 (0.061537) | 0.029999 / 0.023109 (0.006890) | 0.303556 / 0.275898 (0.027658) | 0.335342 / 0.323480 (0.011863) | 0.006996 / 0.007986 (-0.000989) | 0.004183 / 0.004328 (-0.000145) | 0.076434 / 0.004250 (0.072183) | 0.033899 / 0.037052 (-0.003153) | 0.301312 / 0.258489 (0.042823) | 0.343136 / 0.293841 (0.049295) | 0.034062 / 0.128546 (-0.094484) | 0.011465 / 0.075646 (-0.064181) | 0.323134 / 0.419271 (-0.096137) | 0.040820 / 0.043533 (-0.002713) | 0.301708 / 0.255139 (0.046569) | 0.329528 / 0.283200 (0.046328) | 0.088393 / 0.141683 (-0.053290) | 1.460996 / 1.452155 (0.008842) | 1.531145 / 1.492716 (0.038429) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191918 / 0.018006 (0.173912) | 0.414099 / 0.000490 (0.413610) | 0.000411 / 0.000200 (0.000211) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022707 / 0.037411 (-0.014704) | 0.096991 / 0.014526 (0.082465) | 0.106070 / 0.176557 (-0.070487) | 0.151275 / 0.737135 (-0.585860) | 0.108909 / 0.296338 (-0.187430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422499 / 0.215209 (0.207289) | 4.205551 / 2.077655 (2.127896) | 1.918960 / 1.504120 (0.414841) | 1.715421 / 1.541195 (0.174227) | 1.768969 / 1.468490 (0.300479) | 0.692243 / 4.584777 (-3.892534) | 3.382452 / 3.745712 (-0.363260) | 1.943695 / 5.269862 (-3.326166) | 1.250482 / 4.565676 (-3.315195) | 0.082084 / 0.424275 (-0.342191) | 0.012446 / 0.007607 (0.004839) | 0.525584 / 0.226044 (0.299539) | 5.275530 / 2.268929 (3.006602) | 2.386207 / 55.444624 (-53.058418) | 2.043920 / 6.876477 (-4.832557) | 2.030932 / 2.142072 (-0.111140) | 0.810233 / 4.805227 (-3.994994) | 0.148139 / 6.500664 (-6.352525) | 0.064617 / 0.075469 (-0.010852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227352 / 1.841788 (-0.614436) | 13.527623 / 8.074308 (5.453315) | 14.018551 / 10.191392 (3.827159) | 0.140333 / 0.680424 (-0.540091) | 0.028349 / 0.534201 (-0.505852) | 0.394904 / 0.579283 (-0.184379) | 0.406532 / 0.434364 (-0.027831) | 0.471714 / 0.540337 (-0.068624) | 0.568517 / 1.386936 (-0.818419) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006623 / 0.011353 (-0.004730) | 0.004464 / 0.011008 (-0.006544) | 0.076342 / 0.038508 (0.037834) | 0.027451 / 0.023109 (0.004341) | 0.343851 / 0.275898 (0.067953) | 0.385723 / 0.323480 (0.062243) | 0.005624 / 0.007986 (-0.002362) | 0.004685 / 0.004328 (0.000356) | 0.075669 / 0.004250 (0.071419) | 0.037297 / 0.037052 (0.000244) | 0.343363 / 0.258489 (0.084874) | 0.396115 / 0.293841 (0.102274) | 0.031577 / 0.128546 (-0.096970) | 0.011557 / 0.075646 (-0.064090) | 0.085626 / 0.419271 (-0.333645) | 0.041699 / 0.043533 (-0.001834) | 0.340826 / 0.255139 (0.085687) | 0.377167 / 0.283200 (0.093967) | 0.088632 / 0.141683 (-0.053051) | 1.464500 / 1.452155 (0.012345) | 1.556686 / 1.492716 (0.063969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231136 / 0.018006 (0.213130) | 0.402687 / 0.000490 (0.402197) | 0.000590 / 0.000200 (0.000390) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024926 / 0.037411 (-0.012485) | 0.101062 / 0.014526 (0.086536) | 0.106481 / 0.176557 (-0.070075) | 0.159167 / 0.737135 (-0.577968) | 0.110948 / 0.296338 (-0.185390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441813 / 0.215209 (0.226603) | 4.416332 / 2.077655 (2.338677) | 2.080621 / 1.504120 (0.576501) | 1.877832 / 1.541195 (0.336637) | 1.944778 / 1.468490 (0.476288) | 0.704634 / 4.584777 (-3.880143) | 3.433955 / 3.745712 (-0.311758) | 1.863493 / 5.269862 (-3.406368) | 1.168869 / 4.565676 (-3.396807) | 0.084095 / 0.424275 (-0.340180) | 0.012440 / 0.007607 (0.004833) | 0.545122 / 0.226044 (0.319077) | 5.472214 / 2.268929 (3.203285) | 2.514580 / 55.444624 (-52.930044) | 2.164570 / 6.876477 (-4.711907) | 2.193467 / 2.142072 (0.051395) | 0.809056 / 4.805227 (-3.996171) | 0.152343 / 6.500664 (-6.348321) | 0.067610 / 0.075469 (-0.007859) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280968 / 1.841788 (-0.560820) | 13.887674 / 8.074308 (5.813366) | 13.160405 / 10.191392 (2.969013) | 0.128601 / 0.680424 (-0.551823) | 0.016420 / 0.534201 (-0.517780) | 0.382810 / 0.579283 (-0.196473) | 0.394386 / 0.434364 (-0.039978) | 0.470254 / 0.540337 (-0.070083) | 0.566907 / 1.386936 (-0.820029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8cc6950322337ea8873939541c53858b10c0f3b9 \"CML watermark\")\n",
"_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) | 0.008673 / 0.011353 (-0.002679) | 0.004475 / 0.011008 (-0.006533) | 0.102060 / 0.038508 (0.063552) | 0.029438 / 0.023109 (0.006329) | 0.351785 / 0.275898 (0.075887) | 0.388199 / 0.323480 (0.064719) | 0.007011 / 0.007986 (-0.000974) | 0.003317 / 0.004328 (-0.001012) | 0.080931 / 0.004250 (0.076681) | 0.033449 / 0.037052 (-0.003603) | 0.360329 / 0.258489 (0.101840) | 0.400069 / 0.293841 (0.106228) | 0.033628 / 0.128546 (-0.094918) | 0.011462 / 0.075646 (-0.064184) | 0.323781 / 0.419271 (-0.095490) | 0.040686 / 0.043533 (-0.002847) | 0.332715 / 0.255139 (0.077576) | 0.370339 / 0.283200 (0.087139) | 0.084633 / 0.141683 (-0.057050) | 1.459452 / 1.452155 (0.007297) | 1.547719 / 1.492716 (0.055003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187051 / 0.018006 (0.169045) | 0.402625 / 0.000490 (0.402135) | 0.002218 / 0.000200 (0.002018) | 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) | 0.025240 / 0.037411 (-0.012171) | 0.102201 / 0.014526 (0.087675) | 0.108629 / 0.176557 (-0.067927) | 0.156686 / 0.737135 (-0.580449) | 0.111383 / 0.296338 (-0.184955) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418099 / 0.215209 (0.202890) | 4.163345 / 2.077655 (2.085690) | 1.868419 / 1.504120 (0.364300) | 1.662066 / 1.541195 (0.120871) | 1.705912 / 1.468490 (0.237422) | 0.696391 / 4.584777 (-3.888386) | 3.338307 / 3.745712 (-0.407405) | 1.923255 / 5.269862 (-3.346607) | 1.249220 / 4.565676 (-3.316457) | 0.082037 / 0.424275 (-0.342238) | 0.012232 / 0.007607 (0.004624) | 0.523913 / 0.226044 (0.297869) | 5.290036 / 2.268929 (3.021107) | 2.319729 / 55.444624 (-53.124896) | 1.987345 / 6.876477 (-4.889132) | 2.044516 / 2.142072 (-0.097556) | 0.812098 / 4.805227 (-3.993129) | 0.147327 / 6.500664 (-6.353337) | 0.063838 / 0.075469 (-0.011631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219652 / 1.841788 (-0.622136) | 13.271513 / 8.074308 (5.197205) | 13.799982 / 10.191392 (3.608590) | 0.150055 / 0.680424 (-0.530369) | 0.028804 / 0.534201 (-0.505397) | 0.395452 / 0.579283 (-0.183831) | 0.398758 / 0.434364 (-0.035606) | 0.468575 / 0.540337 (-0.071763) | 0.553324 / 1.386936 (-0.833612) |\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) | 0.006498 / 0.011353 (-0.004855) | 0.004439 / 0.011008 (-0.006569) | 0.076525 / 0.038508 (0.038017) | 0.027184 / 0.023109 (0.004074) | 0.364705 / 0.275898 (0.088807) | 0.409481 / 0.323480 (0.086001) | 0.004831 / 0.007986 (-0.003154) | 0.004524 / 0.004328 (0.000196) | 0.075403 / 0.004250 (0.071153) | 0.039013 / 0.037052 (0.001960) | 0.364042 / 0.258489 (0.105553) | 0.413090 / 0.293841 (0.119249) | 0.032052 / 0.128546 (-0.096495) | 0.011514 / 0.075646 (-0.064132) | 0.085219 / 0.419271 (-0.334053) | 0.041448 / 0.043533 (-0.002085) | 0.350371 / 0.255139 (0.095232) | 0.386670 / 0.283200 (0.103470) | 0.089824 / 0.141683 (-0.051859) | 1.487392 / 1.452155 (0.035238) | 1.537201 / 1.492716 (0.044485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231555 / 0.018006 (0.213549) | 0.407505 / 0.000490 (0.407016) | 0.000382 / 0.000200 (0.000182) | 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) | 0.026665 / 0.037411 (-0.010747) | 0.105852 / 0.014526 (0.091326) | 0.108228 / 0.176557 (-0.068328) | 0.164164 / 0.737135 (-0.572972) | 0.114284 / 0.296338 (-0.182054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448957 / 0.215209 (0.233748) | 4.500058 / 2.077655 (2.422403) | 2.331660 / 1.504120 (0.827541) | 2.119904 / 1.541195 (0.578710) | 2.101489 / 1.468490 (0.632999) | 0.696580 / 4.584777 (-3.888197) | 3.364206 / 3.745712 (-0.381506) | 2.550157 / 5.269862 (-2.719704) | 1.496455 / 4.565676 (-3.069222) | 0.083289 / 0.424275 (-0.340986) | 0.012283 / 0.007607 (0.004676) | 0.555581 / 0.226044 (0.329537) | 5.556284 / 2.268929 (3.287355) | 2.595261 / 55.444624 (-52.849363) | 2.234793 / 6.876477 (-4.641683) | 2.280150 / 2.142072 (0.138078) | 0.817885 / 4.805227 (-3.987343) | 0.151481 / 6.500664 (-6.349183) | 0.066764 / 0.075469 (-0.008705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318875 / 1.841788 (-0.522913) | 14.220380 / 8.074308 (6.146072) | 13.922773 / 10.191392 (3.731381) | 0.154608 / 0.680424 (-0.525816) | 0.016343 / 0.534201 (-0.517858) | 0.380758 / 0.579283 (-0.198525) | 0.392595 / 0.434364 (-0.041769) | 0.468844 / 0.540337 (-0.071493) | 0.561047 / 1.386936 (-0.825889) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d57fdcf2c8110b4b599289695fa065d1fc4936d4 \"CML watermark\")\n"
] |
1,602,961,544
| 5,586
|
.sort() is broken when used after .filter(), only in 2.10.0
|
closed
| 2023-02-28T12:18:09
| 2023-02-28T18:17:26
| 2023-02-28T17:21:59
|
https://github.com/huggingface/datasets/issues/5586
| null |
MattYoon
| false
|
[
"Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix"
] |
1,602,190,030
| 5,585
|
Cache is not transportable
|
closed
| 2023-02-28T00:53:06
| 2023-02-28T21:26:52
| 2023-02-28T21:26:52
|
https://github.com/huggingface/datasets/issues/5585
| null |
davidgilbertson
| false
|
[
"Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.\r\n\r\nFinally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.",
"OK good to know. Thanks @lhoestq !"
] |
1,601,821,808
| 5,584
|
Unable to load coyo700M dataset
|
closed
| 2023-02-27T19:35:03
| 2023-02-28T07:27:59
| 2023-02-28T07:27:58
|
https://github.com/huggingface/datasets/issues/5584
| null |
manuaero
| false
|
[
"Hi @manuaero \r\n\r\nThank you for your interest in the COYO dataset.\r\n\r\nOur dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.\r\n\r\nWe provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out.\r\n\r\nThank you."
] |
1,601,583,625
| 5,583
|
Do no write index by default when exporting a dataset
|
closed
| 2023-02-27T17:04:46
| 2023-02-28T13:52:15
| 2023-02-28T13:44:04
|
https://github.com/huggingface/datasets/pull/5583
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5583",
"html_url": "https://github.com/huggingface/datasets/pull/5583",
"diff_url": "https://github.com/huggingface/datasets/pull/5583.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5583.patch",
"merged_at": "2023-02-28T13:44:04"
}
|
mariosasko
| true
|
[
"_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) | 0.009044 / 0.011353 (-0.002309) | 0.004244 / 0.011008 (-0.006765) | 0.106705 / 0.038508 (0.068197) | 0.029779 / 0.023109 (0.006670) | 0.289684 / 0.275898 (0.013786) | 0.347100 / 0.323480 (0.023620) | 0.007071 / 0.007986 (-0.000915) | 0.003734 / 0.004328 (-0.000595) | 0.077971 / 0.004250 (0.073720) | 0.035323 / 0.037052 (-0.001730) | 0.334520 / 0.258489 (0.076031) | 0.375804 / 0.293841 (0.081964) | 0.049211 / 0.128546 (-0.079335) | 0.016992 / 0.075646 (-0.058654) | 0.337208 / 0.419271 (-0.082064) | 0.053700 / 0.043533 (0.010167) | 0.295750 / 0.255139 (0.040611) | 0.330157 / 0.283200 (0.046958) | 0.097017 / 0.141683 (-0.044666) | 1.379353 / 1.452155 (-0.072802) | 1.402670 / 1.492716 (-0.090047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012685 / 0.018006 (-0.005321) | 0.474541 / 0.000490 (0.474051) | 0.006752 / 0.000200 (0.006552) | 0.000097 / 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) | 0.025735 / 0.037411 (-0.011676) | 0.092507 / 0.014526 (0.077982) | 0.100275 / 0.176557 (-0.076281) | 0.180359 / 0.737135 (-0.556777) | 0.104312 / 0.296338 (-0.192026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456558 / 0.215209 (0.241349) | 4.786667 / 2.077655 (2.709012) | 1.873169 / 1.504120 (0.369050) | 1.640935 / 1.541195 (0.099741) | 1.614543 / 1.468490 (0.146053) | 0.936144 / 4.584777 (-3.648633) | 4.699886 / 3.745712 (0.954174) | 2.398545 / 5.269862 (-2.871317) | 1.642808 / 4.565676 (-2.922868) | 0.124803 / 0.424275 (-0.299472) | 0.011848 / 0.007607 (0.004241) | 0.631684 / 0.226044 (0.405639) | 6.096052 / 2.268929 (3.827124) | 2.463052 / 55.444624 (-52.981572) | 1.928551 / 6.876477 (-4.947926) | 1.927790 / 2.142072 (-0.214283) | 1.098912 / 4.805227 (-3.706315) | 0.196343 / 6.500664 (-6.304321) | 0.063296 / 0.075469 (-0.012173) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255032 / 1.841788 (-0.586755) | 13.853623 / 8.074308 (5.779315) | 16.303280 / 10.191392 (6.111888) | 0.227287 / 0.680424 (-0.453137) | 0.037527 / 0.534201 (-0.496674) | 0.449345 / 0.579283 (-0.129938) | 0.522054 / 0.434364 (0.087690) | 0.552848 / 0.540337 (0.012511) | 0.642994 / 1.386936 (-0.743942) |\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) | 0.008470 / 0.011353 (-0.002883) | 0.005167 / 0.011008 (-0.005841) | 0.077794 / 0.038508 (0.039286) | 0.029228 / 0.023109 (0.006119) | 0.340828 / 0.275898 (0.064930) | 0.400170 / 0.323480 (0.076691) | 0.005485 / 0.007986 (-0.002500) | 0.003854 / 0.004328 (-0.000475) | 0.077597 / 0.004250 (0.073346) | 0.036519 / 0.037052 (-0.000533) | 0.335522 / 0.258489 (0.077033) | 0.412622 / 0.293841 (0.118781) | 0.044587 / 0.128546 (-0.083959) | 0.016024 / 0.075646 (-0.059623) | 0.092312 / 0.419271 (-0.326960) | 0.055660 / 0.043533 (0.012127) | 0.343140 / 0.255139 (0.088001) | 0.386403 / 0.283200 (0.103203) | 0.098634 / 0.141683 (-0.043049) | 1.326126 / 1.452155 (-0.126029) | 1.430316 / 1.492716 (-0.062400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222807 / 0.018006 (0.204801) | 0.473622 / 0.000490 (0.473132) | 0.000376 / 0.000200 (0.000176) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024599 / 0.037411 (-0.012813) | 0.100743 / 0.014526 (0.086217) | 0.112086 / 0.176557 (-0.064471) | 0.198294 / 0.737135 (-0.538842) | 0.111210 / 0.296338 (-0.185129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494120 / 0.215209 (0.278911) | 5.117958 / 2.077655 (3.040303) | 2.305131 / 1.504120 (0.801011) | 2.015591 / 1.541195 (0.474396) | 2.027284 / 1.468490 (0.558794) | 1.014241 / 4.584777 (-3.570536) | 4.738836 / 3.745712 (0.993124) | 2.519718 / 5.269862 (-2.750143) | 1.706379 / 4.565676 (-2.859298) | 0.122452 / 0.424275 (-0.301824) | 0.011500 / 0.007607 (0.003893) | 0.632864 / 0.226044 (0.406820) | 6.295457 / 2.268929 (4.026529) | 2.824897 / 55.444624 (-52.619727) | 2.324359 / 6.876477 (-4.552117) | 2.281046 / 2.142072 (0.138974) | 1.173570 / 4.805227 (-3.631657) | 0.197195 / 6.500664 (-6.303469) | 0.064845 / 0.075469 (-0.010624) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273224 / 1.841788 (-0.568563) | 14.531155 / 8.074308 (6.456847) | 15.892176 / 10.191392 (5.700784) | 0.208051 / 0.680424 (-0.472373) | 0.023119 / 0.534201 (-0.511082) | 0.422317 / 0.579283 (-0.156966) | 0.519946 / 0.434364 (0.085582) | 0.544517 / 0.540337 (0.004179) | 0.605955 / 1.386936 (-0.780981) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#337a4a91d0268c68f26760321c9b45bb4a98832a \"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) | 0.010806 / 0.011353 (-0.000547) | 0.005631 / 0.011008 (-0.005378) | 0.113166 / 0.038508 (0.074657) | 0.042980 / 0.023109 (0.019871) | 0.344856 / 0.275898 (0.068958) | 0.404417 / 0.323480 (0.080938) | 0.012222 / 0.007986 (0.004236) | 0.004470 / 0.004328 (0.000141) | 0.088072 / 0.004250 (0.083822) | 0.049815 / 0.037052 (0.012763) | 0.366532 / 0.258489 (0.108043) | 0.392558 / 0.293841 (0.098717) | 0.045411 / 0.128546 (-0.083135) | 0.014118 / 0.075646 (-0.061529) | 0.392894 / 0.419271 (-0.026378) | 0.067713 / 0.043533 (0.024181) | 0.353013 / 0.255139 (0.097874) | 0.378375 / 0.283200 (0.095175) | 0.123686 / 0.141683 (-0.017996) | 1.665272 / 1.452155 (0.213118) | 1.748383 / 1.492716 (0.255667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011672 / 0.018006 (-0.006335) | 0.481667 / 0.000490 (0.481178) | 0.003644 / 0.000200 (0.003444) | 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) | 0.030436 / 0.037411 (-0.006976) | 0.122577 / 0.014526 (0.108052) | 0.135409 / 0.176557 (-0.041148) | 0.220385 / 0.737135 (-0.516750) | 0.143140 / 0.296338 (-0.153199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471146 / 0.215209 (0.255937) | 4.645023 / 2.077655 (2.567368) | 2.126783 / 1.504120 (0.622663) | 1.907905 / 1.541195 (0.366710) | 1.969561 / 1.468490 (0.501071) | 0.798670 / 4.584777 (-3.786107) | 4.394787 / 3.745712 (0.649075) | 2.353535 / 5.269862 (-2.916327) | 1.501013 / 4.565676 (-3.064664) | 0.097472 / 0.424275 (-0.326803) | 0.014015 / 0.007607 (0.006408) | 0.589365 / 0.226044 (0.363320) | 5.897331 / 2.268929 (3.628402) | 2.656198 / 55.444624 (-52.788427) | 2.256082 / 6.876477 (-4.620395) | 2.271122 / 2.142072 (0.129050) | 0.961566 / 4.805227 (-3.843661) | 0.188303 / 6.500664 (-6.312361) | 0.073258 / 0.075469 (-0.002211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445266 / 1.841788 (-0.396522) | 16.876710 / 8.074308 (8.802402) | 16.004287 / 10.191392 (5.812895) | 0.212252 / 0.680424 (-0.468172) | 0.033186 / 0.534201 (-0.501015) | 0.520564 / 0.579283 (-0.058719) | 0.516865 / 0.434364 (0.082501) | 0.638482 / 0.540337 (0.098144) | 0.761959 / 1.386936 (-0.624977) |\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) | 0.008101 / 0.011353 (-0.003252) | 0.005512 / 0.011008 (-0.005497) | 0.086138 / 0.038508 (0.047630) | 0.038605 / 0.023109 (0.015496) | 0.413082 / 0.275898 (0.137184) | 0.444016 / 0.323480 (0.120536) | 0.006196 / 0.007986 (-0.001790) | 0.005736 / 0.004328 (0.001408) | 0.086938 / 0.004250 (0.082688) | 0.052307 / 0.037052 (0.015255) | 0.415206 / 0.258489 (0.156717) | 0.481510 / 0.293841 (0.187669) | 0.041469 / 0.128546 (-0.087077) | 0.013481 / 0.075646 (-0.062165) | 0.101528 / 0.419271 (-0.317744) | 0.056507 / 0.043533 (0.012974) | 0.418166 / 0.255139 (0.163027) | 0.443834 / 0.283200 (0.160634) | 0.116434 / 0.141683 (-0.025249) | 1.651223 / 1.452155 (0.199068) | 1.746429 / 1.492716 (0.253713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242381 / 0.018006 (0.224375) | 0.478826 / 0.000490 (0.478337) | 0.000463 / 0.000200 (0.000264) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031743 / 0.037411 (-0.005668) | 0.126141 / 0.014526 (0.111616) | 0.134539 / 0.176557 (-0.042018) | 0.216546 / 0.737135 (-0.520590) | 0.143513 / 0.296338 (-0.152825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486915 / 0.215209 (0.271706) | 4.833812 / 2.077655 (2.756158) | 2.317785 / 1.504120 (0.813666) | 2.114181 / 1.541195 (0.572986) | 2.153896 / 1.468490 (0.685406) | 0.797490 / 4.584777 (-3.787287) | 4.369950 / 3.745712 (0.624238) | 2.305492 / 5.269862 (-2.964370) | 1.488860 / 4.565676 (-3.076816) | 0.098071 / 0.424275 (-0.326204) | 0.014129 / 0.007607 (0.006522) | 0.611311 / 0.226044 (0.385266) | 6.087482 / 2.268929 (3.818554) | 2.837676 / 55.444624 (-52.606948) | 2.451819 / 6.876477 (-4.424657) | 2.456763 / 2.142072 (0.314690) | 0.957637 / 4.805227 (-3.847590) | 0.190974 / 6.500664 (-6.309690) | 0.074497 / 0.075469 (-0.000972) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.466214 / 1.841788 (-0.375574) | 17.063925 / 8.074308 (8.989617) | 14.630326 / 10.191392 (4.438934) | 0.170570 / 0.680424 (-0.509854) | 0.023794 / 0.534201 (-0.510407) | 0.509175 / 0.579283 (-0.070108) | 0.506485 / 0.434364 (0.072121) | 0.616965 / 0.540337 (0.076628) | 0.718176 / 1.386936 (-0.668760) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4f14de325e26910d026f377756dd8a231150398 \"CML watermark\")\n"
] |
1,600,932,092
| 5,582
|
Add column_names to IterableDataset
|
closed
| 2023-02-27T10:50:07
| 2023-03-13T19:10:22
| 2023-03-13T19:03:32
|
https://github.com/huggingface/datasets/pull/5582
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5582",
"html_url": "https://github.com/huggingface/datasets/pull/5582",
"diff_url": "https://github.com/huggingface/datasets/pull/5582.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5582.patch",
"merged_at": "2023-03-13T19:03:31"
}
|
patrickloeber
| true
|
[
"_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) | 0.006362 / 0.011353 (-0.004991) | 0.004546 / 0.011008 (-0.006462) | 0.097003 / 0.038508 (0.058495) | 0.028007 / 0.023109 (0.004898) | 0.315097 / 0.275898 (0.039199) | 0.365128 / 0.323480 (0.041649) | 0.004819 / 0.007986 (-0.003167) | 0.003335 / 0.004328 (-0.000994) | 0.076665 / 0.004250 (0.072415) | 0.038285 / 0.037052 (0.001233) | 0.322100 / 0.258489 (0.063611) | 0.407466 / 0.293841 (0.113625) | 0.031580 / 0.128546 (-0.096966) | 0.011645 / 0.075646 (-0.064001) | 0.321789 / 0.419271 (-0.097483) | 0.051015 / 0.043533 (0.007483) | 0.331762 / 0.255139 (0.076623) | 0.369727 / 0.283200 (0.086527) | 0.090144 / 0.141683 (-0.051539) | 1.485480 / 1.452155 (0.033326) | 1.562032 / 1.492716 (0.069316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201192 / 0.018006 (0.183186) | 0.409760 / 0.000490 (0.409270) | 0.002220 / 0.000200 (0.002020) | 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) | 0.022361 / 0.037411 (-0.015050) | 0.096375 / 0.014526 (0.081849) | 0.101369 / 0.176557 (-0.075188) | 0.161568 / 0.737135 (-0.575568) | 0.105094 / 0.296338 (-0.191245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426251 / 0.215209 (0.211042) | 4.261374 / 2.077655 (2.183720) | 2.015688 / 1.504120 (0.511569) | 1.833708 / 1.541195 (0.292513) | 1.908994 / 1.468490 (0.440504) | 0.703108 / 4.584777 (-3.881669) | 3.420767 / 3.745712 (-0.324945) | 1.844776 / 5.269862 (-3.425086) | 1.158470 / 4.565676 (-3.407207) | 0.083324 / 0.424275 (-0.340951) | 0.013054 / 0.007607 (0.005447) | 0.521473 / 0.226044 (0.295429) | 5.245505 / 2.268929 (2.976576) | 2.349110 / 55.444624 (-53.095515) | 2.011119 / 6.876477 (-4.865358) | 2.217807 / 2.142072 (0.075734) | 0.808584 / 4.805227 (-3.996643) | 0.151337 / 6.500664 (-6.349327) | 0.065815 / 0.075469 (-0.009654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221839 / 1.841788 (-0.619949) | 13.634161 / 8.074308 (5.559853) | 13.915360 / 10.191392 (3.723968) | 0.126448 / 0.680424 (-0.553976) | 0.016614 / 0.534201 (-0.517587) | 0.379150 / 0.579283 (-0.200133) | 0.382134 / 0.434364 (-0.052230) | 0.442845 / 0.540337 (-0.097493) | 0.519578 / 1.386936 (-0.867358) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006238 / 0.011353 (-0.005115) | 0.004591 / 0.011008 (-0.006418) | 0.076652 / 0.038508 (0.038144) | 0.026882 / 0.023109 (0.003773) | 0.341948 / 0.275898 (0.066050) | 0.375244 / 0.323480 (0.051764) | 0.004770 / 0.007986 (-0.003215) | 0.004703 / 0.004328 (0.000374) | 0.075797 / 0.004250 (0.071547) | 0.035001 / 0.037052 (-0.002051) | 0.341670 / 0.258489 (0.083181) | 0.383028 / 0.293841 (0.089187) | 0.031756 / 0.128546 (-0.096791) | 0.011714 / 0.075646 (-0.063933) | 0.085552 / 0.419271 (-0.333720) | 0.047697 / 0.043533 (0.004164) | 0.340805 / 0.255139 (0.085666) | 0.365478 / 0.283200 (0.082278) | 0.093146 / 0.141683 (-0.048537) | 1.465100 / 1.452155 (0.012945) | 1.552708 / 1.492716 (0.059992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209117 / 0.018006 (0.191111) | 0.402622 / 0.000490 (0.402132) | 0.003940 / 0.000200 (0.003740) | 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) | 0.026027 / 0.037411 (-0.011385) | 0.098346 / 0.014526 (0.083820) | 0.107349 / 0.176557 (-0.069207) | 0.157846 / 0.737135 (-0.579289) | 0.109566 / 0.296338 (-0.186772) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445088 / 0.215209 (0.229879) | 4.450727 / 2.077655 (2.373072) | 2.237798 / 1.504120 (0.733678) | 2.026060 / 1.541195 (0.484866) | 2.020464 / 1.468490 (0.551974) | 0.700155 / 4.584777 (-3.884622) | 3.435497 / 3.745712 (-0.310215) | 2.851970 / 5.269862 (-2.417891) | 1.512689 / 4.565676 (-3.052988) | 0.083717 / 0.424275 (-0.340558) | 0.012466 / 0.007607 (0.004859) | 0.545130 / 0.226044 (0.319085) | 5.478228 / 2.268929 (3.209300) | 2.554169 / 55.444624 (-52.890456) | 2.214703 / 6.876477 (-4.661774) | 2.229997 / 2.142072 (0.087925) | 0.809851 / 4.805227 (-3.995376) | 0.151019 / 6.500664 (-6.349645) | 0.066354 / 0.075469 (-0.009115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281016 / 1.841788 (-0.560772) | 14.071312 / 8.074308 (5.997004) | 14.682465 / 10.191392 (4.491073) | 0.144197 / 0.680424 (-0.536227) | 0.017088 / 0.534201 (-0.517113) | 0.379049 / 0.579283 (-0.200234) | 0.390713 / 0.434364 (-0.043650) | 0.435804 / 0.540337 (-0.104534) | 0.518895 / 1.386936 (-0.868041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fc5c84f36684343bff3e424cb0fd1ac5ecdd66da \"CML watermark\")\n"
] |
1,600,675,489
| 5,581
|
[DOC] Mistaken docs on set_format
|
closed
| 2023-02-27T08:03:09
| 2023-02-28T19:19:17
| 2023-02-28T19:19:17
|
https://github.com/huggingface/datasets/issues/5581
| null |
NightMachinery
| false
|
[
"Thanks for reporting!"
] |
1,600,431,792
| 5,580
|
Support cloud storage in load_dataset via fsspec
|
closed
| 2023-02-27T04:06:05
| 2024-11-27T01:25:39
| 2023-03-11T00:55:40
|
https://github.com/huggingface/datasets/pull/5580
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5580",
"html_url": "https://github.com/huggingface/datasets/pull/5580",
"diff_url": "https://github.com/huggingface/datasets/pull/5580.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5580.patch",
"merged_at": "2023-03-11T00:55:40"
}
|
dwyatte
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"> Regarding the tests I think it should be possible to use the mockfs fixture, it allows to play with a dummy fsspec FileSystem with the \"mock://\" protocol.\r\n\r\n> However it requires some storage_options to be passed. Maybe it can be added to DownloadConfig which is passed to cached_path, so that fsspec_get and fsspec_head can use the user's storage_options ?\r\n\r\n@lhoestq I went ahead and tested this with a patch so that I could assign the mockfs as a return value. Let me know if I'm missing something though and we need to pass storage_options down",
"> Instead of patching think it would be better to have a new filesystem TmpDirFileSystem (tmpfs) that doesn't need storage_options for the tests, and that is based on a temporary directory created just for the fixture. Maybe something like this ?\r\n\r\nThanks for the recommendation, this works great.",
"Feel free to merge `main` into your PR to fix the CI :)",
"Should be good to go. Thanks!",
"<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) | 0.006183 / 0.011353 (-0.005170) | 0.004180 / 0.011008 (-0.006829) | 0.095965 / 0.038508 (0.057457) | 0.026754 / 0.023109 (0.003645) | 0.339724 / 0.275898 (0.063826) | 0.381628 / 0.323480 (0.058149) | 0.004615 / 0.007986 (-0.003371) | 0.004469 / 0.004328 (0.000140) | 0.074035 / 0.004250 (0.069784) | 0.035089 / 0.037052 (-0.001963) | 0.352253 / 0.258489 (0.093764) | 0.389598 / 0.293841 (0.095757) | 0.032262 / 0.128546 (-0.096285) | 0.011392 / 0.075646 (-0.064254) | 0.323884 / 0.419271 (-0.095388) | 0.042658 / 0.043533 (-0.000874) | 0.331533 / 0.255139 (0.076394) | 0.364723 / 0.283200 (0.081523) | 0.086349 / 0.141683 (-0.055334) | 1.465687 / 1.452155 (0.013533) | 1.559782 / 1.492716 (0.067066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198562 / 0.018006 (0.180556) | 0.457170 / 0.000490 (0.456680) | 0.000409 / 0.000200 (0.000209) | 0.000061 / 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) | 0.022439 / 0.037411 (-0.014973) | 0.096551 / 0.014526 (0.082025) | 0.102230 / 0.176557 (-0.074326) | 0.160878 / 0.737135 (-0.576257) | 0.109348 / 0.296338 (-0.186990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456635 / 0.215209 (0.241426) | 4.563571 / 2.077655 (2.485916) | 2.313048 / 1.504120 (0.808928) | 2.117433 / 1.541195 (0.576239) | 2.127478 / 1.468490 (0.658988) | 0.699478 / 4.584777 (-3.885299) | 3.358955 / 3.745712 (-0.386757) | 1.821437 / 5.269862 (-3.448424) | 1.158239 / 4.565676 (-3.407438) | 0.083207 / 0.424275 (-0.341068) | 0.012925 / 0.007607 (0.005318) | 0.556526 / 0.226044 (0.330482) | 5.552364 / 2.268929 (3.283435) | 2.744696 / 55.444624 (-52.699928) | 2.374455 / 6.876477 (-4.502022) | 2.442021 / 2.142072 (0.299949) | 0.809393 / 4.805227 (-3.995834) | 0.152305 / 6.500664 (-6.348359) | 0.066164 / 0.075469 (-0.009305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258268 / 1.841788 (-0.583520) | 13.402391 / 8.074308 (5.328083) | 13.816927 / 10.191392 (3.625535) | 0.148466 / 0.680424 (-0.531958) | 0.016487 / 0.534201 (-0.517714) | 0.385888 / 0.579283 (-0.193395) | 0.378840 / 0.434364 (-0.055524) | 0.444527 / 0.540337 (-0.095810) | 0.531011 / 1.386936 (-0.855925) |\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) | 0.006230 / 0.011353 (-0.005123) | 0.004488 / 0.011008 (-0.006520) | 0.077539 / 0.038508 (0.039031) | 0.026611 / 0.023109 (0.003502) | 0.342093 / 0.275898 (0.066195) | 0.371555 / 0.323480 (0.048075) | 0.004665 / 0.007986 (-0.003321) | 0.003289 / 0.004328 (-0.001039) | 0.078378 / 0.004250 (0.074128) | 0.035223 / 0.037052 (-0.001829) | 0.339972 / 0.258489 (0.081483) | 0.378755 / 0.293841 (0.084914) | 0.031331 / 0.128546 (-0.097215) | 0.011406 / 0.075646 (-0.064241) | 0.086891 / 0.419271 (-0.332381) | 0.047713 / 0.043533 (0.004180) | 0.342678 / 0.255139 (0.087539) | 0.364536 / 0.283200 (0.081337) | 0.092132 / 0.141683 (-0.049551) | 1.537050 / 1.452155 (0.084895) | 1.639927 / 1.492716 (0.147211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219933 / 0.018006 (0.201927) | 0.391627 / 0.000490 (0.391137) | 0.002238 / 0.000200 (0.002038) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024890 / 0.037411 (-0.012521) | 0.098989 / 0.014526 (0.084464) | 0.104505 / 0.176557 (-0.072052) | 0.156252 / 0.737135 (-0.580884) | 0.108027 / 0.296338 (-0.188312) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443957 / 0.215209 (0.228748) | 4.450850 / 2.077655 (2.373196) | 2.076043 / 1.504120 (0.571923) | 1.866396 / 1.541195 (0.325202) | 1.902692 / 1.468490 (0.434202) | 0.703160 / 4.584777 (-3.881617) | 3.373761 / 3.745712 (-0.371951) | 2.615649 / 5.269862 (-2.654213) | 1.340612 / 4.565676 (-3.225065) | 0.083836 / 0.424275 (-0.340439) | 0.012619 / 0.007607 (0.005012) | 0.553410 / 0.226044 (0.327365) | 5.526500 / 2.268929 (3.257571) | 2.513213 / 55.444624 (-52.931411) | 2.152701 / 6.876477 (-4.723776) | 2.165092 / 2.142072 (0.023019) | 0.818381 / 4.805227 (-3.986846) | 0.152118 / 6.500664 (-6.348546) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291468 / 1.841788 (-0.550320) | 13.694828 / 8.074308 (5.620520) | 13.821019 / 10.191392 (3.629627) | 0.126077 / 0.680424 (-0.554347) | 0.016543 / 0.534201 (-0.517658) | 0.381399 / 0.579283 (-0.197884) | 0.377326 / 0.434364 (-0.057038) | 0.439275 / 0.540337 (-0.101063) | 0.524021 / 1.386936 (-0.862915) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6269979fc80ae8939294d26298897f0db5b84d \"CML watermark\")\n",
"@dwyatte \r\n> (tested manually with GCS)\r\n\r\nCan you please paste the code you used to test this with? It's not clear how one would go about actually using this to access datasets in Google Cloud Storage or S3.",
"@Xe \r\n\r\nWith GCS, this can be completely seamless if you have an activated set of credentials with access to the file (which is how I use the functionality). You should be able to pass `storage_options` with credentials too\r\n\r\n```\r\ndwyatte-mac:tmp dwyatte$ gcloud auth list\r\n Credentialed Accounts\r\nACTIVE ACCOUNT\r\n* [YOUR_EMAIL]\r\n```\r\n\r\n```\r\nIn [1]: from datasets import load_dataset\r\n ...: \r\n ...: dataset = load_dataset(\"parquet\", data_files=\"gs://path/to/file.pq\")\r\n\r\nDownloading data: 100%|████████████████████████████████████████| 26.5M/26.5M [00:01<00:00, 20.7MB/s]\r\nGenerating train split: 2000 examples [00:00, 26434.38 examples/s]\r\n```\r\n"
] |
1,599,732,211
| 5,579
|
Add instructions to create `DataLoader` from augmented dataset in object detection guide
|
closed
| 2023-02-25T14:53:17
| 2023-03-23T19:24:59
| 2023-03-23T19:24:50
|
https://github.com/huggingface/datasets/pull/5579
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5579",
"html_url": "https://github.com/huggingface/datasets/pull/5579",
"diff_url": "https://github.com/huggingface/datasets/pull/5579.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5579.patch",
"merged_at": null
}
|
Laurent2916
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.",
"I'm not sure we need this part as we provide a link to the notebook that shows how to train an object detection model, and this notebook instantiates a `DataLoader` before training the model. I'd like to hear what @stevhliu thinks.\r\n\r\nPS: Your `collate_fn` calls `torch.stack` on the `bbox` tensors, which don't have the same shape, so this will fail.",
"I agree with @mariosasko; we also have a [Use with PyTorch](https://huggingface.co/docs/datasets/use_with_pytorch) guide that shows how you can create a `DataLoader`. "
] |
1,598,863,119
| 5,578
|
Add `huggingface_hub` version to env cli command
|
closed
| 2023-02-24T15:37:43
| 2023-02-27T17:28:25
| 2023-02-27T17:21:09
|
https://github.com/huggingface/datasets/pull/5578
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5578",
"html_url": "https://github.com/huggingface/datasets/pull/5578",
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"patch_url": "https://github.com/huggingface/datasets/pull/5578.patch",
"merged_at": "2023-02-27T17:21:09"
}
|
mariosasko
| true
|
[
"_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) | 0.008124 / 0.011353 (-0.003229) | 0.004594 / 0.011008 (-0.006414) | 0.101575 / 0.038508 (0.063066) | 0.029074 / 0.023109 (0.005965) | 0.314641 / 0.275898 (0.038743) | 0.372006 / 0.323480 (0.048526) | 0.006882 / 0.007986 (-0.001103) | 0.003371 / 0.004328 (-0.000958) | 0.078800 / 0.004250 (0.074550) | 0.034030 / 0.037052 (-0.003023) | 0.326917 / 0.258489 (0.068428) | 0.357628 / 0.293841 (0.063788) | 0.033076 / 0.128546 (-0.095470) | 0.011552 / 0.075646 (-0.064094) | 0.321715 / 0.419271 (-0.097557) | 0.040426 / 0.043533 (-0.003107) | 0.315091 / 0.255139 (0.059952) | 0.339291 / 0.283200 (0.056091) | 0.087280 / 0.141683 (-0.054403) | 1.443445 / 1.452155 (-0.008710) | 1.489233 / 1.492716 (-0.003483) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182643 / 0.018006 (0.164637) | 0.390205 / 0.000490 (0.389716) | 0.001361 / 0.000200 (0.001161) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022767 / 0.037411 (-0.014644) | 0.095744 / 0.014526 (0.081219) | 0.102763 / 0.176557 (-0.073794) | 0.166760 / 0.737135 (-0.570375) | 0.106393 / 0.296338 (-0.189945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424649 / 0.215209 (0.209440) | 4.257982 / 2.077655 (2.180327) | 2.135847 / 1.504120 (0.631727) | 1.924810 / 1.541195 (0.383615) | 1.813797 / 1.468490 (0.345307) | 0.695467 / 4.584777 (-3.889310) | 3.330164 / 3.745712 (-0.415548) | 2.665606 / 5.269862 (-2.604255) | 1.458619 / 4.565676 (-3.107058) | 0.082408 / 0.424275 (-0.341867) | 0.012259 / 0.007607 (0.004652) | 0.527737 / 0.226044 (0.301693) | 5.271119 / 2.268929 (3.002191) | 2.618655 / 55.444624 (-52.825970) | 2.312321 / 6.876477 (-4.564155) | 2.270096 / 2.142072 (0.128023) | 0.811563 / 4.805227 (-3.993664) | 0.148512 / 6.500664 (-6.352152) | 0.064562 / 0.075469 (-0.010907) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212483 / 1.841788 (-0.629304) | 13.471679 / 8.074308 (5.397371) | 13.691054 / 10.191392 (3.499662) | 0.137399 / 0.680424 (-0.543025) | 0.028489 / 0.534201 (-0.505711) | 0.398879 / 0.579283 (-0.180404) | 0.396712 / 0.434364 (-0.037652) | 0.458879 / 0.540337 (-0.081458) | 0.537143 / 1.386936 (-0.849793) |\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) | 0.006911 / 0.011353 (-0.004442) | 0.004941 / 0.011008 (-0.006067) | 0.078606 / 0.038508 (0.040098) | 0.028411 / 0.023109 (0.005302) | 0.352172 / 0.275898 (0.076274) | 0.401155 / 0.323480 (0.077675) | 0.005433 / 0.007986 (-0.002552) | 0.003704 / 0.004328 (-0.000625) | 0.076615 / 0.004250 (0.072365) | 0.043814 / 0.037052 (0.006761) | 0.346928 / 0.258489 (0.088439) | 0.405587 / 0.293841 (0.111746) | 0.032176 / 0.128546 (-0.096370) | 0.011863 / 0.075646 (-0.063783) | 0.087209 / 0.419271 (-0.332063) | 0.042977 / 0.043533 (-0.000556) | 0.345366 / 0.255139 (0.090227) | 0.419664 / 0.283200 (0.136464) | 0.093862 / 0.141683 (-0.047821) | 1.490968 / 1.452155 (0.038813) | 1.566644 / 1.492716 (0.073927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216703 / 0.018006 (0.198697) | 0.472411 / 0.000490 (0.471921) | 0.002234 / 0.000200 (0.002034) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.109793 / 0.014526 (0.095267) | 0.110720 / 0.176557 (-0.065837) | 0.182342 / 0.737135 (-0.554793) | 0.116150 / 0.296338 (-0.180188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438165 / 0.215209 (0.222956) | 4.366213 / 2.077655 (2.288558) | 2.065036 / 1.504120 (0.560917) | 1.860105 / 1.541195 (0.318911) | 1.966885 / 1.468490 (0.498395) | 0.705194 / 4.584777 (-3.879583) | 3.389408 / 3.745712 (-0.356304) | 2.632155 / 5.269862 (-2.637707) | 1.471090 / 4.565676 (-3.094587) | 0.083579 / 0.424275 (-0.340697) | 0.012643 / 0.007607 (0.005036) | 0.542230 / 0.226044 (0.316186) | 5.416293 / 2.268929 (3.147365) | 2.517391 / 55.444624 (-52.927233) | 2.160159 / 6.876477 (-4.716317) | 2.167104 / 2.142072 (0.025031) | 0.807142 / 4.805227 (-3.998085) | 0.152249 / 6.500664 (-6.348415) | 0.067559 / 0.075469 (-0.007910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.399516 / 1.841788 (-0.442272) | 15.289898 / 8.074308 (7.215590) | 14.188758 / 10.191392 (3.997366) | 0.161277 / 0.680424 (-0.519147) | 0.016854 / 0.534201 (-0.517347) | 0.382091 / 0.579283 (-0.197192) | 0.396639 / 0.434364 (-0.037725) | 0.467932 / 0.540337 (-0.072405) | 0.552017 / 1.386936 (-0.834919) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2e050273ec3d2a7e53d817544318b23fb51430d0 \"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) | 0.011038 / 0.011353 (-0.000315) | 0.005878 / 0.011008 (-0.005130) | 0.118247 / 0.038508 (0.079739) | 0.043988 / 0.023109 (0.020879) | 0.350823 / 0.275898 (0.074925) | 0.430350 / 0.323480 (0.106870) | 0.009259 / 0.007986 (0.001274) | 0.004614 / 0.004328 (0.000286) | 0.089366 / 0.004250 (0.085116) | 0.049993 / 0.037052 (0.012941) | 0.367620 / 0.258489 (0.109131) | 0.404809 / 0.293841 (0.110968) | 0.044078 / 0.128546 (-0.084468) | 0.014226 / 0.075646 (-0.061421) | 0.397707 / 0.419271 (-0.021565) | 0.056631 / 0.043533 (0.013098) | 0.355942 / 0.255139 (0.100803) | 0.375537 / 0.283200 (0.092338) | 0.121956 / 0.141683 (-0.019727) | 1.757958 / 1.452155 (0.305803) | 1.822183 / 1.492716 (0.329466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.024505 / 0.018006 (0.006499) | 0.488754 / 0.000490 (0.488265) | 0.011032 / 0.000200 (0.010832) | 0.000540 / 0.000054 (0.000486) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032895 / 0.037411 (-0.004516) | 0.132496 / 0.014526 (0.117970) | 0.140620 / 0.176557 (-0.035937) | 0.220628 / 0.737135 (-0.516507) | 0.147622 / 0.296338 (-0.148717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471335 / 0.215209 (0.256126) | 4.699792 / 2.077655 (2.622137) | 2.119782 / 1.504120 (0.615662) | 1.894784 / 1.541195 (0.353590) | 2.002694 / 1.468490 (0.534204) | 0.822610 / 4.584777 (-3.762167) | 4.511510 / 3.745712 (0.765797) | 2.467017 / 5.269862 (-2.802845) | 1.568500 / 4.565676 (-2.997177) | 0.101488 / 0.424275 (-0.322787) | 0.014567 / 0.007607 (0.006960) | 0.603033 / 0.226044 (0.376989) | 6.041397 / 2.268929 (3.772468) | 2.759140 / 55.444624 (-52.685484) | 2.397192 / 6.876477 (-4.479285) | 2.491986 / 2.142072 (0.349914) | 1.021198 / 4.805227 (-3.784029) | 0.196415 / 6.500664 (-6.304249) | 0.076409 / 0.075469 (0.000939) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.406816 / 1.841788 (-0.434972) | 17.740263 / 8.074308 (9.665954) | 16.926489 / 10.191392 (6.735097) | 0.235302 / 0.680424 (-0.445122) | 0.036829 / 0.534201 (-0.497372) | 0.525326 / 0.579283 (-0.053957) | 0.530905 / 0.434364 (0.096541) | 0.650357 / 0.540337 (0.110019) | 0.770641 / 1.386936 (-0.616295) |\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) | 0.008728 / 0.011353 (-0.002625) | 0.006023 / 0.011008 (-0.004985) | 0.088694 / 0.038508 (0.050186) | 0.040345 / 0.023109 (0.017236) | 0.408126 / 0.275898 (0.132228) | 0.461178 / 0.323480 (0.137698) | 0.007456 / 0.007986 (-0.000529) | 0.004722 / 0.004328 (0.000394) | 0.087340 / 0.004250 (0.083090) | 0.055826 / 0.037052 (0.018774) | 0.422432 / 0.258489 (0.163942) | 0.466308 / 0.293841 (0.172467) | 0.043637 / 0.128546 (-0.084909) | 0.014602 / 0.075646 (-0.061044) | 0.103610 / 0.419271 (-0.315662) | 0.069999 / 0.043533 (0.026466) | 0.410676 / 0.255139 (0.155537) | 0.434551 / 0.283200 (0.151351) | 0.127699 / 0.141683 (-0.013984) | 1.699858 / 1.452155 (0.247703) | 1.830331 / 1.492716 (0.337615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235217 / 0.018006 (0.217211) | 0.494814 / 0.000490 (0.494325) | 0.004942 / 0.000200 (0.004742) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035996 / 0.037411 (-0.001416) | 0.139419 / 0.014526 (0.124893) | 0.146859 / 0.176557 (-0.029698) | 0.234793 / 0.737135 (-0.502343) | 0.152495 / 0.296338 (-0.143843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509812 / 0.215209 (0.294603) | 5.067227 / 2.077655 (2.989572) | 2.455505 / 1.504120 (0.951385) | 2.223516 / 1.541195 (0.682321) | 2.367783 / 1.468490 (0.899293) | 0.852550 / 4.584777 (-3.732227) | 4.517284 / 3.745712 (0.771572) | 4.860399 / 5.269862 (-0.409462) | 2.175290 / 4.565676 (-2.390386) | 0.106155 / 0.424275 (-0.318120) | 0.015023 / 0.007607 (0.007416) | 0.633753 / 0.226044 (0.407708) | 6.316214 / 2.268929 (4.047285) | 3.021118 / 55.444624 (-52.423506) | 2.601317 / 6.876477 (-4.275160) | 2.807988 / 2.142072 (0.665916) | 1.028695 / 4.805227 (-3.776532) | 0.204387 / 6.500664 (-6.296277) | 0.077368 / 0.075469 (0.001899) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540299 / 1.841788 (-0.301489) | 18.311957 / 8.074308 (10.237649) | 16.139892 / 10.191392 (5.948500) | 0.217231 / 0.680424 (-0.463193) | 0.020544 / 0.534201 (-0.513657) | 0.505589 / 0.579283 (-0.073694) | 0.506694 / 0.434364 (0.072330) | 0.622162 / 0.540337 (0.081824) | 0.739537 / 1.386936 (-0.647399) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f595fc2aa4786720f7a21da56069a1c46b4552a \"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) | 0.009465 / 0.011353 (-0.001887) | 0.005307 / 0.011008 (-0.005701) | 0.104111 / 0.038508 (0.065603) | 0.036083 / 0.023109 (0.012974) | 0.296608 / 0.275898 (0.020710) | 0.351365 / 0.323480 (0.027885) | 0.008309 / 0.007986 (0.000323) | 0.004383 / 0.004328 (0.000055) | 0.078297 / 0.004250 (0.074047) | 0.044062 / 0.037052 (0.007009) | 0.295592 / 0.258489 (0.037103) | 0.354442 / 0.293841 (0.060602) | 0.038651 / 0.128546 (-0.089896) | 0.012311 / 0.075646 (-0.063335) | 0.337933 / 0.419271 (-0.081338) | 0.048179 / 0.043533 (0.004646) | 0.308320 / 0.255139 (0.053181) | 0.335028 / 0.283200 (0.051829) | 0.105394 / 0.141683 (-0.036289) | 1.444104 / 1.452155 (-0.008050) | 1.573953 / 1.492716 (0.081237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236548 / 0.018006 (0.218542) | 0.552862 / 0.000490 (0.552372) | 0.003925 / 0.000200 (0.003726) | 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) | 0.026386 / 0.037411 (-0.011025) | 0.108002 / 0.014526 (0.093476) | 0.118327 / 0.176557 (-0.058230) | 0.182861 / 0.737135 (-0.554274) | 0.126032 / 0.296338 (-0.170307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397037 / 0.215209 (0.181827) | 3.960978 / 2.077655 (1.883323) | 1.771955 / 1.504120 (0.267835) | 1.575033 / 1.541195 (0.033839) | 1.696552 / 1.468490 (0.228062) | 0.679013 / 4.584777 (-3.905764) | 3.770136 / 3.745712 (0.024424) | 2.068323 / 5.269862 (-3.201539) | 1.310823 / 4.565676 (-3.254853) | 0.083752 / 0.424275 (-0.340523) | 0.012366 / 0.007607 (0.004759) | 0.512679 / 0.226044 (0.286635) | 5.127036 / 2.268929 (2.858108) | 2.313200 / 55.444624 (-53.131424) | 1.931007 / 6.876477 (-4.945470) | 2.018336 / 2.142072 (-0.123737) | 0.833033 / 4.805227 (-3.972194) | 0.163778 / 6.500664 (-6.336886) | 0.064053 / 0.075469 (-0.011417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234102 / 1.841788 (-0.607685) | 15.227921 / 8.074308 (7.153613) | 14.587146 / 10.191392 (4.395754) | 0.176236 / 0.680424 (-0.504187) | 0.028905 / 0.534201 (-0.505295) | 0.439758 / 0.579283 (-0.139525) | 0.439211 / 0.434364 (0.004848) | 0.544325 / 0.540337 (0.003988) | 0.633804 / 1.386936 (-0.753132) |\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) | 0.007933 / 0.011353 (-0.003420) | 0.005446 / 0.011008 (-0.005563) | 0.077846 / 0.038508 (0.039338) | 0.036017 / 0.023109 (0.012907) | 0.358925 / 0.275898 (0.083027) | 0.402757 / 0.323480 (0.079277) | 0.006478 / 0.007986 (-0.001508) | 0.005708 / 0.004328 (0.001380) | 0.074833 / 0.004250 (0.070583) | 0.053412 / 0.037052 (0.016360) | 0.358587 / 0.258489 (0.100098) | 0.430904 / 0.293841 (0.137063) | 0.037778 / 0.128546 (-0.090768) | 0.012698 / 0.075646 (-0.062948) | 0.087615 / 0.419271 (-0.331657) | 0.050236 / 0.043533 (0.006703) | 0.344160 / 0.255139 (0.089021) | 0.390870 / 0.283200 (0.107670) | 0.111035 / 0.141683 (-0.030648) | 1.446963 / 1.452155 (-0.005192) | 1.566158 / 1.492716 (0.073442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302380 / 0.018006 (0.284373) | 0.554005 / 0.000490 (0.553515) | 0.007244 / 0.000200 (0.007044) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032291 / 0.037411 (-0.005120) | 0.117117 / 0.014526 (0.102591) | 0.127513 / 0.176557 (-0.049044) | 0.204208 / 0.737135 (-0.532927) | 0.133730 / 0.296338 (-0.162608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424597 / 0.215209 (0.209388) | 4.233852 / 2.077655 (2.156198) | 2.029731 / 1.504120 (0.525611) | 1.830075 / 1.541195 (0.288880) | 1.966198 / 1.468490 (0.497707) | 0.697881 / 4.584777 (-3.886896) | 3.758012 / 3.745712 (0.012299) | 3.405319 / 5.269862 (-1.864542) | 1.870816 / 4.565676 (-2.694860) | 0.086892 / 0.424275 (-0.337383) | 0.012438 / 0.007607 (0.004831) | 0.524252 / 0.226044 (0.298207) | 5.209534 / 2.268929 (2.940606) | 2.478608 / 55.444624 (-52.966017) | 2.151535 / 6.876477 (-4.724942) | 2.249260 / 2.142072 (0.107187) | 0.831955 / 4.805227 (-3.973273) | 0.165955 / 6.500664 (-6.334710) | 0.064663 / 0.075469 (-0.010806) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253327 / 1.841788 (-0.588460) | 15.904393 / 8.074308 (7.830085) | 13.253464 / 10.191392 (3.062072) | 0.162148 / 0.680424 (-0.518276) | 0.017643 / 0.534201 (-0.516558) | 0.425028 / 0.579283 (-0.154255) | 0.425615 / 0.434364 (-0.008749) | 0.521503 / 0.540337 (-0.018835) | 0.629473 / 1.386936 (-0.757463) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939b2332115c7ec3dd56f58169800ed81cc4a982 \"CML watermark\")\n"
] |
1,598,587,665
| 5,577
|
Cannot load `the_pile_openwebtext2`
|
closed
| 2023-02-24T13:01:48
| 2023-02-24T14:01:09
| 2023-02-24T14:01:09
|
https://github.com/huggingface/datasets/issues/5577
| null |
wjfwzzc
| false
|
[
"Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n"
] |
1,598,582,744
| 5,576
|
I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
|
closed
| 2023-02-24T12:57:49
| 2023-02-24T12:58:31
| 2023-02-24T12:58:18
|
https://github.com/huggingface/datasets/issues/5576
| null |
wjfwzzc
| false
|
[
"Duplicated issue."
] |
1,598,396,552
| 5,575
|
Metadata for each column
|
open
| 2023-02-24T10:53:44
| 2024-01-05T21:48:35
| null |
https://github.com/huggingface/datasets/issues/5575
| null |
parsa-ra
| false
|
[
"Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows:\r\n```python\r\ncol_feature = Value(\"string\", metadata=\"Some column-level metadata\")\r\n\r\nfeatures = Features({\"col\": col_feature}, metadata=\"Some schema-level metadata\")\r\n```\r\n\r\nWDYT?",
"Sorry for the late reply, \r\nYes, I think this is the most straight-forward approach with the things that we already have.\r\n\r\n",
"@mariosasko Let me know how I can help.",
"Hi, is this feature to be implemented in the near future? It would be really nice if that would be the case! ",
"Hi, I also need this feature for tell my customer if any of the feature is encrypted with a certain key. "
] |
1,598,104,691
| 5,574
|
c4 dataset streaming fails with `FileNotFoundError`
|
closed
| 2023-02-24T07:57:32
| 2023-12-18T07:32:32
| 2023-02-27T04:03:38
|
https://github.com/huggingface/datasets/issues/5574
| null |
krasserm
| false
|
[
"Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nspigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True, use_auth_token=True)\r\nsample = next(iter(spigi))\r\n```\r\n\r\n<details>\r\n<summary> Traceback </summary>\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs)\r\n 405 try:\r\n 406 info.update(\r\n--> 407 await _file_info(\r\n 408 self.encode_url(url),\r\n 409 size_policy=policy,\r\n 410 session=session,\r\n 411 **self.kwargs,\r\n 412 **kwargs,\r\n 413 )\r\n 414 )\r\n 415 if info.get(\"size\") is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs)\r\n 791 async with r:\r\n--> 792 r.raise_for_status()\r\n 794 # TODO:\r\n 795 # recognise lack of 'Accept-Ranges',\r\n 796 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 797 # to mean streaming only, no random access => return None\r\n\r\nFile ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self)\r\n 1004 self.release()\r\n-> 1005 raise ClientResponseError(\r\n 1006 self.request_info,\r\n 1007 self.history,\r\n 1008 status=self.status,\r\n 1009 message=self.reason,\r\n 1010 headers=self.headers,\r\n 1011 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[5], line 4\r\n 1 from datasets import load_dataset\r\n 3 spigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True)\r\n----> 4 sample = next(iter(spigi))\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self)\r\n 934 yield from self._iter_pytorch(ex_iterable)\r\n 935 return\r\n--> 937 for key, example in ex_iterable:\r\n 938 if self.features:\r\n 939 # `IterableDataset` automatically fills missing columns with None.\r\n 940 # This is done with `_apply_feature_types_on_example`.\r\n 941 yield _apply_feature_types_on_example(\r\n 942 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 943 )\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self)\r\n 112 def __iter__(self):\r\n--> 113 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path)\r\n 183 dict_keys = [\"wav_filename\", \"wav_filesize\", \"transcript\"]\r\n 185 logging.info(\"Reading metadata...\")\r\n--> 186 with open(meta_path, encoding=\"utf-8\") as f:\r\n 187 csvreader = csv.DictReader(f, delimiter=\"|\")\r\n 188 metadata = {x[\"wav_filename\"]: dict((k, x[k]) for k in dict_keys) for x in csvreader}\r\n\r\nFile ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs)\r\n 68 @wraps(function)\r\n 69 def wrapper(*args, **kwargs):\r\n---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 493 kwargs = {**kwargs, **new_kwargs}\r\n 494 try:\r\n--> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 496 except ValueError as e:\r\n 497 if str(e) == \"Cannot seek streaming HTTP file\":\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self)\r\n 128 def open(self):\r\n 129 \"\"\"Materialise this as a real open file without context\r\n 130 \r\n 131 The OpenFile object should be explicitly closed to avoid enclosed file\r\n 132 instances persisting. You must, therefore, keep a reference to the OpenFile\r\n 133 during the life of the file-like it generates.\r\n 134 \"\"\"\r\n--> 135 return self.__enter__()\r\n\r\nFile ~/venv/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 ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1104 else:\r\n 1105 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1106 f = self._open(\r\n 1107 path,\r\n 1108 mode=mode,\r\n 1109 block_size=block_size,\r\n 1110 autocommit=ac,\r\n 1111 cache_options=cache_options,\r\n 1112 **kwargs,\r\n 1113 )\r\n 1114 if compression is not None:\r\n 1115 from fsspec.compression import compr\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 344 kw[\"asynchronous\"] = self.asynchronous\r\n 345 kw.update(kwargs)\r\n--> 346 size = size or self.info(path, **kwargs)[\"size\"]\r\n 347 session = sync(self.loop, self.set_session)\r\n 348 if block_size and size:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 110 @functools.wraps(func)\r\n 111 def wrapper(*args, **kwargs):\r\n 112 self = obj or args[0]\r\n--> 113 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs)\r\n 96 raise FSTimeoutError from return_result\r\n 97 elif isinstance(return_result, BaseException):\r\n---> 98 raise return_result\r\n 99 else:\r\n 100 return return_result\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout)\r\n 51 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 52 try:\r\n---> 53 result[0] = await coro\r\n 54 except Exception as ex:\r\n 55 result[0] = ex\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs)\r\n 417 except Exception as exc:\r\n 418 if policy == \"get\":\r\n 419 # If get failed, then raise a FileNotFoundError\r\n--> 420 raise FileNotFoundError(url) from exc\r\n 421 logger.debug(str(exc))\r\n 423 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv\r\n```\r\n</details>",
"Hi ! We're investigating this issue, sorry for the inconvenience",
"This has been resolved ! Thanks for reporting",
"Wow, thanks for the very quick fix!",
"This problem now appears again, this time with an underlying HTTP 502 status code:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz')\r\n```",
"Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX')\r\n```",
"I'm facing the same problem. Interestingly using `wget` I can download the file. ",
"It's been resolved again ;)",
"> It's been resolved again ;)\r\n\r\nI'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`",
"Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.`\r\n\r\nHave made sure to login as well, issue persists.",
"> Experiencing the same issues as above : `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz If the repo is private or gated, make sure to log in with `huggingface-cli login`.`\r\n> \r\n> Have made sure to login as well, issue persists.\r\n\r\nI meet the same issue",
"I meet the same issue"
] |
1,597,400,836
| 5,573
|
Use soundfile for mp3 decoding instead of torchaudio
|
closed
| 2023-02-23T19:19:44
| 2023-02-28T20:25:14
| 2023-02-28T20:16:02
|
https://github.com/huggingface/datasets/pull/5573
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5573",
"html_url": "https://github.com/huggingface/datasets/pull/5573",
"diff_url": "https://github.com/huggingface/datasets/pull/5573.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5573.patch",
"merged_at": "2023-02-28T20:16:02"
}
|
polinaeterna
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connected?",
"@polinaeterna The failure is due to `torch.from_numpy` not being picklable in newer versions of PyTorch. You can replace the current definition of `_save_tensor` in `utils/py_utils.py` with the following one to fix it: \r\n\r\n```python\r\n@pklregister(obj_type)\r\ndef _save_tensor(pickler, obj):\r\n # `torch.from_numpy` is not picklable in `torch>=1.11.0`\r\n def _create_tensor(np_array):\r\n return torch.from_numpy(np_array)\r\n\r\n dill_log(pickler, f\"To: {obj}\")\r\n args = (obj.detach().cpu().numpy(),)\r\n pickler.save_reduce(_create_tensor, args, obj=obj)\r\n dill_log(pickler, \"# To\")\r\n return\r\n```",
"(doing a patch release now - please wait before merging ^^)",
"@mariosasko génial, merci!! i've integrated all your changes, can you pls take a look one more time?",
"Patch release is done (I did it from another branch than `main` anyway)",
"<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) | 0.010927 / 0.011353 (-0.000426) | 0.006232 / 0.011008 (-0.004776) | 0.119815 / 0.038508 (0.081307) | 0.034138 / 0.023109 (0.011029) | 0.349945 / 0.275898 (0.074047) | 0.404967 / 0.323480 (0.081487) | 0.008672 / 0.007986 (0.000687) | 0.005010 / 0.004328 (0.000681) | 0.091931 / 0.004250 (0.087680) | 0.042534 / 0.037052 (0.005482) | 0.374701 / 0.258489 (0.116212) | 0.401027 / 0.293841 (0.107186) | 0.053523 / 0.128546 (-0.075024) | 0.019704 / 0.075646 (-0.055942) | 0.384207 / 0.419271 (-0.035064) | 0.065350 / 0.043533 (0.021817) | 0.375074 / 0.255139 (0.119935) | 0.390458 / 0.283200 (0.107259) | 0.110549 / 0.141683 (-0.031134) | 1.719812 / 1.452155 (0.267657) | 1.748906 / 1.492716 (0.256190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210051 / 0.018006 (0.192045) | 0.546503 / 0.000490 (0.546013) | 0.004078 / 0.000200 (0.003878) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030212 / 0.037411 (-0.007199) | 0.121845 / 0.014526 (0.107319) | 0.136309 / 0.176557 (-0.040247) | 0.204667 / 0.737135 (-0.532468) | 0.157327 / 0.296338 (-0.139012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.672548 / 0.215209 (0.457339) | 6.239409 / 2.077655 (4.161754) | 2.462441 / 1.504120 (0.958322) | 2.063985 / 1.541195 (0.522791) | 2.098858 / 1.468490 (0.630368) | 1.262600 / 4.584777 (-3.322177) | 5.478462 / 3.745712 (1.732750) | 5.454672 / 5.269862 (0.184810) | 2.991866 / 4.565676 (-1.573810) | 0.153415 / 0.424275 (-0.270861) | 0.015061 / 0.007607 (0.007454) | 0.796115 / 0.226044 (0.570071) | 8.206858 / 2.268929 (5.937930) | 3.226395 / 55.444624 (-52.218229) | 2.503522 / 6.876477 (-4.372955) | 2.547489 / 2.142072 (0.405417) | 1.504776 / 4.805227 (-3.300451) | 0.256536 / 6.500664 (-6.244128) | 0.078543 / 0.075469 (0.003073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591109 / 1.841788 (-0.250678) | 18.153317 / 8.074308 (10.079008) | 20.465684 / 10.191392 (10.274292) | 0.229808 / 0.680424 (-0.450616) | 0.045263 / 0.534201 (-0.488938) | 0.556760 / 0.579283 (-0.022524) | 0.614985 / 0.434364 (0.180622) | 0.635675 / 0.540337 (0.095337) | 0.729817 / 1.386936 (-0.657119) |\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) | 0.011247 / 0.011353 (-0.000106) | 0.006823 / 0.011008 (-0.004185) | 0.101989 / 0.038508 (0.063481) | 0.036077 / 0.023109 (0.012968) | 0.413469 / 0.275898 (0.137571) | 0.505560 / 0.323480 (0.182080) | 0.007506 / 0.007986 (-0.000480) | 0.006369 / 0.004328 (0.002040) | 0.099597 / 0.004250 (0.095346) | 0.058115 / 0.037052 (0.021063) | 0.414735 / 0.258489 (0.156246) | 0.466801 / 0.293841 (0.172960) | 0.064771 / 0.128546 (-0.063775) | 0.021100 / 0.075646 (-0.054546) | 0.135407 / 0.419271 (-0.283864) | 0.068784 / 0.043533 (0.025251) | 0.410467 / 0.255139 (0.155328) | 0.465993 / 0.283200 (0.182794) | 0.119404 / 0.141683 (-0.022279) | 1.767107 / 1.452155 (0.314952) | 1.938342 / 1.492716 (0.445626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227038 / 0.018006 (0.209032) | 0.511389 / 0.000490 (0.510899) | 0.006723 / 0.000200 (0.006523) | 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) | 0.033078 / 0.037411 (-0.004333) | 0.133159 / 0.014526 (0.118633) | 0.147928 / 0.176557 (-0.028629) | 0.214005 / 0.737135 (-0.523130) | 0.151655 / 0.296338 (-0.144683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634829 / 0.215209 (0.419620) | 6.578640 / 2.077655 (4.500985) | 2.673598 / 1.504120 (1.169478) | 2.338671 / 1.541195 (0.797476) | 2.389104 / 1.468490 (0.920614) | 1.274938 / 4.584777 (-3.309839) | 5.746524 / 3.745712 (2.000812) | 5.992084 / 5.269862 (0.722222) | 3.092090 / 4.565676 (-1.473587) | 0.150375 / 0.424275 (-0.273900) | 0.015470 / 0.007607 (0.007863) | 0.792962 / 0.226044 (0.566918) | 8.057491 / 2.268929 (5.788563) | 3.483966 / 55.444624 (-51.960659) | 2.715038 / 6.876477 (-4.161438) | 2.747186 / 2.142072 (0.605114) | 1.532951 / 4.805227 (-3.272276) | 0.262214 / 6.500664 (-6.238450) | 0.081308 / 0.075469 (0.005839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.698448 / 1.841788 (-0.143340) | 18.590002 / 8.074308 (10.515694) | 20.584508 / 10.191392 (10.393116) | 0.227237 / 0.680424 (-0.453187) | 0.028445 / 0.534201 (-0.505756) | 0.527874 / 0.579283 (-0.051409) | 0.602844 / 0.434364 (0.168480) | 0.672948 / 0.540337 (0.132611) | 0.788103 / 1.386936 (-0.598833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96547708a889c09ca8a02ed7aadd8c5690503c5 \"CML watermark\")\n"
] |
1,597,257,624
| 5,572
|
Datasets 2.10.0 does not reuse the dataset cache
|
closed
| 2023-02-23T17:28:11
| 2023-02-23T18:03:55
| 2023-02-23T18:03:55
|
https://github.com/huggingface/datasets/issues/5572
| null |
lsb
| false
|
[] |
1,597,198,953
| 5,571
|
load_dataset fails for JSON in windows
|
closed
| 2023-02-23T16:50:11
| 2023-02-24T13:21:47
| 2023-02-24T13:21:47
|
https://github.com/huggingface/datasets/issues/5571
| null |
abinashsahu
| false
|
[
"Hi! \r\n\r\nYou need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error:\r\n```python\r\n ds = load_dataset(\"json\", data_files=args.input_json)\r\n```\r\n\r\n",
"Thanks it worked!"
] |
1,597,190,926
| 5,570
|
load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub
|
closed
| 2023-02-23T16:44:32
| 2023-07-24T15:18:50
| 2023-07-24T15:18:50
|
https://github.com/huggingface/datasets/issues/5570
| null |
buoi
| false
|
[
"Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?",
"The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\n"
] |
1,597,132,383
| 5,569
|
pass the dataset features to the IterableDataset.from_generator function
|
closed
| 2023-02-23T16:06:04
| 2023-02-24T14:06:37
| 2023-02-23T18:15:16
|
https://github.com/huggingface/datasets/pull/5569
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5569",
"html_url": "https://github.com/huggingface/datasets/pull/5569",
"diff_url": "https://github.com/huggingface/datasets/pull/5569.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5569.patch",
"merged_at": "2023-02-23T18:15:16"
}
|
bruno-hays
| true
|
[
"_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) | 0.008753 / 0.011353 (-0.002600) | 0.004877 / 0.011008 (-0.006131) | 0.098320 / 0.038508 (0.059812) | 0.034123 / 0.023109 (0.011014) | 0.289539 / 0.275898 (0.013641) | 0.323584 / 0.323480 (0.000104) | 0.007455 / 0.007986 (-0.000531) | 0.004763 / 0.004328 (0.000434) | 0.074350 / 0.004250 (0.070100) | 0.039018 / 0.037052 (0.001966) | 0.294319 / 0.258489 (0.035830) | 0.348686 / 0.293841 (0.054845) | 0.037814 / 0.128546 (-0.090732) | 0.011808 / 0.075646 (-0.063838) | 0.333808 / 0.419271 (-0.085464) | 0.047758 / 0.043533 (0.004225) | 0.298533 / 0.255139 (0.043394) | 0.320790 / 0.283200 (0.037590) | 0.095909 / 0.141683 (-0.045774) | 1.434422 / 1.452155 (-0.017732) | 1.509703 / 1.492716 (0.016987) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201728 / 0.018006 (0.183722) | 0.432243 / 0.000490 (0.431753) | 0.002760 / 0.000200 (0.002560) | 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) | 0.026090 / 0.037411 (-0.011321) | 0.105914 / 0.014526 (0.091388) | 0.115869 / 0.176557 (-0.060688) | 0.178291 / 0.737135 (-0.558844) | 0.121435 / 0.296338 (-0.174904) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402304 / 0.215209 (0.187095) | 3.995183 / 2.077655 (1.917529) | 1.794548 / 1.504120 (0.290428) | 1.603034 / 1.541195 (0.061839) | 1.643836 / 1.468490 (0.175346) | 0.694934 / 4.584777 (-3.889843) | 3.695128 / 3.745712 (-0.050584) | 2.018582 / 5.269862 (-3.251279) | 1.294315 / 4.565676 (-3.271362) | 0.085346 / 0.424275 (-0.338929) | 0.012201 / 0.007607 (0.004594) | 0.510057 / 0.226044 (0.284012) | 5.123404 / 2.268929 (2.854476) | 2.319089 / 55.444624 (-53.125535) | 1.930935 / 6.876477 (-4.945542) | 1.939700 / 2.142072 (-0.202372) | 0.848282 / 4.805227 (-3.956945) | 0.165561 / 6.500664 (-6.335103) | 0.062028 / 0.075469 (-0.013441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220576 / 1.841788 (-0.621212) | 14.413853 / 8.074308 (6.339544) | 14.027156 / 10.191392 (3.835764) | 0.170109 / 0.680424 (-0.510315) | 0.029412 / 0.534201 (-0.504789) | 0.443898 / 0.579283 (-0.135386) | 0.433059 / 0.434364 (-0.001305) | 0.533465 / 0.540337 (-0.006872) | 0.626562 / 1.386936 (-0.760374) |\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) | 0.007148 / 0.011353 (-0.004205) | 0.005019 / 0.011008 (-0.005989) | 0.073132 / 0.038508 (0.034624) | 0.032763 / 0.023109 (0.009654) | 0.329309 / 0.275898 (0.053411) | 0.361658 / 0.323480 (0.038178) | 0.005683 / 0.007986 (-0.002302) | 0.003793 / 0.004328 (-0.000536) | 0.071858 / 0.004250 (0.067608) | 0.045160 / 0.037052 (0.008107) | 0.335852 / 0.258489 (0.077363) | 0.384274 / 0.293841 (0.090433) | 0.036647 / 0.128546 (-0.091899) | 0.012217 / 0.075646 (-0.063430) | 0.086265 / 0.419271 (-0.333007) | 0.049223 / 0.043533 (0.005690) | 0.331460 / 0.255139 (0.076321) | 0.353175 / 0.283200 (0.069975) | 0.102214 / 0.141683 (-0.039469) | 1.491451 / 1.452155 (0.039296) | 1.553702 / 1.492716 (0.060985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222972 / 0.018006 (0.204966) | 0.432862 / 0.000490 (0.432372) | 0.000421 / 0.000200 (0.000221) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028401 / 0.037411 (-0.009010) | 0.109331 / 0.014526 (0.094805) | 0.119246 / 0.176557 (-0.057311) | 0.187997 / 0.737135 (-0.549138) | 0.124212 / 0.296338 (-0.172127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427240 / 0.215209 (0.212031) | 4.271619 / 2.077655 (2.193964) | 2.104948 / 1.504120 (0.600828) | 1.910624 / 1.541195 (0.369430) | 1.943812 / 1.468490 (0.475322) | 0.711466 / 4.584777 (-3.873311) | 3.778987 / 3.745712 (0.033275) | 2.976258 / 5.269862 (-2.293604) | 1.807591 / 4.565676 (-2.758086) | 0.088286 / 0.424275 (-0.335989) | 0.012461 / 0.007607 (0.004854) | 0.527554 / 0.226044 (0.301509) | 5.279461 / 2.268929 (3.010532) | 2.517911 / 55.444624 (-52.926713) | 2.176557 / 6.876477 (-4.699920) | 2.205322 / 2.142072 (0.063249) | 0.855012 / 4.805227 (-3.950215) | 0.170007 / 6.500664 (-6.330658) | 0.065273 / 0.075469 (-0.010196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282785 / 1.841788 (-0.559003) | 14.819500 / 8.074308 (6.745192) | 13.282211 / 10.191392 (3.090819) | 0.161804 / 0.680424 (-0.518620) | 0.017615 / 0.534201 (-0.516586) | 0.420159 / 0.579283 (-0.159124) | 0.441304 / 0.434364 (0.006940) | 0.531704 / 0.540337 (-0.008634) | 0.627477 / 1.386936 (-0.759459) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b91070b9c09673e2e148eec458036ab6a62ac042 \"CML watermark\")\n",
"Hmm I think we need to add more tests. Not sure what would happen with :\r\n- decodable features that may end up decoded twice \r\n- formatted datasets \r\n\r\nI'd be in favor of reverting this until we checked those"
] |
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