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Seems to freeze when loading audio dataset with wav files from local folder
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[
"I just tried to do the same but changing the `.wav` files to `.mp3` files and that doesn't fix it.",
"I don't know if anyone will ever read this but I've tried to upload the same dataset with google colab and the output seems more clarifying. I didn't specify the train/test split so the dataset wasn't fully uploaded (or that is what I understood, might be wrong!!).\r\n\r\nNow, including the `drop_metadata` flag I can load the dataset normally (at least with colab notebook):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"audiofolder\", data_dir=\"../archive/Dataset\", , drop_metadata=True)\r\n```\r\n\r\nI'll close the issue.",
"@uriii3 Hello, I understand correctly that you converted your wav files to mp3?",
"Yes but it didn't matter. I don't remember which of them I ended up working with."
] | 2022-11-10T10:29:31Z
| 2023-04-25T09:54:05Z
| 2022-11-22T11:24:19Z
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### Describe the bug
I'm following the instructions in [https://huggingface.co/docs/datasets/audio_load#audiofolder-with-metadata](url) to be able to load a dataset from a local folder.
I have everything into a folder, into a train folder and then the audios and csv. When I try to load the dataset and run from terminal, seems to work but then freezes with no apparent reason.
The metadata.csv file contains a few columns but the important ones, `file_name` with the filename and `transcription` with the transcription are okay.
The audios are `.wav` files, I don't know if that might be the problem (I will proceed to try to change them all to `.mp3` and try again).
### Steps to reproduce the bug
The code I'm using:
```python
from datasets import load_dataset
dataset = load_dataset("audiofolder", data_dir="../archive/Dataset")
dataset[0]["audio"]
```
The output I obtain:
```
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 311135.43it/s]
Using custom data configuration default-38d4546ffd010f3e
Downloading and preparing dataset audiofolder/default to /Users/mine/.cache/huggingface/datasets/audiofolder/default-38d4546ffd010f3e/0.0.0/6cbdd16f8688354c63b4e2a36e1585d05de285023ee6443ffd71c4182055c0fc...
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 166467.72it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 187772.74it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 59623.71it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 138090.55it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 106065.64it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 56036.38it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 74004.24it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 162343.45it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 101881.23it/s]
Using custom data configuration default-38d4546ffd010f3e
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 60145.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 80890.02it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 54036.67it/s]
Resolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 95851.09it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 155897.00it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 137656.96it/s]
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 131230.81it/s]
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
Using custom data configuration default-38d4546ffd010f3e
```
And then here it just freezes and nothing more happens.
### Expected behavior
Load the dataset.
### Environment info
Datasets version:
datasets 2.6.1 pypi_0 pypi
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Rename master to main
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"All the mentions I found on google were simple URLs that will be redirected, so it's fine. I also checked the spaces and we should be good:\r\n- dalle-mini used to install the master branch but [it's no longer the case](https://huggingface.co/spaces/flax-community/dalle-mini/commit/b78c972afd5c2d2bed087be6479fe5c9c6cfa741)\r\n- same for [logo generator](https://huggingface.co/spaces/tom-doerr/logo_generator/commit/a9ea330e518870d0ca8f65abb56f71d86750d8e4)\r\n- I opened a PR to fix [vision-datasets-viewer](https://huggingface.co/spaces/nateraw/vision-datasets-viewer/discussions/1)\r\n",
"Ok let's rename the branch, and then we can merge this PR"
] | 2022-07-06T13:34:30Z
| 2022-07-06T15:36:46Z
| 2022-07-06T15:25:08Z
|
MEMBER
| null | null | null |
This PR renames mentions of "master" by "main" in the code base for several cases:
- set the default dataset script version to "main" if the local installation of `datasets` is a dev installation
- update URLs to this github repository to use "main"
- update the DVC benchmark
- update the github workflows
- update docstrings
- update tests to compare the changes in dataset cards against "main"
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[
"Thanks for reporting. It's a known issue that should be fixed soon. Meanwhile, I had to manually trigger the dataset viewer. It's OK now.\r\nNote that the extreme aspect ratio of the images generates another issue, that we're inspecting."
] | 2022-08-04T12:49:16Z
| 2022-08-04T13:43:16Z
| 2022-08-04T13:43:16Z
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### Link
https://huggingface.co/datasets/Team-PIXEL/rendered-wikipedia-english/viewer/rendered-wikipedia-en/train
### Description
The dataset can be loaded fine but the viewer shows this error:
```
Server Error
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
I'm guessing this is because I recently renamed the dataset. Based on related issues (e.g. https://github.com/huggingface/datasets/issues/4759) , is there something server-side that needs to be refreshed?
### Owner
Yes
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Fix JSON generation in benchmarks CI
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"<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) | 0.006186 / 0.011353 (-0.005167) | 0.003744 / 0.011008 (-0.007264) | 0.097295 / 0.038508 (0.058787) | 0.037106 / 0.023109 (0.013997) | 0.424154 / 0.275898 (0.148256) | 0.474536 / 0.323480 (0.151057) | 0.003454 / 0.007986 (-0.004532) | 0.003865 / 0.004328 (-0.000463) | 0.077348 / 0.004250 (0.073097) | 0.051728 / 0.037052 (0.014675) | 0.437120 / 0.258489 (0.178631) | 0.478379 / 0.293841 (0.184538) | 0.028939 / 0.128546 (-0.099608) | 0.008376 / 0.075646 (-0.067270) | 0.312002 / 0.419271 (-0.107270) | 0.053723 / 0.043533 (0.010190) | 0.424815 / 0.255139 (0.169676) | 0.446203 / 0.283200 (0.163004) | 0.026553 / 0.141683 (-0.115130) | 1.479983 / 1.452155 (0.027828) | 1.530613 / 1.492716 (0.037896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196627 / 0.018006 (0.178620) | 0.422361 / 0.000490 (0.421871) | 0.003442 / 0.000200 (0.003242) | 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) | 0.022913 / 0.037411 (-0.014499) | 0.096011 / 0.014526 (0.081485) | 0.104091 / 0.176557 (-0.072466) | 0.163273 / 0.737135 (-0.573862) | 0.109142 / 0.296338 (-0.187197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431032 / 0.215209 (0.215823) | 4.314391 / 2.077655 (2.236737) | 2.003812 / 1.504120 (0.499692) | 1.799538 / 1.541195 (0.258344) | 1.830026 / 1.468490 (0.361536) | 0.560131 / 4.584777 (-4.024646) | 3.368997 / 3.745712 (-0.376715) | 1.703032 / 5.269862 (-3.566830) | 1.026949 / 4.565676 (-3.538727) | 0.067507 / 0.424275 (-0.356768) | 0.010910 / 0.007607 (0.003303) | 0.532606 / 0.226044 (0.306562) | 5.345179 / 2.268929 (3.076250) | 2.368077 / 55.444624 (-53.076548) | 2.028913 / 6.876477 (-4.847564) | 2.147621 / 2.142072 (0.005549) | 0.675696 / 4.805227 (-4.129531) | 0.134902 / 6.500664 (-6.365762) | 0.065004 / 0.075469 (-0.010465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233412 / 1.841788 (-0.608376) | 13.767465 / 8.074308 (5.693157) | 13.933653 / 10.191392 (3.742261) | 0.129010 / 0.680424 (-0.551414) | 0.016708 / 0.534201 (-0.517493) | 0.362341 / 0.579283 (-0.216942) | 0.390902 / 0.434364 (-0.043462) | 0.429156 / 0.540337 (-0.111182) | 0.521166 / 1.386936 (-0.865770) |\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) | 0.006169 / 0.011353 (-0.005184) | 0.003839 / 0.011008 (-0.007169) | 0.078784 / 0.038508 (0.040276) | 0.040218 / 0.023109 (0.017109) | 0.360439 / 0.275898 (0.084541) | 0.423957 / 0.323480 (0.100477) | 0.003456 / 0.007986 (-0.004529) | 0.002900 / 0.004328 (-0.001428) | 0.078820 / 0.004250 (0.074569) | 0.047240 / 0.037052 (0.010187) | 0.372081 / 0.258489 (0.113592) | 0.424263 / 0.293841 (0.130422) | 0.027977 / 0.128546 (-0.100569) | 0.008400 / 0.075646 (-0.067246) | 0.084399 / 0.419271 (-0.334872) | 0.043303 / 0.043533 (-0.000230) | 0.361583 / 0.255139 (0.106444) | 0.394987 / 0.283200 (0.111787) | 0.020006 / 0.141683 (-0.121677) | 1.520208 / 1.452155 (0.068053) | 1.587335 / 1.492716 (0.094619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223847 / 0.018006 (0.205840) | 0.402194 / 0.000490 (0.401704) | 0.000384 / 0.000200 (0.000184) | 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) | 0.024902 / 0.037411 (-0.012509) | 0.099076 / 0.014526 (0.084550) | 0.108041 / 0.176557 (-0.068516) | 0.159385 / 0.737135 (-0.577750) | 0.111442 / 0.296338 (-0.184896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446232 / 0.215209 (0.231023) | 4.464927 / 2.077655 (2.387272) | 2.155234 / 1.504120 (0.651114) | 1.953645 / 1.541195 (0.412450) | 1.965991 / 1.468490 (0.497501) | 0.553473 / 4.584777 (-4.031304) | 3.321397 / 3.745712 (-0.424315) | 1.693761 / 5.269862 (-3.576101) | 1.006299 / 4.565676 (-3.559378) | 0.067013 / 0.424275 (-0.357262) | 0.011116 / 0.007607 (0.003509) | 0.555014 / 0.226044 (0.328970) | 5.535694 / 2.268929 (3.266765) | 2.598339 / 55.444624 (-52.846285) | 2.249298 / 6.876477 (-4.627179) | 2.243419 / 2.142072 (0.101347) | 0.667603 / 4.805227 (-4.137624) | 0.133322 / 6.500664 (-6.367343) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293051 / 1.841788 (-0.548737) | 14.103731 / 8.074308 (6.029423) | 14.215204 / 10.191392 (4.023812) | 0.143990 / 0.680424 (-0.536434) | 0.016805 / 0.534201 (-0.517396) | 0.363264 / 0.579283 (-0.216019) | 0.392769 / 0.434364 (-0.041594) | 0.425291 / 0.540337 (-0.115046) | 0.515479 / 1.386936 (-0.871457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"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) | 0.006346 / 0.011353 (-0.005006) | 0.004130 / 0.011008 (-0.006878) | 0.096898 / 0.038508 (0.058390) | 0.042564 / 0.023109 (0.019455) | 0.343748 / 0.275898 (0.067850) | 0.412515 / 0.323480 (0.089035) | 0.006153 / 0.007986 (-0.001833) | 0.003345 / 0.004328 (-0.000984) | 0.075314 / 0.004250 (0.071064) | 0.061478 / 0.037052 (0.024426) | 0.362948 / 0.258489 (0.104459) | 0.401533 / 0.293841 (0.107692) | 0.032363 / 0.128546 (-0.096184) | 0.008780 / 0.075646 (-0.066867) | 0.328691 / 0.419271 (-0.090580) | 0.054253 / 0.043533 (0.010721) | 0.340783 / 0.255139 (0.085644) | 0.360705 / 0.283200 (0.077505) | 0.023183 / 0.141683 (-0.118500) | 1.484078 / 1.452155 (0.031924) | 1.528581 / 1.492716 (0.035865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208732 / 0.018006 (0.190726) | 0.452572 / 0.000490 (0.452082) | 0.002936 / 0.000200 (0.002737) | 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) | 0.024616 / 0.037411 (-0.012795) | 0.107547 / 0.014526 (0.093021) | 0.114492 / 0.176557 (-0.062065) | 0.171770 / 0.737135 (-0.565365) | 0.122538 / 0.296338 (-0.173800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406140 / 0.215209 (0.190930) | 4.062391 / 2.077655 (1.984736) | 1.865962 / 1.504120 (0.361842) | 1.682236 / 1.541195 (0.141041) | 1.738119 / 1.468490 (0.269629) | 0.532244 / 4.584777 (-4.052533) | 3.816421 / 3.745712 (0.070709) | 2.981205 / 5.269862 (-2.288656) | 1.519497 / 4.565676 (-3.046179) | 0.065904 / 0.424275 (-0.358371) | 0.011277 / 0.007607 (0.003670) | 0.512789 / 0.226044 (0.286745) | 5.107618 / 2.268929 (2.838690) | 2.419399 / 55.444624 (-53.025226) | 2.079262 / 6.876477 (-4.797214) | 2.150447 / 2.142072 (0.008375) | 0.696737 / 4.805227 (-4.108490) | 0.142497 / 6.500664 (-6.358167) | 0.063521 / 0.075469 (-0.011949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180692 / 1.841788 (-0.661095) | 14.343084 / 8.074308 (6.268776) | 13.303719 / 10.191392 (3.112327) | 0.164234 / 0.680424 (-0.516190) | 0.017439 / 0.534201 (-0.516762) | 0.399712 / 0.579283 (-0.179571) | 0.428248 / 0.434364 (-0.006115) | 0.471909 / 0.540337 (-0.068428) | 0.573853 / 1.386936 (-0.813083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006210 / 0.011353 (-0.005143) | 0.004104 / 0.011008 (-0.006905) | 0.075140 / 0.038508 (0.036632) | 0.044647 / 0.023109 (0.021538) | 0.370120 / 0.275898 (0.094222) | 0.452936 / 0.323480 (0.129457) | 0.003943 / 0.007986 (-0.004042) | 0.003285 / 0.004328 (-0.001043) | 0.075267 / 0.004250 (0.071017) | 0.055517 / 0.037052 (0.018465) | 0.396385 / 0.258489 (0.137896) | 0.447870 / 0.293841 (0.154029) | 0.031342 / 0.128546 (-0.097204) | 0.008720 / 0.075646 (-0.066926) | 0.082702 / 0.419271 (-0.336570) | 0.051010 / 0.043533 (0.007477) | 0.350546 / 0.255139 (0.095407) | 0.425395 / 0.283200 (0.142195) | 0.024483 / 0.141683 (-0.117200) | 1.467341 / 1.452155 (0.015186) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218067 / 0.018006 (0.200061) | 0.441603 / 0.000490 (0.441114) | 0.003711 / 0.000200 (0.003512) | 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) | 0.028669 / 0.037411 (-0.008742) | 0.112941 / 0.014526 (0.098415) | 0.122584 / 0.176557 (-0.053972) | 0.176494 / 0.737135 (-0.560641) | 0.129369 / 0.296338 (-0.166970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434543 / 0.215209 (0.219334) | 4.344056 / 2.077655 (2.266401) | 2.079286 / 1.504120 (0.575166) | 1.887264 / 1.541195 (0.346069) | 1.910386 / 1.468490 (0.441896) | 0.538824 / 4.584777 (-4.045953) | 3.844786 / 3.745712 (0.099074) | 2.902091 / 5.269862 (-2.367770) | 1.270852 / 4.565676 (-3.294824) | 0.066324 / 0.424275 (-0.357951) | 0.011346 / 0.007607 (0.003739) | 0.537122 / 0.226044 (0.311078) | 5.367354 / 2.268929 (3.098426) | 2.533672 / 55.444624 (-52.910952) | 2.203260 / 6.876477 (-4.673217) | 2.224310 / 2.142072 (0.082237) | 0.663806 / 4.805227 (-4.141422) | 0.142758 / 6.500664 (-6.357906) | 0.063870 / 0.075469 (-0.011599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260487 / 1.841788 (-0.581301) | 14.800106 / 8.074308 (6.725798) | 13.993488 / 10.191392 (3.802096) | 0.165829 / 0.680424 (-0.514595) | 0.017347 / 0.534201 (-0.516854) | 0.401819 / 0.579283 (-0.177464) | 0.424577 / 0.434364 (-0.009787) | 0.475161 / 0.540337 (-0.065176) | 0.574659 / 1.386936 (-0.812277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n"
] | 2023-06-19T16:56:06Z
| 2023-06-19T17:29:11Z
| 2023-06-19T17:22:10Z
|
COLLABORATOR
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Related to changes made in https://github.com/iterative/dvc/pull/9475
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I_kwDODunzps6VvdDK
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Specifying datatype when adding a column to a dataset.
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"#self-assign"
] | 2024-09-08T07:34:24Z
| 2024-09-17T03:46:32Z
| 2024-09-17T03:46:32Z
|
CONTRIBUTOR
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### Feature request
There should be a way to specify the datatype of a column in `datasets.add_column()`.
### Motivation
To specify a custom datatype, we have to use `datasets.add_column()` followed by `datasets.cast_column()` which is slow for large datasets. Another workaround is to pass a `numpy.array()` of desired type to the `datasets.add_column()` function.
IMO this functionality should be natively supported.
https://discuss.huggingface.co/t/add-column-with-a-particular-type-in-datasets/95674
### Your contribution
I can submit a PR for this.
|
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I_kwDODunzps6twu3g
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Epochs shortened after resuming mid-epoch with Iterable dataset+StatefulDataloader(persistent_workers=True)
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[
"Thanks for reporting ! Maybe we should store the epoch in the state_dict, and then when the dataset is iterated on again after setting a new epoch it should restart from scratch instead of resuming ? wdyt ?",
"But why does this only happen when `persistent_workers=True`? I would expect it to work correctly even without storing the epoch number in the state_dict of the iterable dataset. ",
"I think persistent_workers=False simply ignores the dataset state_dict when it starts a new epoch, that's why the issue doesn't appear in that case",
"I opened https://github.com/huggingface/datasets/pull/7451 to fix the issue, let me know if it works for you",
"I just released `datasets` 3.4 that includes the fix :)\n\nPS: in your script you probably want to set the epoch like this, otherwise it's still set to 0 after the first epoch:\n\n```diff\n if state_dict is None:\n- ds.set_epoch(epoch)\n epoch += 1\n+ ds.set_epoch(epoch)\n```"
] | 2025-03-12T21:41:05Z
| 2025-03-14T17:26:59Z
| 2025-03-14T10:50:10Z
|
NONE
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### Describe the bug
When `torchdata.stateful_dataloader.StatefulDataloader(persistent_workers=True)` the epochs after resuming only iterate through the examples that were left in the epoch when the training was interrupted. For example, in the script below training is interrupted on step 124 (epoch 1) when 3 batches are left. Then after resuming, the rest of epochs (2 and 3) only iterate through these 3 batches.
### Steps to reproduce the bug
Run the following script with and with PERSISTENT_WORKERS=true.
```python
# !/usr/bin/env python3
# torch==2.5.1
# datasets==3.3.2
# torchdata>=0.9.0
import datasets
import pprint
from torchdata.stateful_dataloader import StatefulDataLoader
import os
PERSISTENT_WORKERS = (
os.environ.get("PERSISTENT_WORKERS", "False").lower() == "true"
)
# PERSISTENT_WORKERS = True # Incorrect resume
# ds = datasets.load_from_disk("dataset").to_iterable_dataset(num_shards=4)
def generator():
for i in range(128):
yield {"x": i}
ds = datasets.Dataset.from_generator(
generator, features=datasets.Features({"x": datasets.Value("int32")})
).to_iterable_dataset(num_shards=4)
dl = StatefulDataLoader(
ds, batch_size=2, num_workers=2, persistent_workers=PERSISTENT_WORKERS
)
global_step = 0
epoch = 0
ds_state_dict = None
state_dict = None
resumed = False
while True:
if epoch >= 3:
break
if state_dict is not None:
dl.load_state_dict(state_dict)
state_dict = None
ds_state_dict = None
resumed = True
print("resumed")
for i, batch in enumerate(dl):
print(f"epoch: {epoch}, global_step: {global_step}, batch: {batch}")
global_step += 1 # consume datapoint
# simulate error
if global_step == 124 and not resumed:
ds_state_dict = ds.state_dict()
state_dict = dl.state_dict()
print("checkpoint")
print("ds_state_dict")
pprint.pprint(ds_state_dict)
print("dl_state_dict")
pprint.pprint(state_dict)
break
if state_dict is None:
ds.set_epoch(epoch)
epoch += 1
```
The script checkpoints when there are three batches left in the second epoch. After resuming, only the last three batches are repeated in the rest of the epochs.
If it helps, following are the two state_dicts for the dataloader save at the same step with the two settings. The left one is for `PERSISTENT_WORKERS=False`
### Expected behavior
All the elements in the dataset should be iterated through in the epochs following the one where we resumed. The expected behavior can be seen by setting `PERSISTENT_WORKERS=False`.
### Environment info
torch==2.5.1
datasets==3.3.2
torchdata>=0.9.0
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Include `metadata.jsonl` in resolved data files
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"I still don't know if the way we implemented data files resolution could support the metadata.jsonl file without bad side effects for the other packaged builders. In particular here if you have a folder of csv/parquet/whatever files and a metadata.jsonl file, it would return \r\n```\r\nsplit: patterns_dict[split] + [METADATA_PATTERN]\r\n```\r\nwhich is a bit unexpected and can lead to errors.\r\n\r\nMaybe this logic can be specific to imagefolder somehow ? This could be an additional pattern `[\"metadata.jsonl\", \"**/metadata.jsonl\"]` just for imagefolder, that is only used when `data_files=` is not specified by the user.\r\n\r\nI guess it's ok to have patterns that lead to duplicate metadata.jsonl files for imagefolder, since the imagefolder logic only considers the closest metadata file for each image.\r\n\r\nWhat do you think ?",
"Yes, that's indeed the problem. My solution in https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 that accounts for that (include metadata files only if image files are present; not ideal): https://github.com/huggingface/datasets/blob/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95/src/datasets/data_files.py#L119-L125.\r\nPerhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as `imagefolder` and append metadata files to already resolved data files (if there are any). WDYT?",
"@lhoestq \r\n\r\n> Perhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as imagefolder and append metadata files to already resolved data files (if there are any). WDYT?\r\n\r\nI decided to go with this approach.\r\n\r\n Not sure if you meant the same thing with this comment:\r\n\r\n> Maybe this logic can be specific to imagefolder somehow ? This could be an additional pattern [\"metadata.jsonl\", \"**/metadata.jsonl\"] just for imagefolder, that is only used when data_files= is not specified by the user.\r\n\r\n\r\nIt adds more code but is easy to follow IMO.\r\n",
"The CI still struggles but you can merge since at least one of the two WIN CI succeeded"
] | 2022-06-27T12:01:29Z
| 2022-07-01T12:44:55Z
| 2022-06-30T10:15:32Z
|
COLLABORATOR
| null | null | null |
Include `metadata.jsonl` in resolved data files.
Fix #4548
@lhoestq ~~https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 adds support for metadata files placed at the root, and https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 accounts for nested metadata files also, but this results in more complex code. Let me know which one of these two approaches you prefer.~~ Maybe https://github.com/huggingface/datasets/commit/d94336d30eef17fc9abc67f67fa1c139661f4e75 is good enough for now (for the sake of simplicity). https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 breaks the imagefolder tests due to duplicates in the resolved metadata files. One way to fix this would be to resolve the metadata pattern only on parent directories, but this adds even more logic to `_get_data_files_patterns`, so not sure if this is what we should do.
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A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
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[
"recreated .venv and run this: pip install diffusers[training]==0.11.1"
] | 2025-01-04T18:30:17Z
| 2025-01-08T02:20:58Z
| 2025-01-08T02:20:58Z
|
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### Describe the bug
Following this tutorial: https://huggingface.co/docs/diffusers/en/tutorials/basic_training and running it locally using VSCode on my MacBook. The first line in the tutorial fails: from datasets import load_dataset
dataset = load_dataset('huggan/smithsonian_butterflies_subset', split="train"). with this error:
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2. and ImportError: numpy.core.multiarray failed to import.
Does from datasets import load_dataset really use NumPy 1.x?
### Steps to reproduce the bug
Open VSCode. create a new venv. Create a new ipynb file. Import pip install diffusers[training] try to run this line of code: from datasets import load_dataset
### Expected behavior
data is loaded
### Environment info
ran this: datasets-cli env
and got A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
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Inaccurate Bounding Boxes in "wildreceipt" Dataset
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[
"Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it."
] | 2023-08-05T14:34:13Z
| 2023-08-17T14:25:27Z
| 2023-08-17T14:25:26Z
|
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### Describe the bug
I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset.
To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face:
**Example 1:**
**Example 2:**
It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar).
This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated.
### Steps to reproduce the bug
```python
import matplotlib.pyplot as plt
from datasets import load_dataset
# Define functions to convert bounding box formats
def convert_format1(box):
x, y, w, h = box
x2, y2 = x + w, y + h
return [x, y, x2, y2]
def convert_format2(box):
x1, y1, x2, y2 = box
return [x1, y1, x2, y2]
def plot_cropped_image(image, box, title):
cropped_image = image.crop(box)
plt.imshow(cropped_image)
plt.title(title)
plt.axis('off')
plt.savefig(title+'.png')
plt.show()
doc_index = 1
word_index = 3
dataset = load_dataset("Theivaprakasham/wildreceipt")['train']
bbox_hugging_face = dataset[doc_index]['bboxes'][word_index]
text_unit_face = dataset[doc_index]['words'][word_index]
common_box_hugface_1 = convert_format1(bbox_hugging_face)
common_box_hugface_2 = convert_format2(bbox_hugging_face)
plot_cropped_image(image_hugging, common_box_hugface_1,
f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}')
plot_cropped_image(image_hugging, common_box_hugface_2,
f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}')
```
### Expected behavior
The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset.
### Environment info
- Python version: 3.8
- Hugging Face datasets version: 2.14.2
- Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar
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|
Add information about patterns search order to the doc about structuring repo
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[
"Good idea, I think I've seen this a couple of times before too on the forums. I can work on this :)",
"Closed in #5693 "
] | 2023-03-29T11:44:49Z
| 2023-04-03T18:31:11Z
| 2023-04-03T18:31:11Z
|
CONTRIBUTOR
| null | null |
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Following [this](https://github.com/huggingface/datasets/issues/5650) issue I think we should add a note about the order of patterns that is used to find splits, see [my comment](https://github.com/huggingface/datasets/issues/5650#issuecomment-1488412527). Also we should reference this page in pages about packaged loaders.
I have a déjà vu that it had already been discussed as some point but I don't remember....
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"Also unrelated but still: https://huggingface.co/docs/datasets/image_dataset#generate-the-dataset\r\n```If your loading script passed the test, you should now have a dataset_infos.json file in your dataset folder.```\r\nIt's not the case anymore as it's now in the readme.md, it was confusing to me",
"And here is my data loader script: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/blob/main/SDH_16k.py\r\nI have one file archive to download that contains the images for all splits and one `metadata.jsonl` to download that contains the informations about what image goes into what split.",
"Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.",
"> Hi @lambda-science! It seems that your repo is recognized as a packaged module [ImageFolder](https://huggingface.co/docs/datasets/main/en/image_dataset#imagefolder), not as a dataset with the custom loading script, because loader looks for a script that has the same name as the dataset repo. So please try to rename your script to `MyoQuant-SDH-Data.py`, this should help.\r\n\r\nHi !\r\n\r\nThank you for your answer. That was... embarrassingly easy, sorry for this issue, everything is fixed now ! \r\n\r\nHave a nice day ! :)",
"@lambda-science that's not embarrassing at all! it's actually not clear from the documentation that the script should have the same name, so thank you for the issue, we'll add this information to the docs :) "
] | 2022-11-02T22:46:25Z
| 2022-11-03T13:39:16Z
| 2022-11-03T13:35:44Z
|
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### Describe the bug
When loading my own dataset, on loading it I get an error.
Here is my dataset link: https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data
And the error after loading with:
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
```python
Downloading readme: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.34k/3.34k [00:00<00:00, 4.45MB/s]
Using custom data configuration SDH_16k-53e7301a92ab0025
Downloading and preparing dataset None/SDH_16k to /home/corentin/.cache/huggingface/datasets/corentinm7___imagefolder/SDH_16k-53e7301a92ab0025/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.28M/3.28M [00:00<00:00, 4.31MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00, 1.75s/it]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.13G/1.13G [00:15<00:00, 74.3MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.09s/it]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.16s/it]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/load.py", line 1742, in load_dataset
builder_instance.download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 814, in download_and_prepare
self._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1423, in _download_and_prepare
super()._download_and_prepare(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 905, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/builder.py", line 1374, in _prepare_split
for key, record in logging.tqdm(
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/corentin/code-project/hugging_face_play/.venv/lib/python3.10/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 394, in _generate_examples
raise ValueError(
ValueError: One or several metadata. were found, but not in the same directory or in a parent directory of /home/corentin/.cache/huggingface/datasets/downloads/extracted/60c4aa8d4da3065bb3d310de4373dffd73bd4dc331aedcb4ee867febe4fdb7cd/validation/sick/2_CG_SDH_TAM_Bin1cKO_ko_pla_4_1640.tif.
```
However the test command is working fine. ```datasets-cli test hugging_face_play/ds_test/SDH_16k.py --save_info --all_configs --force_redownload```
```
Using custom data configuration SDH_16k
Testing builder 'SDH_16k' (1/1)
Downloading and preparing dataset sdh_16k/SDH_16k to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d...
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.13G/1.13G [00:14<00:00, 76.5MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:15<00:00, 15.66s/it]
Downloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.28M/3.28M [00:02<00:00, 1.44MB/s]
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00, 3.21s/it]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 11586.48it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.42s/it]
Dataset sdh_16k downloaded and prepared to /home/corentin/.cache/huggingface/datasets/sdh_16k/SDH_16k/1.0.0/21b584239a638aeeda33cba1ac2ca4869d48e4b4f20fb22274d5a5ddc487659d. Subsequent calls will reuse this data.
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.27it/s]
Dataset card saved at hugging_face_play/ds_test/README.md
Test successful.
```
### Steps to reproduce the bug
Simply run on python
```python
from datasets import load_dataset
load_dataset("corentinm7/MyoQuant-SDH-Data")
```
### Expected behavior
As the test command worked, this error should not appear
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 10.0.0
- Pandas version: 1.5.1
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"Hello, It's an interesting issue here. I have the same problem, I have a local dataset and I want to push the dataset to the hub but huggingface does a copy of it.\r\n\r\n```py\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"webdataset\", data_files=\"/media/works/data/*.tar\") # copy here\r\ndataset.push_to_hub(\"WaveGenAI/audios2\")\r\n```\r\n\r\nEdit: I can use HfApi for my use case\r\n"
] | 2024-10-31T11:51:56Z
| 2024-10-31T14:43:47Z
| null |
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### Describe the bug
I have data saved with save_to_disk. The data is big (700Gb). When I try loading it, the only option is load_from_disk, and this function copies the data to a tmp directory, causing me to run out of disk space. Is there an alternative solution to that?
### Steps to reproduce the bug
when trying to load data using load_From_disk after being saved using save_to_disk
### Expected behavior
run out of disk space
### Environment info
lateest version
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Allow dataset row indexing with np.int types (#7423)
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[] | 2025-03-06T03:10:43Z
| 2025-03-06T03:10:43Z
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| null | null | null |
@lhoestq
Proposed fix for #7423. Added a couple simple tests as requested. I had some test failures related to Java and pyspark even when installing with dev but these don't seem to be related to the changes here and fail for me even on clean main.
The typeerror raised when using the wrong type is: "Wrong key type: '{key}' of type '{type(key)}'. Expected one of int, slice, range, str or Iterable." I think that is fine. But I could modify the int part to something more generic (although I'm not sure what) if wanted.
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"Can you try again? Maybe there was a minor outage.",
"Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ",
"we fixed something on the server side, glad it's fixed now"
] | 2023-06-22T19:14:48Z
| 2023-06-27T08:38:19Z
| 2023-06-26T14:42:45Z
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The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams
I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main)
Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.
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[
"I was able to work around it"
] | 2025-03-21T17:23:52Z
| 2025-03-21T19:19:58Z
| 2025-03-21T19:19:58Z
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### Describe the bug
Please see https://huggingface.co/datasets/ejschwartz/idioms/discussions/1
Error code: FileFormatMismatchBetweenSplitsError
All three splits, train, test, and validation, use webdataset. But only the train split has more than one file. How can I force the other two splits to also be interpreted as being the webdataset format? (I don't think there is currently a way, but happy to be told that I am wrong.)
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("ejschwartz/idioms")
### Expected behavior
The dataset loads. Alternatively, there is a YAML syntax for manually specifying the format.
### Environment info
- `datasets` version: 3.2.0
- Platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.28.1
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.9.0
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PR_kwDODunzps5srOtR
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add allow_primitive_to_str and allow_decimal_to_str instead of allow_number_to_str
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6811). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@mariosasko pytest seems to be missing on windows?",
"CI is not behaving well today 🙂 ",
"I couldn't find an instance of the `allow_number_to_str` parameter (or `array_cast`/`cast_array_to_feature` more generally) being used in the wild. So, I think simply removing `allow_number_to_str` instead of deprecating it should be fine, considering `array_cast`/`cast_array_to_feature` are somewhat hidden. Do you agree @lhoestq? ",
"Yup we can remove without any deprecation cycle",
"<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) | 0.005253 / 0.011353 (-0.006100) | 0.003767 / 0.011008 (-0.007241) | 0.064599 / 0.038508 (0.026091) | 0.030758 / 0.023109 (0.007649) | 0.237437 / 0.275898 (-0.038461) | 0.277580 / 0.323480 (-0.045900) | 0.004220 / 0.007986 (-0.003766) | 0.002738 / 0.004328 (-0.001591) | 0.049393 / 0.004250 (0.045143) | 0.045283 / 0.037052 (0.008231) | 0.249907 / 0.258489 (-0.008582) | 0.283301 / 0.293841 (-0.010540) | 0.027722 / 0.128546 (-0.100825) | 0.010842 / 0.075646 (-0.064804) | 0.219197 / 0.419271 (-0.200074) | 0.036449 / 0.043533 (-0.007084) | 0.237774 / 0.255139 (-0.017365) | 0.257981 / 0.283200 (-0.025218) | 0.018098 / 0.141683 (-0.123585) | 1.161778 / 1.452155 (-0.290376) | 1.212707 / 1.492716 (-0.280010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096462 / 0.018006 (0.078456) | 0.305322 / 0.000490 (0.304832) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018438 / 0.037411 (-0.018973) | 0.061633 / 0.014526 (0.047107) | 0.073678 / 0.176557 (-0.102879) | 0.122033 / 0.737135 (-0.615103) | 0.074846 / 0.296338 (-0.221493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279564 / 0.215209 (0.064355) | 2.756984 / 2.077655 (0.679330) | 1.486525 / 1.504120 (-0.017595) | 1.366474 / 1.541195 (-0.174721) | 1.370192 / 1.468490 (-0.098298) | 0.576940 / 4.584777 (-4.007837) | 2.414088 / 3.745712 (-1.331624) | 2.788423 / 5.269862 (-2.481439) | 1.738695 / 4.565676 (-2.826982) | 0.064456 / 0.424275 (-0.359819) | 0.005536 / 0.007607 (-0.002071) | 0.337266 / 0.226044 (0.111222) | 3.327140 / 2.268929 (1.058212) | 1.837553 / 55.444624 (-53.607072) | 1.538955 / 6.876477 (-5.337521) | 1.575624 / 2.142072 (-0.566448) | 0.639960 / 4.805227 (-4.165267) | 0.117607 / 6.500664 (-6.383057) | 0.042077 / 0.075469 (-0.033393) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960488 / 1.841788 (-0.881300) | 11.565280 / 8.074308 (3.490972) | 9.702633 / 10.191392 (-0.488759) | 0.139106 / 0.680424 (-0.541318) | 0.013601 / 0.534201 (-0.520600) | 0.291499 / 0.579283 (-0.287784) | 0.277433 / 0.434364 (-0.156930) | 0.325700 / 0.540337 (-0.214637) | 0.421036 / 1.386936 (-0.965900) |\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) | 0.005405 / 0.011353 (-0.005948) | 0.003816 / 0.011008 (-0.007192) | 0.050422 / 0.038508 (0.011914) | 0.030473 / 0.023109 (0.007364) | 0.275975 / 0.275898 (0.000077) | 0.298002 / 0.323480 (-0.025478) | 0.004280 / 0.007986 (-0.003706) | 0.002746 / 0.004328 (-0.001583) | 0.049649 / 0.004250 (0.045398) | 0.040675 / 0.037052 (0.003623) | 0.287496 / 0.258489 (0.029007) | 0.315140 / 0.293841 (0.021299) | 0.029835 / 0.128546 (-0.098711) | 0.010443 / 0.075646 (-0.065204) | 0.058299 / 0.419271 (-0.360972) | 0.032944 / 0.043533 (-0.010588) | 0.279468 / 0.255139 (0.024329) | 0.296336 / 0.283200 (0.013136) | 0.018572 / 0.141683 (-0.123111) | 1.177622 / 1.452155 (-0.274532) | 1.238240 / 1.492716 (-0.254477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091867 / 0.018006 (0.073861) | 0.299982 / 0.000490 (0.299492) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022649 / 0.037411 (-0.014762) | 0.074948 / 0.014526 (0.060422) | 0.087949 / 0.176557 (-0.088607) | 0.125875 / 0.737135 (-0.611261) | 0.089295 / 0.296338 (-0.207044) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290387 / 0.215209 (0.075178) | 2.820969 / 2.077655 (0.743315) | 1.614607 / 1.504120 (0.110487) | 1.496959 / 1.541195 (-0.044236) | 1.526475 / 1.468490 (0.057985) | 0.570087 / 4.584777 (-4.014690) | 2.423106 / 3.745712 (-1.322606) | 2.825321 / 5.269862 (-2.444540) | 1.765580 / 4.565676 (-2.800097) | 0.063289 / 0.424275 (-0.360986) | 0.005456 / 0.007607 (-0.002151) | 0.344100 / 0.226044 (0.118055) | 3.395733 / 2.268929 (1.126804) | 1.951794 / 55.444624 (-53.492830) | 1.677689 / 6.876477 (-5.198787) | 1.684448 / 2.142072 (-0.457624) | 0.644343 / 4.805227 (-4.160885) | 0.115796 / 6.500664 (-6.384868) | 0.041052 / 0.075469 (-0.034417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031487 / 1.841788 (-0.810301) | 12.116156 / 8.074308 (4.041848) | 10.472247 / 10.191392 (0.280855) | 0.142934 / 0.680424 (-0.537490) | 0.015470 / 0.534201 (-0.518731) | 0.290402 / 0.579283 (-0.288882) | 0.272594 / 0.434364 (-0.161770) | 0.328311 / 0.540337 (-0.212027) | 0.424694 / 1.386936 (-0.962242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8983a3b4dec315bf25331a6065cb74de9017f0e8 \"CML watermark\")\n"
] | 2024-04-15T13:14:38Z
| 2024-07-03T14:59:42Z
| 2024-04-16T17:03:17Z
|
CONTRIBUTOR
| null | null | null |
Fix #6805
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PR_kwDODunzps51iuop
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Adding `Music` feature for symbolic music modality (MIDI, abc)
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[] | 2024-07-16T17:26:04Z
| 2024-07-29T06:47:55Z
| 2024-07-29T06:47:55Z
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NONE
| null | null | null |
⚠️ (WIP) ⚠️
### What this PR does
This PR adds a `Music` feature for the symbolic music modality, in particular [MIDI](https://en.wikipedia.org/wiki/Musical_Instrument_Digital_Interface) and [abc](https://en.wikipedia.org/wiki/ABC_notation) files.
### Motivations
These two file formats are widely used in the [Music Information Retrieval (MIR)](https://en.wikipedia.org/wiki/Music_information_retrieval) for tasks such as music generation, music transcription, music synthesis or music transcription. Having a dedicated feature in the datasets library would allow to both encourage researchers to share datasets of this modality as well as making them more easily usable for end users, benefitting from the perks of the library.
These file formats are supported by [symusic](https://github.com/Yikai-Liao/symusic), a lightweight Python library with C bindings (using nanobind) allowing to efficiently read, write and manipulate them. The library is actively developed, and can in the future also implement other file formats such as [musicXML](https://en.wikipedia.org/wiki/MusicXML). As such, this PR relies on it.
The music data can then easily be tokenized with appropriate tokenizers such as [MidiTok](https://github.com/Natooz/MidiTok) or converted to pianorolls matrices by symusic.
**Jul 16th 2024:**
* the tests for the `Music` feature are currently failing due to non-supported access to the LazyBatch in `test_dataset_with_music_feature_map` and `test_dataset_with_music_feature_map_resample_music` (see TODOs). I am a beginner with pyArrow, I'll take any advice to make this work;
* additional tests including the `Music` feature with parquet and WebDataset should be implemented. As of right now, I am waiting for your feedback before taking further steps;
* a `MusicFolder` should also be implemented to comply with the usages of the `Image` and `Audio` features, waiting for your feedback too.
CCing @lhoestq and @albertvillanova
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I_kwDODunzps5rZha5
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Using a dataset for a use other than it was intended for.
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"I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` "
] | 2023-07-12T22:33:17Z
| 2023-07-13T13:57:36Z
| 2023-07-13T13:57:36Z
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### Describe the bug
Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason?
Here is the full stacktrace
```
File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders
dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted")
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets
return _interleave_iterable_datasets(
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets
info = DatasetInfo.from_merge([d.info for d in datasets])
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp>
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__
self.task_templates = [
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features
raise ValueError(f"Column {self.label_column} is not present in features.")
ValueError: Column label is not present in features.
```
### Steps to reproduce the bug
Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets.
### Expected behavior
Should let me use the dataset with just the `text` field
### Environment info
latest datasets library? I don't think this was an issue in earlier versions.
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Support jax 0.4.27 in CI tests
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6885). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005232 / 0.011353 (-0.006121) | 0.003749 / 0.011008 (-0.007260) | 0.063451 / 0.038508 (0.024943) | 0.031164 / 0.023109 (0.008055) | 0.252024 / 0.275898 (-0.023874) | 0.274479 / 0.323480 (-0.049001) | 0.003238 / 0.007986 (-0.004748) | 0.002668 / 0.004328 (-0.001660) | 0.049570 / 0.004250 (0.045320) | 0.046159 / 0.037052 (0.009107) | 0.273416 / 0.258489 (0.014927) | 0.299064 / 0.293841 (0.005223) | 0.027758 / 0.128546 (-0.100788) | 0.010702 / 0.075646 (-0.064944) | 0.207244 / 0.419271 (-0.212028) | 0.036139 / 0.043533 (-0.007394) | 0.249966 / 0.255139 (-0.005173) | 0.270685 / 0.283200 (-0.012515) | 0.019938 / 0.141683 (-0.121745) | 1.133642 / 1.452155 (-0.318512) | 1.170712 / 1.492716 (-0.322004) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098352 / 0.018006 (0.080346) | 0.310738 / 0.000490 (0.310248) | 0.000225 / 0.000200 (0.000025) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018151 / 0.037411 (-0.019261) | 0.061169 / 0.014526 (0.046644) | 0.073275 / 0.176557 (-0.103281) | 0.120320 / 0.737135 (-0.616815) | 0.083945 / 0.296338 (-0.212394) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283285 / 0.215209 (0.068075) | 2.766129 / 2.077655 (0.688475) | 1.477831 / 1.504120 (-0.026289) | 1.363365 / 1.541195 (-0.177830) | 1.402081 / 1.468490 (-0.066409) | 0.554100 / 4.584777 (-4.030677) | 2.374885 / 3.745712 (-1.370827) | 2.866260 / 5.269862 (-2.403601) | 1.775109 / 4.565676 (-2.790567) | 0.062416 / 0.424275 (-0.361859) | 0.005490 / 0.007607 (-0.002117) | 0.379293 / 0.226044 (0.153248) | 3.330534 / 2.268929 (1.061606) | 1.881648 / 55.444624 (-53.562977) | 1.549847 / 6.876477 (-5.326629) | 1.660350 / 2.142072 (-0.481722) | 0.631013 / 4.805227 (-4.174214) | 0.116646 / 6.500664 (-6.384018) | 0.042977 / 0.075469 (-0.032492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996102 / 1.841788 (-0.845685) | 12.079143 / 8.074308 (4.004835) | 9.903568 / 10.191392 (-0.287824) | 0.141447 / 0.680424 (-0.538976) | 0.014115 / 0.534201 (-0.520086) | 0.287576 / 0.579283 (-0.291707) | 0.262951 / 0.434364 (-0.171413) | 0.325167 / 0.540337 (-0.215170) | 0.425780 / 1.386936 (-0.961156) |\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) | 0.005213 / 0.011353 (-0.006139) | 0.003686 / 0.011008 (-0.007322) | 0.049963 / 0.038508 (0.011455) | 0.030635 / 0.023109 (0.007525) | 0.263992 / 0.275898 (-0.011906) | 0.289960 / 0.323480 (-0.033520) | 0.004281 / 0.007986 (-0.003704) | 0.002709 / 0.004328 (-0.001619) | 0.049147 / 0.004250 (0.044897) | 0.041036 / 0.037052 (0.003984) | 0.277621 / 0.258489 (0.019132) | 0.305689 / 0.293841 (0.011848) | 0.029342 / 0.128546 (-0.099205) | 0.010350 / 0.075646 (-0.065296) | 0.058221 / 0.419271 (-0.361051) | 0.033774 / 0.043533 (-0.009759) | 0.266163 / 0.255139 (0.011024) | 0.286866 / 0.283200 (0.003666) | 0.018463 / 0.141683 (-0.123219) | 1.136930 / 1.452155 (-0.315225) | 1.193974 / 1.492716 (-0.298742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.106787 / 0.018006 (0.088781) | 0.304229 / 0.000490 (0.303740) | 0.000209 / 0.000200 (0.000009) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022066 / 0.037411 (-0.015346) | 0.075510 / 0.014526 (0.060984) | 0.087273 / 0.176557 (-0.089284) | 0.128050 / 0.737135 (-0.609085) | 0.090492 / 0.296338 (-0.205847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299034 / 0.215209 (0.083825) | 2.899115 / 2.077655 (0.821461) | 1.625169 / 1.504120 (0.121049) | 1.456491 / 1.541195 (-0.084703) | 1.433063 / 1.468490 (-0.035427) | 0.565416 / 4.584777 (-4.019361) | 0.979298 / 3.745712 (-2.766415) | 2.748965 / 5.269862 (-2.520897) | 1.738671 / 4.565676 (-2.827005) | 0.062869 / 0.424275 (-0.361407) | 0.005001 / 0.007607 (-0.002606) | 0.348534 / 0.226044 (0.122489) | 3.437791 / 2.268929 (1.168862) | 1.896804 / 55.444624 (-53.547821) | 1.658544 / 6.876477 (-5.217933) | 1.649106 / 2.142072 (-0.492966) | 0.653791 / 4.805227 (-4.151436) | 0.125522 / 6.500664 (-6.375142) | 0.051260 / 0.075469 (-0.024209) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025170 / 1.841788 (-0.816617) | 12.247968 / 8.074308 (4.173660) | 9.863777 / 10.191392 (-0.327615) | 0.140498 / 0.680424 (-0.539926) | 0.015158 / 0.534201 (-0.519043) | 0.288210 / 0.579283 (-0.291073) | 0.128207 / 0.434364 (-0.306157) | 0.398735 / 0.540337 (-0.141603) | 0.418217 / 1.386936 (-0.968719) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#871eabc7b23c27d677bc06ae2cc1ec3a2a04b10f \"CML watermark\")\n"
] | 2024-05-08T09:19:37Z
| 2024-05-08T09:43:19Z
| 2024-05-08T09:35:16Z
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MEMBER
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Support jax 0.4.27 in CI tests by using jax Array `devices` method instead of `device` (which no longer exists).
Fix #6884.
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https://api.github.com/repos/huggingface/datasets/issues/5697
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PR_kwDODunzps5NefxZ
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Raise an error on missing distributed seed
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009644 / 0.011353 (-0.001709) | 0.006407 / 0.011008 (-0.004601) | 0.148353 / 0.038508 (0.109845) | 0.037537 / 0.023109 (0.014428) | 0.379697 / 0.275898 (0.103799) | 0.466260 / 0.323480 (0.142780) | 0.007884 / 0.007986 (-0.000102) | 0.005140 / 0.004328 (0.000812) | 0.111078 / 0.004250 (0.106827) | 0.049429 / 0.037052 (0.012377) | 0.364766 / 0.258489 (0.106277) | 0.453809 / 0.293841 (0.159968) | 0.051918 / 0.128546 (-0.076628) | 0.020081 / 0.075646 (-0.055566) | 0.616041 / 0.419271 (0.196770) | 0.059834 / 0.043533 (0.016301) | 0.373104 / 0.255139 (0.117965) | 0.419304 / 0.283200 (0.136104) | 0.113526 / 0.141683 (-0.028156) | 1.827160 / 1.452155 (0.375006) | 1.912092 / 1.492716 (0.419376) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269584 / 0.018006 (0.251578) | 0.554100 / 0.000490 (0.553610) | 0.006618 / 0.000200 (0.006418) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025280 / 0.037411 (-0.012131) | 0.123116 / 0.014526 (0.108591) | 0.127674 / 0.176557 (-0.048883) | 0.189106 / 0.737135 (-0.548030) | 0.142072 / 0.296338 (-0.154267) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602201 / 0.215209 (0.386992) | 5.959610 / 2.077655 (3.881956) | 2.404856 / 1.504120 (0.900736) | 2.175017 / 1.541195 (0.633823) | 2.154360 / 1.468490 (0.685870) | 1.265339 / 4.584777 (-3.319438) | 5.598429 / 3.745712 (1.852716) | 5.130249 / 5.269862 (-0.139612) | 2.764922 / 4.565676 (-1.800754) | 0.143232 / 0.424275 (-0.281043) | 0.014721 / 0.007607 (0.007114) | 0.764734 / 0.226044 (0.538689) | 7.518810 / 2.268929 (5.249882) | 3.344734 / 55.444624 (-52.099890) | 2.601158 / 6.876477 (-4.275319) | 2.726018 / 2.142072 (0.583945) | 1.397918 / 4.805227 (-3.407309) | 0.253277 / 6.500664 (-6.247387) | 0.077772 / 0.075469 (0.002303) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.499535 / 1.841788 (-0.342253) | 17.782490 / 8.074308 (9.708182) | 21.953064 / 10.191392 (11.761672) | 0.248753 / 0.680424 (-0.431671) | 0.029194 / 0.534201 (-0.505007) | 0.529700 / 0.579283 (-0.049583) | 0.618412 / 0.434364 (0.184048) | 0.605062 / 0.540337 (0.064725) | 0.725661 / 1.386936 (-0.661275) |\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) | 0.009489 / 0.011353 (-0.001864) | 0.006423 / 0.011008 (-0.004585) | 0.096789 / 0.038508 (0.058281) | 0.034639 / 0.023109 (0.011530) | 0.403875 / 0.275898 (0.127977) | 0.439368 / 0.323480 (0.115888) | 0.006354 / 0.007986 (-0.001631) | 0.006794 / 0.004328 (0.002466) | 0.095537 / 0.004250 (0.091287) | 0.047749 / 0.037052 (0.010697) | 0.424157 / 0.258489 (0.165668) | 0.487825 / 0.293841 (0.193984) | 0.054675 / 0.128546 (-0.073872) | 0.021349 / 0.075646 (-0.054297) | 0.108917 / 0.419271 (-0.310354) | 0.075891 / 0.043533 (0.032358) | 0.412889 / 0.255139 (0.157750) | 0.464512 / 0.283200 (0.181312) | 0.118832 / 0.141683 (-0.022850) | 1.721215 / 1.452155 (0.269060) | 1.857195 / 1.492716 (0.364478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248308 / 0.018006 (0.230302) | 0.559496 / 0.000490 (0.559006) | 0.007136 / 0.000200 (0.006936) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031772 / 0.037411 (-0.005639) | 0.123565 / 0.014526 (0.109039) | 0.132660 / 0.176557 (-0.043896) | 0.201428 / 0.737135 (-0.535707) | 0.135238 / 0.296338 (-0.161101) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.646978 / 0.215209 (0.431769) | 6.183477 / 2.077655 (4.105822) | 2.782117 / 1.504120 (1.277997) | 2.294093 / 1.541195 (0.752898) | 2.346932 / 1.468490 (0.878442) | 1.239085 / 4.584777 (-3.345692) | 5.696364 / 3.745712 (1.950652) | 4.980102 / 5.269862 (-0.289759) | 2.278116 / 4.565676 (-2.287560) | 0.157339 / 0.424275 (-0.266936) | 0.014936 / 0.007607 (0.007329) | 0.778001 / 0.226044 (0.551957) | 7.708066 / 2.268929 (5.439138) | 3.412235 / 55.444624 (-52.032389) | 2.670670 / 6.876477 (-4.205806) | 2.731802 / 2.142072 (0.589730) | 1.446516 / 4.805227 (-3.358712) | 0.263689 / 6.500664 (-6.236975) | 0.086359 / 0.075469 (0.010890) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.573169 / 1.841788 (-0.268619) | 17.690842 / 8.074308 (9.616534) | 20.343336 / 10.191392 (10.151944) | 0.231028 / 0.680424 (-0.449396) | 0.025954 / 0.534201 (-0.508247) | 0.570554 / 0.579283 (-0.008729) | 0.610453 / 0.434364 (0.176089) | 0.675830 / 0.540337 (0.135493) | 0.790650 / 1.386936 (-0.596286) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d094ed07823bfb3271f3a9006daa1f92a64967a5 \"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) | 0.007553 / 0.011353 (-0.003800) | 0.005426 / 0.011008 (-0.005582) | 0.096550 / 0.038508 (0.058042) | 0.034393 / 0.023109 (0.011284) | 0.322297 / 0.275898 (0.046399) | 0.340943 / 0.323480 (0.017463) | 0.006350 / 0.007986 (-0.001635) | 0.005700 / 0.004328 (0.001372) | 0.074929 / 0.004250 (0.070678) | 0.054819 / 0.037052 (0.017767) | 0.320151 / 0.258489 (0.061662) | 0.346957 / 0.293841 (0.053116) | 0.036659 / 0.128546 (-0.091887) | 0.012443 / 0.075646 (-0.063204) | 0.332232 / 0.419271 (-0.087040) | 0.051467 / 0.043533 (0.007934) | 0.310952 / 0.255139 (0.055813) | 0.325617 / 0.283200 (0.042417) | 0.104908 / 0.141683 (-0.036775) | 1.446752 / 1.452155 (-0.005403) | 1.558773 / 1.492716 (0.066056) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300639 / 0.018006 (0.282633) | 0.499901 / 0.000490 (0.499411) | 0.007340 / 0.000200 (0.007140) | 0.000255 / 0.000054 (0.000201) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027206 / 0.037411 (-0.010206) | 0.105603 / 0.014526 (0.091077) | 0.118669 / 0.176557 (-0.057887) | 0.174050 / 0.737135 (-0.563086) | 0.125099 / 0.296338 (-0.171239) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404285 / 0.215209 (0.189076) | 4.034587 / 2.077655 (1.956933) | 1.812639 / 1.504120 (0.308519) | 1.625745 / 1.541195 (0.084551) | 1.735523 / 1.468490 (0.267033) | 0.709699 / 4.584777 (-3.875078) | 3.802196 / 3.745712 (0.056484) | 3.656984 / 5.269862 (-1.612877) | 1.968470 / 4.565676 (-2.597206) | 0.086612 / 0.424275 (-0.337663) | 0.012368 / 0.007607 (0.004761) | 0.502622 / 0.226044 (0.276577) | 5.017876 / 2.268929 (2.748948) | 2.279794 / 55.444624 (-53.164831) | 1.956938 / 6.876477 (-4.919538) | 2.150430 / 2.142072 (0.008357) | 0.847691 / 4.805227 (-3.957536) | 0.170157 / 6.500664 (-6.330507) | 0.064141 / 0.075469 (-0.011328) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.172246 / 1.841788 (-0.669542) | 15.229444 / 8.074308 (7.155136) | 14.715913 / 10.191392 (4.524521) | 0.192501 / 0.680424 (-0.487923) | 0.017972 / 0.534201 (-0.516229) | 0.423834 / 0.579283 (-0.155449) | 0.423019 / 0.434364 (-0.011345) | 0.493298 / 0.540337 (-0.047039) | 0.589833 / 1.386936 (-0.797103) |\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) | 0.007773 / 0.011353 (-0.003580) | 0.005449 / 0.011008 (-0.005560) | 0.075180 / 0.038508 (0.036672) | 0.035221 / 0.023109 (0.012111) | 0.338169 / 0.275898 (0.062271) | 0.374002 / 0.323480 (0.050522) | 0.006391 / 0.007986 (-0.001595) | 0.004406 / 0.004328 (0.000078) | 0.074925 / 0.004250 (0.070675) | 0.056527 / 0.037052 (0.019475) | 0.338071 / 0.258489 (0.079582) | 0.391882 / 0.293841 (0.098041) | 0.037241 / 0.128546 (-0.091305) | 0.012546 / 0.075646 (-0.063100) | 0.087331 / 0.419271 (-0.331940) | 0.049851 / 0.043533 (0.006318) | 0.335264 / 0.255139 (0.080125) | 0.354813 / 0.283200 (0.071614) | 0.110614 / 0.141683 (-0.031069) | 1.432782 / 1.452155 (-0.019372) | 1.548800 / 1.492716 (0.056083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307892 / 0.018006 (0.289886) | 0.518809 / 0.000490 (0.518319) | 0.004058 / 0.000200 (0.003858) | 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) | 0.029155 / 0.037411 (-0.008256) | 0.111706 / 0.014526 (0.097180) | 0.122964 / 0.176557 (-0.053592) | 0.170939 / 0.737135 (-0.566196) | 0.128538 / 0.296338 (-0.167801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426529 / 0.215209 (0.211320) | 4.254218 / 2.077655 (2.176563) | 2.011455 / 1.504120 (0.507335) | 1.817397 / 1.541195 (0.276202) | 1.952915 / 1.468490 (0.484425) | 0.705052 / 4.584777 (-3.879725) | 3.844458 / 3.745712 (0.098746) | 3.592754 / 5.269862 (-1.677107) | 1.573567 / 4.565676 (-2.992109) | 0.086834 / 0.424275 (-0.337441) | 0.012389 / 0.007607 (0.004782) | 0.541695 / 0.226044 (0.315650) | 5.224492 / 2.268929 (2.955564) | 2.473648 / 55.444624 (-52.970976) | 2.167458 / 6.876477 (-4.709019) | 2.253319 / 2.142072 (0.111246) | 0.836322 / 4.805227 (-3.968905) | 0.168680 / 6.500664 (-6.331984) | 0.065699 / 0.075469 (-0.009770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281886 / 1.841788 (-0.559902) | 15.451741 / 8.074308 (7.377433) | 14.906870 / 10.191392 (4.715478) | 0.168554 / 0.680424 (-0.511870) | 0.017365 / 0.534201 (-0.516836) | 0.434183 / 0.579283 (-0.145100) | 0.421891 / 0.434364 (-0.012473) | 0.538993 / 0.540337 (-0.001344) | 0.636212 / 1.386936 (-0.750724) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f428b8172319a6bfe95d7a4356b1d14a8d386d8 \"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) | 0.007362 / 0.011353 (-0.003991) | 0.004992 / 0.011008 (-0.006016) | 0.098730 / 0.038508 (0.060222) | 0.033673 / 0.023109 (0.010563) | 0.296334 / 0.275898 (0.020436) | 0.328208 / 0.323480 (0.004728) | 0.005658 / 0.007986 (-0.002327) | 0.004130 / 0.004328 (-0.000199) | 0.074596 / 0.004250 (0.070346) | 0.048230 / 0.037052 (0.011178) | 0.295631 / 0.258489 (0.037142) | 0.347176 / 0.293841 (0.053335) | 0.036359 / 0.128546 (-0.092187) | 0.011889 / 0.075646 (-0.063758) | 0.332889 / 0.419271 (-0.086382) | 0.049708 / 0.043533 (0.006175) | 0.291207 / 0.255139 (0.036068) | 0.311066 / 0.283200 (0.027867) | 0.098418 / 0.141683 (-0.043265) | 1.415450 / 1.452155 (-0.036705) | 1.526928 / 1.492716 (0.034212) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212636 / 0.018006 (0.194630) | 0.432337 / 0.000490 (0.431847) | 0.006839 / 0.000200 (0.006639) | 0.000205 / 0.000054 (0.000150) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026045 / 0.037411 (-0.011366) | 0.107427 / 0.014526 (0.092901) | 0.114634 / 0.176557 (-0.061922) | 0.169943 / 0.737135 (-0.567192) | 0.123290 / 0.296338 (-0.173048) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409432 / 0.215209 (0.194223) | 4.097910 / 2.077655 (2.020255) | 1.857177 / 1.504120 (0.353057) | 1.672355 / 1.541195 (0.131160) | 1.740130 / 1.468490 (0.271640) | 0.706520 / 4.584777 (-3.878257) | 3.773606 / 3.745712 (0.027893) | 2.101635 / 5.269862 (-3.168226) | 1.326295 / 4.565676 (-3.239382) | 0.085672 / 0.424275 (-0.338604) | 0.012142 / 0.007607 (0.004534) | 0.501168 / 0.226044 (0.275123) | 5.049784 / 2.268929 (2.780855) | 2.322477 / 55.444624 (-53.122148) | 1.990105 / 6.876477 (-4.886372) | 2.115003 / 2.142072 (-0.027070) | 0.837518 / 4.805227 (-3.967709) | 0.168457 / 6.500664 (-6.332207) | 0.064622 / 0.075469 (-0.010847) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188152 / 1.841788 (-0.653635) | 14.991585 / 8.074308 (6.917276) | 14.635187 / 10.191392 (4.443795) | 0.183708 / 0.680424 (-0.496716) | 0.017452 / 0.534201 (-0.516749) | 0.418963 / 0.579283 (-0.160320) | 0.428893 / 0.434364 (-0.005471) | 0.502108 / 0.540337 (-0.038229) | 0.596345 / 1.386936 (-0.790591) |\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) | 0.007404 / 0.011353 (-0.003949) | 0.005148 / 0.011008 (-0.005860) | 0.074785 / 0.038508 (0.036277) | 0.033815 / 0.023109 (0.010706) | 0.332752 / 0.275898 (0.056854) | 0.368018 / 0.323480 (0.044538) | 0.005642 / 0.007986 (-0.002344) | 0.004041 / 0.004328 (-0.000287) | 0.073455 / 0.004250 (0.069205) | 0.047380 / 0.037052 (0.010328) | 0.337017 / 0.258489 (0.078528) | 0.384185 / 0.293841 (0.090344) | 0.036592 / 0.128546 (-0.091954) | 0.012109 / 0.075646 (-0.063537) | 0.086862 / 0.419271 (-0.332410) | 0.049030 / 0.043533 (0.005497) | 0.336542 / 0.255139 (0.081403) | 0.350295 / 0.283200 (0.067096) | 0.100998 / 0.141683 (-0.040685) | 1.469749 / 1.452155 (0.017594) | 1.588355 / 1.492716 (0.095639) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227552 / 0.018006 (0.209546) | 0.438087 / 0.000490 (0.437598) | 0.000394 / 0.000200 (0.000194) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030575 / 0.037411 (-0.006836) | 0.111914 / 0.014526 (0.097388) | 0.124583 / 0.176557 (-0.051973) | 0.175471 / 0.737135 (-0.561665) | 0.129535 / 0.296338 (-0.166803) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425625 / 0.215209 (0.210416) | 4.228328 / 2.077655 (2.150673) | 2.021087 / 1.504120 (0.516967) | 1.832550 / 1.541195 (0.291355) | 1.925572 / 1.468490 (0.457082) | 0.690772 / 4.584777 (-3.894005) | 3.724900 / 3.745712 (-0.020813) | 2.080286 / 5.269862 (-3.189576) | 1.316854 / 4.565676 (-3.248822) | 0.085123 / 0.424275 (-0.339152) | 0.012078 / 0.007607 (0.004471) | 0.525802 / 0.226044 (0.299758) | 5.242598 / 2.268929 (2.973670) | 2.491596 / 55.444624 (-52.953028) | 2.125156 / 6.876477 (-4.751320) | 2.185922 / 2.142072 (0.043850) | 0.823116 / 4.805227 (-3.982111) | 0.165188 / 6.500664 (-6.335476) | 0.063970 / 0.075469 (-0.011499) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256948 / 1.841788 (-0.584840) | 14.981990 / 8.074308 (6.907682) | 14.565266 / 10.191392 (4.373874) | 0.175064 / 0.680424 (-0.505360) | 0.017628 / 0.534201 (-0.516573) | 0.429979 / 0.579283 (-0.149304) | 0.422509 / 0.434364 (-0.011855) | 0.546262 / 0.540337 (0.005924) | 0.647103 / 1.386936 (-0.739833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0803a006db1c395ac715662cc6079651f77c11ea \"CML watermark\")\n"
] | 2023-04-03T10:44:58Z
| 2023-04-04T15:05:24Z
| 2023-04-04T14:58:16Z
|
MEMBER
| null | null | null |
close https://github.com/huggingface/datasets/issues/5696
|
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https://api.github.com/repos/huggingface/datasets/issues/6561
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https://github.com/huggingface/datasets/issues/6561
| 2,067,404,951
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I_kwDODunzps57OhiX
| 6,561
|
Document YAML configuration with "data_dir"
|
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[
"In particular, I would like to have an example of how to replace the following configuration (from https://huggingface.co/docs/hub/datasets-manual-configuration#splits)\r\n\r\n```\r\n---\r\nconfigs:\r\n- config_name: default\r\n data_files:\r\n - split: train\r\n path: \"data/*.csv\"\r\n - split: test\r\n path: \"holdout/*.csv\"\r\n---\r\n```\r\n\r\nwith the `data_dir` field."
] | 2024-01-05T14:03:33Z
| 2024-01-05T14:06:18Z
| null |
COLLABORATOR
| null | null |
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See https://huggingface.co/datasets/uonlp/CulturaX/discussions/15#6597e83f185db94370d6bf50 for reference
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https://api.github.com/repos/huggingface/datasets/issues/7030
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| 2,393,411,631
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I_kwDODunzps6OqJAv
| 7,030
|
Add option to disable progress bar when reading a dataset ("Loading dataset from disk")
|
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"You can disable progress bars for all of `datasets` with `disable_progress_bars`. [Link](https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars)\r\n\r\nSo you could do something like:\r\n\r\n```python\r\nfrom datasets import load_from_disk, enable_progress_bars, disable_progress_bars\r\n\r\ndisable_progress_bars()\r\n# Your code\r\nload_from_disk(....)\r\n\r\nenable_progress_bars()\r\n```\r\n",
"Thank you! Closing the issue."
] | 2024-07-06T05:43:37Z
| 2024-07-13T14:35:59Z
| 2024-07-13T14:35:59Z
|
NONE
| null | null |
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### Feature request
Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary.
|
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Unpins sqlAlchemy
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"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 "
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| 2023-04-04T08:20:19Z
| 2023-04-04T08:19:14Z
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NONE
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Closes #5477
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7027). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005612 / 0.011353 (-0.005741) | 0.004023 / 0.011008 (-0.006985) | 0.065578 / 0.038508 (0.027070) | 0.030476 / 0.023109 (0.007367) | 0.237131 / 0.275898 (-0.038767) | 0.269388 / 0.323480 (-0.054092) | 0.003364 / 0.007986 (-0.004622) | 0.002938 / 0.004328 (-0.001390) | 0.050867 / 0.004250 (0.046617) | 0.049456 / 0.037052 (0.012403) | 0.249587 / 0.258489 (-0.008902) | 0.291132 / 0.293841 (-0.002709) | 0.029373 / 0.128546 (-0.099174) | 0.012266 / 0.075646 (-0.063380) | 0.206239 / 0.419271 (-0.213033) | 0.037192 / 0.043533 (-0.006340) | 0.244902 / 0.255139 (-0.010237) | 0.269779 / 0.283200 (-0.013421) | 0.019870 / 0.141683 (-0.121813) | 1.123697 / 1.452155 (-0.328458) | 1.181256 / 1.492716 (-0.311460) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108535 / 0.018006 (0.090529) | 0.317838 / 0.000490 (0.317348) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019097 / 0.037411 (-0.018315) | 0.063836 / 0.014526 (0.049310) | 0.075446 / 0.176557 (-0.101111) | 0.124503 / 0.737135 (-0.612632) | 0.077730 / 0.296338 (-0.218608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284688 / 0.215209 (0.069479) | 2.817832 / 2.077655 (0.740178) | 1.487342 / 1.504120 (-0.016778) | 1.354037 / 1.541195 (-0.187158) | 1.426904 / 1.468490 (-0.041586) | 0.728754 / 4.584777 (-3.856022) | 2.361140 / 3.745712 (-1.384573) | 2.926215 / 5.269862 (-2.343647) | 1.981767 / 4.565676 (-2.583909) | 0.079278 / 0.424275 (-0.344997) | 0.005567 / 0.007607 (-0.002040) | 0.336590 / 0.226044 (0.110546) | 3.371062 / 2.268929 (1.102134) | 1.845343 / 55.444624 (-53.599282) | 1.537699 / 6.876477 (-5.338777) | 1.731407 / 2.142072 (-0.410665) | 0.796148 / 4.805227 (-4.009079) | 0.133830 / 6.500664 (-6.366835) | 0.043117 / 0.075469 (-0.032352) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980786 / 1.841788 (-0.861001) | 12.653553 / 8.074308 (4.579245) | 9.402636 / 10.191392 (-0.788756) | 0.143756 / 0.680424 (-0.536667) | 0.014896 / 0.534201 (-0.519304) | 0.328796 / 0.579283 (-0.250487) | 0.275108 / 0.434364 (-0.159255) | 0.343397 / 0.540337 (-0.196940) | 0.472301 / 1.386936 (-0.914635) |\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) | 0.005882 / 0.011353 (-0.005471) | 0.003982 / 0.011008 (-0.007026) | 0.050484 / 0.038508 (0.011976) | 0.035217 / 0.023109 (0.012108) | 0.271683 / 0.275898 (-0.004215) | 0.291498 / 0.323480 (-0.031982) | 0.004429 / 0.007986 (-0.003557) | 0.002928 / 0.004328 (-0.001401) | 0.049386 / 0.004250 (0.045136) | 0.040868 / 0.037052 (0.003815) | 0.280968 / 0.258489 (0.022479) | 0.314880 / 0.293841 (0.021039) | 0.032590 / 0.128546 (-0.095956) | 0.012319 / 0.075646 (-0.063327) | 0.060354 / 0.419271 (-0.358917) | 0.034138 / 0.043533 (-0.009394) | 0.267491 / 0.255139 (0.012352) | 0.283077 / 0.283200 (-0.000123) | 0.017784 / 0.141683 (-0.123899) | 1.154835 / 1.452155 (-0.297320) | 1.179271 / 1.492716 (-0.313446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100519 / 0.018006 (0.082513) | 0.309043 / 0.000490 (0.308553) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024056 / 0.037411 (-0.013356) | 0.077810 / 0.014526 (0.063284) | 0.092682 / 0.176557 (-0.083875) | 0.132101 / 0.737135 (-0.605034) | 0.091986 / 0.296338 (-0.204352) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298186 / 0.215209 (0.082977) | 2.905134 / 2.077655 (0.827479) | 1.552364 / 1.504120 (0.048245) | 1.424644 / 1.541195 (-0.116551) | 1.457667 / 1.468490 (-0.010823) | 0.717606 / 4.584777 (-3.867171) | 0.944470 / 3.745712 (-2.801242) | 3.056236 / 5.269862 (-2.213626) | 1.946453 / 4.565676 (-2.619223) | 0.080525 / 0.424275 (-0.343750) | 0.005235 / 0.007607 (-0.002372) | 0.348561 / 0.226044 (0.122516) | 3.449350 / 2.268929 (1.180421) | 1.930165 / 55.444624 (-53.514459) | 1.620883 / 6.876477 (-5.255593) | 1.671963 / 2.142072 (-0.470109) | 0.801978 / 4.805227 (-4.003249) | 0.134494 / 6.500664 (-6.366170) | 0.041888 / 0.075469 (-0.033581) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005961 / 1.841788 (-0.835826) | 12.687638 / 8.074308 (4.613330) | 10.398730 / 10.191392 (0.207338) | 0.134503 / 0.680424 (-0.545920) | 0.015839 / 0.534201 (-0.518362) | 0.307465 / 0.579283 (-0.271819) | 0.130805 / 0.434364 (-0.303559) | 0.349079 / 0.540337 (-0.191259) | 0.437609 / 1.386936 (-0.949327) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc6ac9e5f70811a450198203ddc077c0c7bff206 \"CML watermark\")\n"
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[
"Hi, thanks for reporting! The last line should be `dataset = Dataset.from_generator(my_gen)`.",
"Can I work on this one?"
] | 2022-10-11T14:28:58Z
| 2022-10-12T11:31:56Z
| 2022-10-12T11:31:56Z
|
NONE
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## Describe the bug
In HOW-TO-GUIDES > Load > [Python generator](https://huggingface.co/docs/datasets/v2.5.2/en/loading#python-generator), the code example defines the `my_gen` function, but when creating the dataset, an undefined `my_dict` is passed in.
```Python
>>> from datasets import Dataset
>>> def my_gen():
... for i in range(1, 4):
... yield {"a": i}
>>> dataset = Dataset.from_generator(my_dict)
```
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PR_kwDODunzps5YnKlx
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PyArrow 13 CI fixes
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[
"<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) | 0.006095 / 0.011353 (-0.005258) | 0.003580 / 0.011008 (-0.007429) | 0.080146 / 0.038508 (0.041638) | 0.063445 / 0.023109 (0.040336) | 0.321930 / 0.275898 (0.046032) | 0.397933 / 0.323480 (0.074453) | 0.003455 / 0.007986 (-0.004531) | 0.002856 / 0.004328 (-0.001472) | 0.062938 / 0.004250 (0.058687) | 0.048896 / 0.037052 (0.011843) | 0.333070 / 0.258489 (0.074581) | 0.404485 / 0.293841 (0.110644) | 0.027156 / 0.128546 (-0.101390) | 0.007974 / 0.075646 (-0.067672) | 0.261505 / 0.419271 (-0.157766) | 0.045328 / 0.043533 (0.001795) | 0.311203 / 0.255139 (0.056064) | 0.390006 / 0.283200 (0.106806) | 0.023650 / 0.141683 (-0.118033) | 1.468856 / 1.452155 (0.016701) | 1.503867 / 1.492716 (0.011151) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202110 / 0.018006 (0.184103) | 0.436433 / 0.000490 (0.435944) | 0.002278 / 0.000200 (0.002078) | 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) | 0.024575 / 0.037411 (-0.012836) | 0.073005 / 0.014526 (0.058479) | 0.083609 / 0.176557 (-0.092947) | 0.144881 / 0.737135 (-0.592254) | 0.083495 / 0.296338 (-0.212844) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398911 / 0.215209 (0.183702) | 3.994035 / 2.077655 (1.916381) | 2.056768 / 1.504120 (0.552649) | 1.913242 / 1.541195 (0.372047) | 1.932934 / 1.468490 (0.464444) | 0.498953 / 4.584777 (-4.085824) | 3.031107 / 3.745712 (-0.714605) | 2.817165 / 5.269862 (-2.452696) | 1.858886 / 4.565676 (-2.706790) | 0.056977 / 0.424275 (-0.367299) | 0.006634 / 0.007607 (-0.000973) | 0.472580 / 0.226044 (0.246536) | 4.738301 / 2.268929 (2.469372) | 2.373938 / 55.444624 (-53.070686) | 2.021057 / 6.876477 (-4.855420) | 2.195419 / 2.142072 (0.053346) | 0.585182 / 4.805227 (-4.220045) | 0.124260 / 6.500664 (-6.376405) | 0.060250 / 0.075469 (-0.015219) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227350 / 1.841788 (-0.614438) | 18.496525 / 8.074308 (10.422216) | 13.946658 / 10.191392 (3.755266) | 0.140024 / 0.680424 (-0.540399) | 0.017077 / 0.534201 (-0.517124) | 0.334415 / 0.579283 (-0.244868) | 0.351118 / 0.434364 (-0.083246) | 0.379556 / 0.540337 (-0.160782) | 0.525064 / 1.386936 (-0.861872) |\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) | 0.006176 / 0.011353 (-0.005177) | 0.003648 / 0.011008 (-0.007360) | 0.063461 / 0.038508 (0.024953) | 0.062770 / 0.023109 (0.039660) | 0.448786 / 0.275898 (0.172888) | 0.486490 / 0.323480 (0.163010) | 0.005527 / 0.007986 (-0.002458) | 0.002860 / 0.004328 (-0.001469) | 0.063803 / 0.004250 (0.059553) | 0.049657 / 0.037052 (0.012604) | 0.449625 / 0.258489 (0.191136) | 0.489378 / 0.293841 (0.195537) | 0.028406 / 0.128546 (-0.100140) | 0.008062 / 0.075646 (-0.067584) | 0.068417 / 0.419271 (-0.350854) | 0.040854 / 0.043533 (-0.002678) | 0.461670 / 0.255139 (0.206531) | 0.481622 / 0.283200 (0.198423) | 0.021018 / 0.141683 (-0.120665) | 1.450328 / 1.452155 (-0.001826) | 1.501283 / 1.492716 (0.008567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269824 / 0.018006 (0.251817) | 0.412296 / 0.000490 (0.411807) | 0.039582 / 0.000200 (0.039382) | 0.000266 / 0.000054 (0.000211) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026436 / 0.037411 (-0.010976) | 0.080633 / 0.014526 (0.066107) | 0.089786 / 0.176557 (-0.086770) | 0.145020 / 0.737135 (-0.592115) | 0.092327 / 0.296338 (-0.204012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464349 / 0.215209 (0.249140) | 4.630631 / 2.077655 (2.552976) | 2.560527 / 1.504120 (1.056407) | 2.374195 / 1.541195 (0.833000) | 2.424774 / 1.468490 (0.956284) | 0.510428 / 4.584777 (-4.074349) | 3.099805 / 3.745712 (-0.645907) | 2.781096 / 5.269862 (-2.488765) | 1.854276 / 4.565676 (-2.711400) | 0.058102 / 0.424275 (-0.366173) | 0.006365 / 0.007607 (-0.001242) | 0.534082 / 0.226044 (0.308038) | 5.355003 / 2.268929 (3.086074) | 3.012546 / 55.444624 (-52.432078) | 2.665222 / 6.876477 (-4.211255) | 2.821014 / 2.142072 (0.678942) | 0.597733 / 4.805227 (-4.207494) | 0.125433 / 6.500664 (-6.375231) | 0.060802 / 0.075469 (-0.014667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345699 / 1.841788 (-0.496088) | 18.836083 / 8.074308 (10.761774) | 14.895458 / 10.191392 (4.704066) | 0.146843 / 0.680424 (-0.533581) | 0.018082 / 0.534201 (-0.516119) | 0.335729 / 0.579283 (-0.243554) | 0.351013 / 0.434364 (-0.083351) | 0.388435 / 0.540337 (-0.151902) | 0.543826 / 1.386936 (-0.843110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0c7e8c4808a1fb6ee7234b4caa25aa9fcfdc88f \"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) | 0.006593 / 0.011353 (-0.004760) | 0.004089 / 0.011008 (-0.006919) | 0.084753 / 0.038508 (0.046245) | 0.079899 / 0.023109 (0.056790) | 0.311528 / 0.275898 (0.035630) | 0.349722 / 0.323480 (0.026243) | 0.004288 / 0.007986 (-0.003698) | 0.004552 / 0.004328 (0.000224) | 0.065896 / 0.004250 (0.061646) | 0.053813 / 0.037052 (0.016760) | 0.316958 / 0.258489 (0.058469) | 0.367011 / 0.293841 (0.073170) | 0.031082 / 0.128546 (-0.097464) | 0.008684 / 0.075646 (-0.066963) | 0.288003 / 0.419271 (-0.131268) | 0.052560 / 0.043533 (0.009027) | 0.305589 / 0.255139 (0.050450) | 0.349656 / 0.283200 (0.066457) | 0.023857 / 0.141683 (-0.117826) | 1.462360 / 1.452155 (0.010205) | 1.568170 / 1.492716 (0.075454) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272342 / 0.018006 (0.254336) | 0.585108 / 0.000490 (0.584618) | 0.003427 / 0.000200 (0.003227) | 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) | 0.030347 / 0.037411 (-0.007064) | 0.086325 / 0.014526 (0.071799) | 0.100958 / 0.176557 (-0.075598) | 0.156534 / 0.737135 (-0.580601) | 0.102506 / 0.296338 (-0.193832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406625 / 0.215209 (0.191416) | 4.065957 / 2.077655 (1.988302) | 2.075867 / 1.504120 (0.571747) | 1.914390 / 1.541195 (0.373196) | 2.013321 / 1.468490 (0.544831) | 0.486832 / 4.584777 (-4.097945) | 3.545940 / 3.745712 (-0.199772) | 3.323226 / 5.269862 (-1.946635) | 2.067742 / 4.565676 (-2.497934) | 0.057884 / 0.424275 (-0.366391) | 0.007751 / 0.007607 (0.000144) | 0.484923 / 0.226044 (0.258878) | 4.844885 / 2.268929 (2.575956) | 2.569828 / 55.444624 (-52.874796) | 2.224058 / 6.876477 (-4.652419) | 2.485587 / 2.142072 (0.343515) | 0.584311 / 4.805227 (-4.220916) | 0.134984 / 6.500664 (-6.365680) | 0.062164 / 0.075469 (-0.013305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247182 / 1.841788 (-0.594605) | 20.107500 / 8.074308 (12.033192) | 14.194444 / 10.191392 (4.003052) | 0.147134 / 0.680424 (-0.533290) | 0.018062 / 0.534201 (-0.516138) | 0.392029 / 0.579283 (-0.187254) | 0.402991 / 0.434364 (-0.031373) | 0.457600 / 0.540337 (-0.082737) | 0.632553 / 1.386936 (-0.754383) |\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) | 0.006920 / 0.011353 (-0.004433) | 0.004257 / 0.011008 (-0.006751) | 0.065233 / 0.038508 (0.026725) | 0.078151 / 0.023109 (0.055042) | 0.389141 / 0.275898 (0.113243) | 0.431518 / 0.323480 (0.108038) | 0.005752 / 0.007986 (-0.002234) | 0.003584 / 0.004328 (-0.000745) | 0.065173 / 0.004250 (0.060922) | 0.059113 / 0.037052 (0.022060) | 0.398225 / 0.258489 (0.139736) | 0.430980 / 0.293841 (0.137139) | 0.032802 / 0.128546 (-0.095744) | 0.008702 / 0.075646 (-0.066945) | 0.071345 / 0.419271 (-0.347926) | 0.048269 / 0.043533 (0.004736) | 0.389264 / 0.255139 (0.134125) | 0.416008 / 0.283200 (0.132809) | 0.024845 / 0.141683 (-0.116838) | 1.499100 / 1.452155 (0.046945) | 1.576397 / 1.492716 (0.083681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296674 / 0.018006 (0.278668) | 0.540108 / 0.000490 (0.539619) | 0.004293 / 0.000200 (0.004093) | 0.000151 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034108 / 0.037411 (-0.003303) | 0.092747 / 0.014526 (0.078221) | 0.112203 / 0.176557 (-0.064354) | 0.162728 / 0.737135 (-0.574407) | 0.109955 / 0.296338 (-0.186383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432006 / 0.215209 (0.216797) | 4.297591 / 2.077655 (2.219937) | 2.379645 / 1.504120 (0.875525) | 2.218680 / 1.541195 (0.677485) | 2.314608 / 1.468490 (0.846117) | 0.495562 / 4.584777 (-4.089215) | 3.589787 / 3.745712 (-0.155925) | 3.349593 / 5.269862 (-1.920268) | 2.119893 / 4.565676 (-2.445783) | 0.057976 / 0.424275 (-0.366299) | 0.007612 / 0.007607 (0.000005) | 0.509422 / 0.226044 (0.283378) | 5.101444 / 2.268929 (2.832515) | 2.794532 / 55.444624 (-52.650092) | 2.459033 / 6.876477 (-4.417444) | 2.714424 / 2.142072 (0.572352) | 0.588444 / 4.805227 (-4.216784) | 0.135763 / 6.500664 (-6.364901) | 0.062593 / 0.075469 (-0.012876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.361415 / 1.841788 (-0.480372) | 20.940684 / 8.074308 (12.866376) | 15.161364 / 10.191392 (4.969972) | 0.154243 / 0.680424 (-0.526181) | 0.020305 / 0.534201 (-0.513896) | 0.397438 / 0.579283 (-0.181845) | 0.415047 / 0.434364 (-0.019317) | 0.473250 / 0.540337 (-0.067088) | 0.740681 / 1.386936 (-0.646255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e84937af4f24194bf61f09244ebef6528fb7c4c \"CML watermark\")\n"
] | 2023-08-23T15:45:53Z
| 2023-08-25T13:15:59Z
| 2023-08-25T13:06:52Z
|
COLLABORATOR
| null | null | null |
Fixes:
* bumps the PyArrow version check in the `cast_array_to_feature` to avoid the offset bug (still not fixed)
* aligns the Pandas formatting tests with the Numpy ones (the current test fails due to https://github.com/apache/arrow/pull/35656, which requires `.to_pandas(coerce_temporal_nanoseconds=True)` to always return `datetime [ns]` objects)
Fix #6173
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PR_kwDODunzps5DtZjg
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Fix xjoin for Windows pathnames
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-25T13:30:17Z
| 2022-11-29T08:07:39Z
| 2022-11-29T08:05:12Z
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MEMBER
| null | null | null |
This PR fixes a bug in `xjoin` function with Windows pathnames.
Fix #5296.
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PR_kwDODunzps49FJg9
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Fix documentation card of ethos dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-12T09:51:06Z
| 2022-08-12T13:13:55Z
| 2022-08-12T12:59:39Z
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MEMBER
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Fix documentation card of ethos dataset.
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I_kwDODunzps5vtfSB
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Force to reuse cache at given path
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[
"realized that need to pass the path at `cache_file_name` like\r\n\r\n```python\r\ntokenized_datasets = raw_datasets[\"train\"].map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=[text_column_name],\r\n load_from_cache_file=True,\r\n desc=\"Running tokenizer on dataset line_by_line\",\r\n # cache_file_names= {\"train\": \"cache-1982fea76aa54a13.arrow\"}\r\n cache_file_name=\"/project/huggingface_cache/datasets/..../cache-1982fea76aa54a13.arrow\",\r\n new_fingerprint=\"1982fea76aa54a13\"\r\n )\r\n```",
"Thank you so much! I went through a lot of issues before finding similar experiences here. I have to say that the [docs](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Dataset.map) of `.map()` is really misleading, probably making people think that just assigning the file name to cache_file_name is enough."
] | 2023-08-30T18:44:54Z
| 2023-11-03T10:14:21Z
| 2023-08-30T19:00:45Z
|
NONE
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### Describe the bug
I have run the official example of MLM like:
```bash
python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name togethercomputer/RedPajama-Data-1T \
--dataset_config_name arxiv \
--per_device_train_batch_size 10 \
--preprocessing_num_workers 20 \
--validation_split_percentage 0 \
--cache_dir /project/huggingface_cache/datasets \
--line_by_line \
--do_train \
--pad_to_max_length \
--output_dir /project/huggingface_cache/test-mlm
```
it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above.
However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process.
I changed my code to
```python
tokenized_datasets = raw_datasets["train"].map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=True,
desc="Running tokenizer on dataset line_by_line",
# cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"}
cache_file_name="cache-1982fea76aa54a13.arrow",
new_fingerprint="1982fea76aa54a13"
)
```
it still does not recognize the previously cached files and trying to re-run the tokenization process.
### Steps to reproduce the bug
use jupyter notebook for dataset map function.
### Expected behavior
the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files.
### Environment info
- `datasets` version: 2.14.4.dev0
- Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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I_kwDODunzps5Xta6g
| 5,324
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Fix docstrings and types in documentation that appears on the website
|
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[
"I agree we have a mess with docstrings...",
"Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :)",
"Hi @polinaeterna @albertvillanova @stevhliu, I hope you all are doing well.\r\n\r\nIs this issue still unresolved as I am interested in it?",
"It should be mostly fixed for the user-facing APIs, but there may be some Sphinx syntax still lurking around in the non-public APIs. Feel free to open a PR to fix those if you catch any! 🤗 ",
"Thanks for your reply @stevhliu :)\r\nSure, I will try to find out the remaining and fix that.\r\n\r\n"
] | 2022-12-01T15:34:53Z
| 2024-01-23T16:21:54Z
| null |
CONTRIBUTOR
| null | null |
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While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the documentation on the website.
Would be nice someday, maybe before releasing datasets 3.0.0, to unify it......
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PR_kwDODunzps5aDhhK
| 6,232
|
Improve error message for missing function parameters
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"_The documentation is not available anymore as the PR was closed or merged._",
"CI errors are 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) | 0.006681 / 0.011353 (-0.004672) | 0.004132 / 0.011008 (-0.006876) | 0.085045 / 0.038508 (0.046536) | 0.077680 / 0.023109 (0.054571) | 0.382042 / 0.275898 (0.106144) | 0.412932 / 0.323480 (0.089452) | 0.005339 / 0.007986 (-0.002646) | 0.003408 / 0.004328 (-0.000921) | 0.065280 / 0.004250 (0.061030) | 0.055732 / 0.037052 (0.018680) | 0.400231 / 0.258489 (0.141742) | 0.432497 / 0.293841 (0.138656) | 0.031532 / 0.128546 (-0.097014) | 0.008721 / 0.075646 (-0.066925) | 0.289612 / 0.419271 (-0.129660) | 0.053089 / 0.043533 (0.009556) | 0.383300 / 0.255139 (0.128161) | 0.401204 / 0.283200 (0.118004) | 0.023582 / 0.141683 (-0.118100) | 1.493854 / 1.452155 (0.041699) | 1.583497 / 1.492716 (0.090781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239163 / 0.018006 (0.221157) | 0.469555 / 0.000490 (0.469065) | 0.008325 / 0.000200 (0.008125) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028975 / 0.037411 (-0.008436) | 0.084195 / 0.014526 (0.069669) | 0.189394 / 0.176557 (0.012837) | 0.158010 / 0.737135 (-0.579125) | 0.097502 / 0.296338 (-0.198837) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383085 / 0.215209 (0.167876) | 3.827030 / 2.077655 (1.749375) | 1.872279 / 1.504120 (0.368159) | 1.705808 / 1.541195 (0.164613) | 1.833706 / 1.468490 (0.365216) | 0.484744 / 4.584777 (-4.100033) | 3.658221 / 3.745712 (-0.087491) | 3.398462 / 5.269862 (-1.871399) | 2.064974 / 4.565676 (-2.500703) | 0.057740 / 0.424275 (-0.366535) | 0.007926 / 0.007607 (0.000319) | 0.465358 / 0.226044 (0.239314) | 4.652951 / 2.268929 (2.384022) | 2.328390 / 55.444624 (-53.116235) | 2.000606 / 6.876477 (-4.875870) | 2.268391 / 2.142072 (0.126318) | 0.586537 / 4.805227 (-4.218690) | 0.134749 / 6.500664 (-6.365915) | 0.061276 / 0.075469 (-0.014193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.337913 / 1.841788 (-0.503875) | 20.232122 / 8.074308 (12.157814) | 14.478579 / 10.191392 (4.287187) | 0.167545 / 0.680424 (-0.512878) | 0.018745 / 0.534201 (-0.515456) | 0.401209 / 0.579283 (-0.178074) | 0.425748 / 0.434364 (-0.008616) | 0.462539 / 0.540337 (-0.077798) | 0.652446 / 1.386936 (-0.734490) |\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) | 0.007159 / 0.011353 (-0.004194) | 0.004091 / 0.011008 (-0.006917) | 0.066202 / 0.038508 (0.027694) | 0.083096 / 0.023109 (0.059987) | 0.402160 / 0.275898 (0.126261) | 0.440565 / 0.323480 (0.117085) | 0.005757 / 0.007986 (-0.002228) | 0.003445 / 0.004328 (-0.000884) | 0.065498 / 0.004250 (0.061248) | 0.059787 / 0.037052 (0.022735) | 0.407017 / 0.258489 (0.148528) | 0.448270 / 0.293841 (0.154429) | 0.033606 / 0.128546 (-0.094941) | 0.008744 / 0.075646 (-0.066902) | 0.072902 / 0.419271 (-0.346369) | 0.050144 / 0.043533 (0.006611) | 0.401069 / 0.255139 (0.145930) | 0.426389 / 0.283200 (0.143189) | 0.023297 / 0.141683 (-0.118386) | 1.506152 / 1.452155 (0.053998) | 1.570211 / 1.492716 (0.077495) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235759 / 0.018006 (0.217753) | 0.488410 / 0.000490 (0.487921) | 0.004587 / 0.000200 (0.004387) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034123 / 0.037411 (-0.003289) | 0.102163 / 0.014526 (0.087638) | 0.110892 / 0.176557 (-0.065664) | 0.166000 / 0.737135 (-0.571135) | 0.110845 / 0.296338 (-0.185494) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431397 / 0.215209 (0.216188) | 4.291540 / 2.077655 (2.213885) | 2.298248 / 1.504120 (0.794128) | 2.134752 / 1.541195 (0.593557) | 2.207913 / 1.468490 (0.739423) | 0.490607 / 4.584777 (-4.094170) | 3.683078 / 3.745712 (-0.062635) | 3.314266 / 5.269862 (-1.955596) | 2.059488 / 4.565676 (-2.506188) | 0.057876 / 0.424275 (-0.366399) | 0.007696 / 0.007607 (0.000089) | 0.512186 / 0.226044 (0.286142) | 5.124071 / 2.268929 (2.855142) | 2.803913 / 55.444624 (-52.640711) | 2.428558 / 6.876477 (-4.447919) | 2.655207 / 2.142072 (0.513135) | 0.584589 / 4.805227 (-4.220638) | 0.133518 / 6.500664 (-6.367146) | 0.060729 / 0.075469 (-0.014740) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352916 / 1.841788 (-0.488872) | 20.249632 / 8.074308 (12.175323) | 15.283079 / 10.191392 (5.091686) | 0.157601 / 0.680424 (-0.522823) | 0.019650 / 0.534201 (-0.514551) | 0.396398 / 0.579283 (-0.182885) | 0.430111 / 0.434364 (-0.004252) | 0.480627 / 0.540337 (-0.059710) | 0.642165 / 1.386936 (-0.744771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9b21e181b642bd55b3ef68c1948bfbcd388136d6 \"CML watermark\")\n"
] | 2023-09-11T19:11:58Z
| 2023-09-15T18:07:56Z
| 2023-09-15T17:59:02Z
|
CONTRIBUTOR
| null | null | null |
The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature."
This has been fixed.
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PR_kwDODunzps5CI49J
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|
Add note about the name of a dataset script
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-11-03T13:51:32Z
| 2022-11-04T12:47:59Z
| 2022-11-04T12:46:01Z
|
CONTRIBUTOR
| null | null | null |
Add note that a dataset script should has the same name as a repo/dir, a bit related to this issue https://github.com/huggingface/datasets/issues/5193
also fixed two minor issues in audio docs (broken links)
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
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"Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields",
"@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ",
"> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example",
"@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used "
] | 2023-08-18T07:19:39Z
| 2023-08-18T17:00:35Z
| null |
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### Describe the bug
When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**.
When deleting that line the loading is successful.
We also tried loading this file with the discrepancy using this function and it is successful
```python
os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T"
ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train']
```
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. Load one jsonl from the redpajama-data-1T
```bash
wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
2.Load dataset will give error:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
_TypeError: Couldn't cast array of type
Struct
<content_hash: string,
timestamp: string,
source: string,
line_count: int64,
max_line_length: int64,
avg_line_length: double,
alnum_prop: double,
repo_name: string,
id: string,
size: string,
binary: bool,
copies: string,
ref: string,
path: string,
mode: string,
license: string,
language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>**
to
{'content_hash': Value(dtype='string', id=None),
'timestamp': Value(dtype='string', id=None),
'source': Value(dtype='string', id=None),
'line_count': Value(dtype='int64', id=None),
'max_line_length': Value(dtype='int64', id=None),
'avg_line_length': Value(dtype='float64', id=None),
'alnum_prop': Value(dtype='float64', id=None),
'repo_name': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None),
'size': Value(dtype='string', id=None),
'binary': Value(dtype='bool', id=None),
'copies': Value(dtype='string', id=None),
'ref': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'mode': Value(dtype='string', id=None),
'license': Value(dtype='string', id=None),
'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_
3. To remove the line causing the problem that includes the **symlink_target: string>** do:
```bash
sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl
```
4. Rerun the loading function now is succesful:
```python
from datasets import load_dataset
ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl')
```
### Expected behavior
Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out.
### Environment info
- `datasets` version: 2.14.1
- Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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CI tests are broken, raising `AttributeError: 'mappingproxy' object has no attribute 'target'`. See: https://github.com/huggingface/datasets/actions/runs/3966497597/jobs/6797384185
```
...
ERROR tests/test_streaming_download_manager.py::TestxPath::test_xpath_rglob[mock://top_level-date=2019-10-0[1-4]/*-expected_paths4] - AttributeError: 'mappingproxy' object has no attribute 'target'
===== 2076 passed, 19 skipped, 15 warnings, 47 errors in 115.54s (0:01:55) =====
```
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Cannot pickle error in Dataset.from_generator()
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[
"Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions). ",
"> Hi! It should work if you put `model = torch.compile(model)` inside the `generate_data` function. If a referenced object is outside, it needs to be pickable, and that's not the case for the compiled models (or functions).\r\n\r\nHi! Thank you for your reply! Everything works perfectly with your suggestion!\r\n\r\nClosing the issue.\r\n"
] | 2023-05-04T08:39:09Z
| 2023-05-05T19:20:59Z
| 2023-05-05T19:20:58Z
|
NONE
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### Describe the bug
I'm trying to use Dataset.from_generator() to generate a large dataset.
### Steps to reproduce the bug
Code to reproduce:
```
from transformers import T5Tokenizer, T5ForConditionalGeneration, GenerationConfig
import torch
from tqdm import tqdm
from datasets import load_dataset
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small", device_map="auto")
model = torch.compile(model)
def generate_data(data_loader):
model.eval()
for batch in tqdm(data_loader):
input_ids = tokenizer(batch['instruction'], return_tensors='pt', padding=True, truncation=True).input_ids.to("cuda:0")
with torch.no_grad():
outputs = model.generate(input_ids, generation_config=generation_config)
decoder_hidden_states = outputs.decoder_hidden_states
for i, h in zip(batch['instruction'], decoder_hidden_states):
yield {"instruction": i, "decoder_hidden_states": h}
generation_config = GenerationConfig(
temperature=1,
max_new_tokens=1024,
do_sample=False,
num_return_sequences=1,
return_dict_in_generate=True,
output_scores=True,
output_hidden_states=True,
)
from datasets import Dataset, load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("HuggingFaceH4/databricks_dolly_15k")
train_loader = DataLoader(dataset['train'], batch_size=2, shuffle=True)
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
dataset.save_to_disk("data/flant5_small_generation")
```
### Expected behavior
The dataset should be generated and saved.
But the following error occurred:
```
Traceback (most recent call last):
File "/remote-home/xhwang/alpaca-lora/data_collection_t5.py", line 46, in <module>
dataset = Dataset.from_generator(generator=generate_data, gen_kwargs={"data_loader": train_loader})
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1035, in from_generator
return GeneratorDatasetInputStream(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/io/generator.py", line 28, in __init__
self.builder = Generator(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 336, in __init__
self.config, self.config_id = self._create_builder_config(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 505, in _create_builder_config
config_id = builder_config.create_config_id(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/builder.py", line 179, in create_config_id
suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 236, in hash
return cls.hash_default(value)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/fingerprint.py", line 229, in hash_default
return cls.hash_bytes(dumps(value))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 726, in dumps
dump(obj, file)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 701, in dump
Pickler(file, recurse=True).dump(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 394, in dump
StockPickler.dump(self, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 487, in dump
self.save(obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 603, in save
self.save_reduce(obj=obj, *rv)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1070, in _save_with_postproc
pickler.save_reduce(*reduction, obj=obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 717, in save_reduce
save(state)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 887, in save_tuple
save(element)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1186, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 972, in save_dict
self._batch_setitems(obj.items())
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 1003, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 560, in save
f(self, obj) # Call unbound method with explicit self
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1311, in save_function
dill._dill._save_with_postproc(
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 1084, in _save_with_postproc
pickler._batch_setitems(iter(source.items()))
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 998, in _batch_setitems
save(v)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 691, in save
dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/site-packages/dill/_dill.py", line 388, in save
StockPickler.save(self, obj, save_persistent_id)
File "/remote-home/xhwang/anaconda3/envs/alpaca-lora/lib/python3.10/pickle.py", line 578, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'ConfigModuleInstance' object
```
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-4.15.0-156-generic-x86_64-with-glibc2.31
- Python version: 3.10.10
- Huggingface_hub version: 0.13.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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I_kwDODunzps5iYCPR
| 5,694
|
Dataset configuration
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open
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[] | null |
[
"Originally we also though about adding it to the YAML part of the README.md:\r\n\r\n```yaml\r\nbuilder_config:\r\n data_dir: data\r\n data_files:\r\n - split: train\r\n pattern: \"train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*\"\r\n```\r\n\r\nHaving it in the README.md could make it easier to modify it in the UI on HF, and for validation on commit",
"From internal discussions we agreed to go with the YAML approach, since it's the one that seems more appropriate to be modified by a human on the Hub or locally (while JSON e.g. for models are usually created programmatically).",
"Current format:\r\n```yaml\r\nbuilder_config:\r\n data_files:\r\n - split: train\r\n pattern: data/train-*\r\n```"
] | 2023-04-01T13:08:05Z
| 2023-04-04T14:54:37Z
| null |
MEMBER
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Following discussions from https://github.com/huggingface/datasets/pull/5331
We could have something like `config.json` to define the configuration of a dataset.
```json
{
"data_dir": "data"
"data_files": {
"train": "train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"
}
}
```
we could also support a list for several configs with a 'config_name' field.
The alternative was to use YAML in the README.md.
I think it could also support a `dataset_type` field to specify which dataset builder class to use, and the other parameters would be the builder's parameters. Some parameters exist for all builders like `data_files` and `data_dir`, but some parameters are builder specific like `sep` for csv.
This format would be used in `push_to_hub` to be able to push multiple configs.
cc @huggingface/datasets
EDIT: actually we're going for the YAML approach in README.md
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[] | 2023-12-05T15:35:34Z
| 2023-12-06T08:17:34Z
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MEMBER
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See: https://github.com/huggingface/datasets/actions/runs/7100835633/job/19327734359
```
Would reformat: src/datasets/features/image.py
1 file would be reformatted, 253 files left unchanged
```
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Fix spark imports
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010844 / 0.011353 (-0.000509) | 0.007329 / 0.011008 (-0.003680) | 0.133764 / 0.038508 (0.095256) | 0.040213 / 0.023109 (0.017103) | 0.413466 / 0.275898 (0.137568) | 0.452860 / 0.323480 (0.129380) | 0.008109 / 0.007986 (0.000123) | 0.005773 / 0.004328 (0.001444) | 0.109969 / 0.004250 (0.105718) | 0.053001 / 0.037052 (0.015949) | 0.416377 / 0.258489 (0.157888) | 0.477486 / 0.293841 (0.183645) | 0.056556 / 0.128546 (-0.071990) | 0.024322 / 0.075646 (-0.051324) | 0.437750 / 0.419271 (0.018479) | 0.087732 / 0.043533 (0.044199) | 0.421540 / 0.255139 (0.166401) | 0.429143 / 0.283200 (0.145944) | 0.144864 / 0.141683 (0.003181) | 1.882785 / 1.452155 (0.430631) | 1.980721 / 1.492716 (0.488005) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285497 / 0.018006 (0.267491) | 0.601820 / 0.000490 (0.601331) | 0.005003 / 0.000200 (0.004804) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030673 / 0.037411 (-0.006739) | 0.126883 / 0.014526 (0.112357) | 0.137677 / 0.176557 (-0.038880) | 0.211504 / 0.737135 (-0.525632) | 0.144752 / 0.296338 (-0.151587) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665845 / 0.215209 (0.450636) | 6.369040 / 2.077655 (4.291385) | 2.708979 / 1.504120 (1.204859) | 2.370842 / 1.541195 (0.829647) | 2.445987 / 1.468490 (0.977497) | 1.260806 / 4.584777 (-3.323971) | 5.979216 / 3.745712 (2.233504) | 3.334350 / 5.269862 (-1.935512) | 2.187298 / 4.565676 (-2.378379) | 0.155494 / 0.424275 (-0.268781) | 0.017351 / 0.007607 (0.009744) | 0.853626 / 0.226044 (0.627581) | 8.375001 / 2.268929 (6.106072) | 3.528312 / 55.444624 (-51.916313) | 2.890509 / 6.876477 (-3.985968) | 3.051016 / 2.142072 (0.908944) | 1.529811 / 4.805227 (-3.275416) | 0.273883 / 6.500664 (-6.226781) | 0.086617 / 0.075469 (0.011148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648231 / 1.841788 (-0.193557) | 19.487109 / 8.074308 (11.412801) | 23.474621 / 10.191392 (13.283229) | 0.221392 / 0.680424 (-0.459032) | 0.028878 / 0.534201 (-0.505323) | 0.582302 / 0.579283 (0.003019) | 0.615059 / 0.434364 (0.180695) | 0.656082 / 0.540337 (0.115745) | 0.740544 / 1.386936 (-0.646392) |\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) | 0.010687 / 0.011353 (-0.000665) | 0.007114 / 0.011008 (-0.003894) | 0.135426 / 0.038508 (0.096918) | 0.041027 / 0.023109 (0.017918) | 0.466441 / 0.275898 (0.190543) | 0.503545 / 0.323480 (0.180065) | 0.009418 / 0.007986 (0.001432) | 0.004976 / 0.004328 (0.000647) | 0.101342 / 0.004250 (0.097092) | 0.058289 / 0.037052 (0.021237) | 0.473715 / 0.258489 (0.215226) | 0.539556 / 0.293841 (0.245715) | 0.063138 / 0.128546 (-0.065408) | 0.020429 / 0.075646 (-0.055217) | 0.124179 / 0.419271 (-0.295093) | 0.066400 / 0.043533 (0.022867) | 0.450793 / 0.255139 (0.195654) | 0.494163 / 0.283200 (0.210964) | 0.131179 / 0.141683 (-0.010504) | 1.876396 / 1.452155 (0.424241) | 1.974148 / 1.492716 (0.481432) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313362 / 0.018006 (0.295356) | 0.602618 / 0.000490 (0.602129) | 0.008279 / 0.000200 (0.008079) | 0.000155 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037250 / 0.037411 (-0.000161) | 0.144151 / 0.014526 (0.129625) | 0.155733 / 0.176557 (-0.020824) | 0.214334 / 0.737135 (-0.522801) | 0.167124 / 0.296338 (-0.129214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686471 / 0.215209 (0.471262) | 6.749174 / 2.077655 (4.671520) | 3.024941 / 1.504120 (1.520821) | 2.553363 / 1.541195 (1.012168) | 2.679107 / 1.468490 (1.210617) | 1.317212 / 4.584777 (-3.267565) | 5.917575 / 3.745712 (2.171862) | 3.412715 / 5.269862 (-1.857146) | 2.203478 / 4.565676 (-2.362198) | 0.150387 / 0.424275 (-0.273888) | 0.015977 / 0.007607 (0.008370) | 0.862999 / 0.226044 (0.636954) | 8.706459 / 2.268929 (6.437530) | 3.762648 / 55.444624 (-51.681977) | 2.992544 / 6.876477 (-3.883933) | 3.135796 / 2.142072 (0.993724) | 1.504140 / 4.805227 (-3.301088) | 0.268265 / 6.500664 (-6.232399) | 0.083297 / 0.075469 (0.007828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.690193 / 1.841788 (-0.151594) | 19.912854 / 8.074308 (11.838546) | 23.568217 / 10.191392 (13.376825) | 0.285125 / 0.680424 (-0.395299) | 0.030593 / 0.534201 (-0.503608) | 0.565305 / 0.579283 (-0.013978) | 0.659283 / 0.434364 (0.224919) | 0.678864 / 0.540337 (0.138527) | 0.793634 / 1.386936 (-0.593302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d0edbe3f3258b7e580d1b58c0eea6637b5e22b2 \"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) | 0.011615 / 0.011353 (0.000262) | 0.006716 / 0.011008 (-0.004292) | 0.146868 / 0.038508 (0.108360) | 0.037621 / 0.023109 (0.014512) | 0.425563 / 0.275898 (0.149664) | 0.483217 / 0.323480 (0.159737) | 0.007830 / 0.007986 (-0.000156) | 0.005940 / 0.004328 (0.001612) | 0.100771 / 0.004250 (0.096521) | 0.063907 / 0.037052 (0.026854) | 0.422993 / 0.258489 (0.164503) | 0.496514 / 0.293841 (0.202673) | 0.056004 / 0.128546 (-0.072542) | 0.021441 / 0.075646 (-0.054206) | 0.453589 / 0.419271 (0.034317) | 0.067555 / 0.043533 (0.024022) | 0.442490 / 0.255139 (0.187351) | 0.503941 / 0.283200 (0.220742) | 0.134023 / 0.141683 (-0.007660) | 1.886329 / 1.452155 (0.434175) | 2.030867 / 1.492716 (0.538150) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288063 / 0.018006 (0.270057) | 0.627177 / 0.000490 (0.626687) | 0.006335 / 0.000200 (0.006135) | 0.000171 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032424 / 0.037411 (-0.004987) | 0.132749 / 0.014526 (0.118223) | 0.144727 / 0.176557 (-0.031829) | 0.232577 / 0.737135 (-0.504558) | 0.157315 / 0.296338 (-0.139024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623058 / 0.215209 (0.407849) | 6.272447 / 2.077655 (4.194792) | 2.506778 / 1.504120 (1.002658) | 2.203094 / 1.541195 (0.661899) | 2.346972 / 1.468490 (0.878482) | 1.358498 / 4.584777 (-3.226279) | 5.879670 / 3.745712 (2.133958) | 5.818406 / 5.269862 (0.548545) | 3.231936 / 4.565676 (-1.333741) | 0.154013 / 0.424275 (-0.270263) | 0.021541 / 0.007607 (0.013934) | 0.823746 / 0.226044 (0.597702) | 8.140304 / 2.268929 (5.871375) | 3.366911 / 55.444624 (-52.077714) | 2.696856 / 6.876477 (-4.179621) | 2.845743 / 2.142072 (0.703671) | 1.522363 / 4.805227 (-3.282864) | 0.278938 / 6.500664 (-6.221726) | 0.085044 / 0.075469 (0.009575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.681348 / 1.841788 (-0.160440) | 19.686703 / 8.074308 (11.612395) | 22.995655 / 10.191392 (12.804263) | 0.218876 / 0.680424 (-0.461548) | 0.029334 / 0.534201 (-0.504867) | 0.560846 / 0.579283 (-0.018438) | 0.645210 / 0.434364 (0.210846) | 0.697842 / 0.540337 (0.157505) | 0.832875 / 1.386936 (-0.554061) |\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) | 0.009509 / 0.011353 (-0.001844) | 0.006471 / 0.011008 (-0.004537) | 0.101477 / 0.038508 (0.062969) | 0.035281 / 0.023109 (0.012171) | 0.470032 / 0.275898 (0.194134) | 0.501475 / 0.323480 (0.177995) | 0.007641 / 0.007986 (-0.000344) | 0.006784 / 0.004328 (0.002455) | 0.096111 / 0.004250 (0.091861) | 0.055199 / 0.037052 (0.018146) | 0.470095 / 0.258489 (0.211606) | 0.530955 / 0.293841 (0.237114) | 0.056161 / 0.128546 (-0.072385) | 0.022055 / 0.075646 (-0.053591) | 0.121585 / 0.419271 (-0.297686) | 0.063736 / 0.043533 (0.020203) | 0.470771 / 0.255139 (0.215632) | 0.490546 / 0.283200 (0.207346) | 0.128825 / 0.141683 (-0.012858) | 1.898639 / 1.452155 (0.446484) | 2.052305 / 1.492716 (0.559589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322526 / 0.018006 (0.304520) | 0.628096 / 0.000490 (0.627607) | 0.006837 / 0.000200 (0.006637) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033830 / 0.037411 (-0.003581) | 0.136217 / 0.014526 (0.121691) | 0.147006 / 0.176557 (-0.029551) | 0.203950 / 0.737135 (-0.533185) | 0.150327 / 0.296338 (-0.146011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654287 / 0.215209 (0.439078) | 6.430306 / 2.077655 (4.352651) | 2.881750 / 1.504120 (1.377630) | 2.489505 / 1.541195 (0.948310) | 2.543037 / 1.468490 (1.074547) | 1.226682 / 4.584777 (-3.358094) | 5.902076 / 3.745712 (2.156364) | 3.335344 / 5.269862 (-1.934518) | 2.156738 / 4.565676 (-2.408939) | 0.151804 / 0.424275 (-0.272472) | 0.015238 / 0.007607 (0.007631) | 0.816364 / 0.226044 (0.590319) | 8.126367 / 2.268929 (5.857438) | 3.653222 / 55.444624 (-51.791402) | 2.886667 / 6.876477 (-3.989809) | 3.120852 / 2.142072 (0.978779) | 1.421423 / 4.805227 (-3.383804) | 0.264590 / 6.500664 (-6.236074) | 0.085716 / 0.075469 (0.010247) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745258 / 1.841788 (-0.096530) | 19.379253 / 8.074308 (11.304945) | 23.827046 / 10.191392 (13.635654) | 0.267702 / 0.680424 (-0.412722) | 0.030253 / 0.534201 (-0.503948) | 0.542037 / 0.579283 (-0.037246) | 0.655946 / 0.434364 (0.221582) | 0.683525 / 0.540337 (0.143188) | 0.831333 / 1.386936 (-0.555603) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b011a258329375aa4dc7b414bd4e7b6363c5357 \"CML watermark\")\n"
] | 2023-04-26T17:09:32Z
| 2023-04-26T17:49:03Z
| 2023-04-26T17:39:12Z
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MEMBER
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I_kwDODunzps5o_7p6
| 5,962
|
Issue with train_test_split maintaining the same underlying PyArrow Table
|
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[] | 2023-06-17T02:19:58Z
| 2023-06-17T02:19:58Z
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### Describe the bug
I've been using the train_test_split method in the datasets module to split my HuggingFace Dataset into separate training, validation, and testing subsets. However, I've noticed an issue where the split datasets appear to maintain the same underlying PyArrow Table.
### Steps to reproduce the bug
1. Load any dataset ```dataset = load_dataset("lhoestq/demo1")```
2. Try the next code:
```python
from datasets import Dataset, DatasetDict
train_size = 0.6
split_train = dataset["train"].train_test_split(
train_size=train_size,
)
separate_dataset_dict = DatasetDict({
"train": split_train["train"],
"test": split_train["test"],
})
```
3. The next code ```print(separate_dataset_dict)``` when printing the dataset it gives the indication that they have 3 and 2 rows respectively.
4. But the next code:
```python
print(len(separate_dataset_dict["train"].data['id']))
print(len(separate_dataset_dict["test"].data['id']))
```
Indicates that both tables still have 5 rows.
### Expected behavior
However, I've noticed that train_test_split["train"].data, test_val_split["train"].data, and test_val_split["test"].data are identical, suggesting that they all point to the same underlying PyArrow Table. This means that the split datasets are not independent, as I expected.
I believe this is a bug in the train_test_split implementation, as I would expect this function to return datasets with separate underlying PyArrow Tables. Could you please help me understand if this is expected behavior, or if there's a workaround to create truly independent split datasets?
I would appreciate any assistance with this issue. Thank you.
### Environment info
I tried in Colab:
- `datasets` version: 2.13.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
and my PC:
- `datasets` version: 2.13.0
- Platform: Linux-5.15.107+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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| 2,904,702,329
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I_kwDODunzps6tIj15
| 7,441
|
`drop_last_batch` does not drop the last batch using IterableDataset + interleave_datasets + multi_worker
|
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[
"Hi @memray, I’d like to help fix the issue with `drop_last_batch` not working when `num_workers > 1`. I’ll investigate and propose a solution. Thanks!\n",
"Thank you very much for offering to help! I also noticed a problem related to a previous issue and left a comment [here](https://github.com/huggingface/datasets/issues/6565#issuecomment-2708169303) (the code checks the validity before certain columns removed). Can you take a look as well?"
] | 2025-03-08T10:28:44Z
| 2025-03-09T21:27:33Z
| null |
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### Describe the bug
See the script below
`drop_last_batch=True` is defined using map() for each dataset.
The last batch for each dataset is expected to be dropped, id 21-25.
The code behaves as expected when num_workers=0 or 1.
When using num_workers>1, 'a-11', 'b-11', 'a-12', 'b-12' are gone and instead 21 and 22 are sampled.
### Steps to reproduce the bug
```
from datasets import Dataset
from datasets import interleave_datasets
from torch.utils.data import DataLoader
def convert_to_str(batch, dataset_name):
batch['a'] = [f"{dataset_name}-{e}" for e in batch['a']]
return batch
def gen1():
for ii in range(1, 25):
yield {"a": ii}
def gen2():
for ii in range(1, 25):
yield {"a": ii}
# https://github.com/huggingface/datasets/issues/6565
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=2)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=2)
dataset1 = dataset1.map(lambda x: convert_to_str(x, dataset_name="a"), batched=True, batch_size=10, drop_last_batch=True)
dataset2 = dataset2.map(lambda x: convert_to_str(x, dataset_name="b"), batched=True, batch_size=10, drop_last_batch=True)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
print(f"num_workers=0")
loader = DataLoader(interleaved, batch_size=5, num_workers=0)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=1")
loader = DataLoader(interleaved, batch_size=5, num_workers=1)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=2")
loader = DataLoader(interleaved, batch_size=5, num_workers=2)
i = 0
for b in loader:
print(i, b['a'])
i += 1
print('=-' * 20)
print(f"num_workers=3")
loader = DataLoader(interleaved, batch_size=5, num_workers=3)
i = 0
for b in loader:
print(i, b['a'])
i += 1
```
output is:
```
num_workers=0
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=1
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
2 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
3 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
4 ['a-11', 'b-11', 'a-12', 'b-12', 'a-13']
5 ['b-13', 'a-14', 'b-14', 'a-15', 'b-15']
6 ['a-16', 'b-16', 'a-17', 'b-17', 'a-18']
7 ['b-18', 'a-19', 'b-19', 'a-20', 'b-20']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=2
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
num_workers=3
Too many dataloader workers: 3 (max is dataset.num_shards=2). Stopping 1 dataloader workers.
0 ['a-1', 'b-1', 'a-2', 'b-2', 'a-3']
1 ['a-13', 'b-13', 'a-14', 'b-14', 'a-15']
2 ['b-3', 'a-4', 'b-4', 'a-5', 'b-5']
3 ['b-15', 'a-16', 'b-16', 'a-17', 'b-17']
4 ['a-6', 'b-6', 'a-7', 'b-7', 'a-8']
5 ['a-18', 'b-18', 'a-19', 'b-19', 'a-20']
6 ['b-8', 'a-9', 'b-9', 'a-10', 'b-10']
7 ['b-20', 'a-21', 'b-21', 'a-22', 'b-22']
```
### Expected behavior
`'a-21', 'b-21', 'a-22', 'b-22'` should be dropped
### Environment info
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-1056-aws-x86_64-with-glibc2.31
- Python version: 3.10.16
- `huggingface_hub` version: 0.28.0
- PyArrow version: 19.0.0
- Pandas version: 2.2.3
- `fsspec` version: 2024.6.1
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I_kwDODunzps6xuPCa
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HfHubHTTPError: 429 Client Error: Too Many Requests for URL when trying to access Fineweb-10BT on 4A100 GPUs using SLURM
|
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[
"Hi ! make sure to be logged in with your HF account (e.g. using `huggingface-cli login` or passing `token=` to `load_dataset()`), otherwise you'll get rate limited at one point"
] | 2025-04-09T06:32:04Z
| 2025-04-15T13:04:31Z
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### Describe the bug
I am trying to run some finetunings on 4 A100 GPUs using SLURM using axolotl training framework which in turn uses Huggingface's Trainer and Accelerate on [Fineweb-10BT](https://huggingface.co/datasets/HuggingFaceFW/fineweb), but I end up running into 429 Client Error: Too Many Requests for URL error when I call next(dataloader_iter). Funny is, that I can run some test fine tuning (for just 200 training steps) in 1 A100 GPU using SLURM. Is there any rate limiter set for querying dataset? I could run the fine tuning with the same settings (4 A100 GPUs in SLURM) last month.
### Steps to reproduce the bug
You would need a server installed with SLURM
1. Create conda environment
1.1 conda create -n example_env -c conda-forge gxx=11 python=3.10
1.2 conda activate example_env
1.3 pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
1.4 conda install nvidia/label/cuda-12.4.0::cuda-toolkit
1.5 Download flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
1.6 pip3 install packaging
1.7 pip3 install ninja
1.8 pip3 install mlflow
1.9 Clone https://github.com/calvintanama/axolotl.git
1.10 `cd` to `axolotl`
1.11 pip3 install -e '.[deepspeed]'
2. Run the training
2.1. Create a folder called `config_run` in axolotl directory
2.2. Copy `config/phi3_pruned_extra_pretrain_22_29_bottleneck_residual_8_a100_4.yaml` to `config_run`
2.3. Change yaml file in the `config_run` accordingly
2.4. Change directory and conda environment name in `jobs/train_phi3_22_29_bottleneck_residual_8_a100_4_temp.sh`
2.5. `jobs/train_phi3_22_29_bottleneck_residual_8_a100_4_temp.sh`
### Expected behavior
This should not cause any error, but gotten
```
File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/accelerate/data_loader.py", line 552, in __iter__
[rank3]: current_batch = next(dataloader_iter)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 701, in __next__
[rank3]: data = self._next_data()
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 757, in _next_data
[rank3]: data = self._dataset_fetcher.fetch(index) # may raise StopIteration
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 33, in fetch
[rank3]: data.append(next(self.dataset_iter))
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/accelerate/data_loader.py", line 338, in __iter__
[rank3]: for element in self.dataset:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 2266, in __iter__
[rank3]: for key, example in ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1866, in __iter__
[rank3]: for key, example in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1084, in __iter__
[rank3]: yield from self._iter()
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1263, in _iter
[rank3]: for key, transformed_example in outputs:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1258, in <genexpr>
[rank3]: outputs = (
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1244, in iter_outputs
[rank3]: for i, key_example in inputs_iterator:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1106, in iter_batched_inputs
[rank3]: for key, example in iterator:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1866, in __iter__
[rank3]: for key, example in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1535, in __iter__
[rank3]: for x in self.ex_iterable:
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 374, in __iter__
[rank3]: for key, pa_table in self.generate_tables_fn(**gen_kwags):
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 90, in _generate_tables
[rank3]: if parquet_fragment.row_groups:
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 386, in pyarrow._dataset_parquet.ParquetFileFragment.row_groups.__get__
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 393, in pyarrow._dataset_parquet.ParquetFileFragment.metadata.__get__
[rank3]: File "pyarrow/_dataset_parquet.pyx", line 382, in pyarrow._dataset_parquet.ParquetFileFragment.ensure_complete_metadata
[rank3]: File "pyarrow/error.pxi", line 89, in pyarrow.lib.check_status
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 827, in read_with_retries
[rank3]: out = read(*args, **kwargs)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 1013, in read
[rank3]: return super().read(length)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/fsspec/spec.py", line 1941, in read
[rank3]: out = self.cache._fetch(self.loc, self.loc + length)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/fsspec/caching.py", line 234, in _fetch
[rank3]: self.cache = self.fetcher(start, end) # new block replaces old
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py", line 976, in _fetch_range
[rank3]: hf_raise_for_status(r)
[rank3]: File "/home/hk-project-test-p0023745/cd7437/miniconda3/envs/llmpruning_train_temp/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 482, in hf_raise_for_status
[rank3]: raise _format(HfHubHTTPError, str(e), response) from e
[rank3]: huggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/0f039043b23fe1d4eed300b504aa4b4a68f1c7ba/sample/10BT/006_00000.parquet
```
### Environment info
- datasets 3.5.0
- torch 2.5.1
- transformers 4.46.2
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PR_kwDODunzps6FiGlt
| 7,341
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minor video docs on how to install
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7341). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-12-17T18:06:17Z
| 2024-12-17T18:11:17Z
| 2024-12-17T18:11:15Z
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MEMBER
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I_kwDODunzps5lU02t
| 5,829
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(mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))
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[
"Can you paste the error stack trace?",
"That is weird. I can't reproduce it again after reboot.\r\n```python\r\nIn [2]: import platform\r\n\r\nIn [3]: platform.platform()\r\nOut[3]: 'macOS-13.2-arm64-arm-64bit'\r\n\r\nIn [4]: from datasets import load_dataset\r\n ...:\r\n ...: jazzy = load_dataset(\"nomic-ai/gpt4all-j-prompt-generations\", revision='v1.2-jazzy')\r\nFound cached dataset parquet (/Users/sarit/.cache/huggingface/datasets/nomic-ai___parquet/nomic-ai--gpt4all-j-prompt-generations-a3b62015e2e52043/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 63.25it/s]\r\n```"
] | 2023-05-08T10:07:14Z
| 2023-06-30T11:39:14Z
| 2023-05-09T00:46:42Z
|
NONE
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### Describe the bug
M2 MBP can't run
```python
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Steps to reproduce the bug
1. Use M2 MBP
2. Python 3.10.10 from pyenv
3. Run
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
### Expected behavior
Be able to run normally
### Environment info
```
from datasets import load_dataset
jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy')
```
OSX: 13.2
CPU: M2
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ds.map(f, num_proc=10) is slower than df.apply
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"Hi ! `map()` reads all the columns and writes the resulting dataset with all the columns as well, while df.column_name.apply only reads and writes one column and does it in memory. So this is speed difference is actually expected.\r\n\r\nMoreover using multiprocessing on a dataset that lives in memory (from_pandas uses the same in-memory data as the pandas DataFrame while load_dataset or from_generator load from disk) requires to copy the data to each subprocess which can also be slow. Data loaded from disk don't need to be copied though since they work as a form of shared memory thanks to memory mapping.\r\n\r\nHowever you can make you map() call much faster by making it read and write only the column you want:\r\n\r\n```python\r\nhas_cover_ds = ds.map(lambda song_name: {'has_cover': has_cover(song_name)}, input_columns=[\"song_name\"], remove_columns=ds.column_names) # outputs a dataset with 1 column\r\nds = ds.concatenate_datasets([ds, has_cover_ds], axis=1)\r\n```\r\n\r\nand if your dataset is loaded from disk you can pass num_proc=10 and get a nice speed up as well (no need to copy the data to subprocesses)",
"Isn't there a way to do memory mapping with the in-memory dataset without saving it to disk?",
"Maybe saving it to a memory-mapped filesystem? It'd be like a trick to make datasets save to \"disk\" but actually it's memory. But it feels like there should be a better \"automatic\" way provided by `datasets`."
] | 2024-10-11T11:04:05Z
| 2025-02-28T21:21:01Z
| null |
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### Describe the bug
pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)
def has_cover(song_name):
if song_name is None or pd.isna(song_name):
return False
return 'cover' in song_name.lower()
df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)
time cost:
1. df.apply: 100%|██████████| 12500592/12500592 [00:13<00:00, 959825.47it/s]
2. ds.map: Map (num_proc=10): 31%
3899028/12500592 [00:28<00:38, 222532.89 examples/s]
### Steps to reproduce the bug
pandas columns: song_id, song_name
ds = Dataset.from_pandas(df)
def has_cover(song_name):
if song_name is None or pd.isna(song_name):
return False
return 'cover' in song_name.lower()
df['has_cover'] = df.song_name.progress_apply(has_cover)
ds = ds.map(lambda x: {'has_cover': has_cover(x['song_name'])}, num_proc=10)
### Expected behavior
ds.map is ~num_proc faster than df.apply
### Environment info
pandas: 2.2.2
datasets: 2.19.1
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PR_kwDODunzps47cLkm
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Update create dataset card docs
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-15T00:41:29Z
| 2022-07-18T17:26:00Z
| 2022-07-18T13:24:10Z
|
MEMBER
| null | null | null |
This PR proposes removing the [online dataset card creator](https://huggingface.co/datasets/card-creator/) in favor of simply copy/pasting a template and using the [Datasets Tagger app](https://huggingface.co/spaces/huggingface/datasets-tagging) to generate the tags. The Tagger app provides more guidance by showing all possible values a user can select in the dropdown menus, whereas the online dataset card creator doesn't, which can make it difficult to know what tag values to input.
Let me know what you think! :)
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adding mafand to datasets
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @dadelani, thanks for your awesome contribution!!! :heart: \r\n\r\nHowever, now we are using the Hub to add new datasets, instead of this GitHub repo. \r\n\r\nYou could share this dataset under your Hub organization namespace: [Masakhane NLP](https://huggingface.co/masakhane). This way the dataset will be accessible using:\r\n```python\r\nds = load_dataset(\"masakhane/mafand\")\r\n```\r\n\r\nYou have the procedure documented in our online docs: \r\n- [Create a dataset loading script](https://huggingface.co/docs/datasets/dataset_script)\r\n- [Share](https://huggingface.co/docs/datasets/share)\r\n\r\nMoreover, datasets shared on the Hub no longer need the dummy data files.\r\n\r\nPlease, feel free to ping me if you need any further guidance/support.",
"thank you for the comment. I have moved it to the Hub https://huggingface.co/datasets/masakhane/mafand",
"Great job, @dadelani!!\r\n\r\nPlease, note that in the README.md file, the YAML tags should be preceded and followed by three dashes `---`, so that they are properly parsed. See, e.g.: https://raw.githubusercontent.com/huggingface/datasets/main/templates/README.md",
"Also you could replace the line:\r\n```\r\n# Dataset Card for [Needs More Information]\r\n```\r\nwith\r\n```\r\n# Dataset Card for MAFAND-MT\r\n```",
"Great, thank you for the feedback. I have fixed both issues."
] | 2022-08-20T15:26:14Z
| 2022-08-22T11:00:50Z
| 2022-08-22T08:52:23Z
|
CONTRIBUTOR
| null | null | null |
I'm addding the MAFAND dataset by Masakhane based on the paper/repository below:
Paper: https://aclanthology.org/2022.naacl-main.223/
Code: https://github.com/masakhane-io/lafand-mt
Please, help merge this
Everything works except for creating dummy data file
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I_kwDODunzps6YDIUZ
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[FSTimeoutError] load_dataset
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[
"Is this `FSTimeoutError` due to download network issue from remote resource (from where it is being accessed)?",
"It seems to happen for all datasets, not just a specific one, and especially for versions after 3.0. (3.0.0, 3.0.1 have this problem)\r\n\r\nI had the same error on a different dataset, but after downgrading to datasets==2.21.0, the problem was solved.",
"Same as https://github.com/huggingface/datasets/issues/7164\r\n\r\nThis dataset is made of a python script that downloads data from elsewhere than HF, so availability depends on the original host. Ultimately it would be nice to host the files of this dataset on HF\r\n\r\nin `datasets` <3.0 there were lots of mechanisms that got removed after the decision to make datasets with python loading scripts legacy for security and maintenance reasons (we only do very basic support now)",
"@lhoestq Thank you for the clarification! Closing the issue.",
"I'm getting this too, and also at 5 minutes. But for `CSTR-Edinburgh/vctk`, so it's not just this dataset, it seems to be a timeout that was introduced and needs to be raised. The progress bar was moving along just fine before the timeout, and I get more or less of it depending on how fast the network is.",
"You can change the `aiohttp` timeout from 5min to 1h like this:\r\n\r\n```python\r\nimport datasets, aiohttp\r\ndataset = datasets.load_dataset(\r\n dataset_name,\r\n storage_options={'client_kwargs': {'timeout': aiohttp.ClientTimeout(total=3600)}}\r\n)\r\n```",
"@JonasLoos Solution solved a download timeout error I received when downloading `\"HuggingFaceM4/VQAv2\"` 🎉 "
] | 2024-09-26T15:42:29Z
| 2025-02-01T09:09:35Z
| 2024-09-30T17:28:35Z
|
NONE
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### Describe the bug
When using `load_dataset`to load [HuggingFaceM4/VQAv2](https://huggingface.co/datasets/HuggingFaceM4/VQAv2), I am getting `FSTimeoutError`.
### Error
```
TimeoutError:
The above exception was the direct cause of the following exception:
FSTimeoutError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/fsspec/asyn.py](https://klh9mr78js-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240924-060116_RC00_678132060#) in sync(loop, func, timeout, *args, **kwargs)
99 if isinstance(return_result, asyncio.TimeoutError):
100 # suppress asyncio.TimeoutError, raise FSTimeoutError
--> 101 raise FSTimeoutError from return_result
102 elif isinstance(return_result, BaseException):
103 raise return_result
FSTimeoutError:
```
It usually fails around 5-6 GB.
<img width="847" alt="Screenshot 2024-09-26 at 9 10 19 PM" src="https://github.com/user-attachments/assets/ff91995a-fb55-4de6-8214-94025d6c8470">
### Steps to reproduce the bug
To reproduce it, run this in colab notebook:
```
!pip install -q -U datasets
from datasets import load_dataset
ds = load_dataset('HuggingFaceM4/VQAv2', split="train[:10%]")
```
### Expected behavior
It should download properly.
### Environment info
Using Colab Notebook.
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Prepare tests for hfh 0.14
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007343 / 0.011353 (-0.004010) | 0.005145 / 0.011008 (-0.005863) | 0.099820 / 0.038508 (0.061312) | 0.033487 / 0.023109 (0.010378) | 0.313069 / 0.275898 (0.037171) | 0.335420 / 0.323480 (0.011940) | 0.005959 / 0.007986 (-0.002027) | 0.005373 / 0.004328 (0.001044) | 0.076568 / 0.004250 (0.072317) | 0.048702 / 0.037052 (0.011650) | 0.322957 / 0.258489 (0.064468) | 0.363044 / 0.293841 (0.069203) | 0.035070 / 0.128546 (-0.093476) | 0.012029 / 0.075646 (-0.063618) | 0.334664 / 0.419271 (-0.084607) | 0.050549 / 0.043533 (0.007017) | 0.310113 / 0.255139 (0.054974) | 0.324405 / 0.283200 (0.041205) | 0.097596 / 0.141683 (-0.044087) | 1.440741 / 1.452155 (-0.011414) | 1.531194 / 1.492716 (0.038478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220799 / 0.018006 (0.202793) | 0.438158 / 0.000490 (0.437668) | 0.007737 / 0.000200 (0.007537) | 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) | 0.026888 / 0.037411 (-0.010523) | 0.106281 / 0.014526 (0.091755) | 0.117419 / 0.176557 (-0.059138) | 0.179144 / 0.737135 (-0.557992) | 0.122477 / 0.296338 (-0.173861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412667 / 0.215209 (0.197458) | 4.108784 / 2.077655 (2.031129) | 1.834300 / 1.504120 (0.330180) | 1.627256 / 1.541195 (0.086061) | 1.691036 / 1.468490 (0.222546) | 0.713405 / 4.584777 (-3.871372) | 3.839262 / 3.745712 (0.093550) | 2.108453 / 5.269862 (-3.161408) | 1.340740 / 4.565676 (-3.224936) | 0.087776 / 0.424275 (-0.336499) | 0.012730 / 0.007607 (0.005123) | 0.505323 / 0.226044 (0.279279) | 5.085176 / 2.268929 (2.816247) | 2.307165 / 55.444624 (-53.137459) | 1.936771 / 6.876477 (-4.939706) | 2.097391 / 2.142072 (-0.044681) | 0.856215 / 4.805227 (-3.949012) | 0.171826 / 6.500664 (-6.328838) | 0.066603 / 0.075469 (-0.008866) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202126 / 1.841788 (-0.639661) | 15.173598 / 8.074308 (7.099290) | 15.012645 / 10.191392 (4.821253) | 0.162187 / 0.680424 (-0.518237) | 0.017462 / 0.534201 (-0.516739) | 0.423895 / 0.579283 (-0.155388) | 0.432010 / 0.434364 (-0.002354) | 0.503234 / 0.540337 (-0.037104) | 0.598948 / 1.386936 (-0.787988) |\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) | 0.007099 / 0.011353 (-0.004254) | 0.005167 / 0.011008 (-0.005841) | 0.075551 / 0.038508 (0.037043) | 0.033050 / 0.023109 (0.009940) | 0.339629 / 0.275898 (0.063731) | 0.380486 / 0.323480 (0.057006) | 0.005776 / 0.007986 (-0.002209) | 0.004029 / 0.004328 (-0.000299) | 0.075074 / 0.004250 (0.070823) | 0.046709 / 0.037052 (0.009656) | 0.340203 / 0.258489 (0.081714) | 0.380849 / 0.293841 (0.087008) | 0.035027 / 0.128546 (-0.093519) | 0.012226 / 0.075646 (-0.063420) | 0.087525 / 0.419271 (-0.331747) | 0.049361 / 0.043533 (0.005828) | 0.341854 / 0.255139 (0.086715) | 0.359590 / 0.283200 (0.076390) | 0.100102 / 0.141683 (-0.041581) | 1.482759 / 1.452155 (0.030605) | 1.569905 / 1.492716 (0.077189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213615 / 0.018006 (0.195609) | 0.441117 / 0.000490 (0.440628) | 0.004932 / 0.000200 (0.004732) | 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) | 0.031313 / 0.037411 (-0.006098) | 0.110191 / 0.014526 (0.095665) | 0.125320 / 0.176557 (-0.051237) | 0.177658 / 0.737135 (-0.559477) | 0.127928 / 0.296338 (-0.168410) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426952 / 0.215209 (0.211743) | 4.247731 / 2.077655 (2.170076) | 2.107318 / 1.504120 (0.603198) | 1.843845 / 1.541195 (0.302650) | 1.894822 / 1.468490 (0.426332) | 0.696232 / 4.584777 (-3.888545) | 3.826516 / 3.745712 (0.080804) | 2.126688 / 5.269862 (-3.143174) | 1.327062 / 4.565676 (-3.238615) | 0.085693 / 0.424275 (-0.338582) | 0.012226 / 0.007607 (0.004619) | 0.521904 / 0.226044 (0.295859) | 5.219798 / 2.268929 (2.950869) | 2.524908 / 55.444624 (-52.919716) | 2.212078 / 6.876477 (-4.664399) | 2.373944 / 2.142072 (0.231871) | 0.833846 / 4.805227 (-3.971381) | 0.169639 / 6.500664 (-6.331025) | 0.064538 / 0.075469 (-0.010931) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254930 / 1.841788 (-0.586858) | 15.585277 / 8.074308 (7.510969) | 14.762857 / 10.191392 (4.571465) | 0.146959 / 0.680424 (-0.533465) | 0.017451 / 0.534201 (-0.516750) | 0.424469 / 0.579283 (-0.154814) | 0.422359 / 0.434364 (-0.012004) | 0.489930 / 0.540337 (-0.050408) | 0.595856 / 1.386936 (-0.791080) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#213c72f52ae52b662f967d3218f66c70a3043048 \"CML watermark\")\n",
"@albertvillanova thanks for the review. As you prefer for the github CI config. I just took it from @lhoestq's branch when testing hfh==0.14.0. I think it's still relevant for next releases. In any case, I let you handle merging the 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) | 0.008371 / 0.011353 (-0.002982) | 0.005210 / 0.011008 (-0.005798) | 0.105639 / 0.038508 (0.067131) | 0.045903 / 0.023109 (0.022794) | 0.391231 / 0.275898 (0.115333) | 0.438824 / 0.323480 (0.115345) | 0.006270 / 0.007986 (-0.001715) | 0.005950 / 0.004328 (0.001621) | 0.079685 / 0.004250 (0.075434) | 0.052121 / 0.037052 (0.015069) | 0.387787 / 0.258489 (0.129298) | 0.434322 / 0.293841 (0.140481) | 0.032598 / 0.128546 (-0.095948) | 0.012126 / 0.075646 (-0.063520) | 0.359658 / 0.419271 (-0.059613) | 0.046686 / 0.043533 (0.003154) | 0.391973 / 0.255139 (0.136834) | 0.421149 / 0.283200 (0.137949) | 0.105920 / 0.141683 (-0.035763) | 1.483008 / 1.452155 (0.030854) | 1.617010 / 1.492716 (0.124294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199111 / 0.018006 (0.181105) | 0.407995 / 0.000490 (0.407505) | 0.006706 / 0.000200 (0.006506) | 0.000229 / 0.000054 (0.000175) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030247 / 0.037411 (-0.007164) | 0.115977 / 0.014526 (0.101451) | 0.118112 / 0.176557 (-0.058444) | 0.182710 / 0.737135 (-0.554426) | 0.122483 / 0.296338 (-0.173855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430455 / 0.215209 (0.215246) | 4.314298 / 2.077655 (2.236643) | 1.898124 / 1.504120 (0.394005) | 1.734909 / 1.541195 (0.193715) | 1.802400 / 1.468490 (0.333910) | 0.717237 / 4.584777 (-3.867539) | 4.004705 / 3.745712 (0.258993) | 2.138901 / 5.269862 (-3.130960) | 1.254037 / 4.565676 (-3.311640) | 0.085594 / 0.424275 (-0.338681) | 0.013774 / 0.007607 (0.006166) | 0.535218 / 0.226044 (0.309174) | 5.373730 / 2.268929 (3.104801) | 2.371194 / 55.444624 (-53.073430) | 2.111206 / 6.876477 (-4.765270) | 2.225137 / 2.142072 (0.083064) | 0.838325 / 4.805227 (-3.966902) | 0.159176 / 6.500664 (-6.341488) | 0.072285 / 0.075469 (-0.003184) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352232 / 1.841788 (-0.489555) | 16.926722 / 8.074308 (8.852414) | 16.709531 / 10.191392 (6.518139) | 0.159249 / 0.680424 (-0.521175) | 0.017667 / 0.534201 (-0.516534) | 0.426894 / 0.579283 (-0.152390) | 0.539903 / 0.434364 (0.105539) | 0.537471 / 0.540337 (-0.002866) | 0.619592 / 1.386936 (-0.767344) |\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) | 0.008354 / 0.011353 (-0.002999) | 0.005366 / 0.011008 (-0.005642) | 0.080961 / 0.038508 (0.042453) | 0.046574 / 0.023109 (0.023465) | 0.345949 / 0.275898 (0.070051) | 0.394041 / 0.323480 (0.070562) | 0.006209 / 0.007986 (-0.001777) | 0.005980 / 0.004328 (0.001651) | 0.076235 / 0.004250 (0.071984) | 0.051833 / 0.037052 (0.014780) | 0.348786 / 0.258489 (0.090297) | 0.397421 / 0.293841 (0.103580) | 0.033026 / 0.128546 (-0.095520) | 0.012217 / 0.075646 (-0.063429) | 0.087439 / 0.419271 (-0.331832) | 0.045488 / 0.043533 (0.001955) | 0.352160 / 0.255139 (0.097021) | 0.379079 / 0.283200 (0.095879) | 0.116111 / 0.141683 (-0.025572) | 1.470177 / 1.452155 (0.018022) | 1.587499 / 1.492716 (0.094783) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296149 / 0.018006 (0.278143) | 0.592362 / 0.000490 (0.591872) | 0.000492 / 0.000200 (0.000292) | 0.000064 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036599 / 0.037411 (-0.000813) | 0.113768 / 0.014526 (0.099242) | 0.116198 / 0.176557 (-0.060358) | 0.180329 / 0.737135 (-0.556806) | 0.123942 / 0.296338 (-0.172396) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452445 / 0.215209 (0.237236) | 4.504330 / 2.077655 (2.426675) | 2.275645 / 1.504120 (0.771525) | 2.107765 / 1.541195 (0.566571) | 2.086363 / 1.468490 (0.617873) | 0.723721 / 4.584777 (-3.861056) | 3.825330 / 3.745712 (0.079618) | 2.162743 / 5.269862 (-3.107119) | 1.255953 / 4.565676 (-3.309724) | 0.085860 / 0.424275 (-0.338415) | 0.013790 / 0.007607 (0.006183) | 0.560257 / 0.226044 (0.334213) | 5.618180 / 2.268929 (3.349251) | 2.625423 / 55.444624 (-52.819202) | 2.374381 / 6.876477 (-4.502095) | 2.496560 / 2.142072 (0.354488) | 0.841120 / 4.805227 (-3.964107) | 0.161541 / 6.500664 (-6.339123) | 0.075270 / 0.075469 (-0.000199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432916 / 1.841788 (-0.408872) | 14.858534 / 8.074308 (6.784226) | 14.973521 / 10.191392 (4.782129) | 0.148312 / 0.680424 (-0.532112) | 0.016811 / 0.534201 (-0.517390) | 0.382623 / 0.579283 (-0.196660) | 0.389767 / 0.434364 (-0.044596) | 0.449657 / 0.540337 (-0.090680) | 0.533723 / 1.386936 (-0.853214) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8344350f15265a585188ac986ae49a8ed8289fe \"CML watermark\")\n",
"I agree it is good to have a way to run the CI on push, without needing to open a PR.\r\n\r\nBut I think the branch name should be more generic (and this is not specific to this PR). See:\r\n- #5790 ",
"<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) | 0.007208 / 0.011353 (-0.004145) | 0.005600 / 0.011008 (-0.005408) | 0.096129 / 0.038508 (0.057621) | 0.027834 / 0.023109 (0.004725) | 0.295106 / 0.275898 (0.019208) | 0.323983 / 0.323480 (0.000503) | 0.005164 / 0.007986 (-0.002822) | 0.003962 / 0.004328 (-0.000366) | 0.078339 / 0.004250 (0.074089) | 0.036974 / 0.037052 (-0.000078) | 0.310315 / 0.258489 (0.051826) | 0.338036 / 0.293841 (0.044195) | 0.042124 / 0.128546 (-0.086422) | 0.015886 / 0.075646 (-0.059760) | 0.337961 / 0.419271 (-0.081310) | 0.051507 / 0.043533 (0.007974) | 0.297505 / 0.255139 (0.042366) | 0.310728 / 0.283200 (0.027528) | 0.086312 / 0.141683 (-0.055371) | 1.356923 / 1.452155 (-0.095232) | 1.429366 / 1.492716 (-0.063350) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205495 / 0.018006 (0.187489) | 0.460639 / 0.000490 (0.460149) | 0.003996 / 0.000200 (0.003796) | 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) | 0.021970 / 0.037411 (-0.015442) | 0.090283 / 0.014526 (0.075757) | 0.098579 / 0.176557 (-0.077978) | 0.160437 / 0.737135 (-0.576699) | 0.102738 / 0.296338 (-0.193600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494474 / 0.215209 (0.279265) | 4.967453 / 2.077655 (2.889799) | 2.045852 / 1.504120 (0.541732) | 1.858022 / 1.541195 (0.316827) | 1.771874 / 1.468490 (0.303384) | 1.186368 / 4.584777 (-3.398408) | 4.974762 / 3.745712 (1.229050) | 2.616225 / 5.269862 (-2.653636) | 1.702971 / 4.565676 (-2.862705) | 0.124929 / 0.424275 (-0.299346) | 0.011774 / 0.007607 (0.004167) | 0.569643 / 0.226044 (0.343598) | 5.793114 / 2.268929 (3.524186) | 2.441561 / 55.444624 (-53.003064) | 1.862233 / 6.876477 (-5.014243) | 1.931142 / 2.142072 (-0.210931) | 1.148915 / 4.805227 (-3.656313) | 0.203914 / 6.500664 (-6.296750) | 0.062468 / 0.075469 (-0.013001) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188708 / 1.841788 (-0.653080) | 13.710830 / 8.074308 (5.636522) | 15.695153 / 10.191392 (5.503761) | 0.171467 / 0.680424 (-0.508957) | 0.024509 / 0.534201 (-0.509692) | 0.450270 / 0.579283 (-0.129014) | 0.500712 / 0.434364 (0.066348) | 0.488632 / 0.540337 (-0.051706) | 0.574893 / 1.386936 (-0.812043) |\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) | 0.007254 / 0.011353 (-0.004099) | 0.006199 / 0.011008 (-0.004809) | 0.072079 / 0.038508 (0.033571) | 0.026909 / 0.023109 (0.003800) | 0.355538 / 0.275898 (0.079640) | 0.358625 / 0.323480 (0.035145) | 0.005564 / 0.007986 (-0.002421) | 0.005278 / 0.004328 (0.000950) | 0.076469 / 0.004250 (0.072219) | 0.038269 / 0.037052 (0.001216) | 0.355214 / 0.258489 (0.096725) | 0.383219 / 0.293841 (0.089378) | 0.046516 / 0.128546 (-0.082030) | 0.015393 / 0.075646 (-0.060254) | 0.088506 / 0.419271 (-0.330765) | 0.050326 / 0.043533 (0.006793) | 0.327265 / 0.255139 (0.072126) | 0.370176 / 0.283200 (0.086976) | 0.102438 / 0.141683 (-0.039245) | 1.378969 / 1.452155 (-0.073186) | 1.441998 / 1.492716 (-0.050719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209044 / 0.018006 (0.191038) | 0.455733 / 0.000490 (0.455243) | 0.005856 / 0.000200 (0.005656) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025336 / 0.037411 (-0.012075) | 0.097449 / 0.014526 (0.082923) | 0.106301 / 0.176557 (-0.070255) | 0.153053 / 0.737135 (-0.584082) | 0.107938 / 0.296338 (-0.188401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.491070 / 0.215209 (0.275861) | 5.049637 / 2.077655 (2.971982) | 2.064709 / 1.504120 (0.560589) | 1.782266 / 1.541195 (0.241072) | 1.798570 / 1.468490 (0.330080) | 0.988886 / 4.584777 (-3.595891) | 4.690324 / 3.745712 (0.944612) | 4.317355 / 5.269862 (-0.952507) | 2.347596 / 4.565676 (-2.218081) | 0.117249 / 0.424275 (-0.307026) | 0.011614 / 0.007607 (0.004007) | 0.630033 / 0.226044 (0.403988) | 6.140108 / 2.268929 (3.871180) | 2.638080 / 55.444624 (-52.806545) | 2.133017 / 6.876477 (-4.743459) | 2.123392 / 2.142072 (-0.018680) | 1.178056 / 4.805227 (-3.627171) | 0.209465 / 6.500664 (-6.291199) | 0.063234 / 0.075469 (-0.012235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238089 / 1.841788 (-0.603699) | 14.066866 / 8.074308 (5.992558) | 16.225480 / 10.191392 (6.034088) | 0.206466 / 0.680424 (-0.473958) | 0.027279 / 0.534201 (-0.506922) | 0.443006 / 0.579283 (-0.136277) | 0.509512 / 0.434364 (0.075148) | 0.479075 / 0.540337 (-0.061263) | 0.573546 / 1.386936 (-0.813390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c6015a070c66a5bbd84603d415ccc57cb668b44b \"CML watermark\")\n"
] | 2023-04-24T12:13:03Z
| 2023-04-25T14:32:56Z
| 2023-04-25T14:25:30Z
|
CONTRIBUTOR
| null | null | null |
Related to the coming release of `huggingface_hub==0.14.0`. It will break some internal tests. The PR fixes these tests. Let's double-check the CI but I expect the fixed tests to be running fine with both `hfh<=0.13.4` and `hfh==0.14`. Worth case scenario, existing PRs will have to be rebased once this fix is merged.
See related [discussion](https://huggingface.slack.com/archives/C02V5EA0A95/p1682337463368609?thread_ts=1681994202.635609&cid=C02V5EA0A95) (private slack).
cc @lhoestq
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Missing documentation build for versions 2.7.1 and 2.6.2
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"- Build docs for 2.6.2:\r\n - Commit: a6a5a1cf4cdf1e0be65168aed5a327f543001fe8\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539470622/jobs/5941404044\r\n- Build docs for 2.7.1:\r\n - Commit: 5ef1ab1cc06c2b7a574bf2df454cd9fcb071ccb2\r\n - Build docs GH Action: https://github.com/huggingface/datasets/actions/runs/3539574442/jobs/5941636792"
] | 2022-11-24T09:42:10Z
| 2022-11-24T10:10:02Z
| 2022-11-24T10:10:02Z
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MEMBER
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After the patch releases [2.7.1](https://github.com/huggingface/datasets/releases/tag/2.7.1) and [2.6.2](https://github.com/huggingface/datasets/releases/tag/2.6.2), the online docs were not properly built (the build_documentation workflow was not triggered).
There was a fix by:
- #5291
However, both documentations were built from main branch, instead of their corresponding version branch.
We are rebuilding them.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6969). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005414 / 0.011353 (-0.005939) | 0.003936 / 0.011008 (-0.007073) | 0.064129 / 0.038508 (0.025621) | 0.032985 / 0.023109 (0.009875) | 0.244051 / 0.275898 (-0.031847) | 0.273500 / 0.323480 (-0.049980) | 0.003227 / 0.007986 (-0.004759) | 0.002858 / 0.004328 (-0.001470) | 0.049212 / 0.004250 (0.044962) | 0.046432 / 0.037052 (0.009380) | 0.249543 / 0.258489 (-0.008946) | 0.297339 / 0.293841 (0.003498) | 0.027880 / 0.128546 (-0.100666) | 0.010582 / 0.075646 (-0.065065) | 0.202345 / 0.419271 (-0.216927) | 0.036402 / 0.043533 (-0.007131) | 0.253157 / 0.255139 (-0.001982) | 0.283355 / 0.283200 (0.000155) | 0.021907 / 0.141683 (-0.119776) | 1.174431 / 1.452155 (-0.277723) | 1.172103 / 1.492716 (-0.320613) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097942 / 0.018006 (0.079936) | 0.307114 / 0.000490 (0.306624) | 0.000230 / 0.000200 (0.000030) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019149 / 0.037411 (-0.018262) | 0.064283 / 0.014526 (0.049758) | 0.075643 / 0.176557 (-0.100913) | 0.122531 / 0.737135 (-0.614604) | 0.077360 / 0.296338 (-0.218978) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291790 / 0.215209 (0.076581) | 2.869234 / 2.077655 (0.791580) | 1.550266 / 1.504120 (0.046146) | 1.392392 / 1.541195 (-0.148802) | 1.375700 / 1.468490 (-0.092790) | 0.574963 / 4.584777 (-4.009814) | 2.444746 / 3.745712 (-1.300966) | 2.920602 / 5.269862 (-2.349259) | 1.812720 / 4.565676 (-2.752957) | 0.064811 / 0.424275 (-0.359464) | 0.005163 / 0.007607 (-0.002444) | 0.341306 / 0.226044 (0.115261) | 3.443177 / 2.268929 (1.174249) | 1.843510 / 55.444624 (-53.601115) | 1.534023 / 6.876477 (-5.342454) | 1.603575 / 2.142072 (-0.538498) | 0.656923 / 4.805227 (-4.148304) | 0.120338 / 6.500664 (-6.380326) | 0.042958 / 0.075469 (-0.032511) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975993 / 1.841788 (-0.865795) | 11.942335 / 8.074308 (3.868027) | 9.964277 / 10.191392 (-0.227115) | 0.131247 / 0.680424 (-0.549176) | 0.014166 / 0.534201 (-0.520035) | 0.283994 / 0.579283 (-0.295290) | 0.267516 / 0.434364 (-0.166848) | 0.328363 / 0.540337 (-0.211974) | 0.412204 / 1.386936 (-0.974732) |\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) | 0.005867 / 0.011353 (-0.005486) | 0.003860 / 0.011008 (-0.007148) | 0.050247 / 0.038508 (0.011739) | 0.033819 / 0.023109 (0.010710) | 0.264840 / 0.275898 (-0.011058) | 0.291253 / 0.323480 (-0.032227) | 0.004481 / 0.007986 (-0.003504) | 0.002880 / 0.004328 (-0.001449) | 0.048528 / 0.004250 (0.044278) | 0.041720 / 0.037052 (0.004667) | 0.280467 / 0.258489 (0.021978) | 0.315244 / 0.293841 (0.021404) | 0.030569 / 0.128546 (-0.097977) | 0.010494 / 0.075646 (-0.065152) | 0.058652 / 0.419271 (-0.360620) | 0.034181 / 0.043533 (-0.009352) | 0.266466 / 0.255139 (0.011327) | 0.292038 / 0.283200 (0.008838) | 0.018501 / 0.141683 (-0.123182) | 1.115965 / 1.452155 (-0.336189) | 1.162753 / 1.492716 (-0.329963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101301 / 0.018006 (0.083295) | 0.296812 / 0.000490 (0.296322) | 0.000212 / 0.000200 (0.000012) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023662 / 0.037411 (-0.013749) | 0.080678 / 0.014526 (0.066153) | 0.089867 / 0.176557 (-0.086689) | 0.130803 / 0.737135 (-0.606332) | 0.091479 / 0.296338 (-0.204860) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286028 / 0.215209 (0.070819) | 2.780072 / 2.077655 (0.702418) | 1.520146 / 1.504120 (0.016026) | 1.372952 / 1.541195 (-0.168243) | 1.428734 / 1.468490 (-0.039756) | 0.571484 / 4.584777 (-4.013293) | 0.969643 / 3.745712 (-2.776069) | 2.788157 / 5.269862 (-2.481705) | 1.841166 / 4.565676 (-2.724511) | 0.063311 / 0.424275 (-0.360964) | 0.005320 / 0.007607 (-0.002287) | 0.333341 / 0.226044 (0.107296) | 3.295141 / 2.268929 (1.026213) | 1.865537 / 55.444624 (-53.579088) | 1.584655 / 6.876477 (-5.291821) | 1.747417 / 2.142072 (-0.394655) | 0.634549 / 4.805227 (-4.170678) | 0.116373 / 6.500664 (-6.384291) | 0.041567 / 0.075469 (-0.033902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.023086 / 1.841788 (-0.818702) | 13.091905 / 8.074308 (5.017597) | 10.572164 / 10.191392 (0.380772) | 0.142208 / 0.680424 (-0.538216) | 0.015692 / 0.534201 (-0.518509) | 0.284309 / 0.579283 (-0.294974) | 0.126467 / 0.434364 (-0.307897) | 0.322719 / 0.540337 (-0.217618) | 0.439952 / 1.386936 (-0.946985) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98fdc9e78e6d057ca66e58a37f49d6618aab8130 \"CML watermark\")\n"
] | 2024-06-13T14:48:20Z
| 2024-06-13T15:04:39Z
| 2024-06-13T14:55:53Z
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MEMBER
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I_kwDODunzps55WV36
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429 Client Error
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"Transferring repos as this is a datasets issue ",
"I'm getting a similar issue even though I've already downloaded the dataset 😅 \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight\r\n```"
] | 2023-12-11T15:06:01Z
| 2024-06-20T05:55:45Z
| null |
NONE
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Hello, I was downloading the following dataset and after 20% of data was downloaded, I started getting error 429. It is not resolved since a few days. How should I resolve it?
Thanks
Dataset:
https://huggingface.co/datasets/cerebras/SlimPajama-627B
Error:
`requests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/datasets/cerebras/SlimPajama-627B/resolve/2d0accdd58c5d5511943ca1f5ff0e3eb5e293543/train/chunk1/example_train_3300.jsonl.zst`
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PR_kwDODunzps4_QYas
| 4,993
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fix: avoid casting tuples after Dataset.map
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-20T08:45:16Z
| 2022-09-20T16:11:27Z
| 2022-09-20T13:08:29Z
|
CONTRIBUTOR
| null | null | null |
This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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I_kwDODunzps51CGC8
| 6,354
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`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`
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[
"I am having issues as well with this. \r\n\r\nHowever, the error I am getting is :\r\n`RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.`\r\n\r\nAlso did not work with pyspark==3.3.0 and py4j==0.10.9.5",
"Hi, may you have some solution of this bug now?",
"cc @maddiedawson if you have an idea ?"
] | 2023-10-26T12:43:36Z
| 2024-12-10T14:06:06Z
| null |
NONE
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### Describe the bug
Looks like `IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader` if I'm not missing anything.
Also, returns not consistent error messages, which probably depend on the nondeterministic order of worker executions
Some exampes I've encountered:
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__
yield from self.generate_examples_fn()
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn
df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id"))
File "/databricks/spark/python/pyspark/instrumentation_utils.py", line 54, in wrapper
logger.log_failure(
File "/databricks/spark/python/pyspark/databricks/usage_logger.py", line 70, in log_failure
self.logger.recordFunctionCallFailureEvent(
File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__
return_value = get_return_value(
File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco
return f(*a, **kw)
File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value
return OUTPUT_CONVERTER[type](answer[2:], gateway_client)
KeyError: 'c'
```
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__
yield from self.generate_examples_fn()
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn
df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id"))
File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped
return f(*args, **kwargs)
File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id
return _invoke_function("spark_partition_id")
File "/databricks/spark/python/pyspark/sql/functions.py", line 98, in _invoke_function
return Column(jf(*args))
File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__
return_value = get_return_value(
File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco
return f(*a, **kw)
File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value
return OUTPUT_CONVERTER[type](answer[2:], gateway_client)
KeyError: 'm'
```
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__
yield from self.generate_examples_fn()
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn
df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id"))
File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped
return f(*args, **kwargs)
File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id
return _invoke_function("spark_partition_id")
File "/databricks/spark/python/pyspark/sql/functions.py", line 97, in _invoke_function
jf = _get_jvm_function(name, SparkContext._active_spark_context)
File "/databricks/spark/python/pyspark/sql/functions.py", line 88, in _get_jvm_function
return getattr(sc._jvm.functions, name)
File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1725, in __getattr__
raise Py4JError(message)
py4j.protocol.Py4JError: functions does not exist in the JVM
```
### Steps to reproduce the bug
```python
import pandas as pd
import numpy as np
batch_size = 16
pdf = pd.DataFrame({
key: np.random.rand(16*100) for key in ['feature', 'target']
})
test_df = spark.createDataFrame(pdf)
from datasets import IterableDataset
from torch.utils.data import DataLoader
ids = IterableDataset.from_spark(test_df)
for batch in DataLoader(ids, batch_size=16, num_workers=4):
for k, b in batch.items():
print(k, b.shape, sep='\t')
print('\n')
```
### Expected behavior
For `num_workers` equal to 0 or 1 works fine as expected:
```
feature torch.Size([16])
target torch.Size([16])
feature torch.Size([16])
target torch.Size([16])
....
```
Expected to support workers >1.
### Environment info
Databricks 13.3 LTS ML runtime - Spark 3.4.1
pyspark==3.4.1
py4j==0.10.9.7
datasets==2.13.1 and also tested with datasets==2.14.6
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https://github.com/huggingface/datasets/pull/6334
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PR_kwDODunzps5dgbpR
| 6,334
|
datasets.filesystems: fix is_remote_filesystems
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006648 / 0.011353 (-0.004705) | 0.004104 / 0.011008 (-0.006904) | 0.084718 / 0.038508 (0.046210) | 0.075342 / 0.023109 (0.052232) | 0.332624 / 0.275898 (0.056726) | 0.376758 / 0.323480 (0.053278) | 0.005371 / 0.007986 (-0.002614) | 0.003317 / 0.004328 (-0.001011) | 0.065153 / 0.004250 (0.060902) | 0.055270 / 0.037052 (0.018218) | 0.342410 / 0.258489 (0.083920) | 0.397484 / 0.293841 (0.103643) | 0.031168 / 0.128546 (-0.097379) | 0.008545 / 0.075646 (-0.067101) | 0.297641 / 0.419271 (-0.121631) | 0.052404 / 0.043533 (0.008871) | 0.327633 / 0.255139 (0.072494) | 0.362177 / 0.283200 (0.078977) | 0.025056 / 0.141683 (-0.116627) | 1.459023 / 1.452155 (0.006868) | 1.529651 / 1.492716 (0.036935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242838 / 0.018006 (0.224832) | 0.451007 / 0.000490 (0.450517) | 0.013732 / 0.000200 (0.013532) | 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) | 0.028068 / 0.037411 (-0.009343) | 0.081970 / 0.014526 (0.067444) | 0.096148 / 0.176557 (-0.080409) | 0.151758 / 0.737135 (-0.585377) | 0.095617 / 0.296338 (-0.200721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389188 / 0.215209 (0.173979) | 3.867506 / 2.077655 (1.789852) | 1.941912 / 1.504120 (0.437792) | 1.759270 / 1.541195 (0.218076) | 1.774714 / 1.468490 (0.306224) | 0.476587 / 4.584777 (-4.108190) | 3.539342 / 3.745712 (-0.206370) | 3.434389 / 5.269862 (-1.835472) | 2.047581 / 4.565676 (-2.518096) | 0.056322 / 0.424275 (-0.367954) | 0.007286 / 0.007607 (-0.000321) | 0.461826 / 0.226044 (0.235781) | 4.604179 / 2.268929 (2.335251) | 2.405267 / 55.444624 (-53.039357) | 2.133998 / 6.876477 (-4.742479) | 2.187724 / 2.142072 (0.045652) | 0.566578 / 4.805227 (-4.238650) | 0.130007 / 6.500664 (-6.370657) | 0.059685 / 0.075469 (-0.015784) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256204 / 1.841788 (-0.585584) | 18.829475 / 8.074308 (10.755167) | 13.937879 / 10.191392 (3.746487) | 0.163948 / 0.680424 (-0.516475) | 0.018118 / 0.534201 (-0.516083) | 0.389369 / 0.579283 (-0.189914) | 0.399988 / 0.434364 (-0.034376) | 0.459504 / 0.540337 (-0.080834) | 0.674696 / 1.386936 (-0.712240) |\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) | 0.006806 / 0.011353 (-0.004547) | 0.004103 / 0.011008 (-0.006905) | 0.064477 / 0.038508 (0.025969) | 0.079514 / 0.023109 (0.056405) | 0.391657 / 0.275898 (0.115759) | 0.422997 / 0.323480 (0.099517) | 0.005485 / 0.007986 (-0.002501) | 0.003461 / 0.004328 (-0.000868) | 0.064621 / 0.004250 (0.060371) | 0.057686 / 0.037052 (0.020633) | 0.396885 / 0.258489 (0.138396) | 0.431508 / 0.293841 (0.137667) | 0.032305 / 0.128546 (-0.096241) | 0.008617 / 0.075646 (-0.067030) | 0.071577 / 0.419271 (-0.347694) | 0.047769 / 0.043533 (0.004236) | 0.394037 / 0.255139 (0.138898) | 0.412593 / 0.283200 (0.129393) | 0.023800 / 0.141683 (-0.117883) | 1.479114 / 1.452155 (0.026959) | 1.562422 / 1.492716 (0.069706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.452465 / 0.000490 (0.451975) | 0.005877 / 0.000200 (0.005677) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033528 / 0.037411 (-0.003884) | 0.091819 / 0.014526 (0.077294) | 0.106188 / 0.176557 (-0.070368) | 0.159480 / 0.737135 (-0.577655) | 0.106326 / 0.296338 (-0.190013) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427396 / 0.215209 (0.212187) | 4.275196 / 2.077655 (2.197541) | 2.287446 / 1.504120 (0.783326) | 2.137089 / 1.541195 (0.595894) | 2.198439 / 1.468490 (0.729949) | 0.491006 / 4.584777 (-4.093771) | 3.531067 / 3.745712 (-0.214645) | 3.264357 / 5.269862 (-2.005505) | 2.047760 / 4.565676 (-2.517916) | 0.057982 / 0.424275 (-0.366293) | 0.007278 / 0.007607 (-0.000329) | 0.507471 / 0.226044 (0.281426) | 5.073901 / 2.268929 (2.804973) | 2.781799 / 55.444624 (-52.662825) | 2.410759 / 6.876477 (-4.465718) | 2.623331 / 2.142072 (0.481258) | 0.601601 / 4.805227 (-4.203626) | 0.131461 / 6.500664 (-6.369204) | 0.060045 / 0.075469 (-0.015424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372946 / 1.841788 (-0.468842) | 19.560818 / 8.074308 (11.486509) | 14.388468 / 10.191392 (4.197076) | 0.177310 / 0.680424 (-0.503114) | 0.020233 / 0.534201 (-0.513967) | 0.395938 / 0.579283 (-0.183345) | 0.418336 / 0.434364 (-0.016028) | 0.471731 / 0.540337 (-0.068607) | 0.684679 / 1.386936 (-0.702257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bedb7dcbaedd292ae5764f0fe6d44c16e1c2c10 \"CML watermark\")\n",
"We did a patch release containing your fix @ap-- !"
] | 2023-10-23T09:17:54Z
| 2024-02-07T12:41:15Z
| 2023-10-23T10:14:10Z
|
CONTRIBUTOR
| null | null | null |
Close #6330, close #6333.
`fsspec.implementations.LocalFilesystem.protocol`
was changed from `str` "file" to `tuple[str,...]` ("file", "local") in `fsspec>=2023.10.0`
This commit supports both styles.
|
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I_kwDODunzps5i3G1k
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|
Handle empty audio
|
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[
"Hi! Can you share one of the problematic audio files with us?\r\n\r\nI tried to reproduce the error with the following code: \r\n```python\r\nimport soundfile as sf\r\nimport numpy as np\r\nfrom datasets import Audio\r\n\r\nsf.write(\"empty.wav\", np.array([]), 16000)\r\nAudio(sampling_rate=24000).decode_example({\"path\": \"empty.wav\", \"bytes\": None})\r\n```\r\nBut without success.\r\n\r\nAlso, what version of `librosa` is installed in your env? (You can get this info with `python -c \"import librosa; print(librosa.__version__)`)\r\n\r\n",
"I'm closing this issue as the reproducer hasn't been provided."
] | 2023-04-07T09:51:40Z
| 2023-09-27T17:47:08Z
| 2023-09-27T17:47:08Z
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Some audio paths exist, but they are empty, and an error will be reported when reading the audio path.How to use the filter function to avoid the empty audio path?
when a audio is empty, when do resample , it will break:
`array, sampling_rate = sf.read(f) array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate)`
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.add_faiss_index and .add_elasticsearch_index returns ImportError at Google Colab
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"I can fix this.\nIt's mainly because faiss-gpu requires python<=3.10 but the default python version in colab is 3.11. We just have to downgrade the CPython version down to 3.10 and it should work fine.\n",
"I think I just had no chance to meet with faiss-cpu.\nIt could be import problem? \n_has_faiss gets its value at the beginning of datasets/search.\nI tried to call object before import faiss, so _has_faiss took False. And never updated later. ",
"Yes you can't meet the requirements because faiss-cpu runs only on\r\npython3.10 and lower but the default version for colab is python3.11 which\r\nresults in pip not being able to find wheels for faiss-cpu with python3.11.\r\n\r\nOn Mon, 17 Mar, 2025, 3:56 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>\r\n>\r\n> I think I just had no chance to meet with faiss-cpu.\r\n> It could be import problem?\r\n> _has_faiss gets its value at the beginning of datasets/search.\r\n> I tried to call object before import faiss, so _has_faiss took False. And\r\n> never updated later.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2728975672>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMBVD7LEDDUGALOTVN32U2PMBAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRYHE3TKNRXGI>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> you can't meet the requirements\n\nIt is not the case (or I didn't reach this point) because the same code in notebook\n```importlib.util.find_spec(\"faiss\")```\nfinds faiss. I've mention it.\nI think the problem is in the very moment when _has_faiss takes its value and never try again. \n(or it couldn't find the path that was easily found when started from my code)",
"When you run the first cell containing pip install faiss-cpu does it\r\ninstall it?\r\n\r\nOn Mon, 17 Mar, 2025, 8:01 pm MapleBloom, ***@***.***> wrote:\r\n\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n> [image: MapleBloom]*MapleBloom* left a comment (huggingface/datasets#7456)\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>\r\n>\r\n> you can't meet the requirements\r\n>\r\n> It is not the case (or I didn't reach this point) because the same code in\r\n> notebook\r\n> importlib.util.find_spec(\"faiss\")\r\n> finds faiss. I've mention it.\r\n> I think the problem is in the very moment when _has_faiss takes its value\r\n> and never try again.\r\n> (or it couldn't find the path that was easily found when started from my\r\n> code)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/7456#issuecomment-2729737414>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVUSZMCCE6BPZCOVAWXKIY32U3MFVAVCNFSM6AAAAABZDBA426VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOMRZG4ZTONBRGQ>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"> When you run the first cell containing pip install faiss-cpu does it\n> install it?\n> […](#)\n\nYes. It was installed succesfully. \nMethods of datasets library that depends on _has_faiss constant didn't start to work."
] | 2025-03-16T00:51:49Z
| 2025-03-17T15:57:19Z
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### Describe the bug
At Google Colab
```!pip install faiss-cpu``` works
```import faiss``` no error
but
```embeddings_dataset.add_faiss_index(column='embeddings')```
returns
```
[/usr/local/lib/python3.11/dist-packages/datasets/search.py](https://localhost:8080/#) in init(self, device, string_factory, metric_type, custom_index)
247 self.faiss_index = custom_index
248 if not _has_faiss:
--> 249 raise ImportError(
250 "You must install Faiss to use FaissIndex. To do so you can run conda install -c pytorch faiss-cpu or conda install -c pytorch faiss-gpu. "
251 "A community supported package is also available on pypi: pip install faiss-cpu or pip install faiss-gpu. "
```
because
```_has_faiss = importlib.util.find_spec("faiss") is not None``` at the beginning of ```datasets/search.py``` returns ```False```
when
the same code at colab notebook returns
```ModuleSpec(name='faiss', loader=<_frozen_importlib_external.SourceFileLoader object at 0x7b7851449f50>, origin='/usr/local/lib/python3.11/dist-packages/faiss/init.py', submodule_search_locations=['/usr/local/lib/python3.11/dist-packages/faiss'])```
But
```
import datasets
datasets.search._has_faiss
```
at ```colab notebook``` also returns ```False```
The same story with ```_has_elasticsearch```
### Steps to reproduce the bug
1. Follow https://huggingface.co/learn/nlp-course/chapter5/6?fw=pt at Google Colab
2. till ```embeddings_dataset.add_faiss_index(column='embeddings')```
3. ```embeddings_dataset.add_elasticsearch_index(column='embeddings')```
4. https://colab.research.google.com/drive/1h2cjuiClblqzbNQgrcoLYOC8zBqTLLcv#scrollTo=3ddzRp72auOF
### Expected behavior
I've only started Tutorial and don't know exactly. But something tells me that ```embeddings_dataset.add_faiss_index(column='embeddings')```
should work without ```Import Error```
### Environment info
Google Colab notebook with default config
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6681). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005410 / 0.011353 (-0.005943) | 0.003862 / 0.011008 (-0.007146) | 0.063457 / 0.038508 (0.024949) | 0.030081 / 0.023109 (0.006972) | 0.250657 / 0.275898 (-0.025241) | 0.275483 / 0.323480 (-0.047997) | 0.004048 / 0.007986 (-0.003938) | 0.002818 / 0.004328 (-0.001511) | 0.048940 / 0.004250 (0.044689) | 0.043397 / 0.037052 (0.006345) | 0.262160 / 0.258489 (0.003671) | 0.294154 / 0.293841 (0.000313) | 0.030028 / 0.128546 (-0.098519) | 0.010789 / 0.075646 (-0.064857) | 0.209665 / 0.419271 (-0.209606) | 0.035297 / 0.043533 (-0.008236) | 0.253169 / 0.255139 (-0.001970) | 0.271775 / 0.283200 (-0.011424) | 0.018332 / 0.141683 (-0.123351) | 1.152420 / 1.452155 (-0.299735) | 1.262767 / 1.492716 (-0.229949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089990 / 0.018006 (0.071984) | 0.298552 / 0.000490 (0.298062) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018414 / 0.037411 (-0.018997) | 0.061566 / 0.014526 (0.047040) | 0.075360 / 0.176557 (-0.101196) | 0.123470 / 0.737135 (-0.613665) | 0.075215 / 0.296338 (-0.221124) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279563 / 0.215209 (0.064354) | 2.725212 / 2.077655 (0.647557) | 1.446413 / 1.504120 (-0.057707) | 1.321665 / 1.541195 (-0.219529) | 1.352475 / 1.468490 (-0.116015) | 0.568440 / 4.584777 (-4.016337) | 2.393217 / 3.745712 (-1.352495) | 2.793150 / 5.269862 (-2.476711) | 1.764316 / 4.565676 (-2.801360) | 0.063157 / 0.424275 (-0.361118) | 0.005117 / 0.007607 (-0.002491) | 0.333310 / 0.226044 (0.107265) | 3.291000 / 2.268929 (1.022071) | 1.824654 / 55.444624 (-53.619971) | 1.558681 / 6.876477 (-5.317795) | 1.580558 / 2.142072 (-0.561514) | 0.649831 / 4.805227 (-4.155396) | 0.118674 / 6.500664 (-6.381990) | 0.042247 / 0.075469 (-0.033222) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976552 / 1.841788 (-0.865236) | 11.847361 / 8.074308 (3.773053) | 9.490786 / 10.191392 (-0.700606) | 0.141643 / 0.680424 (-0.538781) | 0.013653 / 0.534201 (-0.520548) | 0.284345 / 0.579283 (-0.294938) | 0.268314 / 0.434364 (-0.166050) | 0.339586 / 0.540337 (-0.200751) | 0.445239 / 1.386936 (-0.941697) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005754 / 0.011353 (-0.005599) | 0.004038 / 0.011008 (-0.006970) | 0.050027 / 0.038508 (0.011519) | 0.033598 / 0.023109 (0.010488) | 0.286514 / 0.275898 (0.010616) | 0.302493 / 0.323480 (-0.020986) | 0.004254 / 0.007986 (-0.003731) | 0.002827 / 0.004328 (-0.001502) | 0.050433 / 0.004250 (0.046182) | 0.046106 / 0.037052 (0.009054) | 0.301522 / 0.258489 (0.043033) | 0.325784 / 0.293841 (0.031943) | 0.030014 / 0.128546 (-0.098532) | 0.010891 / 0.075646 (-0.064756) | 0.059899 / 0.419271 (-0.359373) | 0.057252 / 0.043533 (0.013719) | 0.280276 / 0.255139 (0.025137) | 0.295632 / 0.283200 (0.012433) | 0.019060 / 0.141683 (-0.122622) | 1.141423 / 1.452155 (-0.310731) | 1.226960 / 1.492716 (-0.265757) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091919 / 0.018006 (0.073913) | 0.300769 / 0.000490 (0.300279) | 0.000220 / 0.000200 (0.000020) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022467 / 0.037411 (-0.014945) | 0.075342 / 0.014526 (0.060816) | 0.087988 / 0.176557 (-0.088569) | 0.128304 / 0.737135 (-0.608831) | 0.089058 / 0.296338 (-0.207280) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294662 / 0.215209 (0.079453) | 2.887743 / 2.077655 (0.810088) | 1.591756 / 1.504120 (0.087636) | 1.469249 / 1.541195 (-0.071945) | 1.495639 / 1.468490 (0.027149) | 0.575507 / 4.584777 (-4.009270) | 2.449674 / 3.745712 (-1.296038) | 2.737217 / 5.269862 (-2.532645) | 1.783066 / 4.565676 (-2.782610) | 0.063388 / 0.424275 (-0.360887) | 0.005044 / 0.007607 (-0.002563) | 0.344807 / 0.226044 (0.118763) | 3.410845 / 2.268929 (1.141916) | 1.967452 / 55.444624 (-53.477173) | 1.699884 / 6.876477 (-5.176593) | 1.862466 / 2.142072 (-0.279607) | 0.663714 / 4.805227 (-4.141513) | 0.118356 / 6.500664 (-6.382308) | 0.041176 / 0.075469 (-0.034293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.013523 / 1.841788 (-0.828264) | 12.498866 / 8.074308 (4.424558) | 10.382595 / 10.191392 (0.191203) | 0.141757 / 0.680424 (-0.538667) | 0.015992 / 0.534201 (-0.518209) | 0.295639 / 0.579283 (-0.283644) | 0.278382 / 0.434364 (-0.155982) | 0.330351 / 0.540337 (-0.209986) | 0.431293 / 1.386936 (-0.955643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf1aaa32eddd73076cf6600125661df4a32cb20a \"CML watermark\")\n"
] | 2024-02-19T10:03:08Z
| 2024-02-28T07:23:49Z
| 2024-02-28T07:17:22Z
|
MEMBER
| null | null | null |
Update release instructions.
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PR_kwDODunzps4-g0Hz
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Introduce regex check when pushing as well
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"_The documentation is not available anymore as the PR was closed or merged._",
"Let me take over this PR if you don't mind"
] | 2022-09-07T13:45:58Z
| 2022-09-13T10:19:01Z
| 2022-09-13T10:16:34Z
|
MEMBER
| null | null | null |
Closes https://github.com/huggingface/datasets/issues/4945 by adding a regex check when pushing to hub.
Let me know if this is helpful and if it's the fix you would have in mind for the issue and I'm happy to contribute tests.
|
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I_kwDODunzps57fT1R
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No online docs for 2.16 release
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[
"Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 ",
"Yes, I saw it. Maybe @mishig25 can give us some hint...",
"fixed https://huggingface.co/docs/datasets/v2.16.0/en/index",
"Still missing 2.16.1.",
"> Still missing 2.16.1.\r\n\r\nre-running the doc-buld job for the missing ones should fix\r\n\r\n",
"Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583",
"Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index"
] | 2024-01-09T07:43:30Z
| 2024-01-09T16:45:50Z
| 2024-01-09T16:45:50Z
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We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1).
In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index
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set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7417). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-02-20T17:45:29Z
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Use auth to get parquet export
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\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) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n"
] | 2023-12-04T11:18:27Z
| 2023-12-04T17:21:22Z
| 2023-12-04T17:15:11Z
|
MEMBER
| null | null | null |
added `token` to the `_datasets_server` functions
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I_kwDODunzps53Qna6
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TypeError: <lambda>() takes 0 positional arguments but 1 was given
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[
"This looks like a problem with your environment rather than `datasets`.",
"I meet the same problem,\r\nand originally use\r\n```python\r\nlocale.getpreferredencoding = lambda : \"UTF-8\"\r\n```\r\nand change to\r\n```\r\nlocale.getpreferredencoding = lambda x: \"UTF-8\"\r\n```\r\nand it works."
] | 2023-11-19T13:10:20Z
| 2024-06-25T06:00:31Z
| 2023-11-29T16:28:34Z
|
NONE
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### Describe the bug
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from datasets import Dataset
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
or
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
[<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>()
----> 1 import datasets
9 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.15.0"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
61 import pyarrow.compute as pc
62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi
---> 63 from multiprocess import Pool
64 from requests import HTTPError
65
[/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module>
31
32 import sys
---> 33 from . import context
34
35 #
[/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module>
4
5 from . import process
----> 6 from . import reduction
7
8 __all__ = ()
[/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module>
14 import os
15 try:
---> 16 import dill as pickle
17 except ImportError:
18 import pickle
[/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module>
24
25
---> 26 from ._dill import (
27 dump, dumps, load, loads, copy,
28 Pickler, Unpickler, register, pickle, pickles, check,
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module>
166 try:
167 from _pyio import open as _open
--> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open)
169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open)
170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open)
[/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(*args, **kwargs)
154 def get_file_type(*args, **kwargs):
155 open = kwargs.pop("open", __builtin__.open)
--> 156 f = open(os.devnull, *args, **kwargs)
157 t = type(f)
158 f.close()
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener)
280 return result
281 encoding = text_encoding(encoding)
--> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering)
283 result = text
284 text.mode = mode
[/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through)
2043 encoding = "utf-8"
2044 else:
-> 2045 encoding = locale.getpreferredencoding(False)
2046
2047 if not isinstance(encoding, str):
TypeError: <lambda>() takes 0 positional arguments but 1 was given
```
### Steps to reproduce the bug
`import datasets` on colab
### Expected behavior
work fine
### Environment info
colab
`!pip install datasets`
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I_kwDODunzps56vOBv
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Datasets : wikipedia 20220301.en error
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"Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?\r\n\r\n```python\r\nwiki_dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\r\n```",
"This bug has been fixed in `2.16.1` thanks to https://github.com/huggingface/datasets/pull/6544, feel free to update `datasets` and re-run your code :)\r\n\r\n```\r\npip install -U datasets\r\n```"
] | 2023-12-29T08:34:51Z
| 2024-01-02T13:21:06Z
| 2024-01-02T13:20:30Z
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### Describe the bug
When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist.
### Steps to reproduce the bug
1.I tried downloading directly.
```python
wiki_dataset = load_dataset("wikipedia", "20220301.en")
```
An exception occurred
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
```
2.I modified the code as prompted.
```python
wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
```
An exception occurred:
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
```
### Expected behavior
I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory.
I really need this data set and hope to provide a download method.
### Environment info
python 3.8
datasets 2.16.0
apache-beam 2.52.0
dill 0.3.7
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Request for text deduplication feature
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"The \"exact match\" deduplication will be possible when we resolve https://github.com/huggingface/datasets/issues/2514 (first, https://github.com/apache/arrow/issues/30950 needs to be addressed on the Arrow side). In the meantime, you can use Polars or DuckDB (e.g., via [datasets-sql](https://github.com/mariosasko/datasets_sql)).\r\n\r\nFuzzy deduplication is out-of-scope for now ([splink](https://github.com/moj-analytical-services/splink) is probably the best tool for it).",
"This library can be an intermediate solution : https://github.com/ChenghaoMou/text-dedup/tree/main",
"I have been using polars to remove duplicates but it would be nice to do it directly in pyarrow.\r\n\r\nFor example,\r\n\r\n1. Read dataset with pyarrow\r\n2. Use scan_pyarrow_dataset() with Polars to create a LazyFrame\r\n3. Use sort and unique to remove duplicates based on a subset of columns\r\n4. Convert to table and save data with ds.write_dataset()\r\n\r\nThere are times where that workflow makes perfect sense because I do additional transformations with Polars. Most of the time I am simply just reading dataset A and writing dataset B without duplicates though, and I wish I could use a pyarrow scanner or table directly. ",
"Hi\r\nsee this new release from hf [datatrove](https://github.com/huggingface/datatrove)\r\nDataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality"
] | 2023-05-20T01:56:00Z
| 2024-01-25T14:40:09Z
| null |
NONE
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### Feature request
It would be great if there would be support for high performance, highly scalable text deduplication algorithms as part of the datasets library.
### Motivation
Motivated by this blog post https://huggingface.co/blog/dedup and this library https://github.com/google-research/deduplicate-text-datasets, but slightly frustrated by how its not very easy to work with these tools I am proposing this feature.
### Your contribution
I would be happy to contribute to the development effort of this feature. would love to collaborate with others in the development effort.
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Ci py3.10
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"_The documentation is not available anymore as the PR was closed or merged._",
"Does it sound good to you @albertvillanova ?"
] | 2022-10-04T10:13:51Z
| 2022-11-29T15:28:05Z
| 2022-11-29T15:25:26Z
|
MEMBER
| null | null | null |
Added a CI job for python 3.10
Some dependencies don't work on 3.10 like apache beam, so I remove them from the extras in this case.
I also removed some s3 fixtures that we don't use anymore (and that don't work on 3.10 anyway)
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Fix multi gpu process example
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"Okay nevermind looks like to works both ways for models. but my doubt still remains, isnt this changing the device of the model every batch?"
] | 2025-03-06T11:29:19Z
| 2025-03-06T17:07:28Z
| 2025-03-06T17:06:38Z
|
NONE
| null | null | null |
to is not an inplace function.
But i am not sure about this code anyway, i think this is modifying the global variable `model` everytime the function is called? Which is on every batch? So it is juggling the same model on every gpu right? Isnt that very inefficient?
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load_dataset gives "403" error when using Financial phrasebank
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"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)."
] | 2023-03-11T07:46:39Z
| 2023-03-13T18:27:26Z
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When I try to load this dataset, I receive the following error:
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
Has this been seen before? Thanks. The website loads when I try to access it manually.
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Offline dataset viewer
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"Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.",
"Hi, we are building an offline dataset viewer: https://github.com/Renumics/spotlight\r\nIt supports many HF datasets, but currently you have to use it via Pandas:\r\ndf=ds.to_pandas()\r\nspotlight.show(df)\r\n\r\nWould love to hear from you if that works for your use case. If not, feel free to open an issue on the repo: https://github.com/Renumics/spotlight/issues",
"@ssuwelack thank you! I will definitely try it out.",
"Related issues:\r\n- https://github.com/huggingface/datasets-server/issues/213\r\n- https://github.com/huggingface/datasets-server/issues/441\r\n- https://github.com/huggingface/datasets/issues/6014",
"Closing for now, as developing and maintaining an offline viewer is not planned.",
"@yuvalkirstain the dataset viewer is now available on private datasets for [PRO users](https://huggingface.co/pricing#pro) and [Enterprise Hub orgs](https://huggingface.co/enterprise). Would it fit your needs?",
"Hi @ssuwelack I tried loading a HF dataset with your viewer but got this error https://github.com/Renumics/spotlight/issues/461 hope the team can help me on this. Thanks!"
] | 2023-08-10T11:30:00Z
| 2024-09-24T18:36:35Z
| 2023-09-29T13:10:22Z
|
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### Feature request
The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset.
### Motivation
I want to easily view my dataset even when it is hosted locally.
### Your contribution
N.A.
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Apply ruff flake8-comprehension checks
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009598 / 0.011353 (-0.001755) | 0.005115 / 0.011008 (-0.005893) | 0.100100 / 0.038508 (0.061592) | 0.036193 / 0.023109 (0.013083) | 0.296478 / 0.275898 (0.020580) | 0.355997 / 0.323480 (0.032517) | 0.007846 / 0.007986 (-0.000140) | 0.004082 / 0.004328 (-0.000247) | 0.076949 / 0.004250 (0.072699) | 0.044304 / 0.037052 (0.007252) | 0.310775 / 0.258489 (0.052286) | 0.333914 / 0.293841 (0.040073) | 0.037783 / 0.128546 (-0.090763) | 0.012023 / 0.075646 (-0.063623) | 0.333311 / 0.419271 (-0.085961) | 0.047568 / 0.043533 (0.004035) | 0.295567 / 0.255139 (0.040428) | 0.315707 / 0.283200 (0.032507) | 0.102675 / 0.141683 (-0.039008) | 1.471546 / 1.452155 (0.019391) | 1.507991 / 1.492716 (0.015274) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208658 / 0.018006 (0.190651) | 0.445026 / 0.000490 (0.444536) | 0.002593 / 0.000200 (0.002393) | 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) | 0.026968 / 0.037411 (-0.010444) | 0.108188 / 0.014526 (0.093662) | 0.117965 / 0.176557 (-0.058592) | 0.182769 / 0.737135 (-0.554366) | 0.121671 / 0.296338 (-0.174667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400677 / 0.215209 (0.185468) | 4.012577 / 2.077655 (1.934922) | 1.821324 / 1.504120 (0.317204) | 1.624438 / 1.541195 (0.083244) | 1.731886 / 1.468490 (0.263396) | 0.698089 / 4.584777 (-3.886688) | 3.786165 / 3.745712 (0.040453) | 2.079742 / 5.269862 (-3.190119) | 1.325032 / 4.565676 (-3.240644) | 0.085229 / 0.424275 (-0.339046) | 0.012017 / 0.007607 (0.004410) | 0.511779 / 0.226044 (0.285734) | 5.114358 / 2.268929 (2.845430) | 2.324763 / 55.444624 (-53.119861) | 2.011864 / 6.876477 (-4.864612) | 2.075875 / 2.142072 (-0.066198) | 0.853475 / 4.805227 (-3.951752) | 0.166949 / 6.500664 (-6.333715) | 0.064669 / 0.075469 (-0.010800) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230212 / 1.841788 (-0.611576) | 14.942371 / 8.074308 (6.868063) | 14.075795 / 10.191392 (3.884403) | 0.156920 / 0.680424 (-0.523504) | 0.029002 / 0.534201 (-0.505199) | 0.442213 / 0.579283 (-0.137070) | 0.436888 / 0.434364 (0.002524) | 0.519725 / 0.540337 (-0.020613) | 0.604634 / 1.386936 (-0.782303) |\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) | 0.007649 / 0.011353 (-0.003704) | 0.005298 / 0.011008 (-0.005710) | 0.076559 / 0.038508 (0.038050) | 0.033723 / 0.023109 (0.010614) | 0.334946 / 0.275898 (0.059048) | 0.372785 / 0.323480 (0.049305) | 0.006032 / 0.007986 (-0.001953) | 0.004125 / 0.004328 (-0.000204) | 0.075366 / 0.004250 (0.071116) | 0.049061 / 0.037052 (0.012009) | 0.338188 / 0.258489 (0.079699) | 0.389693 / 0.293841 (0.095852) | 0.037246 / 0.128546 (-0.091301) | 0.012530 / 0.075646 (-0.063116) | 0.088053 / 0.419271 (-0.331219) | 0.049844 / 0.043533 (0.006311) | 0.338476 / 0.255139 (0.083337) | 0.361672 / 0.283200 (0.078473) | 0.101982 / 0.141683 (-0.039701) | 1.479550 / 1.452155 (0.027396) | 1.541031 / 1.492716 (0.048315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226162 / 0.018006 (0.208156) | 0.439108 / 0.000490 (0.438618) | 0.001102 / 0.000200 (0.000902) | 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) | 0.030240 / 0.037411 (-0.007171) | 0.113754 / 0.014526 (0.099229) | 0.122839 / 0.176557 (-0.053717) | 0.192531 / 0.737135 (-0.544604) | 0.129455 / 0.296338 (-0.166884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424701 / 0.215209 (0.209492) | 4.208161 / 2.077655 (2.130507) | 2.045733 / 1.504120 (0.541613) | 1.892369 / 1.541195 (0.351174) | 1.997024 / 1.468490 (0.528534) | 0.739883 / 4.584777 (-3.844894) | 3.760939 / 3.745712 (0.015227) | 3.195748 / 5.269862 (-2.074113) | 1.731480 / 4.565676 (-2.834197) | 0.087013 / 0.424275 (-0.337262) | 0.012550 / 0.007607 (0.004943) | 0.540829 / 0.226044 (0.314785) | 5.329933 / 2.268929 (3.061005) | 2.507572 / 55.444624 (-52.937052) | 2.167761 / 6.876477 (-4.708716) | 2.250298 / 2.142072 (0.108226) | 0.868718 / 4.805227 (-3.936510) | 0.181643 / 6.500664 (-6.319021) | 0.064817 / 0.075469 (-0.010653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295001 / 1.841788 (-0.546787) | 15.236413 / 8.074308 (7.162105) | 13.692212 / 10.191392 (3.500820) | 0.186330 / 0.680424 (-0.494094) | 0.017492 / 0.534201 (-0.516709) | 0.427365 / 0.579283 (-0.151919) | 0.427781 / 0.434364 (-0.006583) | 0.533763 / 0.540337 (-0.006575) | 0.636011 / 1.386936 (-0.750925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94b16b674111ca5e1a03ddcb71dc0b53acc2f934 \"CML watermark\")\n"
] | 2023-02-19T20:09:28Z
| 2023-02-23T14:06:39Z
| 2023-02-23T13:59:39Z
|
CONTRIBUTOR
| null | null | null |
Fix #5548
Apply ruff's flake8-comprehension checks for better performance, and more readable code.
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[
"Thanks for reporting @msummerfield. We are fixing it."
] | 2023-01-05T00:43:17Z
| 2023-01-06T10:59:14Z
| 2023-01-06T10:59:14Z
|
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### Describe the bug
Calling `Datasets.from_sql()` generates a warning:
`.../site-packages/datasets/builder.py:712: FutureWarning: 'use_auth_token' was deprecated in version 2.7.1 and will be removed in 3.0.0. Pass 'use_auth_token' to the initializer/'load_dataset_builder' instead.`
### Steps to reproduce the bug
Any valid call to `Datasets.from_sql()` will produce the deprecation warning.
### Expected behavior
No warning.
The fix should be simply to remove the parameter `use_auth_token` from the call to `builder.download_and_prepare()` at line 43 of `io/sql.py` (it is set to `None` anyway, and is not needed).
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-4.15.0-169-generic-x86_64-with-glibc2.27
- Python version: 3.9.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6794). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"CI is failing because of the missing parquet export of one test dataset, PR to fix this at https://github.com/huggingface/dataset-viewer/pull/2689",
"I took your comments into account :) lmk what you think @mariosasko ",
"<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) | 0.004956 / 0.011353 (-0.006397) | 0.003282 / 0.011008 (-0.007726) | 0.064028 / 0.038508 (0.025520) | 0.030420 / 0.023109 (0.007311) | 0.240097 / 0.275898 (-0.035801) | 0.266356 / 0.323480 (-0.057124) | 0.003116 / 0.007986 (-0.004869) | 0.002597 / 0.004328 (-0.001731) | 0.050230 / 0.004250 (0.045980) | 0.043864 / 0.037052 (0.006812) | 0.258711 / 0.258489 (0.000222) | 0.290816 / 0.293841 (-0.003025) | 0.027898 / 0.128546 (-0.100648) | 0.009941 / 0.075646 (-0.065705) | 0.208917 / 0.419271 (-0.210355) | 0.035891 / 0.043533 (-0.007642) | 0.253332 / 0.255139 (-0.001807) | 0.274300 / 0.283200 (-0.008900) | 0.019466 / 0.141683 (-0.122217) | 1.133896 / 1.452155 (-0.318259) | 1.178130 / 1.492716 (-0.314586) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091093 / 0.018006 (0.073087) | 0.293632 / 0.000490 (0.293142) | 0.000216 / 0.000200 (0.000016) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017722 / 0.037411 (-0.019689) | 0.060241 / 0.014526 (0.045715) | 0.072024 / 0.176557 (-0.104533) | 0.118521 / 0.737135 (-0.618615) | 0.071107 / 0.296338 (-0.225232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280950 / 0.215209 (0.065741) | 2.781361 / 2.077655 (0.703706) | 1.477949 / 1.504120 (-0.026171) | 1.356388 / 1.541195 (-0.184807) | 1.361808 / 1.468490 (-0.106682) | 0.565499 / 4.584777 (-4.019278) | 2.389206 / 3.745712 (-1.356506) | 2.712782 / 5.269862 (-2.557079) | 1.701402 / 4.565676 (-2.864274) | 0.063619 / 0.424275 (-0.360656) | 0.005321 / 0.007607 (-0.002286) | 0.336783 / 0.226044 (0.110739) | 3.299628 / 2.268929 (1.030699) | 1.794686 / 55.444624 (-53.649939) | 1.504207 / 6.876477 (-5.372270) | 1.524637 / 2.142072 (-0.617436) | 0.642833 / 4.805227 (-4.162395) | 0.117808 / 6.500664 (-6.382856) | 0.041539 / 0.075469 (-0.033930) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.960193 / 1.841788 (-0.881595) | 11.229147 / 8.074308 (3.154839) | 9.380653 / 10.191392 (-0.810739) | 0.137184 / 0.680424 (-0.543240) | 0.013399 / 0.534201 (-0.520802) | 0.314904 / 0.579283 (-0.264379) | 0.262539 / 0.434364 (-0.171825) | 0.354007 / 0.540337 (-0.186331) | 0.451698 / 1.386936 (-0.935238) |\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) | 0.005207 / 0.011353 (-0.006146) | 0.003660 / 0.011008 (-0.007348) | 0.049931 / 0.038508 (0.011423) | 0.030918 / 0.023109 (0.007809) | 0.271243 / 0.275898 (-0.004655) | 0.295706 / 0.323480 (-0.027774) | 0.004106 / 0.007986 (-0.003879) | 0.002750 / 0.004328 (-0.001578) | 0.048337 / 0.004250 (0.044086) | 0.039944 / 0.037052 (0.002892) | 0.284013 / 0.258489 (0.025524) | 0.306827 / 0.293841 (0.012987) | 0.029183 / 0.128546 (-0.099363) | 0.010033 / 0.075646 (-0.065613) | 0.058126 / 0.419271 (-0.361146) | 0.032427 / 0.043533 (-0.011106) | 0.276471 / 0.255139 (0.021332) | 0.288428 / 0.283200 (0.005229) | 0.017549 / 0.141683 (-0.124134) | 1.142361 / 1.452155 (-0.309793) | 1.184514 / 1.492716 (-0.308202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090350 / 0.018006 (0.072344) | 0.292511 / 0.000490 (0.292021) | 0.000215 / 0.000200 (0.000015) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021572 / 0.037411 (-0.015840) | 0.074310 / 0.014526 (0.059784) | 0.086102 / 0.176557 (-0.090455) | 0.123507 / 0.737135 (-0.613629) | 0.087397 / 0.296338 (-0.208941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294038 / 0.215209 (0.078829) | 2.889662 / 2.077655 (0.812007) | 1.591775 / 1.504120 (0.087655) | 1.468815 / 1.541195 (-0.072379) | 1.470226 / 1.468490 (0.001736) | 0.574557 / 4.584777 (-4.010220) | 2.481377 / 3.745712 (-1.264335) | 2.763368 / 5.269862 (-2.506493) | 1.713707 / 4.565676 (-2.851969) | 0.064158 / 0.424275 (-0.360117) | 0.005553 / 0.007607 (-0.002054) | 0.353480 / 0.226044 (0.127436) | 3.447689 / 2.268929 (1.178760) | 1.975802 / 55.444624 (-53.468822) | 1.673561 / 6.876477 (-5.202915) | 1.637212 / 2.142072 (-0.504860) | 0.640667 / 4.805227 (-4.164560) | 0.114618 / 6.500664 (-6.386046) | 0.038912 / 0.075469 (-0.036557) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007581 / 1.841788 (-0.834207) | 11.874250 / 8.074308 (3.799942) | 10.312692 / 10.191392 (0.121300) | 0.142705 / 0.680424 (-0.537719) | 0.015438 / 0.534201 (-0.518763) | 0.285919 / 0.579283 (-0.293364) | 0.278223 / 0.434364 (-0.156141) | 0.323806 / 0.540337 (-0.216531) | 0.415007 / 1.386936 (-0.971929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f1f27c69f6cc8d085b66a8a2ba0440a39bc5bce \"CML watermark\")\n"
] | 2024-04-09T11:13:19Z
| 2024-04-15T21:24:13Z
| 2024-04-15T21:18:08Z
|
MEMBER
| null | null | null |
...for faster dataset download when there are many many small files (e.g. imagefolder, audiofolder)
### Behcnmark
for example on [lhoestq/tmp-images-writer_batch_size](https://hf.co/datasets/lhoestq/tmp-images-writer_batch_size) (128 images)
| | duration of the download step in `load_dataset()` |
|--| ----------------------------------------------------------------------|
| Before | 58s |
| Now | 3s |
This should fix issues with the Dataset Viewer taking too much time to show up for imagefolder/audiofolder datasets.
### Implementation details
The main change is in the `DownloadManager`:
```diff
- download_func = partial(self._download, download_config=download_config)
+ download_func = partial(self._download_batched, download_config=download_config)
downloaded_path_or_paths = map_nested(
download_func,
url_or_urls,
map_tuple=True,
num_proc=download_config.num_proc,
desc="Downloading data files",
+ batched=True,
+ batch_size=-1,
)
```
and `_download_batched` is a multithreaded function.
I only enable multithreading if there are more than 16 files and files are small though, otherwise the progress bar that counts the number of downloaded files is not fluid (updating when a big batch of big files are done downloading). To do so I simply check if the first file is smaller than 20MB.
I also had to tweak `map_nested` to support batching. In particular it slices the data correctly if the user also enables multiprocessing.
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size_in_bytes the same for all splits
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[
"Hi @Breakend,\r\n\r\nIndeed, the attribute `size_in_bytes` refers to the size of the entire dataset configuration, for all splits (size of downloaded files + Arrow files), not the specific split.\r\nThis is also the case for `download_size` (downloaded files) and `dataset_size` (Arrow files).\r\n\r\nThe size of the Arrow files for a specific split can be accessed: e.g. size of the \"test\" split only\r\n```python\r\nds[\"train\"].info.splits[\"test\"].num_bytes\r\n```\r\n\r\nI agree this is confusing and maybe we should improve it."
] | 2023-01-03T20:25:48Z
| 2023-01-04T09:22:59Z
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### Describe the bug
Hi, it looks like whenever you pull a dataset and get size_in_bytes, it returns the same size for all splits (and that size is the combined size of all splits). It seems like this shouldn't be the intended behavior since it is misleading. Here's an example:
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
Found cached dataset glue (/Users/breakend/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1097.70it/s]
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
>>>
```
### Steps to reproduce the bug
```
>>> from datasets import load_dataset
>>> x = load_dataset("glue", "wnli")
>>> x["train"].size_in_bytes
186159
>>> x["validation"].size_in_bytes
186159
>>> x["test"].size_in_bytes
186159
```
### Expected behavior
The expected behavior is that it should return the separate sizes for all splits.
### Environment info
- `datasets` version: 2.7.1
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
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Passing numpy array to ClassLabel names causes ValueError
|
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[
"Should `datasets` allow `ClassLabel` input parameter to be an `np.array` even though internally we need to cast it to a Python list? @lhoestq @mariosasko ",
"Hi! No, I don't think so. The `names` parameter is [annotated](https://github.com/huggingface/datasets/blob/582236640b9109988e5f7a16a8353696ffa09a16/src/datasets/features/features.py#L892) as `List[str]` (**NumPy arrays are not lists**), and considering that type checking is not a common practice in Python, I think we can leave the code as-is.",
"I appreciate it is the wrong type, and that type checking is not common, but I think there's a few circumstances that make it a good idea from a usability perspective.\r\n\r\nIt's quite a difficult error to debug because it comes from a utility function (so it's not immediately obvious which parameter caused it). What makes it even more difficult is the exception happens when the features instance is used to instantiate the dataset, **not** when when the wrong type is actually passed when the features is instantiated. When I was debugging the error, I didn't really consider it could be an issue with the features instance because it had instantiated fine. It's also not one of the more common exceptions caused by trying to use a non-list as a list.\r\n\r\nIt's also relatively easy to accidentally get a numpy array of class types (e.g. calling `unique()` on a pandas dataframe column). Additionally, passing in a `set` instead of the list (again, relatively easy because people may run `set(classes)` to generate uniques) causes an error when the features instance is used, albeit a slightly more obvious one.\r\n\r\nThe names list is already being processed and validated in the `__post_init__` method anyway, so it would not really be adding any complexity to check it is actually a list here too. I'm happy to contribute this change if you change your mind about whether it's worthwhile.",
"I agree that it's not easy to debug this issue, so perhaps we could add some basic type checking (e.g. `not isinstance(names, list)` -> error) to make debugging easier. Feel free to submit a PR.\r\n\r\n> Additionally, passing in a set instead of the list (again, relatively easy because people may run set(classes) to generate uniques) causes an error when the features instance is used, albeit a slightly more obvious one.\r\n\r\n`set` is an unordered structure (it's ordered in Python 3.6+, but this is CPython's implementation detail), and the order of ClassLabel `names` matters, so this doesn't require a fix.",
"What about checking for `Sequence` instead? I think users can pass a list or a tuple as well."
] | 2022-12-05T12:59:03Z
| 2022-12-22T16:32:50Z
| 2022-12-22T16:32:50Z
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CONTRIBUTOR
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### Describe the bug
If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX
TLDR:
If I define my classes as:
```
my_classes = np.array(['one', 'two', 'three'])
```
Then this errors:
```py
features = Features({'value': Value('string'), 'label': ClassLabel(names=my_classes)})
dataset = Dataset.from_list(my_data, features=features)
```
```
ValueError Traceback (most recent call last)
[<ipython-input-8-a8a9d53ec82f>](https://localhost:8080/#) in <module>
----> 1 dataset = Dataset.from_list(my_data, features=features)
11 frames
[/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _asdict_inner(obj)
183 for f in fields(obj):
184 value = _asdict_inner(getattr(obj, f.name))
--> 185 if not f.init or value != f.default or f.metadata.get("include_in_asdict_even_if_is_default", False):
186 result[f.name] = value
187 return result
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
But this works:
```
features2 = Features({'value': Value('string'), 'label': ClassLabel(names=list(my_classes))})
dataset2 = Dataset.from_list(my_data, features=features2)
```
### Expected behavior
If I provide a numpy array of class names, I would expect either an error that the names list is the wrong type, or for it to be cast internally.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.10
- Python version: 3.8.15
- PyArrow version: 10.0.1
- Pandas version: 1.5.2
Additionally:
- Numpy version: 1.23.5
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Name the default config `default`
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"Addressed in #5331."
] | 2022-08-26T16:16:22Z
| 2023-07-24T21:15:31Z
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COLLABORATOR
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Currently, if a dataset has no configuration, a default configuration is created from the dataset name.
For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`.
It might be easier to handle to set it to `default`, or another reserved word.
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Throw EnvironmentError when token is not present
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[
"@mariosasko I've raised a PR #5076 against this issue. Please help to review. Thanks."
] | 2022-10-05T14:14:18Z
| 2022-10-07T14:33:28Z
| 2022-10-07T14:33:28Z
|
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Fix multi gpu map example
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[
"<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) | 0.004537 / 0.011353 (-0.006816) | 0.002844 / 0.011008 (-0.008164) | 0.062506 / 0.038508 (0.023998) | 0.029675 / 0.023109 (0.006566) | 0.238080 / 0.275898 (-0.037818) | 0.259858 / 0.323480 (-0.063622) | 0.004015 / 0.007986 (-0.003970) | 0.002432 / 0.004328 (-0.001897) | 0.049477 / 0.004250 (0.045227) | 0.045383 / 0.037052 (0.008331) | 0.241934 / 0.258489 (-0.016555) | 0.270759 / 0.293841 (-0.023082) | 0.023207 / 0.128546 (-0.105339) | 0.007107 / 0.075646 (-0.068539) | 0.207626 / 0.419271 (-0.211645) | 0.056706 / 0.043533 (0.013173) | 0.239713 / 0.255139 (-0.015426) | 0.256639 / 0.283200 (-0.026560) | 0.017514 / 0.141683 (-0.124169) | 1.105201 / 1.452155 (-0.346953) | 1.173087 / 1.492716 (-0.319629) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093391 / 0.018006 (0.075384) | 0.302673 / 0.000490 (0.302184) | 0.000218 / 0.000200 (0.000018) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019447 / 0.037411 (-0.017965) | 0.063349 / 0.014526 (0.048823) | 0.075600 / 0.176557 (-0.100957) | 0.121098 / 0.737135 (-0.616037) | 0.075028 / 0.296338 (-0.221311) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291479 / 0.215209 (0.076270) | 2.787231 / 2.077655 (0.709576) | 1.480205 / 1.504120 (-0.023915) | 1.417656 / 1.541195 (-0.123538) | 1.394529 / 1.468490 (-0.073962) | 0.408843 / 4.584777 (-4.175934) | 2.398691 / 3.745712 (-1.347021) | 2.635457 / 5.269862 (-2.634404) | 1.591722 / 4.565676 (-2.973955) | 0.048445 / 0.424275 (-0.375830) | 0.004864 / 0.007607 (-0.002743) | 0.349014 / 0.226044 (0.122969) | 3.436962 / 2.268929 (1.168033) | 1.839266 / 55.444624 (-53.605359) | 1.535252 / 6.876477 (-5.341225) | 1.581048 / 2.142072 (-0.561025) | 0.491150 / 4.805227 (-4.314078) | 0.101279 / 6.500664 (-6.399385) | 0.041938 / 0.075469 (-0.033532) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946986 / 1.841788 (-0.894801) | 11.766196 / 8.074308 (3.691888) | 10.425615 / 10.191392 (0.234223) | 0.129957 / 0.680424 (-0.550467) | 0.014859 / 0.534201 (-0.519342) | 0.268046 / 0.579283 (-0.311237) | 0.263724 / 0.434364 (-0.170640) | 0.311028 / 0.540337 (-0.229309) | 0.434715 / 1.386936 (-0.952221) |\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) | 0.004874 / 0.011353 (-0.006479) | 0.002942 / 0.011008 (-0.008067) | 0.048250 / 0.038508 (0.009742) | 0.053726 / 0.023109 (0.030617) | 0.268870 / 0.275898 (-0.007028) | 0.289152 / 0.323480 (-0.034328) | 0.003982 / 0.007986 (-0.004004) | 0.002488 / 0.004328 (-0.001840) | 0.047902 / 0.004250 (0.043652) | 0.038732 / 0.037052 (0.001680) | 0.271021 / 0.258489 (0.012532) | 0.299967 / 0.293841 (0.006126) | 0.024672 / 0.128546 (-0.103874) | 0.007311 / 0.075646 (-0.068336) | 0.053721 / 0.419271 (-0.365550) | 0.032407 / 0.043533 (-0.011126) | 0.266604 / 0.255139 (0.011465) | 0.286816 / 0.283200 (0.003617) | 0.018973 / 0.141683 (-0.122710) | 1.122460 / 1.452155 (-0.329695) | 1.177720 / 1.492716 (-0.314997) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093968 / 0.018006 (0.075962) | 0.304010 / 0.000490 (0.303521) | 0.000228 / 0.000200 (0.000028) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021203 / 0.037411 (-0.016208) | 0.070318 / 0.014526 (0.055793) | 0.081688 / 0.176557 (-0.094869) | 0.120916 / 0.737135 (-0.616219) | 0.083452 / 0.296338 (-0.212886) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293961 / 0.215209 (0.078752) | 2.858514 / 2.077655 (0.780860) | 1.556169 / 1.504120 (0.052049) | 1.431523 / 1.541195 (-0.109671) | 1.478145 / 1.468490 (0.009654) | 0.408927 / 4.584777 (-4.175850) | 2.440630 / 3.745712 (-1.305082) | 2.586327 / 5.269862 (-2.683534) | 1.529495 / 4.565676 (-3.036182) | 0.047387 / 0.424275 (-0.376888) | 0.004817 / 0.007607 (-0.002790) | 0.345009 / 0.226044 (0.118965) | 3.386313 / 2.268929 (1.117384) | 1.922361 / 55.444624 (-53.522264) | 1.640814 / 6.876477 (-5.235663) | 1.657005 / 2.142072 (-0.485068) | 0.483844 / 4.805227 (-4.321383) | 0.099470 / 6.500664 (-6.401194) | 0.040735 / 0.075469 (-0.034734) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986311 / 1.841788 (-0.855476) | 12.327425 / 8.074308 (4.253117) | 10.995135 / 10.191392 (0.803743) | 0.146814 / 0.680424 (-0.533610) | 0.015820 / 0.534201 (-0.518381) | 0.272319 / 0.579283 (-0.306964) | 0.274858 / 0.434364 (-0.159506) | 0.305728 / 0.540337 (-0.234609) | 0.421400 / 1.386936 (-0.965536) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a \"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) | 0.007995 / 0.011353 (-0.003358) | 0.004596 / 0.011008 (-0.006412) | 0.099818 / 0.038508 (0.061310) | 0.053539 / 0.023109 (0.030429) | 0.367757 / 0.275898 (0.091859) | 0.409351 / 0.323480 (0.085871) | 0.007423 / 0.007986 (-0.000563) | 0.003770 / 0.004328 (-0.000558) | 0.075635 / 0.004250 (0.071385) | 0.078844 / 0.037052 (0.041791) | 0.374523 / 0.258489 (0.116034) | 0.423378 / 0.293841 (0.129537) | 0.038901 / 0.128546 (-0.089645) | 0.009985 / 0.075646 (-0.065661) | 0.342793 / 0.419271 (-0.076479) | 0.098045 / 0.043533 (0.054512) | 0.368077 / 0.255139 (0.112938) | 0.394251 / 0.283200 (0.111051) | 0.030624 / 0.141683 (-0.111059) | 1.782728 / 1.452155 (0.330574) | 1.867571 / 1.492716 (0.374855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265550 / 0.018006 (0.247544) | 0.504045 / 0.000490 (0.503555) | 0.016523 / 0.000200 (0.016323) | 0.000757 / 0.000054 (0.000702) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034239 / 0.037411 (-0.003172) | 0.099953 / 0.014526 (0.085427) | 0.113728 / 0.176557 (-0.062829) | 0.180113 / 0.737135 (-0.557023) | 0.114506 / 0.296338 (-0.181833) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507186 / 0.215209 (0.291977) | 5.033590 / 2.077655 (2.955935) | 2.480111 / 1.504120 (0.975991) | 2.258966 / 1.541195 (0.717771) | 2.316045 / 1.468490 (0.847555) | 0.622482 / 4.584777 (-3.962295) | 4.400909 / 3.745712 (0.655197) | 4.012443 / 5.269862 (-1.257419) | 2.408294 / 4.565676 (-2.157383) | 0.067608 / 0.424275 (-0.356668) | 0.008638 / 0.007607 (0.001031) | 0.546558 / 0.226044 (0.320513) | 5.472973 / 2.268929 (3.204044) | 2.795147 / 55.444624 (-52.649477) | 2.371153 / 6.876477 (-4.505324) | 2.440883 / 2.142072 (0.298811) | 0.682380 / 4.805227 (-4.122847) | 0.156819 / 6.500664 (-6.343845) | 0.071969 / 0.075469 (-0.003500) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.500200 / 1.841788 (-0.341588) | 22.854103 / 8.074308 (14.779795) | 16.691945 / 10.191392 (6.500553) | 0.210945 / 0.680424 (-0.469479) | 0.023234 / 0.534201 (-0.510967) | 0.475641 / 0.579283 (-0.103642) | 0.491553 / 0.434364 (0.057189) | 0.549311 / 0.540337 (0.008974) | 0.858498 / 1.386936 (-0.528439) |\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) | 0.009020 / 0.011353 (-0.002333) | 0.004768 / 0.011008 (-0.006240) | 0.082841 / 0.038508 (0.044333) | 0.095111 / 0.023109 (0.072002) | 0.486050 / 0.275898 (0.210151) | 0.527074 / 0.323480 (0.203594) | 0.006622 / 0.007986 (-0.001364) | 0.003961 / 0.004328 (-0.000367) | 0.083361 / 0.004250 (0.079111) | 0.068571 / 0.037052 (0.031518) | 0.494575 / 0.258489 (0.236086) | 0.545593 / 0.293841 (0.251752) | 0.047671 / 0.128546 (-0.080875) | 0.010715 / 0.075646 (-0.064932) | 0.096239 / 0.419271 (-0.323033) | 0.061556 / 0.043533 (0.018023) | 0.484301 / 0.255139 (0.229162) | 0.492189 / 0.283200 (0.208989) | 0.029374 / 0.141683 (-0.112309) | 1.911833 / 1.452155 (0.459678) | 2.005744 / 1.492716 (0.513028) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265402 / 0.018006 (0.247396) | 0.501034 / 0.000490 (0.500545) | 0.004039 / 0.000200 (0.003839) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.041005 / 0.037411 (0.003594) | 0.119204 / 0.014526 (0.104678) | 0.134583 / 0.176557 (-0.041973) | 0.195995 / 0.737135 (-0.541140) | 0.133125 / 0.296338 (-0.163214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503012 / 0.215209 (0.287803) | 5.021972 / 2.077655 (2.944318) | 2.912987 / 1.504120 (1.408867) | 2.707637 / 1.541195 (1.166442) | 2.824065 / 1.468490 (1.355575) | 0.664285 / 4.584777 (-3.920492) | 4.341905 / 3.745712 (0.596193) | 4.152839 / 5.269862 (-1.117022) | 2.438138 / 4.565676 (-2.127539) | 0.076169 / 0.424275 (-0.348106) | 0.010471 / 0.007607 (0.002864) | 0.680918 / 0.226044 (0.454874) | 6.424209 / 2.268929 (4.155281) | 3.285353 / 55.444624 (-52.159271) | 2.865458 / 6.876477 (-4.011019) | 2.946246 / 2.142072 (0.804173) | 0.700051 / 4.805227 (-4.105176) | 0.155299 / 6.500664 (-6.345365) | 0.069372 / 0.075469 (-0.006097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.749517 / 1.841788 (-0.092271) | 23.382582 / 8.074308 (15.308274) | 17.708718 / 10.191392 (7.517326) | 0.197042 / 0.680424 (-0.483382) | 0.023874 / 0.534201 (-0.510327) | 0.471631 / 0.579283 (-0.107652) | 0.512649 / 0.434364 (0.078285) | 0.614479 / 0.540337 (0.074142) | 0.771859 / 1.386936 (-0.615077) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f \"CML watermark\")\n",
"Merging this one, but lmk if you have more comments for subsequent improvements @NielsRogge ",
"<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) | 0.004874 / 0.011353 (-0.006479) | 0.002866 / 0.011008 (-0.008142) | 0.061761 / 0.038508 (0.023253) | 0.052185 / 0.023109 (0.029076) | 0.242264 / 0.275898 (-0.033634) | 0.267816 / 0.323480 (-0.055664) | 0.002844 / 0.007986 (-0.005142) | 0.002349 / 0.004328 (-0.001979) | 0.048393 / 0.004250 (0.044142) | 0.038590 / 0.037052 (0.001538) | 0.257483 / 0.258489 (-0.001006) | 0.279704 / 0.293841 (-0.014137) | 0.023125 / 0.128546 (-0.105421) | 0.007044 / 0.075646 (-0.068602) | 0.203606 / 0.419271 (-0.215665) | 0.035489 / 0.043533 (-0.008044) | 0.248419 / 0.255139 (-0.006719) | 0.266357 / 0.283200 (-0.016843) | 0.020178 / 0.141683 (-0.121505) | 1.163674 / 1.452155 (-0.288481) | 1.191340 / 1.492716 (-0.301376) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092972 / 0.018006 (0.074966) | 0.295260 / 0.000490 (0.294770) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018109 / 0.037411 (-0.019302) | 0.061743 / 0.014526 (0.047217) | 0.073965 / 0.176557 (-0.102592) | 0.119493 / 0.737135 (-0.617642) | 0.075646 / 0.296338 (-0.220692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275700 / 0.215209 (0.060491) | 2.666846 / 2.077655 (0.589191) | 1.401452 / 1.504120 (-0.102668) | 1.276009 / 1.541195 (-0.265186) | 1.309914 / 1.468490 (-0.158576) | 0.396411 / 4.584777 (-4.188365) | 2.347193 / 3.745712 (-1.398519) | 2.568006 / 5.269862 (-2.701856) | 1.564572 / 4.565676 (-3.001105) | 0.045450 / 0.424275 (-0.378825) | 0.004827 / 0.007607 (-0.002780) | 0.333092 / 0.226044 (0.107048) | 3.284295 / 2.268929 (1.015367) | 1.809928 / 55.444624 (-53.634696) | 1.486041 / 6.876477 (-5.390436) | 1.528198 / 2.142072 (-0.613875) | 0.470053 / 4.805227 (-4.335174) | 0.098559 / 6.500664 (-6.402105) | 0.041637 / 0.075469 (-0.033832) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.948915 / 1.841788 (-0.892873) | 11.513211 / 8.074308 (3.438903) | 10.386419 / 10.191392 (0.195027) | 0.129513 / 0.680424 (-0.550910) | 0.021772 / 0.534201 (-0.512429) | 0.295627 / 0.579283 (-0.283656) | 0.261008 / 0.434364 (-0.173355) | 0.305869 / 0.540337 (-0.234469) | 0.399676 / 1.386936 (-0.987260) |\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) | 0.004799 / 0.011353 (-0.006553) | 0.002764 / 0.011008 (-0.008244) | 0.048469 / 0.038508 (0.009961) | 0.051346 / 0.023109 (0.028236) | 0.274853 / 0.275898 (-0.001045) | 0.300770 / 0.323480 (-0.022710) | 0.003986 / 0.007986 (-0.003999) | 0.002376 / 0.004328 (-0.001952) | 0.048545 / 0.004250 (0.044294) | 0.039854 / 0.037052 (0.002801) | 0.280053 / 0.258489 (0.021564) | 0.312797 / 0.293841 (0.018957) | 0.024513 / 0.128546 (-0.104033) | 0.006971 / 0.075646 (-0.068675) | 0.053030 / 0.419271 (-0.366241) | 0.035580 / 0.043533 (-0.007953) | 0.276078 / 0.255139 (0.020939) | 0.299345 / 0.283200 (0.016145) | 0.020423 / 0.141683 (-0.121260) | 1.103053 / 1.452155 (-0.349102) | 1.179747 / 1.492716 (-0.312969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093042 / 0.018006 (0.075036) | 0.299421 / 0.000490 (0.298932) | 0.000232 / 0.000200 (0.000033) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021966 / 0.037411 (-0.015445) | 0.070978 / 0.014526 (0.056452) | 0.083841 / 0.176557 (-0.092715) | 0.121223 / 0.737135 (-0.615912) | 0.082829 / 0.296338 (-0.213510) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289436 / 0.215209 (0.074227) | 2.838074 / 2.077655 (0.760419) | 1.597013 / 1.504120 (0.092893) | 1.476888 / 1.541195 (-0.064307) | 1.504582 / 1.468490 (0.036092) | 0.398050 / 4.584777 (-4.186727) | 2.434446 / 3.745712 (-1.311266) | 2.493545 / 5.269862 (-2.776316) | 1.584159 / 4.565676 (-2.981517) | 0.046461 / 0.424275 (-0.377814) | 0.004876 / 0.007607 (-0.002731) | 0.344166 / 0.226044 (0.118122) | 3.388530 / 2.268929 (1.119602) | 1.939585 / 55.444624 (-53.505039) | 1.672495 / 6.876477 (-5.203982) | 1.811825 / 2.142072 (-0.330247) | 0.470798 / 4.805227 (-4.334429) | 0.097522 / 6.500664 (-6.403142) | 0.040887 / 0.075469 (-0.034582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990081 / 1.841788 (-0.851707) | 12.619827 / 8.074308 (4.545519) | 10.748062 / 10.191392 (0.556670) | 0.130409 / 0.680424 (-0.550015) | 0.016624 / 0.534201 (-0.517577) | 0.272381 / 0.579283 (-0.306902) | 0.270597 / 0.434364 (-0.163767) | 0.306458 / 0.540337 (-0.233879) | 0.408700 / 1.386936 (-0.978236) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc44d2188a1baac50d28a6c8110d6e5497f409de \"CML watermark\")\n",
"This is a little hard to follow — where is the documentation currently? I am trying to follow from snippets, here is what I have based on your convo in this thread:\r\n\r\n```>>> import os\r\n>>>\r\n>>> for i in range(torch.cuda.device_count()): # send model to every GPU\r\n... model.to(f\"cuda:{i}\")\r\n>>>\r\n>>> def gpu_computation(example, rank):\r\n... torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU\r\n... inputs = tokenizer(texts, truncation=True, return_tensors=\"pt\").to(f\"cuda:{rank}\")\r\n... outputs = model(**inputs)\r\n... .... \r\n```\r\n\r\nbut I'm getting device errors (data is on device 3, but it thinks model is on device 0, despite setting `torch.cuda.set_device`\r\n\r\nIs this correct? What version of Torch are you using for this? ",
"Anyway, this didn't work for me:\r\n\r\n`torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU`\r\n\r\nbut substituting it for:\r\n\r\n`model.to(f\"cuda:{rank}\")`\r\n\r\n(`.to` doesn't make a million copies of the model on the device, which I was worried it would do... so you can use it in an inner process)\r\n\r\n(btw, versions: `torch==2.1.1`, `cuda=12.2`)",
"Yeah for me this issue isn't resolved yet, we need a better code example",
"Hi @alex2awesome, could you open a PR with your suggestion to improve this code snippet ?",
"i'm happy to when i get it fully working, but i feel like there are some fundamentals that I'm not fully understanding...\r\n\r\nI've set it up twice now, for 2 GPU-processing pipelines. \r\n\r\nIn one pipelines, my memory usage is fine, it delivers me a huge speedup, and everything is great. In the second pipeline, I keep getting OOM errors when `num_proc > 1` that I don't get when `num_proc=1`. \r\n\r\nThere is a discussion here: https://github.com/pytorch/pytorch/issues/44156 about CUDA memory leaks in multiprocessing setups, and I haven't had the time to fully read the source code to `datasets.map` to understand whether the situations are parallel. Also, if they are, then I don't know what the solution is, not really knowing how it is implemented under the hood. In that discussion, one guy offers a work-around, but it doesn't look great.\r\n\r\nSo, I haven't fully tested out enough to see what the issue. If I feel comfortable over the next several days to generate a slimmed-down example that will generalize to real-world cases such as those I'm working with now, then I will contribute it.\r\n\r\n",
"@lhoestq do you know how `datasets` does multiprocessing? Do we use:\r\nhttps://pytorch.org/docs/stable/multiprocessing.html#module-torch.multiprocessing?\r\n\r\nIf so, there are lots of points around memory usage, here:\r\nhttps://pytorch.org/docs/stable/notes/multiprocessing.html\r\n\r\nEDIT: ahh I see it is using python's native multiprocessing library: https://github.com/huggingface/datasets/blob/2.15.0/src/datasets/arrow_dataset.py#L3172-L3189",
"After some more research and playing around, I can't pinpoint the source of my CUDA memory leak nor can I determine with confidence what works and what doesn't in this setup.\r\n\r\nI'm not really an expert on multiprocessing in general, but my gut is that the current set-up isn't ideal for multiprocessing and I'm not sure I would recommend users to do this. \r\n\r\nKinda unfortunate, because I don't see any great tools for distributed inference out there, and in theory, `datasets.map` could be the standard.\r\n\r\nAre either of you more experienced in this?",
"Not sure about your GPU's OOM :/\r\n\r\nStill, I opened a PR with your suggestion here: https://github.com/huggingface/datasets/pull/6550",
"I still get only 0 rank...\r\n\r\nHere is my code: https://pastebin.com/c6du8jaM\r\n\r\nfrom this ^ i just improt one function:\r\n\r\n\r\n```\r\nfrom test import map_train\r\nfrom multiprocess import set_start_method\r\n\r\n\r\nset_start_method(\"spawn\")\r\nmap_train()\r\n```\r\n\r\nAnd here is the traceback:\r\nhttps://pastebin.com/YijspwQK ",
"Also this code from your docs is not valid (source: https://huggingface.co/docs/datasets/main/en/process#multiprocessing):\r\n```\r\nfor i in range(torch.cuda.device_count()):\r\n model.to(f\"cuda:{i}\")\r\n```\r\n\r\n\r\nThis for me sends the model only to the second GPU\r\n```\r\nvae = AutoencoderKL.from_pretrained(\r\n pretrained_model_name_or_path, subfolder=\"vae\"\r\n)\r\nvae.to(\"cuda:0\")\r\nvae.to(\"cuda:1\")\r\n```",
"Could you please provide a working example of multi-GPU mapping?\r\n\r\nNot just an example in docs, but a real working example starting from all imports loading datasets and models.",
"@alex2awesome the same issue with CUDA OOM. It should not be happening, since it should 2 different GPUs be handling different loads. But in fact something wrong is happening.",
"I haven't experimented much with the multi-GPU code documentation.\r\n\r\nCan you try using the code example at https://github.com/huggingface/datasets/pull/6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n\r\nThough if you have some fixes/improvements ideas feel free to open a PR !",
"@lhoestq the mapping does not start at all in this case:\r\n<img width=\"855\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/7f29a3c1-c6dc-4bab-9955-5311256aa217\">\r\n\r\nHere is the updated code: https://pastebin.com/Kn9aGfZr",
"@lhoestq with this code: https://pastebin.com/muDm78kp\r\ni now getting this error:\r\n\r\n```\r\nMap (num_proc=2): 1%| | 26288/3043663 [06:11<11:51:08, 70.72 examples/s]\r\nTraceback (most recent call last):\r\n File \"/workspace/compute.py\", line 229, in <module>\r\n map_train()\r\n File \"/workspace/compute.py\", line 224, in map_train\r\n return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, with_rank=True, num_proc=2, keep_in_memory=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 3193, in map\r\n for rank, done, content in iflatmap_unordered(\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py\", line 658, in iflatmap_unordered\r\n raise RuntimeError(\r\nRuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing.\r\n```\r\n\r\nAlso when trying to download my dataset there were no issues from one machine, but from another:\r\n```\r\nSSLError: (MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/datasets/kopyl/3M_icons_monochrome_only_no_captioning/revision/753dca4be462dad7022f34cc273555ab6deb5832 (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\"), '(Request ID: 7d0881f3-1b93-4d73-bcb6-52e816d84529)')\r\n```\r\n\r\nCan't download my dataset at all...",
"Hmm this is not good, do you know a way to make it work ?\r\n\r\nBasically `map` creates two subprocesses and runs the function in the subprocesses. Since each function has a parameter `rank` it should be possible to choose which GPU to use",
"I can confirm that PR #6550 works. All GPUs are at full throttle. You have to manually move the model to all GPUs. \r\n\r\n> I haven't experimented much with the multi-GPU code documentation.\r\n> \r\n> Can you try using the code example at #6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n> \r\n> Though if you have some fixes/improvements ideas feel free to open a PR !\r\n\r\n",
"I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example by compiling information from several PRs and issues here. Hope it can help. Let me know how it works. \r\n\r\n> Could you please provide a working example of multi-GPU mapping?\r\n> \r\n> Not just an example in docs, but a real working example starting from all imports loading datasets and models.\r\n\r\n"
] | 2023-11-14T14:57:18Z
| 2024-01-31T00:49:15Z
| 2023-11-22T15:42:19Z
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MEMBER
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- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES `
- add `if __name__ == "__main__"`
fix https://github.com/huggingface/datasets/issues/6186
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"Looks like a [similar feature request](https://github.com/huggingface/huggingface_hub/issues/1198) was made to the HF Hub team. Is handling this at the Hub level more appropriate?\r\n\r\n(As an aside, I've gotten the [HTTP-based solution](https://github.com/huggingface/huggingface_hub/issues/1198#issuecomment-1905774983) proposed in that forum to work for simple cases.)",
"yes it's more for https://github.com/huggingface/huggingface_hub cc @hanouticelina ",
"yes i think @Wauplin's comment on that thread is still what we recommend"
] | 2025-01-09T06:21:20Z
| 2025-01-09T11:17:40Z
| 2025-01-09T11:17:20Z
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### Feature request
I would like a programatic way of requesting access to gated datasets. The current solution to gain access forces me to visit a website and physically click an "agreement" button (as per the [documentation](https://huggingface.co/docs/hub/en/datasets-gated#access-gated-datasets-as-a-user)).
An ideal approach would be HF API download methods that negotiate access on my behalf based on information from my CLI login and/or token. I realise that may be naive given the various types of access semantics available to dataset authors (automatic versus manual approval, for example) and complexities it might add to existing methods, but something along those lines would be nice.
Perhaps using the `*_access_request` methods available to dataset authors can be a precedent; see [`reject_access_request`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_api#huggingface_hub.HfApi.reject_access_request) for example.
### Motivation
When trying to download files from a gated dataset, I'm met with a `GatedRepoError` and instructed to visit the repository's website to gain access:
```
Cannot access gated repo for url https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details/resolve/main/meta-llama__Meta-Llama-3.1-70B-Instruct/samples_leaderboard_math_precalculus_hard_2024-07-19T18-47-29.522341.jsonl.
Access to dataset open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details is restricted and you are not in the authorized list. Visit https://huggingface.co/datasets/open-llm-leaderboard/meta-llama__Meta-Llama-3.1-70B-Instruct-details to ask for access.
```
This makes task automation extremely difficult. For example, I'm interested in studying sample-level responses of models on the LLM leaderboard -- how they answered particular questions on a given evaluation framework. As I come across more and more participants that gate their data, it's becoming unwieldy to continue my work (there over 2,000 participants, so in the worst case that's the number of website visits I'd need to manually undertake).
One approach is use Selenium to react to the `GatedRepoError`, but that seems like overkill; and a potential violation HF terms of service (?).
As mentioned in the previous section, there seems to be an [API for gated dataset owners](https://huggingface.co/docs/hub/en/datasets-gated#via-the-api) to managed access requests, and thus some appetite for allowing automated management of gating. This feature request is to extend that to dataset users.
### Your contribution
Whether I can help depends on a few things; one being the complexity of the underlying gated access design. If this feature request is accepted I am open to being involved in discussions and testing, and even development under the right time-outcome tradeoff.
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Add license and citation information to cosmos_qa dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-30T06:23:19Z
| 2022-08-30T09:49:31Z
| 2022-08-30T09:47:35Z
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MEMBER
| null | null | null |
This PR adds the license information to `cosmos_qa` dataset, once reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0.
This PR also updates the citation information.
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Arrow dataset builder to be able to load and stream Arrow datasets
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ",
"Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006384 / 0.011353 (-0.004969) | 0.003788 / 0.011008 (-0.007220) | 0.098524 / 0.038508 (0.060016) | 0.031786 / 0.023109 (0.008677) | 0.307799 / 0.275898 (0.031901) | 0.337329 / 0.323480 (0.013849) | 0.003650 / 0.007986 (-0.004336) | 0.003731 / 0.004328 (-0.000598) | 0.076816 / 0.004250 (0.072566) | 0.041888 / 0.037052 (0.004835) | 0.310702 / 0.258489 (0.052213) | 0.343846 / 0.293841 (0.050005) | 0.027841 / 0.128546 (-0.100705) | 0.008312 / 0.075646 (-0.067334) | 0.320230 / 0.419271 (-0.099042) | 0.047378 / 0.043533 (0.003845) | 0.308683 / 0.255139 (0.053544) | 0.335129 / 0.283200 (0.051930) | 0.096294 / 0.141683 (-0.045389) | 1.485521 / 1.452155 (0.033366) | 1.559868 / 1.492716 (0.067152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197376 / 0.018006 (0.179370) | 0.430461 / 0.000490 (0.429972) | 0.004152 / 0.000200 (0.003953) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023660 / 0.037411 (-0.013751) | 0.103128 / 0.014526 (0.088602) | 0.107549 / 0.176557 (-0.069008) | 0.175934 / 0.737135 (-0.561201) | 0.112210 / 0.296338 (-0.184129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415804 / 0.215209 (0.200595) | 4.216333 / 2.077655 (2.138679) | 1.910354 / 1.504120 (0.406234) | 1.712689 / 1.541195 (0.171494) | 1.754705 / 1.468490 (0.286215) | 0.554647 / 4.584777 (-4.030130) | 3.393592 / 3.745712 (-0.352120) | 1.737504 / 5.269862 (-3.532358) | 1.021213 / 4.565676 (-3.544464) | 0.066908 / 0.424275 (-0.357367) | 0.011446 / 0.007607 (0.003839) | 0.524630 / 0.226044 (0.298585) | 5.243005 / 2.268929 (2.974077) | 2.349685 / 55.444624 (-53.094939) | 2.027457 / 6.876477 (-4.849020) | 2.131053 / 2.142072 (-0.011020) | 0.669070 / 4.805227 (-4.136157) | 0.136317 / 6.500664 (-6.364347) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254102 / 1.841788 (-0.587686) | 13.790492 / 8.074308 (5.716184) | 14.197772 / 10.191392 (4.006380) | 0.143989 / 0.680424 (-0.536434) | 0.016577 / 0.534201 (-0.517624) | 0.375437 / 0.579283 (-0.203846) | 0.398995 / 0.434364 (-0.035369) | 0.445287 / 0.540337 (-0.095050) | 0.538632 / 1.386936 (-0.848304) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006251 / 0.011353 (-0.005101) | 0.004019 / 0.011008 (-0.006989) | 0.077985 / 0.038508 (0.039477) | 0.028705 / 0.023109 (0.005596) | 0.417360 / 0.275898 (0.141462) | 0.463964 / 0.323480 (0.140484) | 0.003489 / 0.007986 (-0.004497) | 0.003032 / 0.004328 (-0.001296) | 0.077953 / 0.004250 (0.073702) | 0.040104 / 0.037052 (0.003051) | 0.405242 / 0.258489 (0.146753) | 0.475029 / 0.293841 (0.181188) | 0.028113 / 0.128546 (-0.100433) | 0.008610 / 0.075646 (-0.067036) | 0.084847 / 0.419271 (-0.334424) | 0.048227 / 0.043533 (0.004694) | 0.417235 / 0.255139 (0.162096) | 0.450470 / 0.283200 (0.167270) | 0.096978 / 0.141683 (-0.044705) | 1.514688 / 1.452155 (0.062533) | 1.560205 / 1.492716 (0.067488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235125 / 0.018006 (0.217119) | 0.409904 / 0.000490 (0.409414) | 0.002474 / 0.000200 (0.002275) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025152 / 0.037411 (-0.012259) | 0.103517 / 0.014526 (0.088991) | 0.110154 / 0.176557 (-0.066402) | 0.161431 / 0.737135 (-0.575704) | 0.114891 / 0.296338 (-0.181448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456077 / 0.215209 (0.240868) | 4.541171 / 2.077655 (2.463517) | 2.297912 / 1.504120 (0.793792) | 2.079337 / 1.541195 (0.538143) | 2.121291 / 1.468490 (0.652801) | 0.560172 / 4.584777 (-4.024605) | 3.421122 / 3.745712 (-0.324590) | 1.764675 / 5.269862 (-3.505186) | 1.043482 / 4.565676 (-3.522195) | 0.067652 / 0.424275 (-0.356623) | 0.011181 / 0.007607 (0.003574) | 0.557232 / 0.226044 (0.331188) | 5.607851 / 2.268929 (3.338922) | 2.783715 / 55.444624 (-52.660909) | 2.380943 / 6.876477 (-4.495534) | 2.378316 / 2.142072 (0.236244) | 0.674356 / 4.805227 (-4.130871) | 0.135912 / 6.500664 (-6.364752) | 0.067009 / 0.075469 (-0.008460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309002 / 1.841788 (-0.532786) | 14.464073 / 8.074308 (6.389765) | 14.418727 / 10.191392 (4.227335) | 0.148486 / 0.680424 (-0.531938) | 0.016650 / 0.534201 (-0.517551) | 0.368786 / 0.579283 (-0.210497) | 0.395026 / 0.434364 (-0.039338) | 0.433565 / 0.540337 (-0.106772) | 0.526603 / 1.386936 (-0.860333) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n"
] | 2023-06-12T14:21:49Z
| 2023-06-13T17:36:02Z
| 2023-06-13T17:29:01Z
|
CONTRIBUTOR
| null | null | null |
This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files.
It's related to https://github.com/huggingface/datasets/issues/3035
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Upgrade pip in WIN CI
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"_The documentation is not available anymore as the PR was closed or merged._",
"It failed terribly"
] | 2022-06-30T10:25:42Z
| 2023-09-24T10:04:25Z
| 2022-06-30T10:43:38Z
|
MEMBER
| null | null | null |
The windows CI is currently flaky: some dependencies like aiobotocore, multiprocess and seqeval sometimes fail to install.
In particular it seems that building the wheels fail. Here is an example of logs
```
Building wheel for seqeval (setup.py): started
Running command 'C:\tools\miniconda3\envs\py37\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"'; __file__='"'"'C:\\Users\\circleci\\AppData\\Local\\Temp\\pip-install-h55pfgbv\\seqeval_d6cdb9d23ff6490b98b6c4bcaecb516e\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6'
No parent package detected, impossible to derive `name`
running bdist_wheel
running build
running build_py
package init file 'seqeval\__init__.py' not found (or not a regular file)
package init file 'seqeval\metrics\__init__.py' not found (or not a regular file)
C:\tools\miniconda3\envs\py37\lib\site-packages\setuptools\command\install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
setuptools.SetuptoolsDeprecationWarning,
installing to build\bdist.win-amd64\wheel
running install
running install_lib
warning: install_lib: 'build\lib' does not exist -- no Python modules to install
running install_egg_info
running egg_info
creating UNKNOWN.egg-info
writing UNKNOWN.egg-info\PKG-INFO
writing dependency_links to UNKNOWN.egg-info\dependency_links.txt
writing top-level names to UNKNOWN.egg-info\top_level.txt
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
reading manifest file 'UNKNOWN.egg-info\SOURCES.txt'
writing manifest file 'UNKNOWN.egg-info\SOURCES.txt'
Copying UNKNOWN.egg-info to build\bdist.win-amd64\wheel\.\UNKNOWN-0.0.0-py3.7.egg-info
running install_scripts
creating build\bdist.win-amd64\wheel\UNKNOWN-0.0.0.dist-info\WHEEL
creating 'C:\Users\circleci\AppData\Local\Temp\pip-wheel-x3cc8ym6\UNKNOWN-0.0.0-py3-none-any.whl' and adding 'build\bdist.win-amd64\wheel' to it
adding 'UNKNOWN-0.0.0.dist-info/METADATA'
adding 'UNKNOWN-0.0.0.dist-info/WHEEL'
adding 'UNKNOWN-0.0.0.dist-info/top_level.txt'
adding 'UNKNOWN-0.0.0.dist-info/RECORD'
removing build\bdist.win-amd64\wheel
Building wheel for seqeval (setup.py): finished with status 'done'
Created wheel for seqeval: filename=UNKNOWN-0.0.0-py3-none-any.whl size=963 sha256=67eb93a6e1ff4796c5882a13f9fa25bb0d3d103796e2525f9cecf3b2ef26d4b1
Stored in directory: c:\users\circleci\appdata\local\pip\cache\wheels\05\96\ee\7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7
WARNING: Built wheel for seqeval is invalid: Wheel has unexpected file name: expected 'seqeval', got 'UNKNOWN'
```
I tried to update pip and re-run the CI several times and I couldn't re-experience this issue for now, so I think upgrading pip may solve the issue
|
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"Hi @prashanth19bolukonda,\r\n\r\nYou have to restart the notebook runtime session after the installation of `datasets`.\r\n\r\nDuplicate of:\r\n- #5923",
"Thank you soo much\r\n\r\nOn Fri, Feb 16, 2024 at 8:14 PM Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> Closed #6670 <https://github.com/huggingface/datasets/issues/6670> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6670#event-11829788289>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YDQOBUFUWMR4C5O3QTYT5WDJAVCNFSM6AAAAABDL24S5SVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHAZDSNZYHAZDQOI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n"
] | 2024-02-16T11:05:17Z
| 2024-02-17T04:26:34Z
| 2024-02-16T14:43:53Z
|
NONE
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### Describe the bug
ValueError Traceback (most recent call last)
[<ipython-input-11-9b99bc80ec23>](https://localhost:8080/#) in <cell line: 11>()
9 import numpy as np
10 import matplotlib.pyplot as plt
---> 11 from datasets import DatasetDict, Dataset
12 from transformers import AutoTokenizer, AutoModelForSequenceClassification
13 from transformers import Trainer, TrainingArguments
5 frames
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
16 __version__ = "2.17.0"
17
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
65
66 from . import config
---> 67 from .arrow_reader import ArrowReader
68 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
69 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
27
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
31
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/__init__.py](https://localhost:8080/#) in <module>
18 # flake8: noqa
19
---> 20 from .core import *
[/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py](https://localhost:8080/#) in <module>
34 import pyarrow as pa
35 import pyarrow.lib as lib
---> 36 import pyarrow._parquet as _parquet
37
38 from pyarrow._parquet import (ParquetReader, Statistics, # noqa
/usr/local/lib/python3.10/dist-packages/pyarrow/_parquet.pyx in init pyarrow._parquet()
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Steps to reproduce the bug
ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject
### Expected behavior
Resolve the binary incompatibility
### Environment info
Google Colab Note book
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008191 / 0.011353 (-0.003162) | 0.004669 / 0.011008 (-0.006339) | 0.101315 / 0.038508 (0.062807) | 0.090235 / 0.023109 (0.067126) | 0.381265 / 0.275898 (0.105367) | 0.418266 / 0.323480 (0.094786) | 0.006292 / 0.007986 (-0.001693) | 0.003979 / 0.004328 (-0.000349) | 0.075946 / 0.004250 (0.071696) | 0.070678 / 0.037052 (0.033625) | 0.378006 / 0.258489 (0.119517) | 0.425825 / 0.293841 (0.131984) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.345687 / 0.419271 (-0.073584) | 0.063846 / 0.043533 (0.020313) | 0.386003 / 0.255139 (0.130864) | 0.400875 / 0.283200 (0.117675) | 0.027806 / 0.141683 (-0.113877) | 1.814810 / 1.452155 (0.362655) | 1.879897 / 1.492716 (0.387180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218684 / 0.018006 (0.200677) | 0.501715 / 0.000490 (0.501225) | 0.004808 / 0.000200 (0.004608) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035494 / 0.037411 (-0.001917) | 0.100949 / 0.014526 (0.086423) | 0.114639 / 0.176557 (-0.061917) | 0.188908 / 0.737135 (-0.548227) | 0.115794 / 0.296338 (-0.180545) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462537 / 0.215209 (0.247328) | 4.612469 / 2.077655 (2.534814) | 2.298065 / 1.504120 (0.793945) | 2.088738 / 1.541195 (0.547543) | 2.188072 / 1.468490 (0.719582) | 0.565412 / 4.584777 (-4.019364) | 4.180394 / 3.745712 (0.434681) | 3.848696 / 5.269862 (-1.421165) | 2.391381 / 4.565676 (-2.174296) | 0.067647 / 0.424275 (-0.356628) | 0.008847 / 0.007607 (0.001240) | 0.553288 / 0.226044 (0.327243) | 5.517962 / 2.268929 (3.249033) | 2.866622 / 55.444624 (-52.578002) | 2.439025 / 6.876477 (-4.437452) | 2.740156 / 2.142072 (0.598084) | 0.694796 / 4.805227 (-4.110431) | 0.159022 / 6.500664 (-6.341642) | 0.074471 / 0.075469 (-0.000998) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.534979 / 1.841788 (-0.306808) | 23.297273 / 8.074308 (15.222965) | 16.859178 / 10.191392 (6.667786) | 0.207594 / 0.680424 (-0.472830) | 0.021990 / 0.534201 (-0.512211) | 0.472059 / 0.579283 (-0.107224) | 0.497632 / 0.434364 (0.063268) | 0.565672 / 0.540337 (0.025335) | 0.772485 / 1.386936 (-0.614451) |\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) | 0.007777 / 0.011353 (-0.003576) | 0.004679 / 0.011008 (-0.006329) | 0.077317 / 0.038508 (0.038809) | 0.087433 / 0.023109 (0.064324) | 0.437389 / 0.275898 (0.161491) | 0.479562 / 0.323480 (0.156082) | 0.006137 / 0.007986 (-0.001849) | 0.003938 / 0.004328 (-0.000390) | 0.074769 / 0.004250 (0.070518) | 0.066605 / 0.037052 (0.029553) | 0.454865 / 0.258489 (0.196376) | 0.485103 / 0.293841 (0.191262) | 0.036540 / 0.128546 (-0.092006) | 0.009983 / 0.075646 (-0.065664) | 0.083566 / 0.419271 (-0.335706) | 0.059527 / 0.043533 (0.015994) | 0.449154 / 0.255139 (0.194015) | 0.462542 / 0.283200 (0.179342) | 0.027581 / 0.141683 (-0.114102) | 1.776720 / 1.452155 (0.324565) | 1.847920 / 1.492716 (0.355204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246792 / 0.018006 (0.228786) | 0.494513 / 0.000490 (0.494024) | 0.004376 / 0.000200 (0.004176) | 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) | 0.037837 / 0.037411 (0.000426) | 0.112752 / 0.014526 (0.098226) | 0.121742 / 0.176557 (-0.054815) | 0.189365 / 0.737135 (-0.547770) | 0.124366 / 0.296338 (-0.171973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492890 / 0.215209 (0.277681) | 4.920270 / 2.077655 (2.842615) | 2.565350 / 1.504120 (1.061230) | 2.378679 / 1.541195 (0.837484) | 2.483794 / 1.468490 (1.015304) | 0.579623 / 4.584777 (-4.005154) | 4.195924 / 3.745712 (0.450212) | 3.903382 / 5.269862 (-1.366479) | 2.466884 / 4.565676 (-2.098793) | 0.064145 / 0.424275 (-0.360130) | 0.008695 / 0.007607 (0.001088) | 0.579300 / 0.226044 (0.353256) | 5.809064 / 2.268929 (3.540136) | 3.145393 / 55.444624 (-52.299232) | 2.832760 / 6.876477 (-4.043717) | 3.020460 / 2.142072 (0.878388) | 0.700235 / 4.805227 (-4.104992) | 0.161262 / 6.500664 (-6.339402) | 0.076484 / 0.075469 (0.001015) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606504 / 1.841788 (-0.235284) | 23.747863 / 8.074308 (15.673555) | 17.281712 / 10.191392 (7.090320) | 0.203874 / 0.680424 (-0.476550) | 0.021839 / 0.534201 (-0.512362) | 0.472365 / 0.579283 (-0.106918) | 0.475150 / 0.434364 (0.040786) | 0.571713 / 0.540337 (0.031376) | 0.759210 / 1.386936 (-0.627726) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n",
"> Some questions: won't this have an impact on downloading time, once we do not longer compress the payload? What is the advantage of this approach over the one with block_size: 0?\r\n\r\nSurely, but this prevents random access which is needed at multiple places in the code (eg to check the compression type).\r\nGithub isn't a good place for big files anyway so we should be fine"
] | 2023-07-26T12:46:07Z
| 2023-07-27T16:15:11Z
| 2023-07-27T16:14:40Z
|
MEMBER
| null | null | null |
Don't accept gzip encoding from github, otherwise some files are not streamable + seekable.
fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84
and making sure https://github.com/huggingface/datasets/issues/2918 works as well
|
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I_kwDODunzps5wRa2P
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`Dataset.to_dict()` ignore `decode=True` with Image feature
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[
"We need to implement the `Image` type as a PyArrow extension type (to allow us to override the Python conversion) for this to work as expected. For now, it's best to use your approach indeed."
] | 2023-09-06T09:26:16Z
| 2023-09-08T17:08:52Z
| null |
MEMBER
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### Describe the bug
`Dataset.to_dict` seems to ignore the decoding instruction passed in features.
### Steps to reproduce the bug
```python
import datasets
import numpy as np
from PIL import Image
img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8)
img = Image.fromarray(img)
features = datasets.Features({"image": datasets.Image(decode=True)})
dataset = datasets.Dataset.from_dict({"image": [img]}, features=features)
print({key: dataset[key] for key in dataset.column_names})
# {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]}
print(dataset.to_dict())
# {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]}
```
### Expected behavior
I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict).
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35
- Python version: 3.9.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- Pillow 9.5.0
- numpy 1.25.2
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I_kwDODunzps5Pi_SD
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|
Outdated Link for mkqa Dataset
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[
"Thanks for reporting @liaeh, we are investigating this. "
] | 2022-08-10T12:45:45Z
| 2022-08-11T09:37:52Z
| 2022-08-11T09:37:52Z
|
NONE
| null | null |
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## Describe the bug
The URL used to download the mkqa dataset is outdated. It seems the URL to download the dataset is currently https://github.com/apple/ml-mkqa/blob/main/dataset/mkqa.jsonl.gz instead of https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz (master branch has been renamed to main).
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("mkqa")
```
## Expected results
downloads the dataset
## Actual results
```python
Downloading builder script:
4.79k/? [00:00<00:00, 201kB/s]
Downloading metadata:
13.2k/? [00:00<00:00, 504kB/s]
Downloading and preparing dataset mkqa/mkqa (download: 11.35 MiB, generated: 34.29 MiB, post-processed: Unknown size, total: 45.65 MiB) to /home/lhr/.cache/huggingface/datasets/mkqa/mkqa/1.0.0/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d...
Downloading data files: 0%
0/1 [00:00<?, ?it/s]
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Input In [3], in <cell line: 3>()
1 from datasets import load_dataset
----> 3 dataset = load_dataset("mkqa")
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/load.py:1746, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1745 # Download and prepare data
-> 1746 builder_instance.download_and_prepare(
1747 download_config=download_config,
1748 download_mode=download_mode,
1749 ignore_verifications=ignore_verifications,
1750 try_from_hf_gcs=try_from_hf_gcs,
1751 use_auth_token=use_auth_token,
1752 )
1754 # Build dataset for splits
1755 keep_in_memory = (
1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1757 )
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos)
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
769 split_dict = SplitDict(dataset_name=self.name)
770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
773 # Checksums verification
774 if verify_infos and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/mkqa/5401489c674c81257cf563417aaaa5de2c7e26a1090ce9b10eb0404f10003d4d/mkqa.py:130, in Mkqa._split_generators(self, dl_manager)
128 # download and extract URLs
129 urls_to_download = _URLS
--> 130 downloaded_files = dl_manager.download_and_extract(urls_to_download)
132 return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]})]
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls)
415 def download_and_extract(self, url_or_urls):
416 """Download and extract given url_or_urls.
417
418 Is roughly equivalent to:
(...)
429 extracted_path(s): `str`, extracted paths of given URL(s).
430 """
--> 431 return self.extract(self.download(url_or_urls))
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:309, in DownloadManager.download(self, url_or_urls)
306 download_func = partial(self._download, download_config=download_config)
308 start_time = datetime.now()
--> 309 downloaded_path_or_paths = map_nested(
310 download_func,
311 url_or_urls,
312 map_tuple=True,
313 num_proc=download_config.num_proc,
314 disable_tqdm=not is_progress_bar_enabled(),
315 desc="Downloading data files",
316 )
317 duration = datetime.now() - start_time
318 logger.info(f"Downloading took {duration.total_seconds() // 60} min")
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:393, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
391 num_proc = 1
392 if num_proc <= 1 or len(iterable) <= num_proc:
--> 393 mapped = [
394 _single_map_nested((function, obj, types, None, True, None))
395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
396 ]
397 else:
398 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:394, in <listcomp>(.0)
391 num_proc = 1
392 if num_proc <= 1 or len(iterable) <= num_proc:
393 mapped = [
--> 394 _single_map_nested((function, obj, types, None, True, None))
395 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
396 ]
397 else:
398 split_kwds = [] # We organize the splits ourselve (contiguous splits)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py:330, in _single_map_nested(args)
328 # Singleton first to spare some computation
329 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 330 return function(data_struct)
332 # Reduce logging to keep things readable in multiprocessing with tqdm
333 if rank is not None and logging.get_verbosity() < logging.WARNING:
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/download/download_manager.py:335, in DownloadManager._download(self, url_or_filename, download_config)
332 if is_relative_path(url_or_filename):
333 # append the relative path to the base_path
334 url_or_filename = url_or_path_join(self._base_path, url_or_filename)
--> 335 return cached_path(url_or_filename, download_config=download_config)
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:185, in cached_path(url_or_filename, download_config, **download_kwargs)
181 url_or_filename = str(url_or_filename)
183 if is_remote_url(url_or_filename):
184 # URL, so get it from the cache (downloading if necessary)
--> 185 output_path = get_from_cache(
186 url_or_filename,
187 cache_dir=cache_dir,
188 force_download=download_config.force_download,
189 proxies=download_config.proxies,
190 resume_download=download_config.resume_download,
191 user_agent=download_config.user_agent,
192 local_files_only=download_config.local_files_only,
193 use_etag=download_config.use_etag,
194 max_retries=download_config.max_retries,
195 use_auth_token=download_config.use_auth_token,
196 ignore_url_params=download_config.ignore_url_params,
197 download_desc=download_config.download_desc,
198 )
199 elif os.path.exists(url_or_filename):
200 # File, and it exists.
201 output_path = url_or_filename
File ~/repos/punc-cap/venv/lib/python3.9/site-packages/datasets/utils/file_utils.py:530, in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
525 raise FileNotFoundError(
526 f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
527 " disabled. To enable file online look-ups, set 'local_files_only' to False."
528 )
529 elif response is not None and response.status_code == 404:
--> 530 raise FileNotFoundError(f"Couldn't find file at {url}")
531 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
532 if head_error is not None:
FileNotFoundError: Couldn't find file at https://github.com/apple/ml-mkqa/raw/master/dataset/mkqa.jsonl.gz
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
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HfHubHTTPError: 500 Server Error: Internal Server Error for url:
|
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[
"same issue here. @albertvillanova @lhoestq ",
"Also impacted by this issue in many of my datasets (though not all) - in my case, this also seems to affect datasets that have been updated recently. Git cloning and the web interface still work:\r\n- https://huggingface.co/api/datasets/acmc/cheat_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_reuter_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_wp_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_essay_reduced\r\n\r\nOddly enough, the system status looks good: https://status.huggingface.co/",
"Hey how to download these datasets using git cloning?",
"Also reported here\r\nhttps://github.com/huggingface/huggingface_hub/issues/2425",
"I have been getting the same error for the past 8 hours as well",
"Same error since yesterday, fails on any new dataset created",
"Same here. I cannot download the HelpSteer2 dataset: https://huggingface.co/datasets/nvidia/HelpSteer2 which has been uploaded about a month ago",
"> Hey how to download these datasets using git cloning?\n\nYou'll find a guide [here](https://huggingface.co/docs/hub/en/datasets-downloading) 👍🏻",
"Same here for imdb dataset",
"It also happens with this dataset: https://huggingface.co/datasets/ylacombe/jenny-tts-6h-tagged",
"same here for all datsets in the sentence-tramsformers repo and related collections.\r\n\r\nsame issue with dataset that i recently uploaded on my repo.\r\nseems that the upload date of the datset is not relevat (getting this issue with both old datasets and newer ones)\r\n\r\nfor some reason, i was able to get the dataset by turning it private and accessing it with the id token (accessing it as public while providing the token doesn not work)..... but i can say if that is just a random coincidence.\r\n\r\nseems not much deterministic, for a specific dataset (sentence-transformer nq ) , that was \"down\" since some hours , worked for like 5-10 minutes, then stopped again\r\n\r\nnow even this dataset (that worked since some min ago, and that i'm in the middle of processing steps) stopped working: _https://huggingface.co/datasets/bobox/msmarco-bm25-EduScore/_\r\n\r\nas already pointed out, there are no updates on **_https://status.huggingface.co/_**\r\n\r\n\\n\r\n\\n\r\n\r\nan example of the whole error message:\r\n``` \r\nHfHubHTTPError \r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\r\n 2592 \r\n 2593 # Create a dataset builder\r\n-> 2594 builder_instance = load_dataset_builder(\r\n 2595 path=path,\r\n 2596 name=name,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\r\n 2264 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 2265 download_config.storage_options.update(storage_options)\r\n-> 2266 dataset_module = dataset_module_factory(\r\n 2267 path,\r\n 2268 revision=revision,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\r\n 1912 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1913 ) from None\r\n-> 1914 raise e1 from None\r\n 1915 else:\r\n 1916 raise FileNotFoundError(\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\r\n 1832 hf_api = HfApi(config.HF_ENDPOINT)\r\n 1833 try:\r\n-> 1834 dataset_info = hf_api.dataset_info(\r\n 1835 repo_id=path,\r\n 1836 revision=revision,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in _inner_fn(*args, **kwargs)\r\n 112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)\r\n 113 \r\n--> 114 return fn(*args, **kwargs)\r\n 115 \r\n 116 return _inner_fn # type: ignore\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in dataset_info(self, repo_id, revision, timeout, files_metadata, token)\r\n 2362 \r\n 2363 r = get_session().get(path, headers=headers, timeout=timeout, params=params)\r\n-> 2364 hf_raise_for_status(r)\r\n 2365 data = r.json()\r\n 2366 return DatasetInfo(**data)\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py](https://localhost:8080/#) in hf_raise_for_status(response, endpoint_name)\r\n 369 # Convert `HTTPError` into a `HfHubHTTPError` to display request information\r\n 370 # as well (request id and/or server error message)\r\n--> 371 raise HfHubHTTPError(str(e), response=response) from e\r\n 372 \r\n 373 \r\n\r\nHfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/bobox/xSum-processed (Request ID: Root=1-66a527f0-756cfbc35cc466f075382289;7d5dc06a-37e9-4c22-874d-92b0b1023276)\r\n\r\nInternal Error - We're working hard to fix this as soon as possible!\r\n``` ",
"we're working on a fix !",
"We fixed the issue, you can load datasets again, sorry for the inconvenience !",
"I can confirm, it's working now. I can load the dataset, yay. Thank you @lhoestq ",
"@lhoestq thank you so much! ",
"Hi I'm getting the same error with this [dataset](https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset) \r\nWorking on the course of stable diffusion , trying to run this [notebook](https://colab.research.google.com/github/huggingface/diffusion-models-class/blob/main/unit1/01_introduction_to_diffusers.ipynb#scrollTo=-yX-MZhSsxwp) \r\nthis is the error: \r\n`HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/datasets/huggan/smithsonian_butterflies_subset/resolve/3cdedf844922ab40393d46d4c7f81c596e1c6d45/data/train-00000-of-00001.parquet (Request ID: Root=1-66ed3481-3393f4ab268b711440d31e02;c3ca2a7d-ae7b-4ba3-9947-9426711946a8)\r\n\r\nInternal Error - We're working hard to fix this as soon as possible!`\r\n\r\n",
"Thanks for reporting, we are investigating !"
] | 2024-07-27T08:21:03Z
| 2024-09-20T13:26:25Z
| 2024-07-27T19:52:30Z
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### Describe the bug
newly uploaded datasets, since yesterday, yields an error.
old datasets, works fine.
Seems like the datasets api server returns a 500
I'm getting the same error, when I invoke `load_dataset` with my dataset.
Long discussion about it here, but I'm not sure anyone from huggingface have seen it.
https://discuss.huggingface.co/t/hfhubhttperror-500-server-error-internal-server-error-for-url/99580/1
### Steps to reproduce the bug
this api url:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
respond with:
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Expected behavior
return no error with newer datasets.
With older datasets I can load the datasets fine.
### Environment info
# Browser
When I access the api in the browser:
https://huggingface.co/api/datasets/neoneye/simon-arc-shape-v4-rev3
```
{"error":"Internal Error - We're working hard to fix this as soon as possible!"}
```
### Request headers
```
Accept text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8
Accept-Encoding gzip, deflate, br, zstd
Accept-Language en-US,en;q=0.5
Connection keep-alive
Host huggingface.co
Priority u=1
Sec-Fetch-Dest document
Sec-Fetch-Mode navigate
Sec-Fetch-Site cross-site
Upgrade-Insecure-Requests 1
User-Agent Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:127.0) Gecko/20100101 Firefox/127.0
```
### Response headers
```
X-Firefox-Spdy h2
access-control-allow-origin https://huggingface.co
access-control-expose-headers X-Repo-Commit,X-Request-Id,X-Error-Code,X-Error-Message,X-Total-Count,ETag,Link,Accept-Ranges,Content-Range
content-length 80
content-type application/json; charset=utf-8
cross-origin-opener-policy same-origin
date Fri, 26 Jul 2024 19:09:45 GMT
etag W/"50-9qrwU+BNI4SD0Fe32p/nofkmv0c"
referrer-policy strict-origin-when-cross-origin
vary Origin
via 1.1 1624c79cd07e6098196697a6a7907e4a.cloudfront.net (CloudFront)
x-amz-cf-id SP8E7n5qRaP6i9c9G83dNAiOzJBU4GXSrDRAcVNTomY895K35H0nJQ==
x-amz-cf-pop CPH50-C1
x-cache Error from cloudfront
x-error-message Internal Error - We're working hard to fix this as soon as possible!
x-powered-by huggingface-moon
x-request-id Root=1-66a3f479-026417465ef42f49349fdca1
```
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CI benchmarks are broken: Unknown arguments: runnerPath, path
|
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Our CI benchmarks are broken, raising `Unknown arguments` error: https://github.com/huggingface/datasets/actions/runs/3932397079/jobs/6724905161
```
Unknown arguments: runnerPath, path
```
Stack trace:
```
100%|██████████| 500/500 [00:01<00:00, 338.98ba/s]
Updating lock file 'dvc.lock'
To track the changes with git, run:
git add dvc.lock
To enable auto staging, run:
dvc config core.autostage true
Use `dvc push` to send your updates to remote storage.
cml send-comment <markdown file>
Global Options:
--log Logging verbosity
[string] [choices: "error", "warn", "info", "debug"] [default: "info"]
--driver Git provider where the repository is hosted
[string] [choices: "github", "gitlab", "bitbucket"] [default: infer from the
environment]
--repo Repository URL or slug
[string] [default: infer from the environment]
--driver-token, --token CI driver personal/project access token (PAT)
[string] [default: infer from the environment]
--help Show help [boolean]
Options:
--target Comment type (`commit`, `pr`, `commit/f00bar`,
`pr/42`, `issue/1337`),default is automatic (`pr`
but fallback to `commit`). [string]
--watch Watch for changes and automatically update the
comment [boolean]
--publish Upload any local images found in the Markdown
report [boolean] [default: true]
--publish-url Self-hosted image server URL
[string] [default: "https://asset.cml.dev/"]
--publish-native, --native Uses driver's native capabilities to upload assets
instead of CML's storage; not available on GitHub
[boolean]
--watermark-title Hidden comment marker (used for targeting in
subsequent `cml comment update`); "{workflow}" &
"{run}" are auto-replaced [string] [default: ""]
Unknown arguments: runnerPath, path
Error: Process completed with exit code 1.
```
Issue reported to iterative/cml:
- iterative/cml#1319
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PR_kwDODunzps6LeQBF
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Set dev version
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7410). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2025-02-17T14:54:39Z
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Pass down storage options
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"_The documentation is not available anymore as the PR was closed or merged._",
"> download_and_prepare is not called when streaming a dataset, so we may need to have storage_options in the DatasetBuilder.__init__ ? This way it could also be passed later to as_streaming_dataset and the StreamingDownloadManager\r\n\r\n> Currently the storage_options parameter in download_and_prepare are for the target filesystem where the dataset must be downloaded and prepared as arrow files\r\n\r\nAh, I noted this when looking for ways to plumb down `storage_options` although I think I was looking at adding to `BuilderConfig`. The `DatasetBuilder` constructor looks more appropriate for this, will get that added in a future commit",
"Noting as experimental SGTM. The only tests I can think of to add at the moment would be mocks that assert the storage options get passed all the way down using `mock.assert_called_with` but if Hugging Face has some S3/GCS buckets for testing, maybe those would be better in a future PR. Let me know what you think",
"I think adding tests with the mockfs fixture will do the job. Tests and docs can be added when request_etag and is_remote_url support fsspec (right now they would fail with mockfs).\r\n\r\nLet's see in a subsequent PR, this is exciting ! :)",
"<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) | 0.009217 / 0.011353 (-0.002136) | 0.006275 / 0.011008 (-0.004733) | 0.124361 / 0.038508 (0.085853) | 0.035680 / 0.023109 (0.012570) | 0.395255 / 0.275898 (0.119357) | 0.426104 / 0.323480 (0.102624) | 0.006822 / 0.007986 (-0.001163) | 0.004467 / 0.004328 (0.000138) | 0.099404 / 0.004250 (0.095153) | 0.051919 / 0.037052 (0.014867) | 0.388286 / 0.258489 (0.129797) | 0.426361 / 0.293841 (0.132520) | 0.053100 / 0.128546 (-0.075446) | 0.019453 / 0.075646 (-0.056194) | 0.433139 / 0.419271 (0.013867) | 0.063240 / 0.043533 (0.019707) | 0.381175 / 0.255139 (0.126036) | 0.411686 / 0.283200 (0.128487) | 0.104843 / 0.141683 (-0.036840) | 1.853582 / 1.452155 (0.401427) | 1.935644 / 1.492716 (0.442928) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218969 / 0.018006 (0.200963) | 0.515011 / 0.000490 (0.514522) | 0.004017 / 0.000200 (0.003818) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028975 / 0.037411 (-0.008437) | 0.125239 / 0.014526 (0.110713) | 0.131371 / 0.176557 (-0.045185) | 0.203864 / 0.737135 (-0.533271) | 0.140784 / 0.296338 (-0.155554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620701 / 0.215209 (0.405492) | 6.263557 / 2.077655 (4.185903) | 2.510058 / 1.504120 (1.005938) | 2.085892 / 1.541195 (0.544697) | 2.170362 / 1.468490 (0.701872) | 1.325600 / 4.584777 (-3.259177) | 5.583355 / 3.745712 (1.837642) | 5.092791 / 5.269862 (-0.177071) | 2.814766 / 4.565676 (-1.750911) | 0.153568 / 0.424275 (-0.270707) | 0.014850 / 0.007607 (0.007243) | 0.787011 / 0.226044 (0.560967) | 7.948813 / 2.268929 (5.679885) | 3.320831 / 55.444624 (-52.123793) | 2.526327 / 6.876477 (-4.350150) | 2.691651 / 2.142072 (0.549579) | 1.521199 / 4.805227 (-3.284028) | 0.269738 / 6.500664 (-6.230926) | 0.082959 / 0.075469 (0.007490) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.740056 / 1.841788 (-0.101732) | 17.699732 / 8.074308 (9.625424) | 22.450689 / 10.191392 (12.259297) | 0.229350 / 0.680424 (-0.451073) | 0.027486 / 0.534201 (-0.506715) | 0.536153 / 0.579283 (-0.043130) | 0.608166 / 0.434364 (0.173802) | 0.629144 / 0.540337 (0.088807) | 0.732671 / 1.386936 (-0.654265) |\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) | 0.010147 / 0.011353 (-0.001206) | 0.006484 / 0.011008 (-0.004524) | 0.098664 / 0.038508 (0.060156) | 0.036400 / 0.023109 (0.013291) | 0.432895 / 0.275898 (0.156997) | 0.466433 / 0.323480 (0.142954) | 0.008102 / 0.007986 (0.000117) | 0.004554 / 0.004328 (0.000225) | 0.100466 / 0.004250 (0.096216) | 0.054066 / 0.037052 (0.017013) | 0.439177 / 0.258489 (0.180688) | 0.502907 / 0.293841 (0.209066) | 0.059210 / 0.128546 (-0.069336) | 0.020220 / 0.075646 (-0.055426) | 0.124671 / 0.419271 (-0.294600) | 0.064278 / 0.043533 (0.020746) | 0.435659 / 0.255139 (0.180520) | 0.459670 / 0.283200 (0.176471) | 0.115574 / 0.141683 (-0.026109) | 1.826360 / 1.452155 (0.374205) | 1.943199 / 1.492716 (0.450483) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238463 / 0.018006 (0.220457) | 0.534889 / 0.000490 (0.534400) | 0.000404 / 0.000200 (0.000204) | 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) | 0.033210 / 0.037411 (-0.004201) | 0.133529 / 0.014526 (0.119003) | 0.143813 / 0.176557 (-0.032743) | 0.213079 / 0.737135 (-0.524056) | 0.148427 / 0.296338 (-0.147912) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.656819 / 0.215209 (0.441610) | 6.414860 / 2.077655 (4.337205) | 2.756182 / 1.504120 (1.252062) | 2.405268 / 1.541195 (0.864073) | 2.436418 / 1.468490 (0.967928) | 1.289828 / 4.584777 (-3.294949) | 5.572731 / 3.745712 (1.827018) | 3.185432 / 5.269862 (-2.084429) | 2.093220 / 4.565676 (-2.472457) | 0.144817 / 0.424275 (-0.279458) | 0.015674 / 0.007607 (0.008067) | 0.801238 / 0.226044 (0.575194) | 7.955925 / 2.268929 (5.686996) | 3.605670 / 55.444624 (-51.838955) | 2.837568 / 6.876477 (-4.038908) | 2.873848 / 2.142072 (0.731775) | 1.493512 / 4.805227 (-3.311715) | 0.266251 / 6.500664 (-6.234413) | 0.082417 / 0.075469 (0.006948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.608685 / 1.841788 (-0.233103) | 18.587875 / 8.074308 (10.513567) | 21.786119 / 10.191392 (11.594727) | 0.261748 / 0.680424 (-0.418675) | 0.026228 / 0.534201 (-0.507973) | 0.553538 / 0.579283 (-0.025745) | 0.599780 / 0.434364 (0.165416) | 0.665663 / 0.540337 (0.125325) | 0.792785 / 1.386936 (-0.594151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1520e017a9bb6f80e82a38b578213e418ad7e845 \"CML watermark\")\n"
] | 2023-03-26T20:09:37Z
| 2023-03-28T15:03:38Z
| 2023-03-28T14:54:17Z
|
CONTRIBUTOR
| null | null | null |
Remove implementation-specific kwargs from `file_utils.fsspec_get` and `file_utils.fsspec_head`, instead allowing them to be passed down via `storage_options`. This fixes an issue where s3fs did not recognize a timeout arg as well as fixes an issue mentioned in https://github.com/huggingface/datasets/issues/5281 by allowing users to pass down `storage_options` all the way from `datasets.load_dataset` to support implementation-specific credentials
Supports something like the following to provide credentials explicitly instead of relying on boto's methods of locating them
```
load_dataset(..., data_files=["s3://..."], storage_options={"profile": "..."})
```
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PR_kwDODunzps5-1jP8
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Fix typo in image dataset docs
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7231). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-16T14:05:46Z
| 2024-10-16T17:06:21Z
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MEMBER
| null | null | null |
Fix typo in image dataset docs.
Typo reported by @datavistics.
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I_kwDODunzps6YuW_q
| 7,192
|
Add repeat() for iterable datasets
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[
"perhaps concatenate_datasets can already be used to achieve almost the same effect? ",
"`concatenate_datasets` does the job when there is a finite number of repetitions, but in case of `.repeat()` forever we need a new logic in `iterable_dataset.py`",
"done in https://github.com/huggingface/datasets/pull/7198"
] | 2024-10-02T17:48:13Z
| 2025-03-18T10:48:33Z
| 2025-03-18T10:48:32Z
|
CONTRIBUTOR
| null | null |
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### Feature request
It would be useful to be able to straightforwardly repeat iterable datasets indefinitely, to provide complete control over starting and ending of iteration to the user.
An IterableDataset.repeat(n) function could do this automatically
### Motivation
This feature was discussed in this issue https://github.com/huggingface/datasets/issues/7147, and would resolve the need to use the hack of interleave datasets with probability 0 as a simple way to achieve this functionality.
An additional benefit might be the simplification of the use of iterable datasets in a distributed setting:
If the user can assume that datasets will repeat indefinitely, then issues around different numbers of samples appearing on different devices (e.g. https://github.com/huggingface/datasets/issues/6437, https://github.com/huggingface/datasets/issues/6594, https://github.com/huggingface/datasets/issues/6623, https://github.com/huggingface/datasets/issues/6719) can potentially be straightforwardly resolved by simply doing:
ids.repeat(None).take(n_samples_per_epoch)
### Your contribution
I'm not familiar enough with the codebase to assess how straightforward this would be to implement.
If it might be very straightforward, I could possibly have a go.
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I_kwDODunzps5MxcTB
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|
Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
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[
"Hi @faizankshaikh\r\n\r\nPlease note that we have recently launched the Community feature, specifically targeted to create Discussions (about issues/questions/asking-for-help) on each Dataset on the Hub:\r\n- Blog post: https://huggingface.co/blog/community-update\r\n- Docs: https://huggingface.co/docs/hub/repositories-pull-requests-discussions\r\n\r\nThe Discussion tab for your \"jalFaizy/detect_chess_pieces\" dataset is here: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions\r\nYou can use it to ask for help by pinging the Datasets maintainers: see our docs here: https://huggingface.co/docs/datasets/master/en/share#ask-for-a-help-and-reviews\r\n\r\nI'm transferring this discussion to your Discussion tab and trying to address it: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/1",
"Thank you @albertvillanova , I will keep that in mind.\r\n\r\nJust a quick note - I posted the issue on Github because the dataset viewer suggested me to \"open an issue for direct support\". Maybe it can be updated with your suggestion\r\n\r\n\r\n\r\n\r\n",
"Thank you pointing this out: yes, definitely, we should fix the error message. We are working on this."
] | 2022-06-29T00:15:54Z
| 2022-06-29T10:30:03Z
| 2022-06-29T07:49:27Z
|
NONE
| null | null |
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### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
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| 2,317,648,021
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PR_kwDODunzps5wlchX
| 6,920
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[WebDataset] Add `.pth` support for torch tensors
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6920). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005643 / 0.011353 (-0.005710) | 0.003810 / 0.011008 (-0.007198) | 0.065896 / 0.038508 (0.027388) | 0.031692 / 0.023109 (0.008583) | 0.258297 / 0.275898 (-0.017601) | 0.294555 / 0.323480 (-0.028925) | 0.004403 / 0.007986 (-0.003583) | 0.002857 / 0.004328 (-0.001472) | 0.049848 / 0.004250 (0.045597) | 0.049719 / 0.037052 (0.012666) | 0.266393 / 0.258489 (0.007904) | 0.306214 / 0.293841 (0.012373) | 0.028283 / 0.128546 (-0.100264) | 0.010450 / 0.075646 (-0.065196) | 0.203064 / 0.419271 (-0.216208) | 0.036535 / 0.043533 (-0.006998) | 0.247839 / 0.255139 (-0.007300) | 0.270538 / 0.283200 (-0.012661) | 0.018748 / 0.141683 (-0.122935) | 1.117478 / 1.452155 (-0.334677) | 1.162575 / 1.492716 (-0.330141) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101074 / 0.018006 (0.083068) | 0.304321 / 0.000490 (0.303831) | 0.000270 / 0.000200 (0.000070) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019036 / 0.037411 (-0.018376) | 0.064496 / 0.014526 (0.049970) | 0.076848 / 0.176557 (-0.099709) | 0.122979 / 0.737135 (-0.614156) | 0.078008 / 0.296338 (-0.218330) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287009 / 0.215209 (0.071800) | 2.839084 / 2.077655 (0.761429) | 1.495977 / 1.504120 (-0.008143) | 1.379147 / 1.541195 (-0.162047) | 1.413170 / 1.468490 (-0.055320) | 0.616408 / 4.584777 (-3.968369) | 2.419183 / 3.745712 (-1.326529) | 2.905720 / 5.269862 (-2.364142) | 1.801634 / 4.565676 (-2.764043) | 0.064034 / 0.424275 (-0.360241) | 0.005098 / 0.007607 (-0.002509) | 0.341732 / 0.226044 (0.115688) | 3.365262 / 2.268929 (1.096334) | 1.844335 / 55.444624 (-53.600289) | 1.561450 / 6.876477 (-5.315027) | 1.646254 / 2.142072 (-0.495819) | 0.654993 / 4.805227 (-4.150234) | 0.119837 / 6.500664 (-6.380827) | 0.043375 / 0.075469 (-0.032094) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.000352 / 1.841788 (-0.841435) | 12.765122 / 8.074308 (4.690813) | 9.818879 / 10.191392 (-0.372513) | 0.133986 / 0.680424 (-0.546438) | 0.014065 / 0.534201 (-0.520136) | 0.295859 / 0.579283 (-0.283424) | 0.268497 / 0.434364 (-0.165867) | 0.330909 / 0.540337 (-0.209429) | 0.449218 / 1.386936 (-0.937718) |\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) | 0.005646 / 0.011353 (-0.005707) | 0.003926 / 0.011008 (-0.007082) | 0.050437 / 0.038508 (0.011929) | 0.031828 / 0.023109 (0.008719) | 0.268218 / 0.275898 (-0.007680) | 0.292987 / 0.323480 (-0.030493) | 0.004353 / 0.007986 (-0.003633) | 0.002933 / 0.004328 (-0.001395) | 0.050357 / 0.004250 (0.046107) | 0.042988 / 0.037052 (0.005935) | 0.281627 / 0.258489 (0.023138) | 0.305664 / 0.293841 (0.011824) | 0.030162 / 0.128546 (-0.098385) | 0.010856 / 0.075646 (-0.064790) | 0.059528 / 0.419271 (-0.359744) | 0.033800 / 0.043533 (-0.009733) | 0.268200 / 0.255139 (0.013061) | 0.284982 / 0.283200 (0.001782) | 0.019105 / 0.141683 (-0.122578) | 1.171714 / 1.452155 (-0.280441) | 1.205690 / 1.492716 (-0.287026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100979 / 0.018006 (0.082973) | 0.314691 / 0.000490 (0.314201) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023816 / 0.037411 (-0.013596) | 0.081749 / 0.014526 (0.067223) | 0.090118 / 0.176557 (-0.086438) | 0.131615 / 0.737135 (-0.605520) | 0.091821 / 0.296338 (-0.204517) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301222 / 0.215209 (0.086013) | 2.835310 / 2.077655 (0.757655) | 1.562396 / 1.504120 (0.058276) | 1.432365 / 1.541195 (-0.108830) | 1.468358 / 1.468490 (-0.000132) | 0.561300 / 4.584777 (-4.023477) | 0.962294 / 3.745712 (-2.783419) | 2.799705 / 5.269862 (-2.470157) | 1.803035 / 4.565676 (-2.762642) | 0.064104 / 0.424275 (-0.360171) | 0.005480 / 0.007607 (-0.002127) | 0.342519 / 0.226044 (0.116475) | 3.406286 / 2.268929 (1.137357) | 1.966962 / 55.444624 (-53.477663) | 1.654664 / 6.876477 (-5.221813) | 1.829303 / 2.142072 (-0.312769) | 0.650932 / 4.805227 (-4.154295) | 0.119211 / 6.500664 (-6.381453) | 0.043739 / 0.075469 (-0.031730) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.006657 / 1.841788 (-0.835130) | 12.915348 / 8.074308 (4.841040) | 10.808156 / 10.191392 (0.616764) | 0.132664 / 0.680424 (-0.547760) | 0.015574 / 0.534201 (-0.518627) | 0.284525 / 0.579283 (-0.294758) | 0.122322 / 0.434364 (-0.312042) | 0.326826 / 0.540337 (-0.213511) | 0.416593 / 1.386936 (-0.970343) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#15ffefe5be194790a50af88ae1236a51b0ac95e6 \"CML watermark\")\n"
] | 2024-05-26T11:12:07Z
| 2024-05-27T09:11:17Z
| 2024-05-27T09:04:54Z
|
MEMBER
| null | null | null |
In this PR I add support for `.pth` but with `weights_only=True` to disallow the use of pickle
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PR_kwDODunzps5jJ63z
| 6,557
|
Support standalone yaml
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6557). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq \r\nhello\r\nI think it should be defined in config.py\r\nDATASET_ README_ FILENAME=\"README. md\"\r\nThis can replace all \"README. md\"\r\n",
"Thanks for the feedback :) merging now",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004890 / 0.011353 (-0.006463) | 0.003535 / 0.011008 (-0.007473) | 0.062894 / 0.038508 (0.024386) | 0.029133 / 0.023109 (0.006024) | 0.242387 / 0.275898 (-0.033511) | 0.262720 / 0.323480 (-0.060760) | 0.002880 / 0.007986 (-0.005106) | 0.002674 / 0.004328 (-0.001655) | 0.048932 / 0.004250 (0.044682) | 0.041669 / 0.037052 (0.004617) | 0.255922 / 0.258489 (-0.002567) | 0.282106 / 0.293841 (-0.011734) | 0.028137 / 0.128546 (-0.100409) | 0.010620 / 0.075646 (-0.065026) | 0.207799 / 0.419271 (-0.211473) | 0.035499 / 0.043533 (-0.008034) | 0.246158 / 0.255139 (-0.008981) | 0.262671 / 0.283200 (-0.020528) | 0.017297 / 0.141683 (-0.124386) | 1.118681 / 1.452155 (-0.333474) | 1.156732 / 1.492716 (-0.335985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091670 / 0.018006 (0.073664) | 0.300327 / 0.000490 (0.299837) | 0.000212 / 0.000200 (0.000012) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018080 / 0.037411 (-0.019332) | 0.060357 / 0.014526 (0.045831) | 0.072221 / 0.176557 (-0.104336) | 0.119281 / 0.737135 (-0.617855) | 0.073861 / 0.296338 (-0.222477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289848 / 0.215209 (0.074639) | 2.845203 / 2.077655 (0.767549) | 1.531271 / 1.504120 (0.027152) | 1.366110 / 1.541195 (-0.175085) | 1.395041 / 1.468490 (-0.073449) | 0.563353 / 4.584777 (-4.021424) | 2.389074 / 3.745712 (-1.356638) | 2.752960 / 5.269862 (-2.516901) | 1.715508 / 4.565676 (-2.850168) | 0.063063 / 0.424275 (-0.361212) | 0.004967 / 0.007607 (-0.002640) | 0.340757 / 0.226044 (0.114713) | 3.387667 / 2.268929 (1.118739) | 1.845182 / 55.444624 (-53.599442) | 1.569616 / 6.876477 (-5.306861) | 1.571393 / 2.142072 (-0.570679) | 0.643455 / 4.805227 (-4.161772) | 0.116919 / 6.500664 (-6.383745) | 0.042551 / 0.075469 (-0.032918) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943761 / 1.841788 (-0.898027) | 11.481068 / 8.074308 (3.406760) | 10.422180 / 10.191392 (0.230788) | 0.132015 / 0.680424 (-0.548408) | 0.013932 / 0.534201 (-0.520268) | 0.288340 / 0.579283 (-0.290943) | 0.263695 / 0.434364 (-0.170669) | 0.324459 / 0.540337 (-0.215878) | 0.415204 / 1.386936 (-0.971732) |\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) | 0.005042 / 0.011353 (-0.006310) | 0.003465 / 0.011008 (-0.007543) | 0.050107 / 0.038508 (0.011599) | 0.029542 / 0.023109 (0.006433) | 0.273645 / 0.275898 (-0.002253) | 0.293661 / 0.323480 (-0.029818) | 0.004099 / 0.007986 (-0.003887) | 0.002667 / 0.004328 (-0.001661) | 0.048281 / 0.004250 (0.044030) | 0.044406 / 0.037052 (0.007353) | 0.284245 / 0.258489 (0.025756) | 0.312303 / 0.293841 (0.018462) | 0.030057 / 0.128546 (-0.098489) | 0.010675 / 0.075646 (-0.064971) | 0.058404 / 0.419271 (-0.360868) | 0.051874 / 0.043533 (0.008342) | 0.273308 / 0.255139 (0.018169) | 0.289356 / 0.283200 (0.006157) | 0.018628 / 0.141683 (-0.123055) | 1.148764 / 1.452155 (-0.303391) | 1.194181 / 1.492716 (-0.298535) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091383 / 0.018006 (0.073376) | 0.300221 / 0.000490 (0.299731) | 0.000232 / 0.000200 (0.000032) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021814 / 0.037411 (-0.015597) | 0.076420 / 0.014526 (0.061894) | 0.087404 / 0.176557 (-0.089152) | 0.126184 / 0.737135 (-0.610951) | 0.089738 / 0.296338 (-0.206600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299839 / 0.215209 (0.084630) | 2.929260 / 2.077655 (0.851605) | 1.608327 / 1.504120 (0.104207) | 1.479757 / 1.541195 (-0.061437) | 1.494768 / 1.468490 (0.026278) | 0.563873 / 4.584777 (-4.020904) | 2.434442 / 3.745712 (-1.311270) | 2.641384 / 5.269862 (-2.628478) | 1.724222 / 4.565676 (-2.841454) | 0.062125 / 0.424275 (-0.362150) | 0.004994 / 0.007607 (-0.002613) | 0.350895 / 0.226044 (0.124851) | 3.448550 / 2.268929 (1.179621) | 1.928910 / 55.444624 (-53.515714) | 1.669887 / 6.876477 (-5.206590) | 1.781304 / 2.142072 (-0.360768) | 0.649301 / 4.805227 (-4.155926) | 0.116255 / 6.500664 (-6.384409) | 0.040947 / 0.075469 (-0.034522) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977537 / 1.841788 (-0.864251) | 12.119913 / 8.074308 (4.045605) | 10.874078 / 10.191392 (0.682686) | 0.130174 / 0.680424 (-0.550250) | 0.016176 / 0.534201 (-0.518025) | 0.287967 / 0.579283 (-0.291316) | 0.280591 / 0.434364 (-0.153773) | 0.324332 / 0.540337 (-0.216005) | 0.419479 / 1.386936 (-0.967457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d6d16117a30ba345b0236407975f701c5b288d4 \"CML watermark\")\n"
] | 2024-01-03T16:47:35Z
| 2024-01-11T17:59:51Z
| 2024-01-11T17:53:42Z
|
MEMBER
| null | null | null |
see (internal) https://huggingface.slack.com/archives/C02V51Q3800/p1703885853581679
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PR_kwDODunzps5VanI8
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Fix style with ruff 0.0.278
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006444 / 0.011353 (-0.004909) | 0.003768 / 0.011008 (-0.007240) | 0.079625 / 0.038508 (0.041117) | 0.064490 / 0.023109 (0.041381) | 0.313858 / 0.275898 (0.037960) | 0.350810 / 0.323480 (0.027330) | 0.004804 / 0.007986 (-0.003182) | 0.002904 / 0.004328 (-0.001425) | 0.061728 / 0.004250 (0.057477) | 0.052265 / 0.037052 (0.015213) | 0.321246 / 0.258489 (0.062757) | 0.353873 / 0.293841 (0.060032) | 0.027510 / 0.128546 (-0.101036) | 0.007942 / 0.075646 (-0.067704) | 0.260518 / 0.419271 (-0.158754) | 0.045686 / 0.043533 (0.002153) | 0.316821 / 0.255139 (0.061682) | 0.337086 / 0.283200 (0.053886) | 0.022188 / 0.141683 (-0.119495) | 1.427345 / 1.452155 (-0.024810) | 1.476059 / 1.492716 (-0.016657) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189640 / 0.018006 (0.171634) | 0.429724 / 0.000490 (0.429235) | 0.005314 / 0.000200 (0.005114) | 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) | 0.024412 / 0.037411 (-0.013000) | 0.073488 / 0.014526 (0.058962) | 0.083843 / 0.176557 (-0.092714) | 0.147849 / 0.737135 (-0.589286) | 0.085465 / 0.296338 (-0.210873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405314 / 0.215209 (0.190105) | 4.071471 / 2.077655 (1.993816) | 1.916252 / 1.504120 (0.412132) | 1.721616 / 1.541195 (0.180422) | 1.807187 / 1.468490 (0.338697) | 0.498045 / 4.584777 (-4.086732) | 3.057526 / 3.745712 (-0.688187) | 4.451424 / 5.269862 (-0.818437) | 2.764020 / 4.565676 (-1.801656) | 0.057665 / 0.424275 (-0.366610) | 0.006679 / 0.007607 (-0.000928) | 0.485733 / 0.226044 (0.259688) | 4.844367 / 2.268929 (2.575438) | 2.435359 / 55.444624 (-53.009265) | 2.111478 / 6.876477 (-4.764999) | 2.377448 / 2.142072 (0.235375) | 0.587997 / 4.805227 (-4.217230) | 0.125545 / 6.500664 (-6.375120) | 0.061509 / 0.075469 (-0.013960) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229210 / 1.841788 (-0.612577) | 18.553994 / 8.074308 (10.479686) | 14.037877 / 10.191392 (3.846485) | 0.144230 / 0.680424 (-0.536194) | 0.016891 / 0.534201 (-0.517310) | 0.329039 / 0.579283 (-0.250244) | 0.357269 / 0.434364 (-0.077095) | 0.384222 / 0.540337 (-0.156115) | 0.521292 / 1.386936 (-0.865644) |\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) | 0.006359 / 0.011353 (-0.004994) | 0.003721 / 0.011008 (-0.007287) | 0.062047 / 0.038508 (0.023539) | 0.065267 / 0.023109 (0.042158) | 0.360164 / 0.275898 (0.084266) | 0.402292 / 0.323480 (0.078812) | 0.005603 / 0.007986 (-0.002382) | 0.002966 / 0.004328 (-0.001363) | 0.062580 / 0.004250 (0.058330) | 0.053634 / 0.037052 (0.016582) | 0.362210 / 0.258489 (0.103721) | 0.404285 / 0.293841 (0.110444) | 0.027567 / 0.128546 (-0.100979) | 0.008119 / 0.075646 (-0.067528) | 0.067577 / 0.419271 (-0.351694) | 0.042867 / 0.043533 (-0.000666) | 0.361576 / 0.255139 (0.106437) | 0.389061 / 0.283200 (0.105862) | 0.021923 / 0.141683 (-0.119760) | 1.446259 / 1.452155 (-0.005895) | 1.490724 / 1.492716 (-0.001992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206433 / 0.018006 (0.188427) | 0.424178 / 0.000490 (0.423688) | 0.002340 / 0.000200 (0.002140) | 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) | 0.024955 / 0.037411 (-0.012456) | 0.077446 / 0.014526 (0.062920) | 0.088540 / 0.176557 (-0.088017) | 0.141225 / 0.737135 (-0.595910) | 0.089747 / 0.296338 (-0.206592) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443738 / 0.215209 (0.228529) | 4.208887 / 2.077655 (2.131233) | 2.155127 / 1.504120 (0.651007) | 2.028178 / 1.541195 (0.486983) | 2.084903 / 1.468490 (0.616413) | 0.497530 / 4.584777 (-4.087247) | 3.069012 / 3.745712 (-0.676700) | 3.025184 / 5.269862 (-2.244678) | 1.904687 / 4.565676 (-2.660990) | 0.057526 / 0.424275 (-0.366749) | 0.006482 / 0.007607 (-0.001125) | 0.494692 / 0.226044 (0.268647) | 4.944437 / 2.268929 (2.675508) | 2.655989 / 55.444624 (-52.788635) | 2.331677 / 6.876477 (-4.544800) | 2.382396 / 2.142072 (0.240324) | 0.582019 / 4.805227 (-4.223209) | 0.125866 / 6.500664 (-6.374799) | 0.062908 / 0.075469 (-0.012561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294612 / 1.841788 (-0.547176) | 19.016152 / 8.074308 (10.941844) | 14.088828 / 10.191392 (3.897436) | 0.160842 / 0.680424 (-0.519582) | 0.017054 / 0.534201 (-0.517146) | 0.333647 / 0.579283 (-0.245636) | 0.348094 / 0.434364 (-0.086270) | 0.394970 / 0.540337 (-0.145367) | 0.551141 / 1.386936 (-0.835795) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e9cfe886792b30b5000808072a0f91ec8536749 \"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) | 0.007442 / 0.011353 (-0.003911) | 0.004302 / 0.011008 (-0.006707) | 0.087159 / 0.038508 (0.048651) | 0.095094 / 0.023109 (0.071985) | 0.315422 / 0.275898 (0.039524) | 0.346672 / 0.323480 (0.023192) | 0.005811 / 0.007986 (-0.002174) | 0.003597 / 0.004328 (-0.000731) | 0.066400 / 0.004250 (0.062150) | 0.065947 / 0.037052 (0.028894) | 0.323269 / 0.258489 (0.064780) | 0.353309 / 0.293841 (0.059468) | 0.032268 / 0.128546 (-0.096278) | 0.008696 / 0.075646 (-0.066950) | 0.291486 / 0.419271 (-0.127786) | 0.054609 / 0.043533 (0.011076) | 0.321061 / 0.255139 (0.065922) | 0.336907 / 0.283200 (0.053707) | 0.027338 / 0.141683 (-0.114345) | 1.496442 / 1.452155 (0.044287) | 1.576946 / 1.492716 (0.084229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229140 / 0.018006 (0.211134) | 0.487500 / 0.000490 (0.487010) | 0.002425 / 0.000200 (0.002225) | 0.000089 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029351 / 0.037411 (-0.008060) | 0.089610 / 0.014526 (0.075084) | 0.097880 / 0.176557 (-0.078676) | 0.155947 / 0.737135 (-0.581189) | 0.098593 / 0.296338 (-0.197745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382911 / 0.215209 (0.167702) | 3.820363 / 2.077655 (1.742708) | 1.866385 / 1.504120 (0.362265) | 1.712910 / 1.541195 (0.171716) | 1.813863 / 1.468490 (0.345373) | 0.484884 / 4.584777 (-4.099893) | 3.678911 / 3.745712 (-0.066801) | 5.249908 / 5.269862 (-0.019953) | 3.099614 / 4.565676 (-1.466063) | 0.057449 / 0.424275 (-0.366826) | 0.007728 / 0.007607 (0.000120) | 0.462123 / 0.226044 (0.236078) | 4.603942 / 2.268929 (2.335014) | 2.380957 / 55.444624 (-53.063668) | 2.059621 / 6.876477 (-4.816856) | 2.293764 / 2.142072 (0.151691) | 0.636471 / 4.805227 (-4.168756) | 0.150112 / 6.500664 (-6.350552) | 0.063705 / 0.075469 (-0.011764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.358099 / 1.841788 (-0.483689) | 20.193750 / 8.074308 (12.119442) | 14.297350 / 10.191392 (4.105958) | 0.164477 / 0.680424 (-0.515947) | 0.018259 / 0.534201 (-0.515942) | 0.399010 / 0.579283 (-0.180273) | 0.417306 / 0.434364 (-0.017058) | 0.456961 / 0.540337 (-0.083377) | 0.631068 / 1.386936 (-0.755868) |\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) | 0.007324 / 0.011353 (-0.004028) | 0.004463 / 0.011008 (-0.006545) | 0.066148 / 0.038508 (0.027640) | 0.093909 / 0.023109 (0.070799) | 0.399122 / 0.275898 (0.123224) | 0.430226 / 0.323480 (0.106746) | 0.005505 / 0.007986 (-0.002481) | 0.003579 / 0.004328 (-0.000749) | 0.066529 / 0.004250 (0.062278) | 0.063471 / 0.037052 (0.026418) | 0.406351 / 0.258489 (0.147862) | 0.439987 / 0.293841 (0.146146) | 0.032640 / 0.128546 (-0.095906) | 0.008770 / 0.075646 (-0.066877) | 0.072592 / 0.419271 (-0.346680) | 0.050429 / 0.043533 (0.006896) | 0.390873 / 0.255139 (0.135734) | 0.412438 / 0.283200 (0.129239) | 0.027113 / 0.141683 (-0.114570) | 1.458281 / 1.452155 (0.006126) | 1.536819 / 1.492716 (0.044103) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228309 / 0.018006 (0.210303) | 0.454042 / 0.000490 (0.453552) | 0.000387 / 0.000200 (0.000187) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029573 / 0.037411 (-0.007838) | 0.086433 / 0.014526 (0.071907) | 0.097992 / 0.176557 (-0.078565) | 0.152464 / 0.737135 (-0.584671) | 0.099901 / 0.296338 (-0.196437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413807 / 0.215209 (0.198598) | 4.126395 / 2.077655 (2.048740) | 2.113544 / 1.504120 (0.609424) | 1.967829 / 1.541195 (0.426635) | 2.037123 / 1.468490 (0.568633) | 0.489403 / 4.584777 (-4.095374) | 3.689508 / 3.745712 (-0.056204) | 3.503909 / 5.269862 (-1.765952) | 2.113812 / 4.565676 (-2.451864) | 0.057988 / 0.424275 (-0.366287) | 0.007336 / 0.007607 (-0.000271) | 0.490840 / 0.226044 (0.264795) | 4.885040 / 2.268929 (2.616112) | 2.627864 / 55.444624 (-52.816760) | 2.231467 / 6.876477 (-4.645010) | 2.251307 / 2.142072 (0.109235) | 0.577370 / 4.805227 (-4.227857) | 0.131770 / 6.500664 (-6.368895) | 0.061313 / 0.075469 (-0.014156) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362052 / 1.841788 (-0.479735) | 21.332694 / 8.074308 (13.258386) | 15.562019 / 10.191392 (5.370627) | 0.170874 / 0.680424 (-0.509550) | 0.019226 / 0.534201 (-0.514975) | 0.400311 / 0.579283 (-0.178972) | 0.423060 / 0.434364 (-0.011304) | 0.469946 / 0.540337 (-0.070391) | 0.647745 / 1.386936 (-0.739191) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aec567c2f224f192e6e1f9799e3afc755eb517b2 \"CML watermark\")\n"
] | 2023-07-13T12:34:24Z
| 2023-07-13T12:46:26Z
| 2023-07-13T12:37:01Z
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cast_column to Sequence(subfeatures_dict) has err
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[
"Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```",
"> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks"
] | 2024-01-23T09:32:32Z
| 2024-01-25T02:15:23Z
| 2024-01-25T02:15:23Z
|
NONE
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### Describe the bug
I am working with the following demo code:
```
from datasets import load_dataset
from datasets.features import Sequence, Value, ClassLabel, Features
ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/")
ais_dataset = ais_dataset["train"]
def add_class(example):
example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"}
return example
ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32)
ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence(
{
"bbox": Sequence(Value(dtype="int64")),
"label": ClassLabel(names=["cat", "dog"])
}))
print(ais_dataset[0])
```
However, executing this code results in an error:
```
File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
int64
to
Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)
```
Upon examining the source code in datasets/table.py at line 2035:
```
if isinstance(feature, Sequence) and isinstance(feature.feature, dict):
feature = {
name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items()
}
```
I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error.
### Steps to reproduce the bug
run my demo code
### Expected behavior
no exception
### Environment info
python 3.9
datasets: 2.16.1
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https://github.com/huggingface/datasets/pull/6180
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PR_kwDODunzps5Yy1r-
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Use `hf-internal-testing` repos for hosting test dataset repos
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006505 / 0.011353 (-0.004847) | 0.003950 / 0.011008 (-0.007058) | 0.084554 / 0.038508 (0.046046) | 0.074376 / 0.023109 (0.051267) | 0.350184 / 0.275898 (0.074286) | 0.380704 / 0.323480 (0.057224) | 0.004011 / 0.007986 (-0.003975) | 0.003890 / 0.004328 (-0.000438) | 0.065483 / 0.004250 (0.061232) | 0.054912 / 0.037052 (0.017860) | 0.359586 / 0.258489 (0.101097) | 0.403360 / 0.293841 (0.109519) | 0.030614 / 0.128546 (-0.097932) | 0.008530 / 0.075646 (-0.067117) | 0.288220 / 0.419271 (-0.131052) | 0.052270 / 0.043533 (0.008737) | 0.352557 / 0.255139 (0.097418) | 0.380509 / 0.283200 (0.097309) | 0.025513 / 0.141683 (-0.116170) | 1.488469 / 1.452155 (0.036315) | 1.559182 / 1.492716 (0.066466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266163 / 0.018006 (0.248157) | 0.596345 / 0.000490 (0.595855) | 0.004368 / 0.000200 (0.004168) | 0.000211 / 0.000054 (0.000156) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027137 / 0.037411 (-0.010274) | 0.082251 / 0.014526 (0.067725) | 0.094745 / 0.176557 (-0.081812) | 0.148756 / 0.737135 (-0.588379) | 0.094580 / 0.296338 (-0.201758) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383506 / 0.215209 (0.168297) | 3.823147 / 2.077655 (1.745493) | 1.859627 / 1.504120 (0.355507) | 1.687969 / 1.541195 (0.146775) | 1.720786 / 1.468490 (0.252296) | 0.476552 / 4.584777 (-4.108225) | 3.539558 / 3.745712 (-0.206154) | 3.209032 / 5.269862 (-2.060830) | 1.999643 / 4.565676 (-2.566034) | 0.056484 / 0.424275 (-0.367791) | 0.007443 / 0.007607 (-0.000164) | 0.456089 / 0.226044 (0.230044) | 4.562522 / 2.268929 (2.293593) | 2.348286 / 55.444624 (-53.096338) | 1.984323 / 6.876477 (-4.892154) | 2.148988 / 2.142072 (0.006915) | 0.570761 / 4.805227 (-4.234466) | 0.131439 / 6.500664 (-6.369225) | 0.059752 / 0.075469 (-0.015717) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276803 / 1.841788 (-0.564985) | 19.406812 / 8.074308 (11.332504) | 13.979088 / 10.191392 (3.787696) | 0.157418 / 0.680424 (-0.523006) | 0.018051 / 0.534201 (-0.516150) | 0.392307 / 0.579283 (-0.186976) | 0.406603 / 0.434364 (-0.027760) | 0.458450 / 0.540337 (-0.081888) | 0.622569 / 1.386936 (-0.764367) |\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) | 0.006552 / 0.011353 (-0.004800) | 0.004060 / 0.011008 (-0.006948) | 0.063522 / 0.038508 (0.025014) | 0.072537 / 0.023109 (0.049428) | 0.398452 / 0.275898 (0.122554) | 0.422059 / 0.323480 (0.098579) | 0.005577 / 0.007986 (-0.002409) | 0.003413 / 0.004328 (-0.000916) | 0.064095 / 0.004250 (0.059845) | 0.056883 / 0.037052 (0.019831) | 0.407119 / 0.258489 (0.148630) | 0.435889 / 0.293841 (0.142048) | 0.031549 / 0.128546 (-0.096998) | 0.008418 / 0.075646 (-0.067228) | 0.070315 / 0.419271 (-0.348957) | 0.047828 / 0.043533 (0.004295) | 0.398705 / 0.255139 (0.143566) | 0.416986 / 0.283200 (0.133786) | 0.022304 / 0.141683 (-0.119379) | 1.512597 / 1.452155 (0.060442) | 1.570588 / 1.492716 (0.077871) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295100 / 0.018006 (0.277094) | 0.541883 / 0.000490 (0.541393) | 0.007375 / 0.000200 (0.007175) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030877 / 0.037411 (-0.006534) | 0.090807 / 0.014526 (0.076281) | 0.106155 / 0.176557 (-0.070402) | 0.155546 / 0.737135 (-0.581589) | 0.103847 / 0.296338 (-0.192492) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441176 / 0.215209 (0.225967) | 4.401025 / 2.077655 (2.323371) | 2.394764 / 1.504120 (0.890644) | 2.226434 / 1.541195 (0.685239) | 2.247248 / 1.468490 (0.778758) | 0.489149 / 4.584777 (-4.095628) | 3.642468 / 3.745712 (-0.103244) | 3.235597 / 5.269862 (-2.034265) | 1.992660 / 4.565676 (-2.573016) | 0.057457 / 0.424275 (-0.366818) | 0.007192 / 0.007607 (-0.000415) | 0.515978 / 0.226044 (0.289934) | 5.147728 / 2.268929 (2.878800) | 2.837394 / 55.444624 (-52.607230) | 2.505753 / 6.876477 (-4.370723) | 2.653090 / 2.142072 (0.511018) | 0.583274 / 4.805227 (-4.221954) | 0.132116 / 6.500664 (-6.368548) | 0.058794 / 0.075469 (-0.016675) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.331630 / 1.841788 (-0.510158) | 20.056890 / 8.074308 (11.982582) | 14.950561 / 10.191392 (4.759169) | 0.165449 / 0.680424 (-0.514975) | 0.020161 / 0.534201 (-0.514040) | 0.395791 / 0.579283 (-0.183492) | 0.415631 / 0.434364 (-0.018733) | 0.474440 / 0.540337 (-0.065898) | 0.643060 / 1.386936 (-0.743876) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#712185ed5e9cb3ff6d6528b4528882d51935f334 \"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) | 0.007440 / 0.011353 (-0.003913) | 0.004456 / 0.011008 (-0.006552) | 0.099498 / 0.038508 (0.060990) | 0.077579 / 0.023109 (0.054470) | 0.374934 / 0.275898 (0.099036) | 0.409590 / 0.323480 (0.086110) | 0.005876 / 0.007986 (-0.002110) | 0.003642 / 0.004328 (-0.000687) | 0.076781 / 0.004250 (0.072531) | 0.060185 / 0.037052 (0.023133) | 0.374762 / 0.258489 (0.116273) | 0.445608 / 0.293841 (0.151767) | 0.036557 / 0.128546 (-0.091990) | 0.009941 / 0.075646 (-0.065706) | 0.345214 / 0.419271 (-0.074058) | 0.061912 / 0.043533 (0.018379) | 0.378346 / 0.255139 (0.123207) | 0.415275 / 0.283200 (0.132076) | 0.027396 / 0.141683 (-0.114287) | 1.776602 / 1.452155 (0.324447) | 1.827683 / 1.492716 (0.334967) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235227 / 0.018006 (0.217221) | 0.491846 / 0.000490 (0.491356) | 0.004987 / 0.000200 (0.004787) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032517 / 0.037411 (-0.004894) | 0.099217 / 0.014526 (0.084691) | 0.109749 / 0.176557 (-0.066807) | 0.176190 / 0.737135 (-0.560946) | 0.109868 / 0.296338 (-0.186471) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455188 / 0.215209 (0.239979) | 4.560143 / 2.077655 (2.482489) | 2.249928 / 1.504120 (0.745809) | 2.032808 / 1.541195 (0.491614) | 2.090096 / 1.468490 (0.621605) | 0.567813 / 4.584777 (-4.016964) | 4.338299 / 3.745712 (0.592587) | 3.701589 / 5.269862 (-1.568273) | 2.404805 / 4.565676 (-2.160871) | 0.067931 / 0.424275 (-0.356344) | 0.009011 / 0.007607 (0.001404) | 0.542565 / 0.226044 (0.316521) | 5.406578 / 2.268929 (3.137650) | 2.773508 / 55.444624 (-52.671116) | 2.402926 / 6.876477 (-4.473550) | 2.679318 / 2.142072 (0.537246) | 0.683781 / 4.805227 (-4.121446) | 0.155970 / 6.500664 (-6.344694) | 0.070108 / 0.075469 (-0.005361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541583 / 1.841788 (-0.300205) | 21.592562 / 8.074308 (13.518254) | 16.426868 / 10.191392 (6.235476) | 0.168618 / 0.680424 (-0.511806) | 0.021560 / 0.534201 (-0.512641) | 0.467062 / 0.579283 (-0.112221) | 0.479968 / 0.434364 (0.045604) | 0.540747 / 0.540337 (0.000410) | 0.775502 / 1.386936 (-0.611434) |\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) | 0.008632 / 0.011353 (-0.002721) | 0.004523 / 0.011008 (-0.006485) | 0.075814 / 0.038508 (0.037306) | 0.087096 / 0.023109 (0.063987) | 0.482136 / 0.275898 (0.206238) | 0.529969 / 0.323480 (0.206489) | 0.006882 / 0.007986 (-0.001103) | 0.003720 / 0.004328 (-0.000609) | 0.076232 / 0.004250 (0.071981) | 0.069307 / 0.037052 (0.032254) | 0.491554 / 0.258489 (0.233065) | 0.528989 / 0.293841 (0.235148) | 0.042219 / 0.128546 (-0.086327) | 0.009717 / 0.075646 (-0.065929) | 0.103006 / 0.419271 (-0.316266) | 0.060377 / 0.043533 (0.016844) | 0.484454 / 0.255139 (0.229315) | 0.536072 / 0.283200 (0.252872) | 0.027482 / 0.141683 (-0.114201) | 1.844677 / 1.452155 (0.392522) | 2.001800 / 1.492716 (0.509083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252367 / 0.018006 (0.234361) | 0.483601 / 0.000490 (0.483111) | 0.007445 / 0.000200 (0.007245) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036463 / 0.037411 (-0.000948) | 0.108837 / 0.014526 (0.094311) | 0.122256 / 0.176557 (-0.054300) | 0.186455 / 0.737135 (-0.550681) | 0.122270 / 0.296338 (-0.174069) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506291 / 0.215209 (0.291082) | 5.038044 / 2.077655 (2.960389) | 2.751017 / 1.504120 (1.246897) | 2.553655 / 1.541195 (1.012460) | 2.612724 / 1.468490 (1.144234) | 0.581755 / 4.584777 (-4.003022) | 4.376012 / 3.745712 (0.630300) | 3.749755 / 5.269862 (-1.520107) | 2.394059 / 4.565676 (-2.171618) | 0.068727 / 0.424275 (-0.355548) | 0.008714 / 0.007607 (0.001107) | 0.607371 / 0.226044 (0.381326) | 6.062053 / 2.268929 (3.793125) | 3.278378 / 55.444624 (-52.166247) | 2.866417 / 6.876477 (-4.010060) | 3.056150 / 2.142072 (0.914077) | 0.695090 / 4.805227 (-4.110137) | 0.155274 / 6.500664 (-6.345390) | 0.071106 / 0.075469 (-0.004363) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584552 / 1.841788 (-0.257236) | 23.092569 / 8.074308 (15.018260) | 17.381905 / 10.191392 (7.190513) | 0.206535 / 0.680424 (-0.473888) | 0.025401 / 0.534201 (-0.508800) | 0.514297 / 0.579283 (-0.064986) | 0.507487 / 0.434364 (0.073123) | 0.566883 / 0.540337 (0.026545) | 0.811074 / 1.386936 (-0.575862) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fb01295bff860f09a4c466e745f3840f851efdc \"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) | 0.008400 / 0.011353 (-0.002953) | 0.004872 / 0.011008 (-0.006136) | 0.104434 / 0.038508 (0.065926) | 0.074411 / 0.023109 (0.051302) | 0.395970 / 0.275898 (0.120072) | 0.431661 / 0.323480 (0.108181) | 0.005365 / 0.007986 (-0.002621) | 0.005495 / 0.004328 (0.001167) | 0.081255 / 0.004250 (0.077004) | 0.057141 / 0.037052 (0.020089) | 0.397242 / 0.258489 (0.138753) | 0.456052 / 0.293841 (0.162211) | 0.048362 / 0.128546 (-0.080184) | 0.014077 / 0.075646 (-0.061569) | 0.351128 / 0.419271 (-0.068143) | 0.067842 / 0.043533 (0.024309) | 0.372820 / 0.255139 (0.117681) | 0.407917 / 0.283200 (0.124717) | 0.037707 / 0.141683 (-0.103976) | 1.677136 / 1.452155 (0.224981) | 1.764614 / 1.492716 (0.271897) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269850 / 0.018006 (0.251844) | 0.601458 / 0.000490 (0.600969) | 0.006500 / 0.000200 (0.006300) | 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) | 0.030340 / 0.037411 (-0.007072) | 0.098041 / 0.014526 (0.083515) | 0.107270 / 0.176557 (-0.069287) | 0.173502 / 0.737135 (-0.563633) | 0.113296 / 0.296338 (-0.183043) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575788 / 0.215209 (0.360579) | 5.723878 / 2.077655 (3.646223) | 2.326339 / 1.504120 (0.822219) | 2.130667 / 1.541195 (0.589472) | 2.080885 / 1.468490 (0.612395) | 0.800936 / 4.584777 (-3.783841) | 5.227888 / 3.745712 (1.482176) | 4.592647 / 5.269862 (-0.677214) | 2.935765 / 4.565676 (-1.629911) | 0.095909 / 0.424275 (-0.328367) | 0.008763 / 0.007607 (0.001156) | 0.697362 / 0.226044 (0.471318) | 6.968105 / 2.268929 (4.699176) | 3.129070 / 55.444624 (-52.315554) | 2.554818 / 6.876477 (-4.321658) | 2.776005 / 2.142072 (0.633933) | 1.017064 / 4.805227 (-3.788163) | 0.211552 / 6.500664 (-6.289112) | 0.072132 / 0.075469 (-0.003338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517072 / 1.841788 (-0.324716) | 23.737742 / 8.074308 (15.663433) | 22.236447 / 10.191392 (12.045055) | 0.235408 / 0.680424 (-0.445016) | 0.031889 / 0.534201 (-0.502312) | 0.458997 / 0.579283 (-0.120286) | 0.610513 / 0.434364 (0.176149) | 0.536508 / 0.540337 (-0.003830) | 0.750137 / 1.386936 (-0.636799) |\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) | 0.008696 / 0.011353 (-0.002657) | 0.005374 / 0.011008 (-0.005634) | 0.077974 / 0.038508 (0.039466) | 0.083471 / 0.023109 (0.060362) | 0.498890 / 0.275898 (0.222992) | 0.517570 / 0.323480 (0.194090) | 0.006523 / 0.007986 (-0.001462) | 0.004315 / 0.004328 (-0.000013) | 0.082262 / 0.004250 (0.078012) | 0.064828 / 0.037052 (0.027776) | 0.473101 / 0.258489 (0.214612) | 0.534172 / 0.293841 (0.240331) | 0.051884 / 0.128546 (-0.076662) | 0.015191 / 0.075646 (-0.060455) | 0.084307 / 0.419271 (-0.334965) | 0.066050 / 0.043533 (0.022517) | 0.518007 / 0.255139 (0.262868) | 0.511145 / 0.283200 (0.227946) | 0.045302 / 0.141683 (-0.096381) | 1.670973 / 1.452155 (0.218818) | 1.829225 / 1.492716 (0.336509) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.436537 / 0.018006 (0.418531) | 0.608380 / 0.000490 (0.607890) | 0.075211 / 0.000200 (0.075011) | 0.000733 / 0.000054 (0.000679) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039117 / 0.037411 (0.001706) | 0.103525 / 0.014526 (0.088999) | 0.124413 / 0.176557 (-0.052144) | 0.192352 / 0.737135 (-0.544783) | 0.120379 / 0.296338 (-0.175959) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.673338 / 0.215209 (0.458129) | 6.799435 / 2.077655 (4.721780) | 3.600913 / 1.504120 (2.096793) | 2.881008 / 1.541195 (1.339814) | 2.667154 / 1.468490 (1.198664) | 0.868775 / 4.584777 (-3.716002) | 5.517063 / 3.745712 (1.771351) | 4.646706 / 5.269862 (-0.623156) | 2.914825 / 4.565676 (-1.650852) | 0.098784 / 0.424275 (-0.325491) | 0.011504 / 0.007607 (0.003897) | 0.724233 / 0.226044 (0.498188) | 7.311045 / 2.268929 (5.042117) | 3.685490 / 55.444624 (-51.759135) | 2.892360 / 6.876477 (-3.984117) | 3.253189 / 2.142072 (1.111117) | 0.983065 / 4.805227 (-3.822162) | 0.201097 / 6.500664 (-6.299567) | 0.068020 / 0.075469 (-0.007450) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793904 / 1.841788 (-0.047884) | 24.451356 / 8.074308 (16.377048) | 21.697191 / 10.191392 (11.505799) | 0.228545 / 0.680424 (-0.451879) | 0.034600 / 0.534201 (-0.499601) | 0.483253 / 0.579283 (-0.096030) | 0.615103 / 0.434364 (0.180739) | 0.564600 / 0.540337 (0.024262) | 0.799688 / 1.386936 (-0.587248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74d60213dcbd7c99484c62ce1d3dfd90a1df0770 \"CML watermark\")\n"
] | 2023-08-25T13:10:26Z
| 2023-08-25T16:58:02Z
| 2023-08-25T16:46:22Z
|
COLLABORATOR
| null | null | null |
Use `hf-internal-testing` for hosting instead of the maintainers' dataset repos.
|
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https://github.com/huggingface/datasets/issues/6774
| 2,222,164,316
|
I_kwDODunzps6Ec4lc
| 6,774
|
Generating split is very slow when Image format is PNG
|
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[
"I think this is due to the speed of reading a `png` image using pillow compared to a `jpg` image.\r\nNotably the same is true with `tiff`, it is even faster than `jpg` in my case."
] | 2024-04-03T07:47:31Z
| 2024-04-10T17:28:17Z
| null |
NONE
| null | null |
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### Describe the bug
When I create a dataset, it gets stuck while generating cached data.
The image format is PNG, and it will not get stuck when the image format is jpeg.
After debugging, I know that it is because of the `pa.array` operation in [arrow_writer](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_writer.py#L553), but i don't why.
### Steps to reproduce the bug
```
from datasets import Dataset
def generator(lines):
for line in lines:
img = Image.open(open(line["url"], "rb"))
# print(img.format) # "PNG"
yield {
"image": img,
}
lines = open(dataset_path, "r")
dataset = Dataset.from_generator(
generator,
gen_kwargs={"lines": lines}
)
```
### Expected behavior
Generating split done.
### Environment info
datasets 2.13.0
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| 1,895,349,382
|
I_kwDODunzps5w-LyG
| 6,239
|
Load local audio data doesn't work
|
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[
"I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.",
"@mariosasko \r\nThanks for your help"
] | 2023-09-13T22:30:01Z
| 2023-09-15T14:32:10Z
| 2023-09-15T14:32:10Z
|
NONE
| null | null |
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### Describe the bug
I get a RuntimeError from the following code:
```python
audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio())
audio_dataset[0]
```
### Traceback
<details>
```python
RuntimeError Traceback (most recent call last)
Cell In[33], line 1
----> 1 train_dataset[0]
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key)
1762 def __getitem__(self, key): # noqa: F811
1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 1764 return self._getitem(
1765 key,
1766 )
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs)
1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 1749 formatted_output = format_table(
1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1751 )
1752 return formatted_output
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
-> 1386 return {
1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0)
1376 def decode_example(self, example: dict):
1377 """Decode example with custom feature decoding.
1378
1379 Args:
(...)
1383 :obj:`dict[str, Any]`
1384 """
1386 return {
-> 1387 column_name: decode_nested_example(feature, value)
1388 if self._column_requires_decoding[column_name]
1389 else value
1390 for column_name, (feature, value) in zip_dict(
1391 {key: value for key, value in self.items() if key in example}, example
1392 )
1393 }
File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj)
1085 # Object with special decoding:
1086 elif isinstance(schema, (Audio, Image)):
-> 1087 return schema.decode_example(obj) if obj is not None else None
1088 return obj
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value)
101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
102 elif path is not None and path.endswith("mp3"):
--> 103 array, sampling_rate = self._decode_mp3(file if file else path)
104 elif path is not None and path.endswith("opus"):
105 if file:
File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file)
238 except RuntimeError as err:
239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err
--> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3")
242 if self.sampling_rate and self.sampling_rate != sampling_rate:
243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate:
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format)
254 if ret is not None:
255 return ret
--> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format)
22 def _fail_load(
23 filepath: str,
24 frame_offset: int = 0,
(...)
28 format: Optional[str] = None,
29 ) -> Tuple[torch.Tensor, int]:
---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath))
RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3
```
</details>
### Steps to reproduce the bug
1. - Create a custom dataset using Local files of type mp3.
3. - Try to read the first audio item.
### Expected behavior
Expected output
```python
audio_dataset[0]["audio"]
{'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414,
0. , 0. ], dtype=float32),
'path': 'path/to/audio_1',
'sampling_rate': 16000}
```
### Environment info
N/A
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| 2,620,879,059
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PR_kwDODunzps6AOWI8
| 7,262
|
Allow video with disabeld decoding without decord
|
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7262). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2024-10-29T10:54:04Z
| 2024-10-29T10:56:19Z
| 2024-10-29T10:55:37Z
|
MEMBER
| null | null | null |
for the viewer, this way it can use Video(decode=False) and doesn't need decord (which causes segfaults)
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