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https://api.github.com/repos/huggingface/datasets/issues/1234
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MDExOlB1bGxSZXF1ZXN0NTMzNDM0ODkz
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Added ade_corpus_v2, with 3 configs for relation extraction and classification task
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[
"@lhoestq I have added the tags they are in separate files for 3 different configs",
"@lhoestq thanks for the review I added your suggested changes.",
"merging since the CI is fixed on master"
] | 2020-12-07T07:05:14Z
| 2020-12-14T17:49:14Z
| 2020-12-14T17:49:14Z
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Adverse Drug Reaction Data: ADE-Corpus-V2 dataset added configs for different tasks with given data
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Add OPUS Bible Corpus (102 Languages)
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"@lhoestq done"
] | 2020-12-08T14:57:08Z
| 2020-12-09T15:30:57Z
| 2020-12-09T15:30:56Z
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MEMBER
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[Question] Create Apache Arrow dataset from raw text file
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[
"We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https://arrow.apache.org/docs/python/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can find inspiration from the ones we've already written in `/datasets`. Here is a link to the steps to [add a dataset](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset)",
"Hello @mrm8488 and @lhoestq \r\n\r\nIs there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nThanks :)",
"> Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nSure, to get a dataset in arrow format you can either:\r\n- [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n- OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n- OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n",
"> > Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n> \r\n> Sure, to get a dataset in arrow format you can either:\r\n> \r\n> * [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n> \r\n> * OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n> \r\n> * OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n\r\nLinks were broken. \r\n\r\nUpdated links provided as below\r\n- [load from local files (txt, json, csv)](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-or-remote-files)\r\n- [load from python data (dict, pandas)](https://huggingface.co/docs/datasets/loading_datasets.html#from-in-memory-data)\r\n- [create your own dataset script](https://huggingface.co/docs/datasets/loading_datasets.html#using-a-custom-dataset-loading-script)\r\n"
] | 2020-05-25T16:42:47Z
| 2021-12-18T01:45:34Z
| 2020-10-27T15:20:22Z
|
CONTRIBUTOR
| null | null | null |
Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?
Is the worth of send it to you and add it to the NLP library?
Thanks, Manu
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ethos first commit
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[
"> Nice thanks !\r\n> \r\n> I left a few comments\r\n> \r\n> Also it looks like this PR includes changes about other files than the ones for ethos\r\n> \r\n> Can you create another branch and another PR please ?\r\n\r\n@lhoestq Should I close this PR? The new one is the: #1453",
"You can create another PR and close this one if you don't mind",
"> You can create another PR and close this one if you don't mind\r\n\r\nPerfect! You should see the #1453 PR for the fixed version! Thanks"
] | 2020-12-08T15:59:47Z
| 2020-12-10T14:45:57Z
| 2020-12-10T14:45:57Z
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Ethos passed all the tests except from this one:
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<your-dataset-name>
with this error:
E OSError: Cannot find data file.
E Original error:
E [Errno 2] No such file or directory:
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maintain YAML structure reading from README
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| 2021-05-19T13:08:38Z
| 2021-05-19T13:08:38Z
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CONTRIBUTOR
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How YAML used be loaded earlier in the string (structure of YAML was affected because of this and YAML for datasets with multiple configs was not being loaded correctly):
```
annotations_creators:
labeled_final:
- expert-generated
labeled_swap:
- expert-generated
unlabeled_final:
- machine-generated
language_creators:
- machine-generated
languages:
- en
licenses:
- other
multilinguality:
- monolingual
size_categories:
labeled_final:
- 10K<n<100K
labeled_swap:
- 10K<n<100K
unlabeled_final:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- text-scoring
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring-other-paraphrase-identification
```
How YAML is loaded in string now:
```
annotations_creators:
labeled_final:
- expert-generated
labeled_swap:
- expert-generated
unlabeled_final:
- machine-generated
language_creators:
- machine-generated
languages:
- en
licenses:
- other
multilinguality:
- monolingual
size_categories:
labeled_final:
- 10K<n<100K
labeled_swap:
- 10K<n<100K
unlabeled_final:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- text-scoring
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring-other-paraphrase-identification
```
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Add WIT Dataset
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[
"Google's version of WIT is now available here: https://huggingface.co/datasets/google/wit"
] | 2021-08-16T19:34:09Z
| 2022-05-06T12:27:29Z
| 2022-05-06T12:26:16Z
|
NONE
| null | 1
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Adds Google's [WIT](https://github.com/google-research-datasets/wit) dataset.
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I_kwDODunzps55tlwi
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Support setting a default config name in push_to_hub
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In order to convert script-datasets to no-script datasets, we need to support setting a default config name for those scripts that set one.
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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) | 0.006827 / 0.011353 (-0.004526) | 0.004468 / 0.011008 (-0.006540) | 0.088687 / 0.038508 (0.050179) | 0.072560 / 0.023109 (0.049451) | 0.333421 / 0.275898 (0.057523) | 0.374977 / 0.323480 (0.051497) | 0.005829 / 0.007986 (-0.002156) | 0.003284 / 0.004328 (-0.001045) | 0.068929 / 0.004250 (0.064678) | 0.057212 / 0.037052 (0.020160) | 0.328911 / 0.258489 (0.070422) | 0.389107 / 0.293841 (0.095266) | 0.033518 / 0.128546 (-0.095029) | 0.009919 / 0.075646 (-0.065728) | 0.308100 / 0.419271 (-0.111171) | 0.059380 / 0.043533 (0.015847) | 0.345587 / 0.255139 (0.090448) | 0.353703 / 0.283200 (0.070503) | 0.026454 / 0.141683 (-0.115229) | 1.573309 / 1.452155 (0.121155) | 1.663812 / 1.492716 (0.171095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255081 / 0.018006 (0.237075) | 0.472613 / 0.000490 (0.472123) | 0.016120 / 0.000200 (0.015920) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028219 / 0.037411 (-0.009192) | 0.086600 / 0.014526 (0.072074) | 0.099484 / 0.176557 (-0.077073) | 0.154604 / 0.737135 (-0.582531) | 0.099168 / 0.296338 (-0.197171) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421703 / 0.215209 (0.206494) | 4.188600 / 2.077655 (2.110945) | 2.037575 / 1.504120 (0.533456) | 1.843389 / 1.541195 (0.302194) | 1.912554 / 1.468490 (0.444064) | 0.517452 / 4.584777 (-4.067325) | 3.838002 / 3.745712 (0.092290) | 3.698899 / 5.269862 (-1.570963) | 2.175393 / 4.565676 (-2.390283) | 0.066059 / 0.424275 (-0.358216) | 0.008455 / 0.007607 (0.000848) | 0.506813 / 0.226044 (0.280768) | 4.826994 / 2.268929 (2.558066) | 2.544437 / 55.444624 (-52.900187) | 2.164938 / 6.876477 (-4.711539) | 2.171725 / 2.142072 (0.029652) | 0.603757 / 4.805227 (-4.201470) | 0.149113 / 6.500664 (-6.351551) | 0.065093 / 0.075469 (-0.010376) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.366887 / 1.841788 (-0.474901) | 20.508089 / 8.074308 (12.433780) | 14.836531 / 10.191392 (4.645139) | 0.167418 / 0.680424 (-0.513006) | 0.019707 / 0.534201 (-0.514494) | 0.409897 / 0.579283 (-0.169387) | 0.439412 / 0.434364 (0.005048) | 0.495784 / 0.540337 (-0.044553) | 0.685367 / 1.386936 (-0.701569) |\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) | 0.007604 / 0.011353 (-0.003749) | 0.004368 / 0.011008 (-0.006640) | 0.072628 / 0.038508 (0.034120) | 0.084187 / 0.023109 (0.061077) | 0.461396 / 0.275898 (0.185498) | 0.481429 / 0.323480 (0.157949) | 0.005894 / 0.007986 (-0.002092) | 0.003472 / 0.004328 (-0.000857) | 0.068717 / 0.004250 (0.064466) | 0.061066 / 0.037052 (0.024014) | 0.464217 / 0.258489 (0.205728) | 0.498061 / 0.293841 (0.204220) | 0.035458 / 0.128546 (-0.093089) | 0.009474 / 0.075646 (-0.066173) | 0.079633 / 0.419271 (-0.339639) | 0.053966 / 0.043533 (0.010433) | 0.454911 / 0.255139 (0.199772) | 0.470837 / 0.283200 (0.187637) | 0.026358 / 0.141683 (-0.115325) | 1.665131 / 1.452155 (0.212976) | 1.730365 / 1.492716 (0.237648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234810 / 0.018006 (0.216804) | 0.453672 / 0.000490 (0.453183) | 0.004620 / 0.000200 (0.004420) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035310 / 0.037411 (-0.002101) | 0.100379 / 0.014526 (0.085853) | 0.118802 / 0.176557 (-0.057754) | 0.173853 / 0.737135 (-0.563282) | 0.115714 / 0.296338 (-0.180624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466797 / 0.215209 (0.251588) | 4.698324 / 2.077655 (2.620670) | 2.446897 / 1.504120 (0.942777) | 2.277346 / 1.541195 (0.736151) | 2.347211 / 1.468490 (0.878721) | 0.514377 / 4.584777 (-4.070400) | 3.931269 / 3.745712 (0.185557) | 3.573575 / 5.269862 (-1.696286) | 2.208122 / 4.565676 (-2.357554) | 0.061081 / 0.424275 (-0.363194) | 0.007803 / 0.007607 (0.000196) | 0.544376 / 0.226044 (0.318332) | 5.440003 / 2.268929 (3.171074) | 3.012559 / 55.444624 (-52.432065) | 2.617286 / 6.876477 (-4.259191) | 2.863978 / 2.142072 (0.721906) | 0.610024 / 4.805227 (-4.195203) | 0.133643 / 6.500664 (-6.367021) | 0.064766 / 0.075469 (-0.010703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465225 / 1.841788 (-0.376563) | 21.308351 / 8.074308 (13.234043) | 15.176634 / 10.191392 (4.985242) | 0.172701 / 0.680424 (-0.507723) | 0.020345 / 0.534201 (-0.513855) | 0.433923 / 0.579283 (-0.145360) | 0.450183 / 0.434364 (0.015819) | 0.514048 / 0.540337 (-0.026289) | 0.736302 / 1.386936 (-0.650634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f1a7d621fff3b08ace02643466097654a5e010f \"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) | 0.008305 / 0.011353 (-0.003048) | 0.006007 / 0.011008 (-0.005001) | 0.103521 / 0.038508 (0.065013) | 0.075776 / 0.023109 (0.052666) | 0.378888 / 0.275898 (0.102990) | 0.405245 / 0.323480 (0.081765) | 0.004596 / 0.007986 (-0.003390) | 0.003687 / 0.004328 (-0.000641) | 0.079043 / 0.004250 (0.074792) | 0.055895 / 0.037052 (0.018843) | 0.406565 / 0.258489 (0.148076) | 0.433869 / 0.293841 (0.140028) | 0.045321 / 0.128546 (-0.083226) | 0.014317 / 0.075646 (-0.061329) | 0.345312 / 0.419271 (-0.073960) | 0.064485 / 0.043533 (0.020953) | 0.381744 / 0.255139 (0.126605) | 0.401162 / 0.283200 (0.117962) | 0.035973 / 0.141683 (-0.105709) | 1.829616 / 1.452155 (0.377461) | 1.868487 / 1.492716 (0.375771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245432 / 0.018006 (0.227426) | 0.494249 / 0.000490 (0.493759) | 0.010878 / 0.000200 (0.010678) | 0.000492 / 0.000054 (0.000437) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032778 / 0.037411 (-0.004633) | 0.103418 / 0.014526 (0.088892) | 0.108010 / 0.176557 (-0.068547) | 0.176477 / 0.737135 (-0.560658) | 0.107732 / 0.296338 (-0.188606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.572471 / 0.215209 (0.357262) | 5.647039 / 2.077655 (3.569384) | 2.385069 / 1.504120 (0.880949) | 2.048928 / 1.541195 (0.507733) | 2.108538 / 1.468490 (0.640048) | 0.861436 / 4.584777 (-3.723341) | 4.933452 / 3.745712 (1.187739) | 4.735219 / 5.269862 (-0.534642) | 2.926971 / 4.565676 (-1.638705) | 0.097687 / 0.424275 (-0.326588) | 0.008346 / 0.007607 (0.000739) | 0.677754 / 0.226044 (0.451709) | 6.798433 / 2.268929 (4.529504) | 3.129862 / 55.444624 (-52.314762) | 2.454033 / 6.876477 (-4.422444) | 2.464590 / 2.142072 (0.322517) | 1.034497 / 4.805227 (-3.770730) | 0.205753 / 6.500664 (-6.294911) | 0.076618 / 0.075469 (0.001149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617569 / 1.841788 (-0.224219) | 22.091489 / 8.074308 (14.017181) | 20.406312 / 10.191392 (10.214920) | 0.222012 / 0.680424 (-0.458411) | 0.027787 / 0.534201 (-0.506414) | 0.441669 / 0.579283 (-0.137615) | 0.564773 / 0.434364 (0.130409) | 0.510389 / 0.540337 (-0.029948) | 0.753672 / 1.386936 (-0.633264) |\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) | 0.011107 / 0.011353 (-0.000246) | 0.004973 / 0.011008 (-0.006035) | 0.078331 / 0.038508 (0.039823) | 0.083964 / 0.023109 (0.060855) | 0.518980 / 0.275898 (0.243082) | 0.528264 / 0.323480 (0.204784) | 0.007452 / 0.007986 (-0.000534) | 0.003931 / 0.004328 (-0.000397) | 0.079724 / 0.004250 (0.075474) | 0.061739 / 0.037052 (0.024686) | 0.517804 / 0.258489 (0.259315) | 0.582764 / 0.293841 (0.288923) | 0.049674 / 0.128546 (-0.078873) | 0.014540 / 0.075646 (-0.061106) | 0.093130 / 0.419271 (-0.326141) | 0.060647 / 0.043533 (0.017114) | 0.492628 / 0.255139 (0.237489) | 0.549761 / 0.283200 (0.266562) | 0.034313 / 0.141683 (-0.107369) | 1.824574 / 1.452155 (0.372419) | 2.013664 / 1.492716 (0.520947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231335 / 0.018006 (0.213329) | 0.521477 / 0.000490 (0.520987) | 0.011314 / 0.000200 (0.011114) | 0.000397 / 0.000054 (0.000343) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033303 / 0.037411 (-0.004108) | 0.098238 / 0.014526 (0.083712) | 0.119527 / 0.176557 (-0.057030) | 0.169163 / 0.737135 (-0.567972) | 0.114536 / 0.296338 (-0.181803) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578401 / 0.215209 (0.363191) | 5.966438 / 2.077655 (3.888783) | 2.646370 / 1.504120 (1.142250) | 2.361833 / 1.541195 (0.820638) | 2.476573 / 1.468490 (1.008083) | 0.777411 / 4.584777 (-3.807366) | 4.811070 / 3.745712 (1.065357) | 4.314221 / 5.269862 (-0.955641) | 2.743317 / 4.565676 (-1.822359) | 0.110394 / 0.424275 (-0.313881) | 0.008333 / 0.007607 (0.000726) | 0.729588 / 0.226044 (0.503543) | 7.743226 / 2.268929 (5.474298) | 3.606294 / 55.444624 (-51.838330) | 2.838069 / 6.876477 (-4.038408) | 3.087494 / 2.142072 (0.945421) | 1.053341 / 4.805227 (-3.751886) | 0.205105 / 6.500664 (-6.295559) | 0.075204 / 0.075469 (-0.000265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561959 / 1.841788 (-0.279829) | 21.407849 / 8.074308 (13.333541) | 19.084263 / 10.191392 (8.892871) | 0.226129 / 0.680424 (-0.454295) | 0.029695 / 0.534201 (-0.504506) | 0.427035 / 0.579283 (-0.152248) | 0.565353 / 0.434364 (0.130989) | 0.526789 / 0.540337 (-0.013548) | 0.734820 / 1.386936 (-0.652116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b52536f4e39df3b98f7e0b03ee71b24c4fff49a \"CML watermark\")\n"
] | 2023-10-19T12:19:13Z
| 2023-10-19T16:27:20Z
| 2023-10-19T16:16:31Z
|
MEMBER
| null | 0
|
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|
Sort the items in a set according to their `datasets.fingerprint.Hasher.hash` hash to get a deterministic hash of sets.
This is useful to get deterministic hashes of tokenizers that use a trie based on python sets.
reported in https://github.com/huggingface/datasets/issues/3847
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PR_kwDODunzps47fG6w
| 4,690
|
Refactor base extractors
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-15T17:47:48Z
| 2022-07-18T08:46:56Z
| 2022-07-18T08:34:49Z
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This PR:
- Refactors base extractors as subclasses of `BaseExtractor`:
- this is an abstract class defining the interface with:
- `is_extractable`: abstract class method
- `extract`: abstract static method
- Implements abstract `MagicNumberBaseExtractor` (as subclass of `BaseExtractor`):
- this has a default implementation of `is_extractable`
- this improves performance (reducing the number of file reads) by allowing passing already read `magic_number`
- Refactors `Extractor`:
- reads magic number from file only once
This PR deprecates:
```python
is_extractable, extractor = self.extractor.is_extractable(input_path, return_extractor=True)
self.extractor.extract(input_path, output_path, extractor=extractor)
```
and uses more Pythonic instead:
```python
extractor_format = self.extractor.infer_extractor_format(input_path)
self.extractor.extract(input_path, output_path, extractor_format)
```
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|
XNLI cache reload is very slow
|
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[
"Hi,\r\nCould you tell us how you are running this code?\r\nI tested on my machine (M1 Mac). And it is running fine both on and off internet.\r\n\r\n<img width=\"1033\" alt=\"Screen Shot 2022-07-03 at 1 32 25 AM\" src=\"https://user-images.githubusercontent.com/8711912/177026364-4ad7cedb-e524-4513-97f7-7961bbb34c90.png\">\r\nTested on both stable and dev version. ",
"Sure, I was running it on a Linux machine.\r\nI found that if I turn the Internet off, it would still try to make a HTTPS call which would slow down the cache loading. If you can't reproduce then we can close the issue.",
"Hi @Muennighoff! You can set the env variable `HF_DATASETS_OFFLINE` to `1` to avoid this behavior in offline mode. More info is available [here](https://huggingface.co/docs/datasets/master/en/loading#offline)."
] | 2022-06-25T16:43:56Z
| 2022-07-04T14:29:40Z
| 2022-07-04T14:29:40Z
|
CONTRIBUTOR
| null | null | null |
### Reproduce
Using `2.3.3.dev0`
`from datasets import load_dataset`
`load_dataset("xnli", "en")`
Turn off Internet
`load_dataset("xnli", "en")`
I cancelled the second `load_dataset` eventually cuz it took super long. It would be great to have something to specify e.g. `only_load_from_cache` and avoid the library trying to download when there is no Internet. If I leave it running it works but takes way longer than when there is Internet. I would expect loading from cache to take the same amount of time regardless of whether there is Internet.
```
---------------------------------------------------------------------------
gaierror Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
/opt/conda/lib/python3.7/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
71
---> 72 for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM):
73 af, socktype, proto, canonname, sa = res
/opt/conda/lib/python3.7/socket.py in getaddrinfo(host, port, family, type, proto, flags)
751 addrlist = []
--> 752 for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
753 af, socktype, proto, canonname, sa = res
gaierror: [Errno -3] Temporary failure in name resolution
During handling of the above exception, another exception occurred:
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_33/3594208039.py in <module>
----> 1 load_dataset("xnli", "en")
/opt/conda/lib/python3.7/site-packages/datasets/load.py 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)
1673 revision=revision,
1674 use_auth_token=use_auth_token,
-> 1675 **config_kwargs,
1676 )
1677
/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1494 download_mode=download_mode,
1495 data_dir=data_dir,
-> 1496 data_files=data_files,
1497 )
1498
/opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1182 download_config=download_config,
1183 download_mode=download_mode,
-> 1184 dynamic_modules_path=dynamic_modules_path,
1185 ).get_module()
1186 elif path.count("/") == 1: # community dataset on the Hub
/opt/conda/lib/python3.7/site-packages/datasets/load.py in __init__(self, name, revision, download_config, download_mode, dynamic_modules_path)
506 self.dynamic_modules_path = dynamic_modules_path
507 assert self.name.count("/") == 0
--> 508 increase_load_count(name, resource_type="dataset")
509
510 def download_loading_script(self, revision: Optional[str]) -> str:
/opt/conda/lib/python3.7/site-packages/datasets/load.py in increase_load_count(name, resource_type)
166 if not config.HF_DATASETS_OFFLINE and config.HF_UPDATE_DOWNLOAD_COUNTS:
167 try:
--> 168 head_hf_s3(name, filename=name + ".py", dataset=(resource_type == "dataset"))
169 except Exception:
170 pass
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in head_hf_s3(identifier, filename, use_cdn, dataset, max_retries)
93 return http_head(
94 hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset),
---> 95 max_retries=max_retries,
96 )
97
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
445 allow_redirects=allow_redirects,
446 timeout=timeout,
--> 447 max_retries=max_retries,
448 )
449 return response
/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
366 tries += 1
367 try:
--> 368 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
369 success = True
370 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/opt/conda/lib/python3.7/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
527 }
528 send_kwargs.update(settings)
--> 529 resp = self.send(prep, **send_kwargs)
530
531 return resp
/opt/conda/lib/python3.7/site-packages/requests/sessions.py in send(self, request, **kwargs)
643
644 # Send the request
--> 645 r = adapter.send(request, **kwargs)
646
647 # Total elapsed time of the request (approximately)
/opt/conda/lib/python3.7/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 decode_content=False,
449 retries=self.max_retries,
--> 450 timeout=timeout
451 )
452
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
708 body=body,
709 headers=headers,
--> 710 chunked=chunked,
711 )
712
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
384 # Trigger any extra validation we need to do.
385 try:
--> 386 self._validate_conn(conn)
387 except (SocketTimeout, BaseSSLError) as e:
388 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
/opt/conda/lib/python3.7/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
1038 # Force connect early to allow us to validate the connection.
1039 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
-> 1040 conn.connect()
1041
1042 if not conn.is_verified:
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in connect(self)
356 def connect(self):
357 # Add certificate verification
--> 358 self.sock = conn = self._new_conn()
359 hostname = self.host
360 tls_in_tls = False
/opt/conda/lib/python3.7/site-packages/urllib3/connection.py in _new_conn(self)
173 try:
174 conn = connection.create_connection(
--> 175 (self._dns_host, self.port), self.timeout, **extra_kw
176 )
177
KeyboardInterrupt:
```
|
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Add license metadata to pg19
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-11T13:52:20Z
| 2022-08-11T15:01:03Z
| 2022-08-11T14:46:38Z
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As reported over email by Roy Rijkers
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add books3
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"> When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797\r\n\r\nThanks for the message, we'll definitely improve this\r\n\r\n> Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675\r\n\r\nWell currently no, but I think @lewtun was about to do it (though he's currently on vacations)",
"> > Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675\r\n> \r\n> Well currently no, but I think @lewtun was about to do it (though he's currently on vacations)\r\n\r\nyes i plan to start working on this next week #2185 \r\n\r\none question for @richarddwang - do you know if eleutherai happened to also release the \"existing\" datasets like enron emails and opensubtitles? \r\n\r\nin appendix c of their paper, they provide details on how they extracted these datasets, but it would be nice if we could just point to a url so we can be as close as possible to original implementation.",
"@lewtun \r\n\r\n> yes i plan to start working on this next week\r\n\r\nNice! Looking forward to it.\r\n\r\n> one question for @richarddwang - do you know if eleutherai happened to also release the \"existing\" datasets like enron emails and opensubtitles?\r\n\r\nSadly, I don't know any existing dataset of enron emails, but I believe opensubtitles dataset is hosted at here. https://the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\n",
"thanks for the link @richarddwang! i think that corpus is actually the youtube subtitles one and my impression is that eleutherai have only uploaded the 14 new datasets they created. i've contacted one of the authors so hopefully they can share some additional info for us :)\r\n\r\nbtw it might take a while to put together all the corpora if i also need to preprocess them (e.g. the open subtitles / enron email etc), but i expect no longer than a few weeks."
] | 2021-08-14T07:04:25Z
| 2021-08-19T16:43:09Z
| 2021-08-18T15:36:59Z
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books3 is part of EleutherAI/The Pile, but AFAIK, The Pile dataset blend all sub datasets together thus we are not able to use just one of its sub dataset from The Pile data. So I create an independent dataset using The Pile preliminary components.
When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797
Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675
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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) | 0.008470 / 0.011353 (-0.002883) | 0.004721 / 0.011008 (-0.006287) | 0.099024 / 0.038508 (0.060516) | 0.029831 / 0.023109 (0.006722) | 0.325887 / 0.275898 (0.049989) | 0.380753 / 0.323480 (0.057273) | 0.007101 / 0.007986 (-0.000885) | 0.004734 / 0.004328 (0.000406) | 0.077576 / 0.004250 (0.073326) | 0.037207 / 0.037052 (0.000154) | 0.320463 / 0.258489 (0.061974) | 0.369284 / 0.293841 (0.075443) | 0.033411 / 0.128546 (-0.095135) | 0.011610 / 0.075646 (-0.064037) | 0.321460 / 0.419271 (-0.097811) | 0.041315 / 0.043533 (-0.002217) | 0.349186 / 0.255139 (0.094047) | 0.384546 / 0.283200 (0.101347) | 0.088045 / 0.141683 (-0.053637) | 1.536341 / 1.452155 (0.084186) | 1.527806 / 1.492716 (0.035089) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193435 / 0.018006 (0.175429) | 0.451732 / 0.000490 (0.451243) | 0.003165 / 0.000200 (0.002965) | 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) | 0.023203 / 0.037411 (-0.014208) | 0.096211 / 0.014526 (0.081685) | 0.105665 / 0.176557 (-0.070891) | 0.141074 / 0.737135 (-0.596061) | 0.108584 / 0.296338 (-0.187755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419041 / 0.215209 (0.203832) | 4.187915 / 2.077655 (2.110261) | 1.855336 / 1.504120 (0.351216) | 1.660046 / 1.541195 (0.118851) | 1.674646 / 1.468490 (0.206156) | 0.692257 / 4.584777 (-3.892520) | 3.466853 / 3.745712 (-0.278860) | 1.900925 / 5.269862 (-3.368936) | 1.294696 / 4.565676 (-3.270980) | 0.082792 / 0.424275 (-0.341483) | 0.012808 / 0.007607 (0.005201) | 0.529622 / 0.226044 (0.303578) | 5.337025 / 2.268929 (3.068096) | 2.326558 / 55.444624 (-53.118066) | 1.956256 / 6.876477 (-4.920221) | 2.035911 / 2.142072 (-0.106161) | 0.815824 / 4.805227 (-3.989403) | 0.148720 / 6.500664 (-6.351944) | 0.064226 / 0.075469 (-0.011243) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231347 / 1.841788 (-0.610440) | 13.724596 / 8.074308 (5.650288) | 13.933878 / 10.191392 (3.742486) | 0.150913 / 0.680424 (-0.529511) | 0.028460 / 0.534201 (-0.505741) | 0.393564 / 0.579283 (-0.185719) | 0.407185 / 0.434364 (-0.027179) | 0.458250 / 0.540337 (-0.082087) | 0.547993 / 1.386936 (-0.838943) |\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) | 0.006653 / 0.011353 (-0.004699) | 0.004615 / 0.011008 (-0.006393) | 0.098062 / 0.038508 (0.059554) | 0.027849 / 0.023109 (0.004740) | 0.409116 / 0.275898 (0.133218) | 0.448770 / 0.323480 (0.125290) | 0.004856 / 0.007986 (-0.003130) | 0.003427 / 0.004328 (-0.000901) | 0.075748 / 0.004250 (0.071498) | 0.037942 / 0.037052 (0.000889) | 0.410232 / 0.258489 (0.151743) | 0.457394 / 0.293841 (0.163553) | 0.031927 / 0.128546 (-0.096620) | 0.011618 / 0.075646 (-0.064028) | 0.321231 / 0.419271 (-0.098040) | 0.041416 / 0.043533 (-0.002117) | 0.413535 / 0.255139 (0.158396) | 0.438196 / 0.283200 (0.154997) | 0.089551 / 0.141683 (-0.052132) | 1.459298 / 1.452155 (0.007143) | 1.552594 / 1.492716 (0.059878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228186 / 0.018006 (0.210180) | 0.404393 / 0.000490 (0.403904) | 0.006944 / 0.000200 (0.006744) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025167 / 0.037411 (-0.012244) | 0.101282 / 0.014526 (0.086756) | 0.107282 / 0.176557 (-0.069275) | 0.139797 / 0.737135 (-0.597339) | 0.110477 / 0.296338 (-0.185861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479121 / 0.215209 (0.263912) | 4.778210 / 2.077655 (2.700555) | 2.464687 / 1.504120 (0.960567) | 2.255312 / 1.541195 (0.714118) | 2.287348 / 1.468490 (0.818858) | 0.694769 / 4.584777 (-3.890008) | 3.460860 / 3.745712 (-0.284852) | 3.078881 / 5.269862 (-2.190980) | 1.297726 / 4.565676 (-3.267950) | 0.082699 / 0.424275 (-0.341576) | 0.012652 / 0.007607 (0.005045) | 0.583308 / 0.226044 (0.357263) | 5.839199 / 2.268929 (3.570271) | 2.893724 / 55.444624 (-52.550900) | 2.546503 / 6.876477 (-4.329974) | 2.559570 / 2.142072 (0.417498) | 0.802357 / 4.805227 (-4.002870) | 0.151890 / 6.500664 (-6.348774) | 0.068593 / 0.075469 (-0.006876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262421 / 1.841788 (-0.579367) | 13.771848 / 8.074308 (5.697540) | 14.046017 / 10.191392 (3.854625) | 0.140950 / 0.680424 (-0.539474) | 0.016839 / 0.534201 (-0.517362) | 0.378870 / 0.579283 (-0.200413) | 0.385908 / 0.434364 (-0.048456) | 0.438539 / 0.540337 (-0.101799) | 0.522761 / 1.386936 (-0.864175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8145ebfd4fc3508d0be0de9a0f9c58877f2b32f8 \"CML watermark\")\n"
] | 2023-01-18T06:53:15Z
| 2023-01-18T13:49:51Z
| 2023-01-18T13:42:49Z
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MEMBER
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This PR updates the "checkout" GitHub Action to its latest version, as previous ones are deprecated: https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/
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MDU6SXNzdWU2MzUwNTc1Njg=
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[Feature request] Be able to remove a specific sample of the dataset
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"Oh yes you can now do that with the `dataset.filter()` method that was added in #214 "
] | 2020-06-09T02:22:13Z
| 2020-06-09T08:41:38Z
| 2020-06-09T08:41:38Z
|
NONE
| null | null | null |
As mentioned in #117, it's currently not possible to remove a sample of the dataset.
But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.
I think it should be a feature. What do you think ?
---
Any work-around in the meantime ?
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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_6420). 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) | 0.004536 / 0.011353 (-0.006816) | 0.002979 / 0.011008 (-0.008030) | 0.061984 / 0.038508 (0.023476) | 0.029382 / 0.023109 (0.006273) | 0.245237 / 0.275898 (-0.030661) | 0.270571 / 0.323480 (-0.052909) | 0.003956 / 0.007986 (-0.004029) | 0.002453 / 0.004328 (-0.001876) | 0.047967 / 0.004250 (0.043717) | 0.043695 / 0.037052 (0.006643) | 0.248457 / 0.258489 (-0.010032) | 0.283293 / 0.293841 (-0.010548) | 0.023603 / 0.128546 (-0.104943) | 0.007225 / 0.075646 (-0.068422) | 0.200533 / 0.419271 (-0.218739) | 0.055310 / 0.043533 (0.011777) | 0.245152 / 0.255139 (-0.009987) | 0.267187 / 0.283200 (-0.016012) | 0.018158 / 0.141683 (-0.123525) | 1.126079 / 1.452155 (-0.326075) | 1.185137 / 1.492716 (-0.307580) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092436 / 0.018006 (0.074430) | 0.300132 / 0.000490 (0.299642) | 0.000206 / 0.000200 (0.000006) | 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) | 0.018476 / 0.037411 (-0.018935) | 0.062827 / 0.014526 (0.048301) | 0.074605 / 0.176557 (-0.101952) | 0.119768 / 0.737135 (-0.617368) | 0.076044 / 0.296338 (-0.220294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279717 / 0.215209 (0.064508) | 2.752308 / 2.077655 (0.674654) | 1.434954 / 1.504120 (-0.069166) | 1.314700 / 1.541195 (-0.226495) | 1.347689 / 1.468490 (-0.120802) | 0.400332 / 4.584777 (-4.184445) | 2.383024 / 3.745712 (-1.362689) | 2.583130 / 5.269862 (-2.686732) | 1.567670 / 4.565676 (-2.998007) | 0.045446 / 0.424275 (-0.378829) | 0.004813 / 0.007607 (-0.002794) | 0.336191 / 0.226044 (0.110147) | 3.319837 / 2.268929 (1.050909) | 1.816808 / 55.444624 (-53.627817) | 1.539052 / 6.876477 (-5.337424) | 1.550765 / 2.142072 (-0.591307) | 0.484253 / 4.805227 (-4.320974) | 0.100494 / 6.500664 (-6.400170) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.940857 / 1.841788 (-0.900931) | 11.784946 / 8.074308 (3.710638) | 10.397038 / 10.191392 (0.205646) | 0.141458 / 0.680424 (-0.538965) | 0.014193 / 0.534201 (-0.520008) | 0.268304 / 0.579283 (-0.310979) | 0.267059 / 0.434364 (-0.167305) | 0.309389 / 0.540337 (-0.230949) | 0.420628 / 1.386936 (-0.966308) |\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) | 0.004776 / 0.011353 (-0.006577) | 0.002941 / 0.011008 (-0.008067) | 0.048659 / 0.038508 (0.010151) | 0.053334 / 0.023109 (0.030225) | 0.273342 / 0.275898 (-0.002556) | 0.302278 / 0.323480 (-0.021202) | 0.004001 / 0.007986 (-0.003984) | 0.002414 / 0.004328 (-0.001914) | 0.047504 / 0.004250 (0.043254) | 0.038581 / 0.037052 (0.001529) | 0.277768 / 0.258489 (0.019279) | 0.306772 / 0.293841 (0.012931) | 0.024146 / 0.128546 (-0.104400) | 0.007233 / 0.075646 (-0.068413) | 0.053308 / 0.419271 (-0.365964) | 0.032617 / 0.043533 (-0.010916) | 0.277390 / 0.255139 (0.022251) | 0.296015 / 0.283200 (0.012816) | 0.018733 / 0.141683 (-0.122950) | 1.124895 / 1.452155 (-0.327260) | 1.182579 / 1.492716 (-0.310137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093375 / 0.018006 (0.075369) | 0.301555 / 0.000490 (0.301066) | 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) | 0.021284 / 0.037411 (-0.016127) | 0.070158 / 0.014526 (0.055632) | 0.080187 / 0.176557 (-0.096370) | 0.119282 / 0.737135 (-0.617854) | 0.081672 / 0.296338 (-0.214666) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.314396 / 0.215209 (0.099187) | 2.975114 / 2.077655 (0.897459) | 1.724658 / 1.504120 (0.220539) | 1.604464 / 1.541195 (0.063269) | 1.652736 / 1.468490 (0.184246) | 0.395064 / 4.584777 (-4.189713) | 2.412768 / 3.745712 (-1.332944) | 2.564427 / 5.269862 (-2.705435) | 1.507627 / 4.565676 (-3.058050) | 0.045463 / 0.424275 (-0.378812) | 0.004797 / 0.007607 (-0.002810) | 0.383115 / 0.226044 (0.157071) | 3.501976 / 2.268929 (1.233048) | 2.087512 / 55.444624 (-53.357113) | 1.793132 / 6.876477 (-5.083345) | 1.804178 / 2.142072 (-0.337895) | 0.468287 / 4.805227 (-4.336940) | 0.097247 / 6.500664 (-6.403417) | 0.041139 / 0.075469 (-0.034330) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976034 / 1.841788 (-0.865754) | 12.431248 / 8.074308 (4.356940) | 10.896064 / 10.191392 (0.704672) | 0.129137 / 0.680424 (-0.551287) | 0.015636 / 0.534201 (-0.518565) | 0.268219 / 0.579283 (-0.311064) | 0.278345 / 0.434364 (-0.156019) | 0.302696 / 0.540337 (-0.237642) | 0.408465 / 1.386936 (-0.978471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51c53e94acd7a273c24899c045446df021314cd2 \"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) | 0.007703 / 0.011353 (-0.003650) | 0.004614 / 0.011008 (-0.006394) | 0.101425 / 0.038508 (0.062917) | 0.040122 / 0.023109 (0.017013) | 0.398890 / 0.275898 (0.122992) | 0.424392 / 0.323480 (0.100912) | 0.005411 / 0.007986 (-0.002575) | 0.003747 / 0.004328 (-0.000582) | 0.080494 / 0.004250 (0.076243) | 0.059392 / 0.037052 (0.022340) | 0.398025 / 0.258489 (0.139536) | 0.454293 / 0.293841 (0.160452) | 0.043662 / 0.128546 (-0.084884) | 0.013726 / 0.075646 (-0.061920) | 0.352910 / 0.419271 (-0.066362) | 0.088572 / 0.043533 (0.045039) | 0.401677 / 0.255139 (0.146538) | 0.421774 / 0.283200 (0.138575) | 0.033377 / 0.141683 (-0.108305) | 1.728499 / 1.452155 (0.276344) | 1.821557 / 1.492716 (0.328841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230744 / 0.018006 (0.212738) | 0.496188 / 0.000490 (0.495698) | 0.010315 / 0.000200 (0.010115) | 0.000402 / 0.000054 (0.000348) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028859 / 0.037411 (-0.008552) | 0.089688 / 0.014526 (0.075163) | 0.111697 / 0.176557 (-0.064860) | 0.183238 / 0.737135 (-0.553898) | 0.112407 / 0.296338 (-0.183931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.558394 / 0.215209 (0.343185) | 5.643048 / 2.077655 (3.565393) | 2.454622 / 1.504120 (0.950502) | 2.183338 / 1.541195 (0.642143) | 2.324793 / 1.468490 (0.856303) | 0.859482 / 4.584777 (-3.725295) | 4.959346 / 3.745712 (1.213634) | 4.599224 / 5.269862 (-0.670638) | 2.764382 / 4.565676 (-1.801295) | 0.089976 / 0.424275 (-0.334299) | 0.008144 / 0.007607 (0.000537) | 0.634675 / 0.226044 (0.408631) | 6.555693 / 2.268929 (4.286765) | 3.080252 / 55.444624 (-52.364373) | 2.442715 / 6.876477 (-4.433762) | 2.475126 / 2.142072 (0.333053) | 0.986459 / 4.805227 (-3.818768) | 0.193859 / 6.500664 (-6.306805) | 0.063652 / 0.075469 (-0.011817) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.545318 / 1.841788 (-0.296469) | 21.928751 / 8.074308 (13.854442) | 20.598229 / 10.191392 (10.406837) | 0.234046 / 0.680424 (-0.446377) | 0.025947 / 0.534201 (-0.508254) | 0.459773 / 0.579283 (-0.119510) | 0.598026 / 0.434364 (0.163662) | 0.555260 / 0.540337 (0.014922) | 0.782767 / 1.386936 (-0.604169) |\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) | 0.009322 / 0.011353 (-0.002030) | 0.004650 / 0.011008 (-0.006358) | 0.079326 / 0.038508 (0.040818) | 0.079112 / 0.023109 (0.056003) | 0.428708 / 0.275898 (0.152810) | 0.481647 / 0.323480 (0.158168) | 0.006419 / 0.007986 (-0.001566) | 0.003878 / 0.004328 (-0.000450) | 0.079013 / 0.004250 (0.074762) | 0.058107 / 0.037052 (0.021055) | 0.436967 / 0.258489 (0.178478) | 0.501120 / 0.293841 (0.207279) | 0.052972 / 0.128546 (-0.075574) | 0.014414 / 0.075646 (-0.061232) | 0.098587 / 0.419271 (-0.320685) | 0.061626 / 0.043533 (0.018093) | 0.451623 / 0.255139 (0.196484) | 0.468893 / 0.283200 (0.185693) | 0.032479 / 0.141683 (-0.109203) | 1.911743 / 1.452155 (0.459588) | 1.969024 / 1.492716 (0.476308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232015 / 0.018006 (0.214009) | 0.508637 / 0.000490 (0.508147) | 0.005470 / 0.000200 (0.005270) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035345 / 0.037411 (-0.002066) | 0.106319 / 0.014526 (0.091794) | 0.117205 / 0.176557 (-0.059352) | 0.176527 / 0.737135 (-0.560608) | 0.121566 / 0.296338 (-0.174773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584920 / 0.215209 (0.369711) | 5.745688 / 2.077655 (3.668034) | 2.519875 / 1.504120 (1.015755) | 2.197593 / 1.541195 (0.656398) | 2.296670 / 1.468490 (0.828180) | 0.831938 / 4.584777 (-3.752839) | 5.130594 / 3.745712 (1.384882) | 4.581385 / 5.269862 (-0.688476) | 2.829516 / 4.565676 (-1.736161) | 0.099015 / 0.424275 (-0.325260) | 0.011468 / 0.007607 (0.003861) | 0.702717 / 0.226044 (0.476672) | 6.856099 / 2.268929 (4.587170) | 3.372966 / 55.444624 (-52.071658) | 2.567664 / 6.876477 (-4.308812) | 2.699200 / 2.142072 (0.557127) | 0.992316 / 4.805227 (-3.812911) | 0.190463 / 6.500664 (-6.310201) | 0.063305 / 0.075469 (-0.012165) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591491 / 1.841788 (-0.250296) | 21.696492 / 8.074308 (13.622184) | 19.695404 / 10.191392 (9.504012) | 0.222853 / 0.680424 (-0.457571) | 0.032936 / 0.534201 (-0.501265) | 0.431209 / 0.579283 (-0.148074) | 0.543101 / 0.434364 (0.108737) | 0.543427 / 0.540337 (0.003089) | 0.742102 / 1.386936 (-0.644834) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#534a227179265df9093230885613c95390325705 \"CML watermark\")\n"
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| 2023-11-15T08:33:36Z
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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) | 0.008959 / 0.011353 (-0.002394) | 0.004549 / 0.011008 (-0.006460) | 0.102012 / 0.038508 (0.063504) | 0.030122 / 0.023109 (0.007013) | 0.303731 / 0.275898 (0.027833) | 0.344418 / 0.323480 (0.020938) | 0.007199 / 0.007986 (-0.000787) | 0.003415 / 0.004328 (-0.000913) | 0.079784 / 0.004250 (0.075534) | 0.034894 / 0.037052 (-0.002158) | 0.304739 / 0.258489 (0.046250) | 0.359457 / 0.293841 (0.065616) | 0.034194 / 0.128546 (-0.094352) | 0.011348 / 0.075646 (-0.064298) | 0.324340 / 0.419271 (-0.094931) | 0.041071 / 0.043533 (-0.002461) | 0.304437 / 0.255139 (0.049298) | 0.335517 / 0.283200 (0.052317) | 0.087787 / 0.141683 (-0.053895) | 1.467293 / 1.452155 (0.015138) | 1.543529 / 1.492716 (0.050813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187654 / 0.018006 (0.169648) | 0.426558 / 0.000490 (0.426068) | 0.003585 / 0.000200 (0.003385) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023410 / 0.037411 (-0.014001) | 0.097065 / 0.014526 (0.082539) | 0.105358 / 0.176557 (-0.071198) | 0.140941 / 0.737135 (-0.596195) | 0.109484 / 0.296338 (-0.186855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420334 / 0.215209 (0.205125) | 4.223235 / 2.077655 (2.145581) | 1.866213 / 1.504120 (0.362093) | 1.673829 / 1.541195 (0.132634) | 1.757828 / 1.468490 (0.289337) | 0.702203 / 4.584777 (-3.882574) | 3.426192 / 3.745712 (-0.319521) | 1.950392 / 5.269862 (-3.319470) | 1.286139 / 4.565676 (-3.279538) | 0.082858 / 0.424275 (-0.341417) | 0.012587 / 0.007607 (0.004980) | 0.531920 / 0.226044 (0.305876) | 5.344425 / 2.268929 (3.075497) | 2.337875 / 55.444624 (-53.106749) | 1.967713 / 6.876477 (-4.908764) | 2.022075 / 2.142072 (-0.119997) | 0.829267 / 4.805227 (-3.975961) | 0.151712 / 6.500664 (-6.348952) | 0.066617 / 0.075469 (-0.008852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251867 / 1.841788 (-0.589921) | 13.861756 / 8.074308 (5.787448) | 14.236309 / 10.191392 (4.044917) | 0.138215 / 0.680424 (-0.542209) | 0.028600 / 0.534201 (-0.505601) | 0.395890 / 0.579283 (-0.183393) | 0.403971 / 0.434364 (-0.030393) | 0.479033 / 0.540337 (-0.061305) | 0.564019 / 1.386936 (-0.822917) |\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) | 0.006845 / 0.011353 (-0.004508) | 0.004544 / 0.011008 (-0.006464) | 0.098719 / 0.038508 (0.060211) | 0.029082 / 0.023109 (0.005973) | 0.426011 / 0.275898 (0.150113) | 0.447185 / 0.323480 (0.123705) | 0.005203 / 0.007986 (-0.002783) | 0.004790 / 0.004328 (0.000462) | 0.076446 / 0.004250 (0.072196) | 0.040649 / 0.037052 (0.003596) | 0.414810 / 0.258489 (0.156321) | 0.452082 / 0.293841 (0.158241) | 0.031842 / 0.128546 (-0.096704) | 0.011575 / 0.075646 (-0.064071) | 0.320710 / 0.419271 (-0.098561) | 0.044994 / 0.043533 (0.001461) | 0.415645 / 0.255139 (0.160506) | 0.435235 / 0.283200 (0.152035) | 0.091756 / 0.141683 (-0.049927) | 1.493900 / 1.452155 (0.041746) | 1.592353 / 1.492716 (0.099637) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264710 / 0.018006 (0.246703) | 0.410553 / 0.000490 (0.410064) | 0.024497 / 0.000200 (0.024297) | 0.000232 / 0.000054 (0.000178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024452 / 0.037411 (-0.012959) | 0.102673 / 0.014526 (0.088147) | 0.107787 / 0.176557 (-0.068770) | 0.147368 / 0.737135 (-0.589767) | 0.112127 / 0.296338 (-0.184211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471294 / 0.215209 (0.256085) | 4.711638 / 2.077655 (2.633983) | 2.436819 / 1.504120 (0.932699) | 2.238540 / 1.541195 (0.697345) | 2.334134 / 1.468490 (0.865644) | 0.697668 / 4.584777 (-3.887108) | 3.414332 / 3.745712 (-0.331380) | 2.783248 / 5.269862 (-2.486614) | 1.529599 / 4.565676 (-3.036078) | 0.082626 / 0.424275 (-0.341649) | 0.012385 / 0.007607 (0.004778) | 0.580486 / 0.226044 (0.354441) | 5.837914 / 2.268929 (3.568986) | 2.915129 / 55.444624 (-52.529495) | 2.606254 / 6.876477 (-4.270223) | 2.659031 / 2.142072 (0.516958) | 0.810431 / 4.805227 (-3.994796) | 0.151666 / 6.500664 (-6.348998) | 0.066873 / 0.075469 (-0.008596) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259933 / 1.841788 (-0.581855) | 14.052388 / 8.074308 (5.978080) | 13.356141 / 10.191392 (3.164749) | 0.138416 / 0.680424 (-0.542008) | 0.016582 / 0.534201 (-0.517619) | 0.378110 / 0.579283 (-0.201173) | 0.385089 / 0.434364 (-0.049275) | 0.465299 / 0.540337 (-0.075038) | 0.559780 / 1.386936 (-0.827156) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d2859fd4d4beca33f21539a6e1df9a7f012cbd10 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011945 / 0.011353 (0.000592) | 0.006128 / 0.011008 (-0.004880) | 0.128926 / 0.038508 (0.090418) | 0.037708 / 0.023109 (0.014599) | 0.373449 / 0.275898 (0.097551) | 0.423567 / 0.323480 (0.100088) | 0.009848 / 0.007986 (0.001863) | 0.006097 / 0.004328 (0.001769) | 0.098275 / 0.004250 (0.094024) | 0.043199 / 0.037052 (0.006147) | 0.376848 / 0.258489 (0.118359) | 0.441819 / 0.293841 (0.147978) | 0.055094 / 0.128546 (-0.073453) | 0.019704 / 0.075646 (-0.055942) | 0.422746 / 0.419271 (0.003474) | 0.061764 / 0.043533 (0.018231) | 0.381056 / 0.255139 (0.125917) | 0.419343 / 0.283200 (0.136144) | 0.116720 / 0.141683 (-0.024963) | 1.763913 / 1.452155 (0.311759) | 1.872306 / 1.492716 (0.379589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198651 / 0.018006 (0.180645) | 0.560565 / 0.000490 (0.560075) | 0.004269 / 0.000200 (0.004069) | 0.000114 / 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) | 0.027307 / 0.037411 (-0.010104) | 0.128276 / 0.014526 (0.113750) | 0.129015 / 0.176557 (-0.047542) | 0.167269 / 0.737135 (-0.569866) | 0.143955 / 0.296338 (-0.152384) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.564954 / 0.215209 (0.349745) | 5.810570 / 2.077655 (3.732916) | 2.456382 / 1.504120 (0.952262) | 2.115809 / 1.541195 (0.574614) | 2.097363 / 1.468490 (0.628873) | 1.189712 / 4.584777 (-3.395065) | 5.318287 / 3.745712 (1.572575) | 2.965763 / 5.269862 (-2.304099) | 2.177958 / 4.565676 (-2.387719) | 0.144135 / 0.424275 (-0.280140) | 0.014348 / 0.007607 (0.006741) | 0.781715 / 0.226044 (0.555670) | 7.688349 / 2.268929 (5.419421) | 3.189260 / 55.444624 (-52.255365) | 2.552340 / 6.876477 (-4.324137) | 2.559312 / 2.142072 (0.417240) | 1.490755 / 4.805227 (-3.314473) | 0.257908 / 6.500664 (-6.242756) | 0.082016 / 0.075469 (0.006547) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565735 / 1.841788 (-0.276053) | 17.660338 / 8.074308 (9.586030) | 19.493573 / 10.191392 (9.302181) | 0.241310 / 0.680424 (-0.439114) | 0.043485 / 0.534201 (-0.490716) | 0.557397 / 0.579283 (-0.021886) | 0.624385 / 0.434364 (0.190021) | 0.634601 / 0.540337 (0.094264) | 0.743140 / 1.386936 (-0.643796) |\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) | 0.010134 / 0.011353 (-0.001219) | 0.005858 / 0.011008 (-0.005150) | 0.128741 / 0.038508 (0.090232) | 0.036769 / 0.023109 (0.013660) | 0.470894 / 0.275898 (0.194996) | 0.524302 / 0.323480 (0.200822) | 0.006830 / 0.007986 (-0.001156) | 0.006166 / 0.004328 (0.001838) | 0.094875 / 0.004250 (0.090625) | 0.051201 / 0.037052 (0.014148) | 0.493992 / 0.258489 (0.235503) | 0.510540 / 0.293841 (0.216699) | 0.056354 / 0.128546 (-0.072192) | 0.020512 / 0.075646 (-0.055134) | 0.417809 / 0.419271 (-0.001463) | 0.061941 / 0.043533 (0.018408) | 0.498883 / 0.255139 (0.243744) | 0.480762 / 0.283200 (0.197563) | 0.110753 / 0.141683 (-0.030930) | 1.914096 / 1.452155 (0.461941) | 1.941338 / 1.492716 (0.448622) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237955 / 0.018006 (0.219949) | 0.518136 / 0.000490 (0.517647) | 0.000475 / 0.000200 (0.000275) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032947 / 0.037411 (-0.004465) | 0.127857 / 0.014526 (0.113331) | 0.133911 / 0.176557 (-0.042646) | 0.188406 / 0.737135 (-0.548729) | 0.143939 / 0.296338 (-0.152400) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.787553 / 0.215209 (0.572344) | 6.976572 / 2.077655 (4.898918) | 2.897964 / 1.504120 (1.393844) | 2.545906 / 1.541195 (1.004711) | 2.622111 / 1.468490 (1.153620) | 1.278283 / 4.584777 (-3.306494) | 5.650447 / 3.745712 (1.904734) | 4.955835 / 5.269862 (-0.314027) | 2.767946 / 4.565676 (-1.797731) | 0.149385 / 0.424275 (-0.274890) | 0.014340 / 0.007607 (0.006733) | 0.861774 / 0.226044 (0.635730) | 8.660985 / 2.268929 (6.392057) | 3.685611 / 55.444624 (-51.759014) | 2.963087 / 6.876477 (-3.913390) | 3.020746 / 2.142072 (0.878673) | 1.538908 / 4.805227 (-3.266319) | 0.285875 / 6.500664 (-6.214789) | 0.080337 / 0.075469 (0.004867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.575155 / 1.841788 (-0.266633) | 17.548946 / 8.074308 (9.474638) | 19.954104 / 10.191392 (9.762712) | 0.242025 / 0.680424 (-0.438398) | 0.025586 / 0.534201 (-0.508615) | 0.515676 / 0.579283 (-0.063607) | 0.607035 / 0.434364 (0.172671) | 0.633597 / 0.540337 (0.093259) | 0.744577 / 1.386936 (-0.642359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6529cada7879496bf18dd686e4d281de81d6203c \"CML watermark\")\n"
] | 2023-01-26T19:34:44Z
| 2023-01-26T19:47:34Z
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MDExOlB1bGxSZXF1ZXN0NTk4NzQzMDM4
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MIAM dataset - new citation details
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"Hi !\r\nLooks like there's a unicode error in the new citation in the miam.py file.\r\nCould you try to fix it ? Not sure from which character it comes from though\r\n\r\nYou can test if it works on your side with\r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_miam\r\n```",
"Unicode error resolved!"
] | 2021-03-23T10:41:23Z
| 2021-03-23T18:08:10Z
| 2021-03-23T18:08:10Z
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CONTRIBUTOR
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Hi @lhoestq, I have updated the citations to reference an OpenReview preprint.
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Fix version in map_nested docstring
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-29T05:44:32Z
| 2022-07-29T11:51:25Z
| 2022-07-29T11:38:36Z
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After latest release, `map_nested` docstring needs being updated with the right version for versionchanged and versionadded.
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Use HTML relative paths for tiles in the docs
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Good catch, @lewtun. Thanks for the fix.\r\n> \r\n> Do you know if there are other absolute paths in the docs that should be fixed as well?\r\n\r\nI found a few more in [0d4796b](https://github.com/huggingface/datasets/pull/5092/commits/0d4796b747e6620d9fcc17a8f74acc5cf4bba7be).\r\n\r\nHowever, I noticed that none of the cross-references (e.g. to API classes / methods) work locally, but that is probably just a limitation of the local build",
"Thanks."
] | 2022-10-10T07:24:27Z
| 2022-10-11T13:25:45Z
| 2022-10-11T13:23:23Z
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This PR replaces the absolute paths in the landing page tiles with relative ones so that one can test navigation both locally in and in future PRs (see [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084/en/index) for an example PR where the links don't work).
I encountered this while working on the `optimum` docs and figured I'd fix it elsewhere too :)
Internal Slack thread: https://huggingface.slack.com/archives/C02GLJ5S0E9/p1665129710176619
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datasets.config.PYARROW_VERSION has no attribute 'major'
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"I have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(\".\")[0]) < 3:` just to get around it.",
"Hi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https://github.com/huggingface/datasets/commit/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n",
"Sorted. Thanks!",
"Reopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps://github.com/huggingface/datasets/pull/2873",
"Hi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci/circleci: run_dataset_script_tests_pyarrow_1\" details](https://circleci.com/gh/huggingface/datasets/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue."
] | 2021-09-06T21:06:57Z
| 2021-09-08T08:51:52Z
| 2021-09-08T08:51:52Z
|
CONTRIBUTOR
| null | null | null |
In the test_dataset_common.py script, line 288-289
```
if datasets.config.PYARROW_VERSION.major < 3:
packaged_datasets = [pd for pd in packaged_datasets if pd["dataset_name"] != "parquet"]
```
which throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.
```
import datasets
datasets.config.PYARROW_VERSION.major
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/var/folders/1f/0wqmlgp90qjd5mpj53fnjq440000gn/T/ipykernel_73361/2547517336.py in <module>
1 import datasets
----> 2 datasets.config.PYARROW_VERSION.major
AttributeError: 'str' object has no attribute 'major'
```
## Environment info
- `datasets` version: 1.11.0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 4.0.1
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Save file name in embed_storage
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"I updated the tests, met le know if it sounds good to you now :)"
] | 2022-11-23T10:55:54Z
| 2022-11-24T14:11:41Z
| 2022-11-24T14:08:37Z
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Having the file name is useful in case we need to check the extension of the file (e.g. mp3), or in general in case it includes some metadata information (track id, image id etc.)
Related to https://github.com/huggingface/datasets/issues/5276
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fix(dataset_wrappers): Fixes access to fsspec.asyn in torch_iterable_dataset.py.
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Fix #4612.
Apparently, newest `fsspec` versions do not allow access to attribute-based modules if they are not imported, such as `fsspec.async`.
Thus, @mariosasko suggested to add the missing part to the module import to allow for its access.
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I_kwDODunzps5Dwqp0
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`FaissIndex` to support multiple GPU and `custom_index`
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"Hi @rentruewang, thansk for reporting and for your PR!!! We should definitely support this. ",
"@albertvillanova Great! :)"
] | 2022-02-14T06:21:43Z
| 2022-03-07T16:28:56Z
| 2022-03-07T16:28:56Z
|
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
Currently, because `device` is of the type `int | None`, to leverage `faiss-gpu`'s multi-gpu support, you need to create a `custom_index`. However, if using a `custom_index` created by e.g. `faiss.index_cpu_to_all_gpus`, then `FaissIndex.save` does not work properly because it checks the device id (which is an int, so no multiple GPUs).
**Describe the solution you'd like**
I would like `FaissIndex` to support multiple GPUs, by passing in a list to `add_faiss_index`.
**Describe alternatives you've considered**
Alternatively, I would like it to at least provide a warning cause it wasn't the behavior that I expected.
**Additional context**
Relavent source code here:
https://github.com/huggingface/datasets/blob/6ed6ac9448311930557810383d2cfd4fe6aae269/src/datasets/search.py#L340-L349
Device management needs changing to support multiple GPUs, probably by `isinstance` calls.
I can provide a PR if you like :)
Thanks for reading!
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"We want to keep the repo as light as possible so that it doesn't take ages to clone, that's why we ask for small dummy data files (especially when there are many of them). Let me know if you have questions or if we can help you on this",
"Hello @lhoestq , made the changes as you suggested and pushed, please review. By default, the dummy data was generated the way it was by the dummy data auto generate command. Thank you."
] | 2020-12-09T18:42:58Z
| 2020-12-13T19:11:18Z
| 2020-12-13T19:11:18Z
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Add code to automate parts of the dataset card
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Most parts of the "Dataset Structure" section can be generated automatically. This PR adds some code to do so.
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Follow cache_dir parameter to gcs downloader
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As noticed in #900 the cache_dir parameter was not followed to the downloader in the case of an already processed dataset hosted on our google storage (one of them is natural questions).
Fix #900
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[Tests] Local => aws
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"For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n\r\nNote: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.",
"> For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n> \r\n> Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.\r\n\r\nDoes it have to download the whole data to check if the checksums are correct? I guess so no? ",
"> > For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n> > Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.\r\n> \r\n> Does it have to download the whole data to check if the checksums are correct? I guess so no?\r\n\r\nYes it has to download them all (unless they were already downloaded in which case it just uses the cached downloaded files)."
] | 2020-05-15T09:12:25Z
| 2020-05-15T10:06:12Z
| 2020-05-15T10:03:26Z
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## Change default Test from local => aws
As a default we set` aws=True`, `Local=False`, `slow=False`
### 1. RUN_AWS=1 (default)
This runs 4 tests per dataset script.
a) Does the dataset script have a valid etag / Can it be reached on AWS?
b) Can we load its `builder_class`?
c) Can we load **all** dataset configs?
d) _Most importantly_: Can we load the dataset?
Important - we currently only test the first config of each dataset to reduce test time. Total test time is around 1min20s.
### 2. RUN_LOCAL=1 RUN_AWS=0
***This should be done when debugging dataset scripts of the ./datasets folder***
This only runs 1 test per dataset test, which is equivalent to aws d) - Can we load the dataset from the local `datasets` directory?
### 3. RUN_SLOW=1
We should set up to run these tests maybe 1 time per week ? @thomwolf
The `slow` tests include two more important tests.
e) Can we load the dataset with all possible configs? This test will probably fail at the moment because a lot of dummy data is missing. We should add the dummy data step by step to be sure that all configs work.
f) Test that the actual dataset can be loaded. This will take quite some time to run, but is important to make sure that the "real" data can be loaded. It will also test whether the dataset script has the correct checksums file which is currently not tested with `aws=True`. @lhoestq - is there an easy way to check cheaply whether the `dataset_info.json` is correct for each dataset script?
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cannot shuffle dataset loaded from disk
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[] | 2021-10-01T13:49:52Z
| 2021-10-01T13:49:52Z
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NONE
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## Describe the bug
dataset loaded from disk cannot be shuffled.
## Steps to reproduce the bug
```
my_dataset = load_from_disk('s3://my_file/validate', fs=s3)
sample = my_dataset.select(range(100)).shuffle(seed=1234)
```
## Actual results
```
sample = my_dataset .select(range(100)).shuffle(seed=1234)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2494, in shuffle
new_fingerprint=new_fingerprint,
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/fingerprint.py", line 398, in wrapper
out = func(self, *args, **kwargs)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 2303, in select
tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 547, in NamedTemporaryFile
(fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)
File "/home/ubuntu/anaconda3/envs/pytorch_p37/lib/python3.7/tempfile.py", line 258, in _mkstemp_inner
fd = _os.open(file, flags, 0o600)
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpnnu5uhnx/my_file/validate/tmpy76d70g4'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Python version: 3.7
- PyArrow version: 5.0.0
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Add the Ud treebank
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"merging since the CI is fixed on master"
] | 2020-12-03T16:56:41Z
| 2020-12-04T16:11:54Z
| 2020-12-04T15:51:46Z
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This PR adds the 183 datasets in 104 languages of the UD Treebank.
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Fix cast to Python scalar in Matthews Correlation metric
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[] | 2021-10-02T11:44:59Z
| 2021-10-04T09:54:04Z
| 2021-10-04T09:26:12Z
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This PR is motivated by issue #2964.
The Matthews Correlation metric relies on sklearn's `matthews_corrcoef` function to compute the result. This function returns either `float` or `np.float64` (see the [source](https://github.com/scikit-learn/scikit-learn/blob/844b4be24d20fc42cc13b957374c718956a0db39/sklearn/metrics/_classification.py#L906-L909)). Obviously, calling `.item()` on the float value will fail, so I'm fixing this with the built-in `float()` function, which covers both cases. Surprisingly, on my machine, casting `np.float64` to a Python scalar with `float()` is even faster than with the `.item()` method.
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Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
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[
"+1 \r\n```\r\nFound cached dataset csv (file:///home/ubuntu/.cache/huggingface/datasets/theSquarePond___csv/theSquarePond--XXXXX-bbf0a8365d693d2c/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[14], line 4\r\n 1 get_ipython().system('pip install -U datasets')\r\n 3 # Load dataset from the hub\r\n----> 4 dataset = load_dataset(dataset_name)\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/load.py:1810, 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, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 # Build dataset for splits\r\n 1807 keep_in_memory = (\r\n 1808 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1809 )\r\n-> 1810 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1811 # Rename and cast features to match task schema\r\n 1812 if task is not None:\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/builder.py:1128, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1126 is_local = not is_remote_filesystem(self._fs)\r\n 1127 if not is_local:\r\n-> 1128 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1129 if not os.path.exists(self._output_dir):\r\n 1130 raise FileNotFoundError(\r\n 1131 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1132 \"builder.download_and_prepare(), or use \"\r\n 1133 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1134 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```",
"+1\r\n\r\n```\r\nFound cached dataset csv ([file://C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1](file:///C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1))\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[38], line 3\r\n 1 huggingface_dataset_name = \"knkarthick/dialogsum\"\r\n----> 3 dataset = load_dataset(huggingface_dataset_name)\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\load.py:1804, 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, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\builder.py:1108, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```",
"This error stems from a breaking change in `fsspec`. It has been fixed in the latest `datasets` release (`2.14.6`). Updating the installation with `pip install -U datasets` should fix the issue.\r\n",
"> 此错误源于 中的重大更改。此问题已在最新版本 （） 中修复。更新安装应该可以解决此问题。`fsspec``datasets``2.14.6``pip install -U datasets`\r\n\r\nthanks , 太好啦，刚好解决了我的问题，GPT都没解决了，终于被你搞定了",
"https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141",
"Fixed by:\r\n- https://github.com/huggingface/datasets/pull/6334\r\n\r\nThe fix was released in `datasets-2.14.6`."
] | 2023-10-25T21:55:31Z
| 2023-11-07T07:26:54Z
| 2023-11-07T07:26:54Z
|
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I was trying to load the wiki dataset, but i got this error
traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset
ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset
raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.
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Update index.rst
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Added downloading to Hyperpartisan news detection
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[
"Thank you @ghomasHudson for making our dataset available! This is great!",
"The test passes since #527 :)"
] | 2020-08-13T21:53:46Z
| 2020-08-27T08:18:41Z
| 2020-08-27T08:18:41Z
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Following the discussion on Slack and #349, I've updated the hyperpartisan dataset to pull directly from Zenodo rather than manual install, which should make this dataset much more accessible. Many thanks to @johanneskiesel !
Currently doesn't pass `test_load_real_dataset` - I'm using `self.config.name` which is `default` in this test. Might be related to #474
|
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Add MSRA NER dataset
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"LGTM, don't forget the tags ;)"
] | 2020-12-01T05:02:11Z
| 2020-12-04T09:29:40Z
| 2020-12-01T07:25:53Z
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Move silicone directory
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The dataset was added in #1761 but not in the right directory. I'm moving it to /datasets
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Create README.md
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[
"@ManuelFay thanks you so much for adding a dataset card, this is such a cool contribution!\r\n\r\nThis looks like it uses an old template for the card we've moved things around a bit and we have an app you should be using to get the tags and the structure of the Data Fields paragraph :) Would you mind moving your text to the newer format (we're also asking contributors to keep the full template structure, even if some sections still have [More Information Needed] for the time being)\r\n\r\nHere's the link to the instructions:\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nOut of curiosity, what was your landing point for filling out the card? Did you follow the \"Update on Github\" when navigating the datasets? Trying to make the instructions as clear as possible :) ",
"@yjernite \r\n\r\nPerfect, I'll follow the instructions when I have a bit more time tomorrow ! I was actually browsing the new contributions after the dataset sprint and realized most of the \"old\" datasets were not tagged, so I just copied and pasted the readme from another dataset and was not aware there was precise instructions... Will fix !\r\n\r\nBTW, amazing job with the retriBert work, I used the contrastive + in-batch negative quite a bit for various projects. Probably neither the time nor place to talk about that but I was curious as to why, in your original work, you prefered using a simple projection in the last layer to differentiate the question vs answer embedding, rather than allowing for bias in the dense layer or even just to fine-tune 2 different embedders for question + answer ? ",
"Cool! Looking forward to the next version!\r\n\r\nQuick answer for retriBERT is that I expected a simple projection to generalize better and more importantly only having to store the gradients for the proj means training with larger batches :) If you want to keep chatting about it, feel free to send me an email!",
"Hi @ManuelFay ! \r\nIf you're still interested in completing the FQuAD dataset card, note that we've generated one that is pre-filled.\r\nTherefore feel free to complete it with the content you already have in your README.md.\r\nThis would be awesome ! And thanks again for your contribution :)",
"Yo @lhoestq , just not sure about the tag table at the top, I used @yjernite eli5 template so hope it's okay ! Also want to signal the streamlit app for dataset tagging has a weird behavior with the size categories when filling in the form. \r\n\r\nThanks to you guys for doing that and sorry about the time it took, i completely forgot about it ! \r\n"
] | 2020-12-14T11:40:23Z
| 2021-03-25T14:01:49Z
| 2021-03-25T14:01:49Z
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I_kwDODunzps5tUNtg
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Cache not being used when loading commonvoice 8.0.0
|
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"You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).",
"Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else."
] | 2023-08-02T23:18:11Z
| 2023-08-18T23:59:00Z
| 2023-08-18T23:59:00Z
|
NONE
| null | null | null |
### Describe the bug
I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially:
```
dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")
```
it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32`
### Steps to reproduce the bug
Steps to reproduce the behavior:
1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")```
2. dataset is updated by maintainers
3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")```
### Expected behavior
I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`.
Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded?
EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example:
```
load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en")
> ...
> File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str)
1937 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
...
1794 e = e.__context__
-> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Environment info
datasets==2.7.0
python==3.10.8
OS: AWS Linux
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feat: Return the name of the currently loaded file
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[
"Your change adds a new element in the key used to avoid duplicates when generating the examples of a dataset. I don't think it fixes the issue you're trying to solve."
] | 2023-08-23T07:08:17Z
| 2023-08-29T12:41:05Z
| null |
NONE
| null | 0
|
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Added an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output.
I added this here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92.
fixes #5806
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I_kwDODunzps5sMDr3
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Why is the speed difference of gen example so big?
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[
"Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\nand share the output?"
] | 2023-07-21T03:34:49Z
| 2023-10-04T18:06:16Z
| 2023-10-04T18:06:15Z
|
NONE
| null | null | null |
```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
with open(metadata_path, 'r') as file:
metadata = json.load(file)
for idx, item in enumerate(metadata):
image_path = item.get('image_path')
text_content = item.get('text_content')
image_data = open(image_path, "rb").read()
yield idx, {
"text": text_content,
"image": {
"path": image_path,
"bytes": image_data,
},
"conditioning_image": {
"path": image_path,
"bytes": image_data,
},
}
```
Hello,
I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**
I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.
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I_kwDODunzps5nbsiN
| 5,918
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File not found for audio dataset
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"load_dataset () did not work for loading local files either "
] | 2023-06-01T02:15:29Z
| 2023-06-11T06:02:25Z
| null |
NONE
| null | null | null |
### Describe the bug
After loading an audio dataset, and looking at a sample entry, the `path` element, which is supposed to be the path to the audio file, doesn't actually exist.
### Steps to reproduce the bug
Run bug.py:
```py
import os.path
from datasets import load_dataset
def run() -> None:
cv13 = load_dataset(
"mozilla-foundation/common_voice_13_0",
"hi",
split="train",
)
print(cv13[0])
audio_file = cv13[0]["path"]
if not os.path.exists(audio_file):
raise ValueError(f'File {audio_file} does not exist.')
if __name__ == "__main__":
run()
```
The result (on my machine):
```json
{'client_id': '0f018a99663f33afbb7d38aee281fb1afcfd07f9e7acd00383f604e1e17c38d6ed8adf1bd2ccbf927a52c5adefb8ac4b158ce27a7c2ed9581e71202eb302dfb3', 'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'audio': {'path': 'C:\\Users\\rober\\.cache\\huggingface\\datasets\\downloads\\extracted\\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\\common_voice_hi_26008353.mp3', 'array': array([ 6.46234854e-26, -1.35709319e-25, -8.07793567e-26, ...,
1.06425944e-07, 4.46417090e-08, 2.61451660e-09]), 'sampling_rate': 48000}, 'sentence': 'हमने उसका जन्मदिन मनाया।', 'up_votes': 2, 'down_votes': 0, 'age': '', 'gender': '', 'accent': '', 'locale': 'hi', 'segment': '' ', 'variant': ''}
```
```txt
Traceback (most recent call last):
File "F:\eo-reco\bug.py", line 18, in <module>
run()
File "F:\eo-reco\bug.py", line 15, in run
raise ValueError(f'File {audio_file} does not exist.')
ValueError: File C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\common_voice_hi_26008353.mp3 does not exist.
```
### Expected behavior
The `path` element points to the correct file, which happens to be:
```
C:\Users\rober\.cache\huggingface\datasets\downloads\extracted\8d1479bc09b4609bc2675bd02d6869a4d5e09f7e6616f540bd55eacef46c6e2b\hi_train_0\common_voice_hi_26008353.mp3
```
That is, there's an extra directory `hi_train_0` that is not in the `path` element.
### Environment info
- `datasets` version: 2.12.0
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.11.3
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
-
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PR_kwDODunzps44RoXs
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Fix metadata validation
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-23T09:11:20Z
| 2022-06-01T09:27:52Z
| 2022-06-01T09:19:25Z
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Since Python 3.8, the typing module:
- raises an AttributeError when trying to access `__args__` on any type, e.g.: `List.__args__`
- provides the `get_args` function instead: `get_args(List)`
This PR implements a fix for Python >=3.8 whereas maintaining backward compatibility.
|
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MDExOlB1bGxSZXF1ZXN0NjkyMjk2NDQ4
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|
Fix potential DuplicatedKeysError in LibriSpeech
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[] | 2021-07-19T06:00:49Z
| 2021-07-19T06:28:57Z
| 2021-07-19T06:28:56Z
|
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DONE:
- Fix unnecessary path join.
- Fix potential DiplicatedKeysError by ensuring keys are unique.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
|
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|
Fingerprint
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[
"I changed the way I implemented fingerprint updates to use decorator functions.\r\n\r\nI also added a new attribute called `_inplace_history` that stores the in-place history of transforms (like cast_, rename_columns, etc.). This history is useful to replay the changes that were done in-place when unpickling a dataset that is memory mapped from a file.\r\n\r\nLet me know what you think @thomwolf "
] | 2020-08-27T16:27:09Z
| 2020-08-31T14:20:40Z
| 2020-08-31T14:20:39Z
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This PR is a continuation of #513 , in which many in-place functions were introduced or updated (cast_, flatten_) etc.
However the caching didn't handle these changes. Indeed the caching took into account only the previous cache file name of the table, and not the possible in-place transforms of the table.
To fix that, I added the concept of dataset fingerprint, that is updated after each transform (in place or not), and stored inside the table metadata.
When a dataset is created, an initial fingerprint is computed. If the dataset is memory-mapped, then the fingerprint generator doesn't read the table and only looks at the filename. However if the table is in-memory, then the fingerprint generator reads the content of the table using a batched non-crypto hashing.
I added a utility class to compute hashes of arbitrary python objects in `fingerprint.py` : `Hasher`. The API is close to standard hashing tools (`.update`, `.hexdigest`). It also supports custom hashing functions depending on object types using a registry like pickle. I added a custom hashing function to hash a `pa.Table` in a batched way, and also for `nlp.DatasetInfo` to leverage its json serialization feature.
Note about this PR:
This is a draft PR because #513 needs to be merged first.
The diff that is shown is for branches fingerprint -> indices (and not master, for now)
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PR_kwDODunzps5eW9kO
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do not try to download from HF GCS for generator
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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) | 0.007617 / 0.011353 (-0.003735) | 0.005371 / 0.011008 (-0.005638) | 0.092110 / 0.038508 (0.053602) | 0.070654 / 0.023109 (0.047544) | 0.362501 / 0.275898 (0.086603) | 0.412835 / 0.323480 (0.089355) | 0.006752 / 0.007986 (-0.001234) | 0.003752 / 0.004328 (-0.000576) | 0.075644 / 0.004250 (0.071394) | 0.055666 / 0.037052 (0.018614) | 0.355906 / 0.258489 (0.097417) | 0.405078 / 0.293841 (0.111237) | 0.045767 / 0.128546 (-0.082779) | 0.013778 / 0.075646 (-0.061868) | 0.324696 / 0.419271 (-0.094575) | 0.062200 / 0.043533 (0.018667) | 0.359571 / 0.255139 (0.104432) | 0.387274 / 0.283200 (0.104075) | 0.035323 / 0.141683 (-0.106360) | 1.586294 / 1.452155 (0.134139) | 1.707564 / 1.492716 (0.214847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303940 / 0.018006 (0.285934) | 0.583349 / 0.000490 (0.582859) | 0.014845 / 0.000200 (0.014645) | 0.000698 / 0.000054 (0.000643) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028994 / 0.037411 (-0.008417) | 0.085555 / 0.014526 (0.071029) | 0.097856 / 0.176557 (-0.078701) | 0.161480 / 0.737135 (-0.575655) | 0.098573 / 0.296338 (-0.197766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.591294 / 0.215209 (0.376085) | 5.751350 / 2.077655 (3.673695) | 2.241620 / 1.504120 (0.737500) | 1.991083 / 1.541195 (0.449888) | 2.006711 / 1.468490 (0.538221) | 0.832339 / 4.584777 (-3.752438) | 5.213808 / 3.745712 (1.468095) | 4.650355 / 5.269862 (-0.619506) | 2.860494 / 4.565676 (-1.705182) | 0.093090 / 0.424275 (-0.331185) | 0.009740 / 0.007607 (0.002133) | 0.693509 / 0.226044 (0.467464) | 6.828735 / 2.268929 (4.559807) | 2.967763 / 55.444624 (-52.476862) | 2.311461 / 6.876477 (-4.565016) | 2.400051 / 2.142072 (0.257979) | 0.914753 / 4.805227 (-3.890474) | 0.202804 / 6.500664 (-6.297860) | 0.076905 / 0.075469 (0.001436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.576424 / 1.841788 (-0.265363) | 22.963472 / 8.074308 (14.889164) | 19.948105 / 10.191392 (9.756713) | 0.228982 / 0.680424 (-0.451442) | 0.029038 / 0.534201 (-0.505163) | 0.477715 / 0.579283 (-0.101568) | 0.554924 / 0.434364 (0.120560) | 0.532118 / 0.540337 (-0.008219) | 0.775096 / 1.386936 (-0.611840) |\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) | 0.009127 / 0.011353 (-0.002226) | 0.004978 / 0.011008 (-0.006030) | 0.084166 / 0.038508 (0.045658) | 0.083391 / 0.023109 (0.060282) | 0.420760 / 0.275898 (0.144862) | 0.459072 / 0.323480 (0.135592) | 0.007102 / 0.007986 (-0.000883) | 0.004175 / 0.004328 (-0.000154) | 0.082922 / 0.004250 (0.078672) | 0.059010 / 0.037052 (0.021957) | 0.416959 / 0.258489 (0.158470) | 0.472220 / 0.293841 (0.178379) | 0.049999 / 0.128546 (-0.078547) | 0.014126 / 0.075646 (-0.061520) | 0.096894 / 0.419271 (-0.322378) | 0.057920 / 0.043533 (0.014387) | 0.405779 / 0.255139 (0.150640) | 0.464286 / 0.283200 (0.181087) | 0.034957 / 0.141683 (-0.106726) | 1.637921 / 1.452155 (0.185767) | 1.768231 / 1.492716 (0.275515) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.354875 / 0.018006 (0.336868) | 0.554667 / 0.000490 (0.554177) | 0.074127 / 0.000200 (0.073927) | 0.000411 / 0.000054 (0.000357) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027681 / 0.037411 (-0.009730) | 0.087746 / 0.014526 (0.073220) | 0.093714 / 0.176557 (-0.082843) | 0.145380 / 0.737135 (-0.591755) | 0.095686 / 0.296338 (-0.200652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522079 / 0.215209 (0.306870) | 5.197366 / 2.077655 (3.119711) | 2.300744 / 1.504120 (0.796624) | 2.056846 / 1.541195 (0.515652) | 2.009897 / 1.468490 (0.541407) | 0.813025 / 4.584777 (-3.771751) | 5.177732 / 3.745712 (1.432020) | 4.076749 / 5.269862 (-1.193112) | 2.545588 / 4.565676 (-2.020088) | 0.083507 / 0.424275 (-0.340769) | 0.007011 / 0.007607 (-0.000596) | 0.598820 / 0.226044 (0.372776) | 6.203730 / 2.268929 (3.934801) | 2.945385 / 55.444624 (-52.499239) | 2.304849 / 6.876477 (-4.571628) | 2.599035 / 2.142072 (0.456962) | 1.002721 / 4.805227 (-3.802506) | 0.191781 / 6.500664 (-6.308883) | 0.064178 / 0.075469 (-0.011292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549560 / 1.841788 (-0.292228) | 22.395727 / 8.074308 (14.321418) | 20.537895 / 10.191392 (10.346503) | 0.246542 / 0.680424 (-0.433882) | 0.031673 / 0.534201 (-0.502528) | 0.442490 / 0.579283 (-0.136793) | 0.589838 / 0.434364 (0.155474) | 0.535201 / 0.540337 (-0.005136) | 0.733660 / 1.386936 (-0.653276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n"
] | 2023-11-01T17:57:11Z
| 2023-11-02T16:02:52Z
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attempt to fix https://github.com/huggingface/datasets/issues/6371
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Consistent ner features
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As discussed in #613 , this PR aims at making NER feature names consistent across datasets.
I changed the feature names of LinCE and XTREME/PAN-X
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Support DataLoader with num_workers > 0 in streaming mode
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright this is finally ready for review ! It's quite long I'm sorry, but it's not easy to disentangle everything ^^'\r\n\r\nThe main additions are in\r\n- src/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py\r\n- src/datasets/iterable_dataset.py\r\n- src/datasets/utils/patching.py",
"Added some comments and an error when lists have different lengths for sharding :)",
"Let's resolve the merge conflict and the CI error (if it's related to the changes), and I can review the PR again.",
"Feel free to review again :) The CI fail is unrelated to this PR and will be fixed by https://github.com/huggingface/datasets/pull/4472 (the hub now returns 401 instead of 404 for unauthenticated requests to non-existing repos)",
"CI failures are unrelated to this PR - merging :)\r\n\r\n(CI fails are a mix of pip install fails and Hub fails)",
"@lhoestq you're our hero :)"
] | 2022-05-19T15:00:31Z
| 2022-07-04T16:05:14Z
| 2022-06-10T20:47:27Z
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### Issue
It's currently not possible to properly stream a dataset using multiple `torch.utils.data.DataLoader` workers:
- the `TorchIterableDataset` can't be pickled and passed to the subprocesses: https://github.com/huggingface/datasets/issues/3950
- streaming extension is failing: https://github.com/huggingface/datasets/issues/3951
- `fsspec` doesn't work out of the box in subprocesses
### Solution in this PR
I fixed these to enable passing an `IterableDataset` to a `torch.utils.data.DataLoader` with `num_workers > 0`.
I also had to shard the `IterableDataset` to give each worker a shard, otherwise data would be duplicated. This is implemented in `TorchIterableDataset.__iter__` and uses the new `IterableDataset._iter_shard(shard_idx)` method
I also had to do a few changes the patching that enable streaming in dataset scripts:
- the patches are now always applied - not just for streaming mode. They're applied when a builder is instantiated
- I improved it to also check for renamed modules or attributes (ex: pandas vs pd)
- I grouped all the patches of pathlib.Path into a class `xPath`, so that `Path` outside of dataset scripts stay unchanged - otherwise I didn't change the content of the extended Path methods for streaming
- I fixed a bug with the `pd.read_csv` patch, opening the file in "rb" mode was missing and causing some datasets to not work in streaming mode, and compression inference was missing
### A few details regarding `fsspec` in multiprocessing
From https://github.com/fsspec/filesystem_spec/pull/963#issuecomment-1131709948 :
> Non-async instances might be safe in the forked child, if they hold no open files/sockets etc.; I'm not sure any implementations pass this test!
> If any async instance has been created, the newly forked processes must:
> 1. discard references to locks, threads and event loops and make new ones
> 2. not use any async fsspec instances from the parent process
> 3. clear all class instance caches
Therefore in a DataLoader's worker, I clear the reference to the loop and thread (1). We should be fine for 2 and 3 already since we don't use fsspec class instances from the parent process.
Fix https://github.com/huggingface/datasets/issues/3950
Fix https://github.com/huggingface/datasets/issues/3951
TODO:
- [x] fix tests
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"Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L103)",
"Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de/2017/7006210/7063319.xml.gz en/2017/7006210/7050201.xml.gz 335 339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang/year/imdb_id/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps://www.imdb.com/title/tt7006210/, https://www.opensubtitles.org/en/subtitles/7063319 and https://www.opensubtitles.org/en/subtitles/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n",
"I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is fine.",
"Thanks for improving it @Valahaar :) ",
"Something like this? (adapted from [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n sentence_counter,\r\n {\r\n \"id\": str(sentence_counter),\r\n \"meta\": {\r\n \"year\": year,\r\n \"imdbId\": imdb_id,\r\n \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n # or maybe src/tgt? I'd go with the first one for consistency with 'translation'\r\n \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n },\r\n \"translation\": {l1: x, l2: y},\r\n },\r\n )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?",
"Merged in #1865, closing. Thanks :)"
] | 2021-02-08T13:55:13Z
| 2021-02-12T17:38:58Z
| 2021-02-12T17:38:58Z
|
CONTRIBUTOR
| null | null | null |
Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles).
I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.
I think I should tag @abhishekkrthakur as he's the one who added it in the first place.
Thanks!
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fix ETT m1/m2 test/val dataset
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thansk for the fix ! Can you regenerate the datasets_infos.json please ? This way it will update the expected number of examples in the test and val splits",
"ah yes!"
] | 2022-06-15T11:51:02Z
| 2022-06-15T14:55:56Z
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Make RedCaps streamable
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Make RedCaps streamable.
@lhoestq Using `data/redcaps_v1.0_annotations.zip` as a download URL gives an error locally when running `datasets-cli test` (will investigate this another time)
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`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext
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[
"Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.",
"It makes sense :D\r\n\r\nIt seems to work! Thanks a lot :))\r\n\r\nClosing the issue"
] | 2021-01-04T18:47:53Z
| 2021-01-04T19:04:45Z
| 2021-01-04T19:04:45Z
|
CONTRIBUTOR
| null | null | null |
It seems to fail the final batch ):
steps to reproduce:
```
from datasets import load_dataset
from elasticsearch import Elasticsearch
import torch
from transformers import file_utils, set_seed
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
MAX_SEQ_LENGTH = 256
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", cache_dir="../datasets/")
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-single-nq-base",
cache_dir="..datasets/"
)
dataset = load_dataset('text',
data_files='data/raw/ARC_Corpus.txt',
cache_dir='../datasets')
torch.set_grad_enabled(False)
ds_with_embeddings = dataset.map(
lambda example: {
'embeddings': ctx_encoder(
**ctx_tokenizer(
example["text"],
padding='max_length',
truncation=True,
max_length=MAX_SEQ_LENGTH,
return_tensors="pt"
)
)[0][0].numpy(),
},
batched=True,
load_from_cache_file=False,
batch_size=1000
)
```
ARC Corpus can be obtained from [here](https://ai2-datasets.s3-us-west-2.amazonaws.com/arc/ARC-V1-Feb2018.zip)
And then the error:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-67d139bb2ed3> in <module>
14 batched=True,
15 load_from_cache_file=False,
---> 16 batch_size=1000
17 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
161 # Call actual function
162
--> 163 out = func(self, *args, **kwargs)
164
165 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)
1526 if update_data:
1527 batch = cast_to_python_objects(batch)
-> 1528 writer.write_batch(batch)
1529 if update_data:
1530 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
276 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)
277 typed_sequence_examples[col] = typed_sequence
--> 278 pa_table = pa.Table.from_pydict(typed_sequence_examples)
279 self.write_table(pa_table)
280
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.validate()
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Column 1 named text expected length 768 but got length 1000
```
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Update tatoeba to v2021-07-22
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[
"How about this? @lhoestq @abhishekkrthakur ",
"Hi ! I think it would be nice if people could still be able to load the old version.\r\nMaybe this can be a parameter ? For example to load the old version they could do\r\n```python\r\nload_dataset(\"tatoeba\", lang1=\"en\", lang2=\"mr\", date=\"v2020-11-09\")\r\n```\r\n\r\nIf it sounds good to you, we can add this parameter to the TatoebaConfig:\r\n```python\r\nclass TatoebaConfig(datasets.BuilderConfig):\r\n def __init__(self, *args, lang1=None, lang2=None, date=\"v2021-07-22\", **kwargs):\r\n self.date = date\r\n```\r\nand then pass the date to the URL\r\n```python\r\n_BASE_URL = \"https://object.pouta.csc.fi/OPUS-Tatoeba/{}/moses/{}-{}.txt.zip\"\r\n```\r\n```python\r\n def _base_url(lang1, lang2, date):\r\n return _BASE_URL.format(date, lang1, lang2)\r\n```\r\n\r\nWhat do you think ?",
"`_DATE = \"v\" + \"-\".join(s.zfill(2) for s in _VERSION.split(\".\"))` seems rather tricky but works well. How about this? @lhoestq \r\n",
"The CI is only failing because of the missing sections in the dataset card, and because of an issue with the CER metric that is unrelated to this PR"
] | 2021-11-06T15:14:31Z
| 2021-11-12T11:13:13Z
| 2021-11-12T11:13:13Z
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Tatoeba's latest version is v2021-07-22
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Deprecate `Dataset.export`
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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) | 0.006680 / 0.011353 (-0.004673) | 0.003987 / 0.011008 (-0.007021) | 0.084677 / 0.038508 (0.046169) | 0.076800 / 0.023109 (0.053691) | 0.358338 / 0.275898 (0.082440) | 0.386573 / 0.323480 (0.063094) | 0.005370 / 0.007986 (-0.002616) | 0.003323 / 0.004328 (-0.001005) | 0.064238 / 0.004250 (0.059988) | 0.057859 / 0.037052 (0.020806) | 0.355408 / 0.258489 (0.096919) | 0.388302 / 0.293841 (0.094461) | 0.030784 / 0.128546 (-0.097762) | 0.008381 / 0.075646 (-0.067266) | 0.287971 / 0.419271 (-0.131300) | 0.053078 / 0.043533 (0.009545) | 0.352719 / 0.255139 (0.097580) | 0.370319 / 0.283200 (0.087119) | 0.023064 / 0.141683 (-0.118619) | 1.480661 / 1.452155 (0.028507) | 1.555711 / 1.492716 (0.062995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211289 / 0.018006 (0.193283) | 0.466957 / 0.000490 (0.466467) | 0.003760 / 0.000200 (0.003561) | 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) | 0.028552 / 0.037411 (-0.008859) | 0.084469 / 0.014526 (0.069943) | 0.096027 / 0.176557 (-0.080529) | 0.152170 / 0.737135 (-0.584965) | 0.096513 / 0.296338 (-0.199825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382940 / 0.215209 (0.167731) | 3.841735 / 2.077655 (1.764080) | 1.850575 / 1.504120 (0.346455) | 1.676554 / 1.541195 (0.135360) | 1.765241 / 1.468490 (0.296751) | 0.482131 / 4.584777 (-4.102646) | 3.512739 / 3.745712 (-0.232973) | 3.977042 / 5.269862 (-1.292820) | 2.387568 / 4.565676 (-2.178109) | 0.056657 / 0.424275 (-0.367618) | 0.007283 / 0.007607 (-0.000324) | 0.468193 / 0.226044 (0.242149) | 4.704077 / 2.268929 (2.435149) | 2.373467 / 55.444624 (-53.071157) | 2.002470 / 6.876477 (-4.874007) | 2.228280 / 2.142072 (0.086208) | 0.576908 / 4.805227 (-4.228320) | 0.132000 / 6.500664 (-6.368664) | 0.060544 / 0.075469 (-0.014926) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256168 / 1.841788 (-0.585619) | 19.965458 / 8.074308 (11.891150) | 14.521435 / 10.191392 (4.330043) | 0.159156 / 0.680424 (-0.521268) | 0.018170 / 0.534201 (-0.516031) | 0.393019 / 0.579283 (-0.186264) | 0.415002 / 0.434364 (-0.019362) | 0.471810 / 0.540337 (-0.068528) | 0.658907 / 1.386936 (-0.728029) |\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) | 0.006836 / 0.011353 (-0.004517) | 0.004067 / 0.011008 (-0.006942) | 0.066242 / 0.038508 (0.027734) | 0.078601 / 0.023109 (0.055491) | 0.369371 / 0.275898 (0.093473) | 0.402026 / 0.323480 (0.078546) | 0.006097 / 0.007986 (-0.001889) | 0.003337 / 0.004328 (-0.000991) | 0.065854 / 0.004250 (0.061603) | 0.057665 / 0.037052 (0.020612) | 0.379709 / 0.258489 (0.121219) | 0.406868 / 0.293841 (0.113027) | 0.031946 / 0.128546 (-0.096600) | 0.008691 / 0.075646 (-0.066955) | 0.071430 / 0.419271 (-0.347841) | 0.049518 / 0.043533 (0.005986) | 0.370439 / 0.255139 (0.115300) | 0.389235 / 0.283200 (0.106036) | 0.023730 / 0.141683 (-0.117953) | 1.509035 / 1.452155 (0.056880) | 1.548890 / 1.492716 (0.056173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229264 / 0.018006 (0.211258) | 0.445801 / 0.000490 (0.445312) | 0.000363 / 0.000200 (0.000163) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032377 / 0.037411 (-0.005034) | 0.091082 / 0.014526 (0.076556) | 0.104816 / 0.176557 (-0.071740) | 0.161040 / 0.737135 (-0.576095) | 0.105165 / 0.296338 (-0.191173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411012 / 0.215209 (0.195803) | 4.097256 / 2.077655 (2.019602) | 2.088686 / 1.504120 (0.584566) | 1.934429 / 1.541195 (0.393234) | 2.027387 / 1.468490 (0.558896) | 0.476262 / 4.584777 (-4.108515) | 3.518416 / 3.745712 (-0.227296) | 3.260919 / 5.269862 (-2.008943) | 2.041441 / 4.565676 (-2.524235) | 0.056302 / 0.424275 (-0.367973) | 0.007750 / 0.007607 (0.000143) | 0.489966 / 0.226044 (0.263922) | 4.915844 / 2.268929 (2.646916) | 2.617001 / 55.444624 (-52.827623) | 2.333557 / 6.876477 (-4.542920) | 2.484530 / 2.142072 (0.342458) | 0.572009 / 4.805227 (-4.233219) | 0.142557 / 6.500664 (-6.358107) | 0.066711 / 0.075469 (-0.008758) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359929 / 1.841788 (-0.481859) | 20.332252 / 8.074308 (12.257943) | 14.585842 / 10.191392 (4.394450) | 0.170498 / 0.680424 (-0.509926) | 0.018450 / 0.534201 (-0.515751) | 0.395449 / 0.579283 (-0.183834) | 0.409666 / 0.434364 (-0.024698) | 0.467937 / 0.540337 (-0.072401) | 0.616078 / 1.386936 (-0.770858) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a888bc94dc6bce7815e3061a28e718097f4b8b9e \"CML watermark\")\n"
] | 2023-07-27T14:22:18Z
| 2023-07-28T11:09:54Z
| 2023-07-28T11:01:04Z
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Deprecate `Dataset.export` that generates a TFRecord file from a dataset as this method is undocumented, and the usage seems low. Users should use [TFRecordWriter](https://www.tensorflow.org/api_docs/python/tf/io/TFRecordWriter#write) or the official [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) tutorial (on which this method is based) to write TFRecord files instead.
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Depracate `num_proc` parameter in `DownloadManager.extract`
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"I can take this! #self-assign",
"#self-assign",
"@lazarust i'm already working on this issue :smile: ",
"#self-assign",
"hey @mariosasko , i made a pr for this issue. Could you please review it."
] | 2022-10-18T17:41:05Z
| 2022-10-25T15:56:46Z
| 2022-10-25T15:56:46Z
|
CONTRIBUTOR
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The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`.
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load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied
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"Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB/s]\r\nDownloading: 2.71kB [00:00, ?B/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory",
"Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue.",
"Hi @albertvillanova, even I am facing the same issue on my work machine:\r\n\r\n`Downloading and preparing dataset json/c4-en-html-with-metadata to C:\\Users\\......\\.cache\\huggingface\\datasets\\json\\c4-en-html-with-metadata-4635c2fd9249f62d\\0.0.0\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde...\r\n100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 983.42it/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 209.01it/s]\r\nTraceback (most recent call last):\r\n File \"bsmetadata/preprocessing_utils.py\", line 710, in <module>\r\n ds = load_dataset(\r\n File \"C:\\Users\\.......\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\load.py\", line 1694, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\........\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 603, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\..........\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\.....\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 557, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\.........\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde.incomplete' -> 'C:\\\\Users\\\\I355109\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde'`",
"I'm facing the same issue.\r\n\r\n## System Information\r\n\r\n- OS Edition: Windows 10 21H1\r\n- OS build: 19043.1826\r\n- Python version: 3.10.6 (installed using `choco install python`)\r\n- datasets: 2.4.0\r\n- PyArrow: 6.0.1\r\n\r\n## Troubleshooting steps\r\n\r\n- Restart the computer, unfortunately doesn't work! 🌚\r\n- Checked the permissions of `~./cache/...`, looks fine.\r\n- Tested with a simple file operation using the `open()` function and writing a hello_world.txt, it works fine.\r\n- Tested with a different `cache_dir` value on the `load_dataset()`, e.g. \"./data\"\r\n- Tested different datasets: `conll2003`, `squad_v2`, and `wiki_bio`.\r\n- Downgraded datasets from `2.4.0` to `2.1.0`, issue persists.\r\n- Tested it on WSL (Ubuntu 20.04), and it works! \r\n- Python reinstallation, in the first time downloading `conll2003` works fine, but `squad` or `squad_v2` raises Access Denied.\r\n - After the system or VSCode restart, the issue comes back.\r\n\r\n## Resolution\r\n\r\nI fixed it by changing the following command:\r\n\r\nhttps://github.com/huggingface/datasets/blob/68cffe30917a9abed68d28caf54b40c10f977602/src/datasets/builder.py#L666\r\n\r\nfor\r\n\r\n```python\r\nshutil.move(tmp_dir, dirname)\r\n```"
] | 2021-09-17T16:52:10Z
| 2022-08-24T13:09:08Z
| 2022-08-24T13:09:08Z
|
NONE
| null | null | null |
## Describe the bug
Standard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('wiki_bio')
```
## Expected results
It is expected that the dataset downloads without any errors.
## Actual results
PermissionError see trace below:
```
Using custom data configuration default
Downloading and preparing dataset wiki_bio/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\Users\username\.cache\huggingface\datasets\wiki_bio\default\1.1.0\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\load.py", line 1112, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 644, in download_and_prepare
self._save_info()
File "C:\Users\username\.conda\envs\hf\lib\contextlib.py", line 120, in __exit__
next(self.gen)
File "C:\Users\username\.conda\envs\hf\lib\site-packages\datasets\builder.py", line 598, in incomplete_dir
os.rename(tmp_dir, dirname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'
```
By commenting out the os.rename() [L604](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L604) and the shutil.rmtree() [L607](https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.
It seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https://github.com/conan-io/conan/issues/6560) with similar issue with os.rename() if it helps debug this issue.
## Environment info
- `datasets` version: 1.12.1
- Platform: Windows-10-10.0.22449-SP0
- Python version: 3.8.12
- PyArrow version: 5.0.0
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"This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\nTry to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\nSee issues #242 and #307 ",
"It should be in `xtreme.py:L755`:\r\n```python\r\n if self.config.name == \"tydiqa\" or self.config.name.startswith(\"MLQA\") or self.config.name == \"SQuAD\":\r\n with open(filepath) as f:\r\n data = json.load(f)\r\n```\r\n\r\nCould you try to add the encoding parameter:\r\n```python\r\nopen(filepath, encoding='utf-8')\r\n```",
"Hello @jerryIsHere :) Did it work ?\r\nIf so we may change the dataset script to force the utf-8 encoding",
"@lhoestq sorry for being that late, I found 4 copy of xtreme.py. I did the changes as what has been told to all of them.\r\nThe problem is not solved",
"Could you provide a better error message so that we can make sure it comes from the opening of the `tydiqa`'s json files ?\r\n",
"@lhoestq \r\nThe error message is same as before:\r\nException has occurred: UnicodeDecodeError\r\n'cp950' codec can't decode byte 0xe2 in position 111: illegal multibyte sequence\r\n File \"D:\\python\\test\\test.py\", line 3, in <module>\r\n dataset = load_dataset('xtreme', 'tydiqa')\r\n\r\n\r\n\r\nI said that I found 4 copy of xtreme.py and add the 「, encoding='utf-8'」 parameter to the open() function\r\nthese python script was found under this directory\r\nC:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python37\\Lib\\site-packages\\nlp\\datasets\\xtreme\r\n",
"Hi there !\r\nI encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\nI added ```encoding='UTF-8'``` to both lines that have ```open``` in ```imdb.py``` (108 and 114) and it worked for me.\r\nThank you !",
"> Hi there !\r\n> I encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\n> I added `encoding='UTF-8'` to both lines that have `open` in `imdb.py` (108 and 114) and it worked for me.\r\n> Thank you !\r\n\r\nHello !\r\nGlad you managed to fix this issue on your side.\r\nDo you mind opening a PR for IMDB ?",
"> This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\n> Try to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\n> See issues #242 and #307\r\n\r\nSorry for not responding for about a month.\r\nI have just found that it is necessary to change / add the environment variable as what was told in #242.\r\nEverything works after I add the new environment variable and restart my PC.\r\n\r\nI think the encoding issue for windows isn't limited to the open() function call specific to few dataset, but actually in the entire library, depends on the machine / os you use.",
"Since #481 we shouldn't have other issues with encodings as they need to be set to \"utf-8\" be default.\r\n\r\nClosing this one, but feel free to re-open if you gave other questions"
] | 2020-07-07T08:14:23Z
| 2020-09-07T14:51:45Z
| 2020-09-07T14:51:45Z
|
CONTRIBUTOR
| null | null | null |
I guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :
https://www.python.org/dev/peps/pep-0263/
I guess the error was triggered by the code " module = importlib.import_module(module_path)" at line 57 in the source code: nlp/src/nlp/load.py / (https://github.com/huggingface/nlp/blob/911d5596f9b500e39af8642fe3d1b891758999c7/src/nlp/load.py#L51)
Any ideas?
p.s. tried the same code on colab, that runs perfectly
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[] | 2020-09-10T10:47:44Z
| 2020-09-10T11:02:05Z
| 2020-09-10T11:02:04Z
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By default the scripts version is master, so that if the library is installed with
```
pip install git+http://github.com/huggingface/nlp.git
```
or
```
git clone http://github.com/huggingface/nlp.git
pip install -e ./nlp
```
will use the latest scripts, and not the ones from the previous version.
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Add XFUN dataset
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"Not sure how to generate dummy data.\r\n\r\nThe downloaded file structure is \r\n\r\n- document file paths\r\n - (a json file containing all documents info, document images folder)\r\n - (a json file containing all documents info, document images folder)\r\n - ...",
"Hey @mariosasko, thanks for the review. I'm not sure how to suggest these changes to the owner @ranpox, and I did spend some time to write the model card and hope to get it on the official repo. Is that possible?",
"Since the author is not responding, maybe we can go ahead with this PR ?",
"Go for it!\n\nOn Tue, Apr 12, 2022 at 10:24 AM Quentin Lhoest ***@***.***>\nwrote:\n\n> Since the author is not responding, maybe we can go ahead with this PR ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/3974#issuecomment-1096797650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ATFNL66EVUFWS3P2FOAS7SLVEWBP3ANCNFSM5RFH3MXA>\n> .\n> You are receiving this because you are subscribed to this thread.Message\n> ID: ***@***.***>\n>\n",
"@qqaatw Do you plan to finish this PR? I can give you some pointers and help you with the code if needed.",
"@mariosasko Yes, I'll apply all of the suggestions when I have some time.",
"Thanks for your contribution, @qqaatw.\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you propose this changes there to the original repo. Please, feel free to tell us if you need some help."
] | 2022-03-20T09:24:54Z
| 2022-10-03T09:38:16Z
| 2022-10-03T09:36:22Z
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This PR adds XFUN dataset.
Home page and repository: https://github.com/doc-analysis/XFUND
Source code: https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/data/datasets/xfun.py
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Read GeoParquet files using parquet reader
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6508). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2023-12-18T04:50:37Z
| 2023-12-18T10:36:34Z
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NONE
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Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader.
Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75
Addresses https://github.com/huggingface/datasets/issues/6438
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speedup
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] | 2023-06-27T09:17:58Z
| 2023-06-27T09:23:07Z
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Newline characters don't behave as expected when calling dataset.info
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[] | 2023-12-12T23:07:51Z
| 2023-12-13T13:24:22Z
| null |
NONE
| null | null | null |
### System Info
- `transformers` version: 4.32.1
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.11.5
- Huggingface_hub version: 0.15.1
- Safetensors version: 0.3.2
- Accelerate version: not installed
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.1+cpu (False)
- Tensorflow version (GPU?): 2.15.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no
### Who can help?
@marios
### Information
- [X] The official example scripts
- [ ] My own modified scripts
### Tasks
- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
[Source](https://huggingface.co/docs/datasets/v2.2.1/en/access)
```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset.info
```
DatasetInfo(description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n', citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398', license='', features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='glue', dataset_name=None, config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943843, num_examples=3668, shard_lengths=None, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105879, num_examples=408, shard_lengths=None, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442410, num_examples=1725, shard_lengths=None, dataset_name='glue')}, download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': None}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': None}}, download_size=1494541, post_processing_size=None, dataset_size=1492132, size_in_bytes=2986673)
### Expected behavior
```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset.info
```
DatasetInfo(
description='GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n',
citation='@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n', homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398',
license='',
features={'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None), 'idx': Value(dtype='int32', id=None)}, post_processed=None, supervised_keys=None, builder_name='glue', config_name='mrpc', version=1.0.0, splits={'train': SplitInfo(name='train', num_bytes=943851, num_examples=3668, dataset_name='glue'), 'validation': SplitInfo(name='validation', num_bytes=105887, num_examples=408, dataset_name='glue'), 'test': SplitInfo(name='test', num_bytes=442418, num_examples=1725, dataset_name='glue')},
download_checksums={'https://dl.fbaipublicfiles.com/glue/data/mrpc_dev_ids.tsv': {'num_bytes': 6222, 'checksum': '971d7767d81b997fd9060ade0ec23c4fc31cbb226a55d1bd4a1bac474eb81dc7'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt': {'num_bytes': 1047044, 'checksum': '60a9b09084528f0673eedee2b69cb941920f0b8cd0eeccefc464a98768457f89'}, 'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt': {'num_bytes': 441275, 'checksum': 'a04e271090879aaba6423d65b94950c089298587d9c084bf9cd7439bd785f784'}},
download_size=1494541,
post_processing_size=None,
dataset_size=1492156,
size_in_bytes=2986697
)
|
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MDExOlB1bGxSZXF1ZXN0NzMxNjQ0NTY2
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|
Use pyarrow.Table.replace_schema_metadata instead of pyarrow.Table.cast
|
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| 2021-09-21T22:50:01Z
| 2021-09-21T08:18:35Z
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|
This PR partially addresses #2252.
``update_metadata_with_features`` uses ``Table.cast`` which slows down ``load_from_disk`` (and possibly other methods that use it) for very large datasets. Since ``update_metadata_with_features`` is only updating the schema metadata, it makes more sense to use ``pyarrow.Table.replace_schema_metadata`` which is much faster. This PR adds a ``replace_schema_metadata`` method to all table classes, and modifies ``update_metadata_with_features`` to use it instead of ``cast``.
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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) | 0.006897 / 0.011353 (-0.004456) | 0.004207 / 0.011008 (-0.006802) | 0.104828 / 0.038508 (0.066320) | 0.048054 / 0.023109 (0.024945) | 0.373991 / 0.275898 (0.098093) | 0.426740 / 0.323480 (0.103260) | 0.005540 / 0.007986 (-0.002446) | 0.003531 / 0.004328 (-0.000797) | 0.079304 / 0.004250 (0.075053) | 0.066996 / 0.037052 (0.029944) | 0.370675 / 0.258489 (0.112186) | 0.414154 / 0.293841 (0.120313) | 0.031567 / 0.128546 (-0.096979) | 0.008843 / 0.075646 (-0.066803) | 0.357426 / 0.419271 (-0.061845) | 0.067040 / 0.043533 (0.023508) | 0.362384 / 0.255139 (0.107245) | 0.376056 / 0.283200 (0.092856) | 0.032985 / 0.141683 (-0.108697) | 1.560603 / 1.452155 (0.108448) | 1.619024 / 1.492716 (0.126308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229059 / 0.018006 (0.211053) | 0.440513 / 0.000490 (0.440023) | 0.004647 / 0.000200 (0.004447) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029517 / 0.037411 (-0.007894) | 0.120974 / 0.014526 (0.106448) | 0.125070 / 0.176557 (-0.051486) | 0.184695 / 0.737135 (-0.552441) | 0.130244 / 0.296338 (-0.166095) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436930 / 0.215209 (0.221721) | 4.356118 / 2.077655 (2.278463) | 2.049169 / 1.504120 (0.545049) | 1.842898 / 1.541195 (0.301703) | 1.918948 / 1.468490 (0.450458) | 0.553573 / 4.584777 (-4.031204) | 3.883195 / 3.745712 (0.137483) | 3.209780 / 5.269862 (-2.060081) | 1.551707 / 4.565676 (-3.013970) | 0.068181 / 0.424275 (-0.356094) | 0.012370 / 0.007607 (0.004762) | 0.539899 / 0.226044 (0.313854) | 5.380008 / 2.268929 (3.111079) | 2.518178 / 55.444624 (-52.926446) | 2.174190 / 6.876477 (-4.702286) | 2.317812 / 2.142072 (0.175740) | 0.674154 / 4.805227 (-4.131073) | 0.149313 / 6.500664 (-6.351351) | 0.068297 / 0.075469 (-0.007172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261426 / 1.841788 (-0.580362) | 15.316378 / 8.074308 (7.242070) | 13.573512 / 10.191392 (3.382120) | 0.190022 / 0.680424 (-0.490401) | 0.018697 / 0.534201 (-0.515504) | 0.448122 / 0.579283 (-0.131161) | 0.435044 / 0.434364 (0.000681) | 0.550065 / 0.540337 (0.009728) | 0.653547 / 1.386936 (-0.733389) |\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) | 0.007116 / 0.011353 (-0.004237) | 0.004375 / 0.011008 (-0.006633) | 0.081793 / 0.038508 (0.043285) | 0.047980 / 0.023109 (0.024871) | 0.392185 / 0.275898 (0.116287) | 0.462263 / 0.323480 (0.138783) | 0.005574 / 0.007986 (-0.002412) | 0.003552 / 0.004328 (-0.000776) | 0.080413 / 0.004250 (0.076162) | 0.065539 / 0.037052 (0.028487) | 0.413137 / 0.258489 (0.154648) | 0.467377 / 0.293841 (0.173536) | 0.034386 / 0.128546 (-0.094160) | 0.009183 / 0.075646 (-0.066464) | 0.087542 / 0.419271 (-0.331730) | 0.053954 / 0.043533 (0.010421) | 0.385096 / 0.255139 (0.129957) | 0.404900 / 0.283200 (0.121701) | 0.025908 / 0.141683 (-0.115775) | 1.550159 / 1.452155 (0.098005) | 1.598794 / 1.492716 (0.106078) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246222 / 0.018006 (0.228216) | 0.441095 / 0.000490 (0.440605) | 0.006863 / 0.000200 (0.006663) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032179 / 0.037411 (-0.005233) | 0.120112 / 0.014526 (0.105586) | 0.129326 / 0.176557 (-0.047230) | 0.184542 / 0.737135 (-0.552593) | 0.135038 / 0.296338 (-0.161300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459002 / 0.215209 (0.243793) | 4.580258 / 2.077655 (2.502604) | 2.296689 / 1.504120 (0.792569) | 2.104338 / 1.541195 (0.563143) | 2.182896 / 1.468490 (0.714406) | 0.546447 / 4.584777 (-4.038330) | 3.854047 / 3.745712 (0.108335) | 1.873829 / 5.269862 (-3.396032) | 1.116484 / 4.565676 (-3.449193) | 0.067158 / 0.424275 (-0.357117) | 0.012035 / 0.007607 (0.004428) | 0.556642 / 0.226044 (0.330597) | 5.574436 / 2.268929 (3.305508) | 2.828223 / 55.444624 (-52.616402) | 2.519851 / 6.876477 (-4.356626) | 2.668594 / 2.142072 (0.526521) | 0.675989 / 4.805227 (-4.129238) | 0.146075 / 6.500664 (-6.354589) | 0.067788 / 0.075469 (-0.007681) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345958 / 1.841788 (-0.495830) | 15.672748 / 8.074308 (7.598440) | 14.937583 / 10.191392 (4.746191) | 0.163479 / 0.680424 (-0.516945) | 0.018364 / 0.534201 (-0.515837) | 0.433296 / 0.579283 (-0.145987) | 0.432463 / 0.434364 (-0.001901) | 0.512000 / 0.540337 (-0.028338) | 0.619397 / 1.386936 (-0.767539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"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) | 0.010097 / 0.011353 (-0.001256) | 0.005070 / 0.011008 (-0.005939) | 0.118638 / 0.038508 (0.080130) | 0.043651 / 0.023109 (0.020542) | 0.356074 / 0.275898 (0.080176) | 0.414578 / 0.323480 (0.091098) | 0.005939 / 0.007986 (-0.002046) | 0.004927 / 0.004328 (0.000598) | 0.089545 / 0.004250 (0.085294) | 0.067533 / 0.037052 (0.030481) | 0.371550 / 0.258489 (0.113061) | 0.417808 / 0.293841 (0.123967) | 0.045186 / 0.128546 (-0.083361) | 0.015763 / 0.075646 (-0.059883) | 0.393304 / 0.419271 (-0.025967) | 0.065123 / 0.043533 (0.021591) | 0.345057 / 0.255139 (0.089918) | 0.378809 / 0.283200 (0.095610) | 0.033243 / 0.141683 (-0.108440) | 1.679956 / 1.452155 (0.227802) | 1.775456 / 1.492716 (0.282739) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229723 / 0.018006 (0.211717) | 0.554630 / 0.000490 (0.554140) | 0.008729 / 0.000200 (0.008529) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027284 / 0.037411 (-0.010128) | 0.114741 / 0.014526 (0.100215) | 0.129188 / 0.176557 (-0.047369) | 0.189270 / 0.737135 (-0.547866) | 0.126000 / 0.296338 (-0.170339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580417 / 0.215209 (0.365208) | 5.829337 / 2.077655 (3.751683) | 2.421191 / 1.504120 (0.917071) | 2.063673 / 1.541195 (0.522479) | 2.133427 / 1.468490 (0.664937) | 0.830964 / 4.584777 (-3.753813) | 5.107139 / 3.745712 (1.361427) | 4.599451 / 5.269862 (-0.670410) | 2.406502 / 4.565676 (-2.159175) | 0.100422 / 0.424275 (-0.323853) | 0.011850 / 0.007607 (0.004243) | 0.741881 / 0.226044 (0.515836) | 7.425689 / 2.268929 (5.156760) | 3.068948 / 55.444624 (-52.375676) | 2.496292 / 6.876477 (-4.380184) | 2.566420 / 2.142072 (0.424348) | 1.093084 / 4.805227 (-3.712144) | 0.224106 / 6.500664 (-6.276558) | 0.084549 / 0.075469 (0.009080) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.416315 / 1.841788 (-0.425473) | 16.306901 / 8.074308 (8.232593) | 19.792419 / 10.191392 (9.601027) | 0.224223 / 0.680424 (-0.456201) | 0.026385 / 0.534201 (-0.507816) | 0.463460 / 0.579283 (-0.115823) | 0.598385 / 0.434364 (0.164021) | 0.543981 / 0.540337 (0.003644) | 0.647454 / 1.386936 (-0.739482) |\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) | 0.009470 / 0.011353 (-0.001883) | 0.004800 / 0.011008 (-0.006208) | 0.094276 / 0.038508 (0.055768) | 0.045157 / 0.023109 (0.022048) | 0.397302 / 0.275898 (0.121404) | 0.474213 / 0.323480 (0.150733) | 0.005826 / 0.007986 (-0.002160) | 0.003724 / 0.004328 (-0.000605) | 0.090060 / 0.004250 (0.085809) | 0.066671 / 0.037052 (0.029618) | 0.439560 / 0.258489 (0.181071) | 0.468598 / 0.293841 (0.174757) | 0.044549 / 0.128546 (-0.083997) | 0.014000 / 0.075646 (-0.061646) | 0.110457 / 0.419271 (-0.308815) | 0.065898 / 0.043533 (0.022365) | 0.408101 / 0.255139 (0.152962) | 0.433473 / 0.283200 (0.150273) | 0.038438 / 0.141683 (-0.103245) | 1.767781 / 1.452155 (0.315626) | 1.791575 / 1.492716 (0.298859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230257 / 0.018006 (0.212251) | 0.492280 / 0.000490 (0.491790) | 0.005110 / 0.000200 (0.004910) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028854 / 0.037411 (-0.008557) | 0.111702 / 0.014526 (0.097176) | 0.122040 / 0.176557 (-0.054517) | 0.179103 / 0.737135 (-0.558032) | 0.128869 / 0.296338 (-0.167470) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634795 / 0.215209 (0.419586) | 6.204760 / 2.077655 (4.127105) | 2.692479 / 1.504120 (1.188359) | 2.324260 / 1.541195 (0.783066) | 2.380640 / 1.468490 (0.912149) | 0.887827 / 4.584777 (-3.696950) | 5.251648 / 3.745712 (1.505935) | 2.632767 / 5.269862 (-2.637095) | 1.745721 / 4.565676 (-2.819955) | 0.108364 / 0.424275 (-0.315911) | 0.013409 / 0.007607 (0.005802) | 0.783427 / 0.226044 (0.557383) | 7.765144 / 2.268929 (5.496216) | 3.340686 / 55.444624 (-52.103938) | 2.715340 / 6.876477 (-4.161137) | 2.768604 / 2.142072 (0.626531) | 1.119746 / 4.805227 (-3.685481) | 0.210804 / 6.500664 (-6.289860) | 0.072600 / 0.075469 (-0.002869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517334 / 1.841788 (-0.324454) | 17.046837 / 8.074308 (8.972529) | 19.371090 / 10.191392 (9.179698) | 0.194275 / 0.680424 (-0.486148) | 0.026712 / 0.534201 (-0.507488) | 0.462731 / 0.579283 (-0.116552) | 0.568958 / 0.434364 (0.134595) | 0.555707 / 0.540337 (0.015370) | 0.663654 / 1.386936 (-0.723283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d20476b1d4c8e11e0ffafc1570cbf4bd19011cf \"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) | 0.006423 / 0.011353 (-0.004930) | 0.003882 / 0.011008 (-0.007126) | 0.082976 / 0.038508 (0.044468) | 0.071281 / 0.023109 (0.048171) | 0.311367 / 0.275898 (0.035469) | 0.348228 / 0.323480 (0.024748) | 0.005315 / 0.007986 (-0.002671) | 0.003326 / 0.004328 (-0.001003) | 0.064641 / 0.004250 (0.060391) | 0.056134 / 0.037052 (0.019081) | 0.314071 / 0.258489 (0.055582) | 0.360534 / 0.293841 (0.066693) | 0.030642 / 0.128546 (-0.097904) | 0.008301 / 0.075646 (-0.067345) | 0.285820 / 0.419271 (-0.133451) | 0.069241 / 0.043533 (0.025708) | 0.313995 / 0.255139 (0.058856) | 0.336656 / 0.283200 (0.053457) | 0.031686 / 0.141683 (-0.109997) | 1.467627 / 1.452155 (0.015472) | 1.536493 / 1.492716 (0.043777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196518 / 0.018006 (0.178512) | 0.458235 / 0.000490 (0.457745) | 0.005599 / 0.000200 (0.005399) | 0.000088 / 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) | 0.027371 / 0.037411 (-0.010040) | 0.080986 / 0.014526 (0.066460) | 0.093296 / 0.176557 (-0.083260) | 0.150592 / 0.737135 (-0.586543) | 0.094150 / 0.296338 (-0.202188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379412 / 0.215209 (0.164202) | 3.797927 / 2.077655 (1.720272) | 1.830654 / 1.504120 (0.326534) | 1.669569 / 1.541195 (0.128374) | 1.746738 / 1.468490 (0.278248) | 0.479536 / 4.584777 (-4.105241) | 3.592867 / 3.745712 (-0.152845) | 5.468098 / 5.269862 (0.198237) | 3.268013 / 4.565676 (-1.297663) | 0.056635 / 0.424275 (-0.367640) | 0.007224 / 0.007607 (-0.000383) | 0.456681 / 0.226044 (0.230636) | 4.566736 / 2.268929 (2.297807) | 2.362831 / 55.444624 (-53.081793) | 1.965141 / 6.876477 (-4.911336) | 2.156905 / 2.142072 (0.014833) | 0.572543 / 4.805227 (-4.232684) | 0.132203 / 6.500664 (-6.368461) | 0.059254 / 0.075469 (-0.016215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256134 / 1.841788 (-0.585654) | 19.905438 / 8.074308 (11.831130) | 14.179556 / 10.191392 (3.988164) | 0.168043 / 0.680424 (-0.512381) | 0.018215 / 0.534201 (-0.515986) | 0.392740 / 0.579283 (-0.186543) | 0.398397 / 0.434364 (-0.035967) | 0.463806 / 0.540337 (-0.076531) | 0.616248 / 1.386936 (-0.770688) |\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) | 0.006564 / 0.011353 (-0.004789) | 0.003923 / 0.011008 (-0.007085) | 0.063929 / 0.038508 (0.025421) | 0.073780 / 0.023109 (0.050671) | 0.360242 / 0.275898 (0.084344) | 0.395078 / 0.323480 (0.071598) | 0.005265 / 0.007986 (-0.002720) | 0.003229 / 0.004328 (-0.001100) | 0.064094 / 0.004250 (0.059843) | 0.057468 / 0.037052 (0.020416) | 0.369530 / 0.258489 (0.111041) | 0.411159 / 0.293841 (0.117318) | 0.031278 / 0.128546 (-0.097268) | 0.008424 / 0.075646 (-0.067222) | 0.070411 / 0.419271 (-0.348860) | 0.048714 / 0.043533 (0.005181) | 0.361280 / 0.255139 (0.106141) | 0.382468 / 0.283200 (0.099269) | 0.023059 / 0.141683 (-0.118624) | 1.452369 / 1.452155 (0.000215) | 1.519192 / 1.492716 (0.026475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223745 / 0.018006 (0.205739) | 0.442086 / 0.000490 (0.441596) | 0.000379 / 0.000200 (0.000179) | 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) | 0.030919 / 0.037411 (-0.006493) | 0.088483 / 0.014526 (0.073958) | 0.101165 / 0.176557 (-0.075391) | 0.154332 / 0.737135 (-0.582804) | 0.103030 / 0.296338 (-0.193309) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414520 / 0.215209 (0.199311) | 4.126754 / 2.077655 (2.049099) | 2.142677 / 1.504120 (0.638557) | 1.995300 / 1.541195 (0.454106) | 2.101678 / 1.468490 (0.633188) | 0.481099 / 4.584777 (-4.103678) | 3.562813 / 3.745712 (-0.182900) | 3.392463 / 5.269862 (-1.877399) | 1.983943 / 4.565676 (-2.581734) | 0.056594 / 0.424275 (-0.367681) | 0.007216 / 0.007607 (-0.000391) | 0.495085 / 0.226044 (0.269041) | 4.955640 / 2.268929 (2.686712) | 2.629434 / 55.444624 (-52.815191) | 2.269577 / 6.876477 (-4.606900) | 2.357708 / 2.142072 (0.215635) | 0.612370 / 4.805227 (-4.192857) | 0.131169 / 6.500664 (-6.369495) | 0.061029 / 0.075469 (-0.014440) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.339438 / 1.841788 (-0.502350) | 19.757611 / 8.074308 (11.683303) | 14.246254 / 10.191392 (4.054862) | 0.170750 / 0.680424 (-0.509674) | 0.018192 / 0.534201 (-0.516009) | 0.395693 / 0.579283 (-0.183590) | 0.411003 / 0.434364 (-0.023361) | 0.478531 / 0.540337 (-0.061806) | 0.650291 / 1.386936 (-0.736645) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e34d06d746688dd5d26e4c85517b7e1a2f361ca \"CML watermark\")\n"
] | 2023-07-03T18:29:14Z
| 2023-07-06T17:04:11Z
| 2023-07-06T16:55:25Z
|
CONTRIBUTOR
| null | 0
|
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Contains the following improvements:
* fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section
* updates `Makefile` to also run the style checks on `utils` and `setup.py`
* deletes a test for GH-hosted datasets (no longer supported)
* deletes `convert_dataset.sh` (outdated)
* aligns `utils/release.py` with `transformers` (the current version is outdated)
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MDExOlB1bGxSZXF1ZXN0NTgyNzgyNTU0
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Fix ArrowWriter closes stream at exit
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"Oh nice thanks for adding the context manager ! All the streams and RecordBatchWriter will be properly closed now. Hopefully this gives a better experience on windows on which it's super important to close stuff.\r\n\r\nNot sure about the error, it looks like a process crashed silently.\r\nLet me take a look",
"> Hopefully this gives a better experience on windows on which it's super important to close stuff.\r\n\r\nExactly! On Windows, you got:\r\n> PermissionError: [WinError 32] The process cannot access the file because it is being used by another process\r\n\r\nwhen trying to access the unclosed `stream` file, e.g. by `with incomplete_dir(self._cache_dir) as tmp_data_dir`: `shutil.rmtree(tmp_dir)`\r\n\r\nThe reason is: https://docs.python.org/3/library/os.html#os.remove\r\n\r\n> On Windows, attempting to remove a file that is in use causes an exception to be raised; on Unix, the directory entry is removed but the storage allocated to the file is not made available until the original file is no longer in use.\r\n\r\n\r\n",
"The test passes on my windows. This was probably a circleCI issue. I re-ran the circleCI tests",
"NICE! It passed!",
"Maybe you can merge master into this branch and check the CI before merging ?",
"@lhoestq done! ;)",
"Thanks ! merging"
] | 2021-03-02T07:12:34Z
| 2021-03-10T16:36:57Z
| 2021-03-10T16:36:57Z
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MEMBER
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Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method.
Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.
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I_kwDODunzps5PDy-Q
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Add Multiface dataset
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"Hi @osanseviero I would like to add this dataset.",
"Hey @nandwalritik! Thanks for offering to help!\r\n\r\nThis dataset might be somewhat complex and I'm concerned about it being 65 TB, which would be quite expensive to host. @lhoestq @mariosasko I would love your input if you think it's worth adding this dataset.",
"Thanks for proposing this interesting dataset, @osanseviero.\r\n\r\nPlease note that the data files are already hosted in a third-party server: e.g. the index of data files for entity \"6795937\" is at https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/index.html \r\n- audio.tar: https://fb-baas-f32eacb9-8abb-11eb-b2b8-4857dd089e15.s3.amazonaws.com/MugsyDataRelease/v0.0/identities/6795937/audio.tar\r\n- ...\r\n\r\nTherefore, in principle, we don't need to host them on our Hub: it would be enough to just implement a loading script in the corresponding Hub dataset repo, e.g. \"facebook/multiface\"..."
] | 2022-08-02T21:00:22Z
| 2022-08-08T14:42:36Z
| null |
MEMBER
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## Adding a Dataset
- **Name:** Multiface dataset
- **Description:** f high quality recordings of the faces of 13 identities, each captured in a multi-view capture stage performing various facial expressions. An average of 12,200 (v1 scripts) to 23,000 (v2 scripts) frames per subject with capture rate at 30 fps
- **Data:** https://github.com/facebookresearch/multiface
The whole dataset is 65TB though, so I'm not sure
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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fix typo readme
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Implicit type conversion of lists in to_pandas
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[
"I think this behavior comes from PyArrow:\r\n```python\r\nimport pyarrow as pa\r\nt = pa.table({\"a\": [[0]]})\r\nt.to_pandas().a.values[0]\r\n# array([0])\r\n```\r\n\r\nI believe this has to do with zero-copy: you can get a pandas DataFrame without copying the buffers from arrow, and therefore end up with numpy arrays.",
"That's interesting, I guess not much to do here then."
] | 2022-11-09T08:40:18Z
| 2022-11-10T16:12:26Z
| 2022-11-10T16:12:26Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
```
ds = Dataset.from_list([{'a':[1,2,3]}])
ds.to_pandas().a.values[0]
```
Results in `array([1, 2, 3])` -- a rather unexpected conversion of types which made downstream tools expecting lists not happy.
### Steps to reproduce the bug
See snippet
### Expected behavior
Keep the original type
### Environment info
datasets 2.6.1
python 3.8.10
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Fix dependencies conflicts in Windows CI after conda update to 4.11
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For some reason the CI wasn't using python 3.6 but python 3.7 after the update to conda 4.11
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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_6103). 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) | 0.006528 / 0.011353 (-0.004825) | 0.003909 / 0.011008 (-0.007099) | 0.083954 / 0.038508 (0.045446) | 0.070513 / 0.023109 (0.047404) | 0.344362 / 0.275898 (0.068464) | 0.370278 / 0.323480 (0.046798) | 0.005395 / 0.007986 (-0.002591) | 0.003323 / 0.004328 (-0.001005) | 0.064538 / 0.004250 (0.060288) | 0.055616 / 0.037052 (0.018564) | 0.353590 / 0.258489 (0.095101) | 0.382159 / 0.293841 (0.088318) | 0.031133 / 0.128546 (-0.097414) | 0.008429 / 0.075646 (-0.067217) | 0.288665 / 0.419271 (-0.130606) | 0.052626 / 0.043533 (0.009093) | 0.347676 / 0.255139 (0.092537) | 0.363726 / 0.283200 (0.080526) | 0.021956 / 0.141683 (-0.119727) | 1.506091 / 1.452155 (0.053936) | 1.563940 / 1.492716 (0.071223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207658 / 0.018006 (0.189652) | 0.473411 / 0.000490 (0.472922) | 0.005437 / 0.000200 (0.005237) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027769 / 0.037411 (-0.009643) | 0.082566 / 0.014526 (0.068040) | 0.092700 / 0.176557 (-0.083857) | 0.152589 / 0.737135 (-0.584546) | 0.093772 / 0.296338 (-0.202566) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401072 / 0.215209 (0.185863) | 3.997922 / 2.077655 (1.920267) | 2.028223 / 1.504120 (0.524103) | 1.845229 / 1.541195 (0.304035) | 1.883980 / 1.468490 (0.415489) | 0.485112 / 4.584777 (-4.099665) | 3.657048 / 3.745712 (-0.088664) | 4.998475 / 5.269862 (-0.271386) | 3.007417 / 4.565676 (-1.558259) | 0.057003 / 0.424275 (-0.367272) | 0.007270 / 0.007607 (-0.000338) | 0.482220 / 0.226044 (0.256176) | 4.817560 / 2.268929 (2.548631) | 2.484285 / 55.444624 (-52.960340) | 2.163327 / 6.876477 (-4.713149) | 2.326412 / 2.142072 (0.184339) | 0.600349 / 4.805227 (-4.204878) | 0.134245 / 6.500664 (-6.366419) | 0.060705 / 0.075469 (-0.014764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281440 / 1.841788 (-0.560347) | 19.165591 / 8.074308 (11.091283) | 14.007728 / 10.191392 (3.816336) | 0.168367 / 0.680424 (-0.512057) | 0.018149 / 0.534201 (-0.516052) | 0.391688 / 0.579283 (-0.187595) | 0.414528 / 0.434364 (-0.019836) | 0.456964 / 0.540337 (-0.083373) | 0.613807 / 1.386936 (-0.773129) |\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) | 0.006502 / 0.011353 (-0.004851) | 0.003956 / 0.011008 (-0.007052) | 0.064297 / 0.038508 (0.025789) | 0.073430 / 0.023109 (0.050321) | 0.364113 / 0.275898 (0.088215) | 0.389021 / 0.323480 (0.065541) | 0.005375 / 0.007986 (-0.002611) | 0.003363 / 0.004328 (-0.000966) | 0.064404 / 0.004250 (0.060153) | 0.056664 / 0.037052 (0.019612) | 0.365504 / 0.258489 (0.107015) | 0.398477 / 0.293841 (0.104636) | 0.031739 / 0.128546 (-0.096807) | 0.008663 / 0.075646 (-0.066984) | 0.070757 / 0.419271 (-0.348515) | 0.051014 / 0.043533 (0.007481) | 0.368287 / 0.255139 (0.113148) | 0.382941 / 0.283200 (0.099742) | 0.024642 / 0.141683 (-0.117041) | 1.516721 / 1.452155 (0.064567) | 1.557625 / 1.492716 (0.064908) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208248 / 0.018006 (0.190242) | 0.443560 / 0.000490 (0.443070) | 0.004004 / 0.000200 (0.003805) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031116 / 0.037411 (-0.006295) | 0.086814 / 0.014526 (0.072288) | 0.099111 / 0.176557 (-0.077445) | 0.155032 / 0.737135 (-0.582104) | 0.098938 / 0.296338 (-0.197401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413080 / 0.215209 (0.197871) | 4.115546 / 2.077655 (2.037891) | 2.162073 / 1.504120 (0.657953) | 2.008107 / 1.541195 (0.466912) | 2.052317 / 1.468490 (0.583827) | 0.485158 / 4.584777 (-4.099619) | 3.617478 / 3.745712 (-0.128234) | 5.030564 / 5.269862 (-0.239298) | 2.787812 / 4.565676 (-1.777865) | 0.057466 / 0.424275 (-0.366809) | 0.007656 / 0.007607 (0.000049) | 0.490037 / 0.226044 (0.263993) | 4.887896 / 2.268929 (2.618968) | 2.639644 / 55.444624 (-52.804981) | 2.258051 / 6.876477 (-4.618426) | 2.417573 / 2.142072 (0.275500) | 0.604473 / 4.805227 (-4.200754) | 0.134770 / 6.500664 (-6.365894) | 0.061709 / 0.075469 (-0.013760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.342500 / 1.841788 (-0.499288) | 19.354990 / 8.074308 (11.280682) | 14.161975 / 10.191392 (3.970583) | 0.157084 / 0.680424 (-0.523339) | 0.018227 / 0.534201 (-0.515974) | 0.391819 / 0.579283 (-0.187464) | 0.399157 / 0.434364 (-0.035207) | 0.460582 / 0.540337 (-0.079756) | 0.612183 / 1.386936 (-0.774753) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b20f6a82410dd47e89585bb932616a22e0eaf2e6 \"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) | 0.009318 / 0.011353 (-0.002035) | 0.005515 / 0.011008 (-0.005493) | 0.108532 / 0.038508 (0.070024) | 0.103583 / 0.023109 (0.080473) | 0.419249 / 0.275898 (0.143351) | 0.453573 / 0.323480 (0.130093) | 0.006601 / 0.007986 (-0.001384) | 0.005297 / 0.004328 (0.000968) | 0.082737 / 0.004250 (0.078487) | 0.064708 / 0.037052 (0.027656) | 0.425679 / 0.258489 (0.167190) | 0.462028 / 0.293841 (0.168187) | 0.048104 / 0.128546 (-0.080442) | 0.014069 / 0.075646 (-0.061577) | 0.377780 / 0.419271 (-0.041491) | 0.067510 / 0.043533 (0.023977) | 0.422421 / 0.255139 (0.167282) | 0.447127 / 0.283200 (0.163927) | 0.037745 / 0.141683 (-0.103938) | 1.855306 / 1.452155 (0.403152) | 1.943876 / 1.492716 (0.451160) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280161 / 0.018006 (0.262155) | 0.598001 / 0.000490 (0.597512) | 0.001130 / 0.000200 (0.000930) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036064 / 0.037411 (-0.001347) | 0.113256 / 0.014526 (0.098730) | 0.120598 / 0.176557 (-0.055959) | 0.191386 / 0.737135 (-0.545750) | 0.118125 / 0.296338 (-0.178214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616887 / 0.215209 (0.401678) | 6.085498 / 2.077655 (4.007844) | 2.639428 / 1.504120 (1.135308) | 2.215444 / 1.541195 (0.674249) | 2.311990 / 1.468490 (0.843500) | 0.820539 / 4.584777 (-3.764238) | 5.306010 / 3.745712 (1.560298) | 4.731726 / 5.269862 (-0.538136) | 3.053933 / 4.565676 (-1.511744) | 0.098862 / 0.424275 (-0.325413) | 0.009456 / 0.007607 (0.001849) | 0.725455 / 0.226044 (0.499411) | 7.367385 / 2.268929 (5.098457) | 3.464921 / 55.444624 (-51.979703) | 2.833868 / 6.876477 (-4.042608) | 3.033008 / 2.142072 (0.890935) | 1.036751 / 4.805227 (-3.768476) | 0.243646 / 6.500664 (-6.257018) | 0.081079 / 0.075469 (0.005610) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584695 / 1.841788 (-0.257093) | 25.150355 / 8.074308 (17.076047) | 21.826622 / 10.191392 (11.635230) | 0.212502 / 0.680424 (-0.467921) | 0.029865 / 0.534201 (-0.504335) | 0.496814 / 0.579283 (-0.082470) | 0.611959 / 0.434364 (0.177595) | 0.550434 / 0.540337 (0.010097) | 0.800897 / 1.386936 (-0.586039) |\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) | 0.009117 / 0.011353 (-0.002236) | 0.005236 / 0.011008 (-0.005772) | 0.082402 / 0.038508 (0.043894) | 0.090578 / 0.023109 (0.067468) | 0.487302 / 0.275898 (0.211404) | 0.523639 / 0.323480 (0.200159) | 0.006684 / 0.007986 (-0.001302) | 0.004306 / 0.004328 (-0.000023) | 0.083273 / 0.004250 (0.079023) | 0.068585 / 0.037052 (0.031532) | 0.487751 / 0.258489 (0.229262) | 0.538972 / 0.293841 (0.245131) | 0.048915 / 0.128546 (-0.079632) | 0.014312 / 0.075646 (-0.061335) | 0.091863 / 0.419271 (-0.327409) | 0.066114 / 0.043533 (0.022581) | 0.483552 / 0.255139 (0.228413) | 0.522250 / 0.283200 (0.239050) | 0.038533 / 0.141683 (-0.103150) | 1.803834 / 1.452155 (0.351680) | 1.891927 / 1.492716 (0.399211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.336662 / 0.018006 (0.318656) | 0.611408 / 0.000490 (0.610918) | 0.014310 / 0.000200 (0.014110) | 0.000152 / 0.000054 (0.000097) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034755 / 0.037411 (-0.002656) | 0.101008 / 0.014526 (0.086483) | 0.124530 / 0.176557 (-0.052026) | 0.179844 / 0.737135 (-0.557292) | 0.125027 / 0.296338 (-0.171312) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618341 / 0.215209 (0.403132) | 6.146848 / 2.077655 (4.069193) | 2.893305 / 1.504120 (1.389185) | 2.608722 / 1.541195 (1.067528) | 2.671276 / 1.468490 (1.202786) | 0.860096 / 4.584777 (-3.724681) | 5.440671 / 3.745712 (1.694959) | 4.776958 / 5.269862 (-0.492903) | 3.098300 / 4.565676 (-1.467376) | 0.098664 / 0.424275 (-0.325611) | 0.009270 / 0.007607 (0.001663) | 0.712780 / 0.226044 (0.486735) | 7.199721 / 2.268929 (4.930793) | 3.620723 / 55.444624 (-51.823902) | 3.052218 / 6.876477 (-3.824259) | 3.321093 / 2.142072 (1.179021) | 1.070992 / 4.805227 (-3.734235) | 0.224091 / 6.500664 (-6.276573) | 0.083395 / 0.075469 (0.007926) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.716867 / 1.841788 (-0.124921) | 25.534617 / 8.074308 (17.460309) | 25.221014 / 10.191392 (15.029621) | 0.248098 / 0.680424 (-0.432326) | 0.029659 / 0.534201 (-0.504542) | 0.492929 / 0.579283 (-0.086355) | 0.618253 / 0.434364 (0.183889) | 0.577108 / 0.540337 (0.036771) | 0.803188 / 1.386936 (-0.583748) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#584db360eed9155e173b199ba5fc037562b7b862 \"CML watermark\")\n"
] | 2023-07-31T06:44:05Z
| 2023-07-31T06:55:58Z
| 2023-07-31T06:45:41Z
|
MEMBER
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| 643,611,557
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MDExOlB1bGxSZXF1ZXN0NDM4Mzg0NDgw
| 299
|
remove some print in snli file
|
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"I guess you can just rebase from master to fix the CI"
] | 2020-06-23T07:46:06Z
| 2020-06-23T08:10:46Z
| 2020-06-23T08:10:44Z
|
CONTRIBUTOR
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This PR removes unwanted `print` statements in some files such as `snli.py`
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MDU6SXNzdWU2OTQ2MDcxNDg=
| 577
|
Some languages in wikipedia dataset are not loading
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[
"Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for languages with hundreds of MB of xml.\r\n\r\nLet me know if you encounter an error or if you feel that is is taking too long for you.\r\nWe could process those that really take too much time",
"Ok, thanks for clarifying, that makes sense. I will time those examples later today and post back here.\r\n\r\nAlso, it seems that not all dumps should use the same date. For instance, I was checking the Spanish dump doing the following:\r\n```\r\ndata = nlp.load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner', split='train')\r\n```\r\n\r\nI got the error below because this URL does not exist: https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json. So I checked the actual available dates here https://dumps.wikimedia.org/eswiki/ and there is no 20200501. If one tries for a date available in the link, then the nlp library does not allow such a request because is not in the list of expected datasets.\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20200501.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 965, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 518, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 422, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 220, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 155, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 163, in map_nested\r\n return {\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 164, in <dictcomp>\r\n k: map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 191, in map_nested\r\n return function(data_struct)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 156, in <lambda>\r\n lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 191, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 356, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json\r\n```",
"Thanks ! This will be very helpful.\r\n\r\nAbout the date issue, I think it's possible to use another date with\r\n\r\n```python\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n```\r\n\r\nHowever we've not processed wikipedia dumps for other dates than 20200501 (yet ?)\r\n\r\nOne more thing that is specific to 20200501.es: it was available once but the `mwparserfromhell` was not able to parse it for some reason, so we didn't manage to get a processed version of 20200501.es (see #321 )",
"Cool! Thanks for the trick regarding different dates!\r\n\r\nI checked the download/processing time for retrieving the Arabic Wikipedia dump, and it took about 3.2 hours. I think that this may be a bit impractical when it comes to working with multiple languages (although I understand that storing those datasets in your Google storage may not be very appealing either). \r\n\r\nFor the record, here's what I did:\r\n```python\r\nimport nlp\r\nimport time\r\n\r\ndef timeit(filename):\r\n elapsed = time.time()\r\n data = nlp.load_dataset('wikipedia', filename, beam_runner='DirectRunner', split='train')\r\n elapsed = time.time() - elapsed\r\n print(f\"Loading the '{filename}' data took {elapsed:,.1f} seconds...\")\r\n return data\r\n\r\ndata = timeit('20200501.ar')\r\n```\r\n\r\nHere's the output:\r\n```\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13.0k/13.0k [00:00<00:00, 8.34MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28.7k/28.7k [00:00<00:00, 954kB/s]\r\nDownloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47.4k/47.4k [00:00<00:00, 1.40MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79.8M/79.8M [00:15<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 171M/171M [00:33<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 103M/103M [00:20<00:00, 5.14MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 227M/227M [00:44<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 140M/140M [00:28<00:00, 4.96MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 160M/160M [00:30<00:00, 5.20MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 97.5M/97.5M [00:19<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222M/222M [00:42<00:00, 5.21MB/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [03:16<00:00, 196.39s/sources]\r\nDataset wikipedia downloaded and prepared to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50. Subsequent calls will reuse this data.\r\nLoading the '20200501.ar' data took 11,582.7 seconds...\r\n````",
"> About the date issue, I think it's possible to use another date with\r\n> ```python\r\n> load_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n> ```\r\n\r\nI tried your suggestion about the date and the function does not accept the language and date keywords. I tried both on `nlp` v0.4 and the new `datasets` library (v1.0.2):\r\n```\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"20200601\", beam_runner='DirectRunner', split='train')\r\n```\r\nFor now, my quick workaround to keep things moving was to simply change the date inside the library at this line: [https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403](https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403)\r\n\r\nNote that the date and languages are valid: [https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json](https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json)\r\n\r\nAny suggestion is welcome :) @lhoestq \r\n\r\n\r\n## **[UPDATE]**\r\n\r\nThe workaround I mentioned fetched the data, but then I faced another issue (even the log says to report this as bug):\r\n```\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\n```\r\n\r\nHere's the full stack (which says that there is a key error caused by this key: `KeyError: '000nbsp'`):\r\n\r\n```Downloading and preparing dataset wikipedia/20200601.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gustavoag/.cache/huggingface/datasets/wikipedia/20200601.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 74.7k/74.7k [00:00<00:00, 1.53MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 232M/232M [00:48<00:00, 4.75MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 442M/442M [01:39<00:00, 4.44MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 173M/173M [00:33<00:00, 5.12MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 344M/344M [01:14<00:00, 4.59MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 541M/541M [01:59<00:00, 4.52MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 476M/476M [01:31<00:00, 5.18MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 545M/545M [02:02<00:00, 4.46MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 299M/299M [01:01<00:00, 4.89MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.60M/9.60M [00:01<00:00, 4.84MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 423M/423M [01:36<00:00, 4.38MB/s]\r\nWARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--lang', 'es', '--date', '20200601', '--tokenizer', 'bert-base-multilingual-cased', '--cache', 'train', 'valid', '--max_dataset_length', '200000', '10000']\r\n\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 96, in <module>\r\n main()\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 65, in main\r\n data = nlp.load_dataset('wikipedia', f'{args.date}.{args.lang}', beam_runner='DirectRunner', split='train')\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 969, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 534, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 119, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 172, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 183, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 338, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 512, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 556, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 940, in process_bundle\r\n for result, split_result in executor.map(execute, zip(part_inputs, # pylint: disable=zip-builtin-not-iterating\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 611, in result_iterator\r\n yield fs.pop().result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 439, in result\r\n return self.__get_result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 388, in __get_result\r\n raise self._exception\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/utils/thread_pool_executor.py\", line 44, in run\r\n self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 932, in execute\r\n return bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 837, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 352, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 479, in do_instruction\r\n return getattr(self, request_type)(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 515, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 977, in process_bundle\r\n input_op_by_transform_id[element.transform_id].process_encoded(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 218, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 330, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 332, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1045, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"```",
"@lhoestq Any updates on this? I have similar issues with the Romanian dump, tnx.",
"Hey @gaguilar ,\r\n\r\nI just found the [\"char2subword\" paper](https://arxiv.org/pdf/2010.12730.pdf) and I'm really interested in trying it out on own vocabs/datasets like for historical texts (I've already [trained some lms](https://github.com/stefan-it/europeana-bert) on newspaper articles with OCR errors).\r\n\r\nDo you plan to release the code for your paper or is it possible to get the implementation 🤔 Many thanks :hugs: ",
"Hi @stefan-it! Thanks for your interest in our work! We do plan to release the code, but we will make it available once the paper has been published at a conference. Sorry for the inconvenience!\r\n\r\nHi @lhoestq, do you have any insights for this issue by any chance? Thanks!",
"This is an issue on the `mwparserfromhell` side. You could try to update `mwparserfromhell` and see if it fixes the issue. If it doesn't we'll have to create an issue on their repo for them to fix it.\r\nBut first let's see if the latest version of `mwparserfromhell` does the job.",
"I think the work around as suggested in the issue [#886] is not working for several languages, such as `id`. For example, I tried all the dates to download dataset for `id` langauge from the following link: (https://github.com/huggingface/datasets/pull/886) [https://dumps.wikimedia.org/idwiki/](https://dumps.wikimedia.org/idwiki/ )\r\n\r\n> >>> dataset = load_dataset('wikipedia', language='id', date=\"20210501\", beam_runner='DirectRunner')\r\nWARNING:datasets.builder:Using custom data configuration 20210501.id-date=20210501,language=id\r\nDownloading and preparing dataset wikipedia/20210501.id (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/.cache/huggingface/datasets/wikipedia/20210501.id-date=20210501,language=id/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 1139, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 420, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 623, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json\r\n\r\nMoreover the downloading speed for `non-en` language is very very slow. And interestingly the download stopped after approx a couple minutes due to the read time-out. I tried numerous times and the results is same. Is there any feasible way to download non-en language using huggingface?\r\n\r\n> File \"/Users/miislamg/opt/anaconda3/envs/proj-semlm/lib/python3.9/site-packages/requests/models.py\", line 760, in generate\r\n raise ConnectionError(e)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='dumps.wikimedia.org', port=443): Read timed out.\r\nDownloading: 7%|████████▎ | 10.2M/153M [03:35<50:07, 47.4kB/s]",
"Hi ! The link https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json seems to be working fine for me.\r\n\r\nRegarding the time outs, it must come either from an issue on the wikimedia host side, or from your internet connection.\r\nFeel free to try again several times.",
"I was trying to download dataset for `es` language, however I am getting the following error:\r\n```\r\ndataset = load_dataset('wikipedia', language='es', date=\"20210320\", beam_runner='DirectRunner') \r\n```\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20210320.es (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /scratch/user_name/datasets/wikipedia/20210320.es-date=20210320,language=es/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_dataset_all.py\", line 8, in <module>\r\n dataset = load_dataset('wikipedia', language=language, date=\"20210320\", beam_runner='DirectRunner') \r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 748, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 575, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 1152, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/pipeline.py\", line 564, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/direct/direct_runner.py\", line 131, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 190, in run_pipeline\r\n pipeline.to_runner_api(default_environment=self._default_environment))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 200, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 366, in run_stages\r\n bundle_context_manager,\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 562, in _run_stage\r\n bundle_manager)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 602, in _run_bundle\r\n data_input, data_output, input_timers, expected_timer_output)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 903, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 378, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 610, in do_instruction\r\n getattr(request, request_type), request.instruction_id)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 647, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 1001, in process_bundle\r\n element.data)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 229, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 356, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 358, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1315, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/opt/conda/lib/python3.7/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```",
"Hi ! This looks related to this issue: https://github.com/huggingface/datasets/issues/1994\r\nBasically the parser that is used (mwparserfromhell) has some issues for some pages in `es`.\r\nWe already reported some issues for `es` on their repo at https://github.com/earwig/mwparserfromhell/issues/247 but it looks like there are still a few issues. Might be a good idea to open a new issue on the mwparserfromhell repo",
"Any updates on this so far?",
"The issue:\r\n```\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```\r\nreported in comments:\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-701890059 (by @gaguilar)\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-879513227 (by @mmiakashs)\r\n\r\nwas normally fixed in the `mwparserfromhell` library and will be accessible in their next release version `0.7`:\r\n- https://github.com/earwig/mwparserfromhell/issues/288",
"mwparserfromhell 0.7 has still not been released, but you might have luck with the dev version:\r\n`pip install git+https://github.com/earwig/mwparserfromhell.git@0f89f44`"
] | 2020-09-07T01:16:29Z
| 2023-04-11T22:50:48Z
| 2022-10-11T11:16:04Z
|
CONTRIBUTOR
| null | null | null |
Hi,
I am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:
```
import nlp
langs = ['ar'. 'af', 'an']
for lang in langs:
data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train')
print(lang, len(data))
```
Here's what I see for 'ar' (it gets stuck there):
```
Downloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...
```
Note that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!
|
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| 1,764
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Connection Issues
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"Academic WIFI was blocking."
] | 2021-01-21T20:56:09Z
| 2021-01-21T21:00:19Z
| 2021-01-21T21:00:02Z
|
NONE
| null | null | null |
Today, I am getting connection issues while loading a dataset and the metric.
```
Traceback (most recent call last):
File "src/train.py", line 180, in <module>
train_dataset, dev_dataset, test_dataset = create_race_dataset()
File "src/train.py", line 130, in create_race_dataset
train_dataset = load_dataset("race", "all", split="train")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 591, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/datasets/race/race.py
```
Or
```
Traceback (most recent call last):
File "src/train.py", line 105, in <module>
rouge = datasets.load_metric("rouge")
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 500, in load_metric
dataset=False,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 343, in cached_path
max_retries=download_config.max_retries,
File "/Users/saeed/Desktop/codes/repos/dreamscape-qa/env/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 617, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.2.1/metrics/rouge/rouge.py
```
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Add time series data - stock market
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[
"Can I use instructions present in below link for time series dataset as well? \r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md ",
"cc'ing @kashif and @NielsRogge for visibility!",
"@INF800 happy to add this dataset! I will try to set a PR by the end of the day... if you can kindly point me to the dataset? Also, note we have a bunch of time series datasets checked in e.g. `electricity_load_diagrams` or `monash_tsf`, and ideally this dataset could also be in a similar format. ",
"Thankyou. This is how raw data looks like before cleaning for an individual stocks:\r\n\r\n1. https://github.com/INF800/marktech/tree/raw-data/f/data/raw\r\n2. https://github.com/INF800/marktech/tree/raw-data/t/data/raw\r\n3. https://github.com/INF800/marktech/tree/raw-data/rdfn/data/raw\r\n4. https://github.com/INF800/marktech/tree/raw-data/irbt/data/raw\r\n5. https://github.com/INF800/marktech/tree/raw-data/hll/data/raw\r\n6. https://github.com/INF800/marktech/tree/raw-data/infy/data/raw\r\n7. https://github.com/INF800/marktech/tree/raw-data/reli/data/raw\r\n8. https://github.com/INF800/marktech/tree/raw-data/hdbk/data/raw\r\n\r\n> Scraping is automated using GitHub Actions. So, everyday we will see a new file added in the above links.\r\n\r\nI can rewrite the cleaning scripts to make sure it fits HF dataset standards. (P.S I am very much new to HF dataset)\r\n\r\nThe data set above can be converted into univariate regression / multivariate regression / sequence to sequence generation dataset etc. So, do we have some kind of transformation modules that will read the dataset as some type of dataset (`GenericTimeData`) and convert it to other possible dataset relating to a specific ML task. **By having this kind of transformation module, I only have to add data once** and use transformation module whenever necessary\r\n\r\nAdditionally, having some kind of versioning for the dataset will be really helpful because it will keep on updating - especially time series datasets ",
"thanks @INF800 I'll have a look. I believe it should be possible to incorporate this into the time-series format.",
"Referencing https://github.com/qingsongedu/time-series-transformers-review",
"@INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet.\r\n\r\nIn any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with... \r\n\r\nDo you think you can make a version with just numerical data?",
"> @INF800 yes I am aware of the review repository and paper which is more or less a collection of abstracts etc. I am working on a unified library of implementations of these papers together with datasets to be then able to compare/contrast and build upon the research etc. but I am not ready to share them publicly just yet.\r\n> \r\n> In any case regarding your dataset at the moment its seems from looking at the csv files, its mixture of textual and numerical data, sometimes in the same column etc. As you know, for time series models we would need just numeric data so I would need your help in disambiguating the dataset you have collected and also perhaps starting with just numerical data to start with...\r\n> \r\n> Do you think you can make a version with just numerical data?\r\n\r\nWill share the numeric data and conversion script within end of this week. \r\n\r\nI am on a business trip currently - it is in my desktop."
] | 2022-04-06T05:46:58Z
| 2022-04-11T09:07:10Z
| null |
NONE
| null | null | null |
## Adding a Time Series Dataset
- **Name:** 2min ticker data for stock market
- **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image
- **Data:** Collected by myself from investing.com
- **Motivation:** Test applicability of transformer based model on stock market / time series problem
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Adding Scielo
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[] | 2020-12-09T12:02:48Z
| 2020-12-09T17:53:37Z
| 2020-12-09T17:53:37Z
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MEMBER
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Adding Scielo: Parallel corpus of full-text articles in Portuguese, English and Spanish from SciELO
https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB
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FileNotFoundError: [Errno 2] No such file or directory: 'nul'
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[
"Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519. "
] | 2023-12-11T08:52:13Z
| 2023-12-14T08:09:08Z
| 2023-12-14T08:09:08Z
|
NONE
| null | null | null |
### Describe the bug
it seems that sth wrong with my terrible "bug body" life, When i run this code, "import datasets"
i meet this error FileNotFoundError: [Errno 2] No such file or directory: 'nul'
### Steps to reproduce the bug
1.import datasets
### Expected behavior
i just run a single line code and stuct in this bug
### Environment info
OS: Windows10
Datasets==2.15.0
python=3.10
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Add TF-based Features to handle different modes of data
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[] | 2021-03-09T18:29:52Z
| 2021-03-17T12:32:08Z
| 2021-03-17T12:32:07Z
|
CONTRIBUTOR
| null | 1
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Hi,
I am creating this draft PR to work on add features similar to [TF datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/core/features). I'll be starting with `Tensor` and `FeatureConnector` classes, and build upon them to add other features as well. This is a work in progress.
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Make Dataset streaming queries retryable
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[
"Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader\r\n\r\nA minor Hub outage that we experienced yesterday could be the cause.",
"I wanted something similar. I have a huge dataset I want to process (laion-2b), but after processing several batches, it sometimes fails with this error: `HTTP 502 Bad Gateway for url`. I had the following code to handle it but this way I believe it restarts processing the data from the first batch? How can I set the attribute values you mention above?\r\n\r\n```\r\niterable_dataset = load_dataset(\"laion/laion2B-multi\", streaming=True, split='train')\r\ndataloader = DataLoader(iterable_dataset, batch_size=131072, collate_fn=custom_collate_fn, num_workers=8)\r\n\r\nMAX_RETRIES = 5\r\nRETRY_WAIT = 10 # wait 10 seconds before retry\r\n\r\n for retry in range(MAX_RETRIES):\r\n try:\r\n for j, batch in enumerate(dataloader):\r\n < process batch>\r\n\r\n except HfHubHTTPError as e:\r\n if \"502\" in str(e) and retry < MAX_RETRIES - 1:\r\n logging.warning(f\"Encountered a 502 error on batch {j}. Waiting for {RETRY_WAIT} seconds before retrying.\")\r\n time.sleep(RETRY_WAIT)\r\n continue\r\n else:\r\n raise",
"Hey all! Wondering if there's a way of making Datasets streaming mode somewhat robust to Hub outages? Over the weekend, I got two quite cryptic errors, which I reckon were probably from Hub issues:\r\n\r\n<details>\r\n<summary> Stack Trace 1 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nConnectionError: Caught ConnectionError in DataLoader worker process 8.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1155, in _create_direct_connection\r\n hosts = await asyncio.shield(host_resolved)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 874, in _resolve_host\r\n addrs = await self._resolver.resolve(host, port, family=self._family)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/resolver.py\", line 33, in resolve\r\n infos = await self._loop.getaddrinfo(\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 863, in getaddrinfo\r\n return await self.run_in_executor(\r\n File \"/usr/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/usr/lib/python3.10/socket.py\", line 955, in getaddrinfo\r\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\r\nsocket.gaierror: [Errno -3] Temporary failure in name resolution\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 612, in read\r\n return super().read(length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 439, in _fetch\r\n new = self.fetcher(self.end, bend)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 118, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 103, in sync\r\n raise return_result\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 56, in _runner\r\n result[0] = await coro\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 660, in async_fetch_range\r\n r = await self.session.get(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/client.py\", line 562, in _request\r\n conn = await self._connector.connect(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 540, in connect\r\n proto = await self._create_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 901, in _create_connection\r\n _, proto = await self._create_direct_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1169, in _create_direct_connection\r\n raise ClientConnectorError(req.connection_key, exc) from exc\r\naiohttp.client_exceptions.ClientConnectorError: Cannot connect to host huggingface.co:443 ssl:default [Temporary failure in name resolution]\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/.cache/huggingface/modules/datasets_modules/datasets/distil-whisper--switchboard-data/9472ee64cca0e1a7e11909c7033c2354511fa62805f81a2e07616980c765abfe/switchboard-data.py\", line 247, in _generate_tables\r\n for record_batch in pf.iter_batches():\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retries\r\n raise ConnectionError(\"Server Disconnected\") from disconnect_err\r\nConnectionError: Server Disconnected\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n<summary> Stack Trace 2 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nrequests.exceptions.ConnectionError: Caught ConnectionError in DataLoader worker process 13.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nhttp.client.RemoteDisconnected: Remote end closed connection without response\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 486, in send\r\n resp = conn.urlopen(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 845, in urlopen\r\n retries = retries.increment(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/retry.py\", line 470, in increment\r\n raise reraise(type(error), error, _stacktrace)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/util.py\", line 38, in reraise\r\n raise value.with_traceback(tb)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nurllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 410, in _fetch_range\r\n r = http_backoff(\"GET\", url, headers=headers)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 258, in http_backoff\r\n response = session.request(method=method, url=url, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 589, in request\r\n resp = self.send(prep, **send_kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 703, in send\r\n r = adapter.send(request, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 63, in send\r\n return super().send(request, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 501, in send\r\n raise ConnectionError(err, request=request)\r\nrequests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 5fce9fc2-e22f-41c2-91af-529f13f1d611)')\r\n```\r\n\r\n</details>\r\n\r\nHaving streaming mode fail when the Hub goes down makes using it problematic for long training runs where large amounts of data is involved. However, this is the precise situation for which streaming mode is so appealing!\r\n\r\nWondering if there were a 'common' set of Hub errors that we could catch in `iterable_datasets` and prevent from crashing the script?\r\n\r\ncc @lhoestq @mariosasko ",
"Errors are already caught and requests are already retried.\r\n\r\nWhat you can do is increase the number of retries before an error is raised.\r\n\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.STREAMING_READ_MAX_RETRIES = 20 # default\r\ndatasets.config.STREAMING_READ_RETRY_INTERVAL = 5 # default\r\n```"
] | 2023-08-23T13:15:38Z
| 2023-11-06T13:54:16Z
| null |
NONE
| null | null | null |
### Feature request
Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy).
### Motivation
I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy.
### Your contribution
It would be better if someone having experience in this area takes this up as this would require some testing.
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MDExOlB1bGxSZXF1ZXN0NTM3MDY1NDMx
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Adding the Mac-Morpho dataset
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[] | 2020-12-11T16:01:38Z
| 2020-12-21T10:03:37Z
| 2020-12-21T10:03:37Z
|
CONTRIBUTOR
| null | 0
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Adding the Mac-Morpho dataset, a Portuguese language dataset for Part-of-speech tagging tasks
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Support streaming tar files
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[
"Hi ! Why do we need the custom `readline` for exactly ? feel free to add a comment to say why it's needed"
] | 2021-08-14T04:40:17Z
| 2021-08-26T10:02:30Z
| 2021-08-14T04:55:57Z
|
MEMBER
| null | 0
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This PR adds support to stream tar files by using the `fsspec` tar protocol.
It also uses the custom `readline` implemented in PR #2786.
The corresponding test is implemented in PR #2786.
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Dataset Viewer issue for ett
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[
"Thanks for reporting @dgcnz.\r\n\r\nI have checked that the dataset works fine in streaming mode.\r\n\r\nAdditionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co/datasets/blbooks\r\n\r\nI have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out\r\n\r\nCC: @severo ",
"I've just resent the refresh of the preview to the new endpoint, without success.\r\n\r\nCC: @severo ",
"Fixed!\r\n\r\nhttps://huggingface.co/datasets/ett/viewer/h1/test\r\n\r\n<img width=\"982\" alt=\"Capture d’écran 2022-06-15 à 09 30 22\" src=\"https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png\">\r\n"
] | 2022-05-27T02:12:35Z
| 2022-06-15T07:30:46Z
| 2022-06-15T07:30:46Z
|
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/ett
### Description
Timestamp is not JSON serializable.
```
Status code: 500
Exception: Status500Error
Message: Type is not JSON serializable: Timestamp
```
### Owner
No
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py3.7: TypeError: can't pickle _LazyModule objects
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[
"\r\nThis wasn't a `datasets` problem, but `transformers`' and it was solved here https://github.com/huggingface/transformers/pull/11168\r\n"
] | 2021-04-08T21:02:48Z
| 2021-04-09T16:56:50Z
| 2021-04-09T01:52:57Z
|
CONTRIBUTOR
| null | null | null |
While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install:
```
git clone https://github.com/huggingface/transformers
cd transformers
pip install -e .[testing]
export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \
examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \
--per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \
--fp16
```
```
Traceback (most recent call last):
File "examples/language-modeling/run_clm.py", line 453, in <module>
main()
File "examples/language-modeling/run_clm.py", line 336, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map
update_data=update_data,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper
self._fingerprint, transform, kwargs_for_fingerprint
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint
hasher.update(transform_args[key])
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps
dump(obj, file)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump
Pickler(file, recurse=True).dump(obj)
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function
obj=obj,
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save
rv = reduce(self.proto)
TypeError: can't pickle _LazyModule objects
```
```
$ python --version
Python 3.7.4
$ python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 1.8.0.dev20210110+cu110
Is debug build: False
CUDA used to build PyTorch: 11.0
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: 10.0.0-4ubuntu1
CMake version: version 3.16.3
```
Thanks.
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Support remote file systems for `Audio`
|
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[
"Just seen https://github.com/huggingface/datasets/issues/5281"
] | 2022-12-12T13:22:13Z
| 2022-12-12T13:37:14Z
| 2022-12-12T13:37:14Z
|
NONE
| null | null | null |
### Feature request
Hi there!
It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system.
### Motivation
Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly.
### Your contribution
Something like this (for Google Cloud Platform in this instance):
```python
from datasets import Dataset, Audio
import gcsfs
fs = gcsfs.GCSFileSystem()
list_of_audio_fp = {'audio': ['1', '2', '3']}
ds = Dataset.from_dict(list_of_audio_fp)
ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs))
```
Under the hood:
```python
import librosa
from io import BytesIO
def load_audio(fp, sampling_rate=None, fs=None):
if fs is not None:
with fs.open(fp, 'rb') as f:
arr, sr = librosa.load(BytesIO(f), sr=sampling_rate)
else:
# Perform existing io operations
```
Written from memory so some things could be wrong.
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Error when downloading datasets to non-traditional cache directories
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[
"Same here !"
] | 2021-09-15T19:59:46Z
| 2021-11-24T21:42:31Z
| null |
NONE
| null | null | null |
## Describe the bug
When the cache directory is linked (soft link) to a directory on a NetApp device, the download fails.
## Steps to reproduce the bug
```bash
ln -s /path/to/netapp/.cache ~/.cache
```
```python
load_dataset("imdb")
```
## Expected results
Successfully loading IMDB dataset
## Actual results
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33432835,
num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0,
dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'),
'recorded': SplitInfo(name='test', num_bytes=659932, num_examples=503, dataset_name='imdb')}, {'expected':
SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded':
SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.1.2
- Platform: Ubuntu
- Python version: 3.8
## Extra notes
Stranger yet, trying to debug the phenomenon, I found the range of results to vary a lot without clear direction:
- With `cache_dir="/path/to/netapp/.cache"` the same thing happens.
- However, when linking `~/netapp/` to `/path/to/netapp` *and* setting `cache_dir="~/netapp/.cache/huggingface/datasets"` - it does work
- On the other hand, when linking `~/.cache` to `~/netapp/.cache` without using `cache_dir`, it does work anymore.
While I could test it only for a NetApp device, it might have to do with any other mounted FS.
Thanks :)
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Add meta-data to the HANS dataset
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| 2020-12-03T13:38:34Z
| 2020-12-03T13:38:34Z
|
MEMBER
| null | null | null |
The current version of the [HANS dataset](https://github.com/huggingface/datasets/blob/master/datasets/hans/hans.py) is missing the additional information provided for each example, including the sentence parses, heuristic and subcase.
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Fix config creation for data files with NamedSplit
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During config creation, we need to iterate through the data files of all the splits to compute a hash.
To make sure the hash is unique given a certain combination of files/splits, we sort the split names.
However the `NamedSplit` objects can't be passed to `sorted` and currently it raises an error: we need to sort the string of their names instead.
Fix #705
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Add FAISS .range_search() method for retrieving all texts from dataset above similarity threshold
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[
"Hi ! You can access the faiss index with\r\n```python\r\nfaiss_index = my_dataset.get_index(\"my_index_name\").faiss_index\r\n```\r\nand then do whatever you want with it, e.g. query it using range_search:\r\n```python\r\nthreshold = 0.95\r\nlimits, distances, indices = faiss_index.range_search(x=xq, thresh=threshold)\r\n\r\ntexts = dataset[indices]\r\n```",
"wow, that's great, thank you for the explanation. (if that's not already in the documentation, could be worth adding it)\r\n\r\nwhich type of faiss index is Datasets using? I looked into faiss recently and I understand that there are several different types of indexes and the choice is important, e.g. regarding which distance metric you use (euclidian vs. cosine/dot product), the size of my dataset etc. can I chose the type of index somehow as well?",
"`Dataset.add_faiss_index` has a `string_factory` parameter, used to set the type of index (see the faiss documentation about [index factory](https://github.com/facebookresearch/faiss/wiki/The-index-factory)). Alternatively, you can pass an index you've defined yourself using faiss with the `custom_index` parameter of `Dataset.add_faiss_index` \r\n\r\nHere is the full documentation of `Dataset.add_faiss_index`: https://huggingface.co/docs/datasets/v2.0.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index",
"great thanks, I will try it out"
] | 2022-03-25T17:31:33Z
| 2022-05-06T08:35:52Z
| 2022-05-06T08:35:52Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
I would like to retrieve all texts from a dataset, which are semantically similar to a specific input text (query), above a certain (cosine) similarity threshold. My dataset is very large (Wikipedia), so I need to use Datasets and FAISS for this. I would like to be able to repeat many different queries on the dataset quickly.
**Describe the solution you'd like**
dataset objects currently have the .get_nearest_examples() method for text retrieval via FAISS. But this only allows retrieving a specific number of K texts instead of everything above a specified similarity threshold.
It would be great if HF Datasets would also support the FAISS method .range_search() for retrieving texts above a certain similarity threshold.
see details here: https://github.com/facebookresearch/faiss/issues/1273
**Describe alternatives you've considered**
I've considered using native FAISS, but doing this via HF datasets would be better. My assumption is that Dataset features like dataset streaming make it easier to work with large datasets
**Additional context**
The concrete use-case is: I have a large dataset (wikipedia) and I would like to retrieve all paragraphs which are similar to a query. I will use sentence-transformers for encoding the texts.
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"dataset_infos.json" missing for chr_en and mc4
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[
"Hi ! Thanks for reporting :) \r\nWe can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language",
"No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? \r\n\r\nI noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?\r\n\r\n\r\n",
"The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)"
] | 2021-11-21T00:07:22Z
| 2022-01-19T13:55:32Z
| null |
NONE
| null | null | null |
## Describe the bug
In the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.
## Steps to reproduce the bug
Check [chr_en](https://github.com/huggingface/datasets/tree/master/datasets/chr_en) and [mc4](https://github.com/huggingface/datasets/tree/master/datasets/mc4)
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Importing PyTorch reduces multiprocessing performance for map
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[
"Hi! The times match when I run this code locally or on Colab.\r\n\r\nAlso, we use `multiprocess`, not `multiprocessing`, for parallelization, and torch's `__init__.py` (executed on `import torch` ) slightly modifies the latter.",
"Hey Mariosasko,\r\n\r\nThanks for looking into it. We further did some investigations after your comment and figured out it's only affecting some hardware/software configurations with the `pytorch` installation of `conda-forge`. Based on this we found the following issue in PyTorch: https://github.com/pytorch/pytorch/issues/102269 with a quick fix for now.\r\n\r\nSince it seems to be a deeper issue with forking processes, the difference between`multiprocess` and `multiprocessing` didn't make a difference.\r\n\r\nClosing this, since the issue comes from `pytorch` not `dataset`. \r\n"
] | 2023-06-06T19:42:25Z
| 2023-06-16T13:09:12Z
| 2023-06-16T13:09:12Z
|
NONE
| null | null | null |
### Describe the bug
I noticed that the performance of my dataset preprocessing with `map(...,num_proc=32)` decreases when PyTorch is imported.
### Steps to reproduce the bug
I created two example scripts to reproduce this behavior:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
Takes around 4 seconds on my machine.
While the same code, but with an `import torch`:
```
import datasets
datasets.disable_caching()
from datasets import Dataset
import time
import torch
PROC=32
if __name__ == "__main__":
dataset = [True] * 10000000
dataset = Dataset.from_dict({'train': dataset})
start = time.time()
dataset.map(lambda x: x, num_proc=PROC)
end = time.time()
print(end - start)
```
takes around 22 seconds.
### Expected behavior
I would expect that the import of torch to not have such a significant effect on the performance of map using multiprocessing.
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.35
- Python version: 3.11.3
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
- torch: 2.0.1
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Support PIL Image objects in `add_item`/`add_column`
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4828). All of your documentation changes will be reflected on that endpoint.",
"Hey @mariosasko could we please merge this? I'm still getting the original error at #4796 .",
"Are you planning to continue working on this?"
] | 2022-08-11T14:25:45Z
| 2023-09-24T10:15:33Z
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Fix #4796
PS: We should also improve the type inference in `OptimizedTypeSequence` to make it possible to also infer the complex types (only `Image` currently) in nested arrays (e.g. `[[pil_image], [pil_image, pil_image]]` or `[{"img": pil_image}`]), but I plan to address this in a separate PR.
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I_kwDODunzps5VahFi
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`path` is `None` when downloading a custom audio dataset from the Hub
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[
"Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes. \r\n"
] | 2022-11-02T11:51:25Z
| 2022-11-02T12:55:02Z
| 2022-11-02T12:55:02Z
|
MEMBER
| null | null | null |
### Describe the bug
I've created an [audio dataset](https://huggingface.co/datasets/lewtun/audio-test-push) using the `audiofolder` feature desribed in the [docs](https://huggingface.co/docs/datasets/audio_dataset#audiofolder) and then pushed it to the Hub.
Locally, I can see the `audio.path` feature is of the expected form `path/to/data_dir`, but when I download the dataset from the Hub, I see `audio.path` is `None`
Here's an example:
```python
from datasets import load_dataset
ds = load_dataset("lewtun/audio-test-push")
ds["train"][0]
# {
# "audio": {
# "path": None, <-- Is this expected?
# "array": array(
# [
# 3.97140226e-07,
# 7.30310290e-07,
# 7.56406735e-07,
# ...,
# -1.19636677e-01,
# -1.16811886e-01,
# -1.12441722e-01,
# ]
# ),
# "sampling_rate": 44100,
# },
# "song_id": 0,
# "genre_id": 0,
# "genre": "Electronic",
# }
```
Is this expected behaviour? If yes, feel free to close this issue as it's not a true bug then :)
### Steps to reproduce the bug
1. Create an audio dataset with the `audiofolder` feature
2. Push the dataset to the Hub with `push_to_hub()`
3. Download the Hub dataset and inspect the `audio.path` feature
### Expected behavior
`audio.path` points to the file associated with the audio data
### Environment info
- `datasets` version: 2.6.2.dev0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1
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Opus montenegrinsubs
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[
"merging since the CI is fixed on master"
] | 2020-12-05T17:00:44Z
| 2020-12-07T11:02:49Z
| 2020-12-07T11:02:49Z
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Opus montenegrinsubs - language pair en-me
more info : http://opus.nlpl.eu/MontenegrinSubs.php
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MDU6SXNzdWU4MzExMzU3MDQ=
| 2,052
|
Timit_asr dataset repeats examples
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[
"Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```",
"Ty!"
] | 2021-03-14T11:43:43Z
| 2021-03-15T10:37:16Z
| 2021-03-15T10:37:16Z
|
NONE
| null | null | null |
Summary
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce
As an example, on this code there is the text from the training part:
Code snippet:
```
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
timit['train']['text']
#['Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
```
The same behavior happens for other columns
Expected behavior:
Different info on the actual timit_asr dataset
Actual behavior:
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different
Debug info
Streamlit version: (get it with $ streamlit version)
Python version: Python 3.6.12
Using Conda? PipEnv? PyEnv? Pex? Using pip
OS version: Centos-release-7-9.2009.1.el7.centos.x86_64
Additional information
You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr
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Add the Winograd Schema Challenge
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| 2020-12-09T15:11:31Z
| 2020-12-09T09:32:34Z
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Adds the Winograd Schema Challenge, including configs for the more canonical wsc273 as well as wsc285 with 12 new examples.
- https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
The data format was a bit of a nightmare but I think I got it to a workable format.
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common_language: Fix license in README.md
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| 2021-10-04T09:27:01Z
| 2021-10-04T09:27:01Z
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...it's correct elsewhere
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#3337 Add typing overloads to Dataset.__getitem__ for mypy
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[
"Locally the `make quality` passes with the same dependencies. I would suggest upgrading flake8. (I can take care of it in another PR)\r\ncc @lhoestq ",
"Thank you for fixing flake8! I think we are ready to merge then. "
] | 2021-12-04T20:54:49Z
| 2021-12-14T10:28:55Z
| 2021-12-14T10:28:55Z
|
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| null | 0
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Add typing overloads to Dataset.__getitem__ for mypy
Fixes #3337
**Iterable**
Iterable from `collections` cannot have a type, so you can't do `Iterable[int]` for example. `typing` has a Generic version that builds upon the one from `collections`.
**Flake8**
I had to add `# noqa: F811`, this is a bug from Flake8.
datasets uses flake8==3.7.9 which released in October 2019 if I update flake8 (4.0.1), I no longer get these errors, but I did not want to make the update without your approval. (It also triggers other errors like no args in f-strings.)
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MDExOlB1bGxSZXF1ZXN0NDU5MTc1MTY0
| 456
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add crd3(ACL 2020) dataset
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[] | 2020-07-30T13:28:35Z
| 2023-09-24T09:48:47Z
| 2020-08-03T11:28:52Z
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This PR adds the **Critical Role Dungeons and Dragons Dataset** published at ACL 2020
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PR_kwDODunzps4sn_YG
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Fix Windows paths in SUPERB benchmark datasets
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[] | 2021-10-04T08:13:49Z
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Minor fix in SUPERB benchmark datasets for Windows pathname component separator.
Related to #2884, #2783 and #2619.
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Update BibTeX entry
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