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Add Tatoeba Dataset
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Support streaming JSON files
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Use open in JSON dataset builder, so that it can be patched with xopen for streaming.
Close #2607.
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Fix `Overview.ipynb` & detach Jupyter Notebooks from `datasets` repository
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"Random fact: previous run was showing that the Hub was hosting 13336 datasets, while the most recent run shows 36662 👀🎉",
"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks! \r\n\r\nHowever, I think we should stop linking this notebook and use the notebook version of the Quickstart doc page instead of it for easier maintenance (we would have the \"Open in Colab\" button in the Quickstart doc as Transformers [does](https://huggingface.co/docs/transformers/quicktour)). \r\n\r\n@stevhliu should be able to help with this. If I'm not mistaken, this can be done by adding the `[[open in colab]]` marker to the doc page.\r\n\r\nAlso, if some useful info from the Overview notebook is not in the docs, feel free to add it so we don't lose it 🙂.",
"Cool, makes sense @mariosasko, then I'll check both notebooks and see whether there's something in `Overview.ipynb` worth including in the `docs/source/quickstart.mdx` and remove `Overview.ipynb` and update references in favour of `docs/source/quickstart.mdx`\r\n\r\nAre you OK if I do that @stevhliu @mariosasko? Thanks 🤗 ",
"For the moment I've just updated the `quickstart.mdx` to be more similar to [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx), but regarding the `Overview.ipynb` notebook I was planning to create a PR in https://github.com/huggingface/notebooks to add it there, does that make sense @stevhliu? And then to create a `README.md` in this repository in `notebooks/` as `transformers` does to point to the related notebooks hosted in https://github.com/huggingface/notebooks, WDYT? 🤗 ",
"Hi @stevhliu thanks for the feedback! Already applied your suggestions, I'll also add the pointers to both audio and image datasets in the \"What's next\" section.\r\n\r\nBesides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!",
"Thanks a lot for the detailed feedback @mariosasko, I'll apply the changes today!",
"> Besides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!\r\n\r\nAwesome! If you're up for it, I think you can go ahead and open a PR with the changes I've outlined [here](https://github.com/huggingface/datasets/pull/5902#pullrequestreview-1475236887) to add the notebook building workflow. ",
"Hi @stevhliu @mariosasko, sorry for the delay I had a busy week, I'll tackle this either today or tomorrow to ideally close it before the weekend, thanks again for the help and guidance 😄 ",
"Hi guys @stevhliu @mariosasko sorry for the delay! I've resolved all the comments and applied your reviews 👍🏻 Let me know if this works and we can finally close this PR, thanks for the help in the meantime!",
"> Thanks for iterating on this and wrapping it up! 🤗\r\n\r\nNo need to! Always a pleasure to collaborate with you guys 🤗 ",
"<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) | 0.009814 / 0.011353 (-0.001539) | 0.004632 / 0.011008 (-0.006376) | 0.103059 / 0.038508 (0.064551) | 0.090277 / 0.023109 (0.067167) | 0.389344 / 0.275898 (0.113446) | 0.464536 / 0.323480 (0.141056) | 0.008196 / 0.007986 (0.000210) | 0.003872 / 0.004328 (-0.000457) | 0.081912 / 0.004250 (0.077662) | 0.073197 / 0.037052 (0.036145) | 0.407545 / 0.258489 (0.149056) | 0.458035 / 0.293841 (0.164194) | 0.037485 / 0.128546 (-0.091061) | 0.010141 / 0.075646 (-0.065505) | 0.365998 / 0.419271 (-0.053273) | 0.065218 / 0.043533 (0.021685) | 0.414091 / 0.255139 (0.158952) | 0.435617 / 0.283200 (0.152417) | 0.028850 / 0.141683 (-0.112833) | 1.883510 / 1.452155 (0.431355) | 1.979986 / 1.492716 (0.487269) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236623 / 0.018006 (0.218616) | 0.467128 / 0.000490 (0.466638) | 0.008273 / 0.000200 (0.008074) | 0.000699 / 0.000054 (0.000645) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033061 / 0.037411 (-0.004350) | 0.101381 / 0.014526 (0.086856) | 0.110862 / 0.176557 (-0.065695) | 0.180982 / 0.737135 (-0.556154) | 0.113791 / 0.296338 (-0.182548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450805 / 0.215209 (0.235596) | 4.478374 / 2.077655 (2.400719) | 2.190814 / 1.504120 (0.686694) | 1.976726 / 1.541195 (0.435532) | 2.078527 / 1.468490 (0.610037) | 0.569150 / 4.584777 (-4.015627) | 4.557790 / 3.745712 (0.812078) | 3.794964 / 5.269862 (-1.474898) | 2.555689 / 4.565676 (-2.009987) | 0.067380 / 0.424275 (-0.356896) | 0.008741 / 0.007607 (0.001134) | 0.536913 / 0.226044 (0.310868) | 5.364588 / 2.268929 (3.095659) | 2.725602 / 55.444624 (-52.719022) | 2.332012 / 6.876477 (-4.544465) | 2.560550 / 2.142072 (0.418477) | 0.672490 / 4.805227 (-4.132738) | 0.153629 / 6.500664 (-6.347035) | 0.070583 / 0.075469 (-0.004886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620083 / 1.841788 (-0.221704) | 23.094248 / 8.074308 (15.019939) | 17.797625 / 10.191392 (7.606233) | 0.167993 / 0.680424 (-0.512430) | 0.021151 / 0.534201 (-0.513050) | 0.470216 / 0.579283 (-0.109067) | 0.515492 / 0.434364 (0.081128) | 0.666359 / 0.540337 (0.126021) | 0.772928 / 1.386936 (-0.614008) |\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) | 0.007853 / 0.011353 (-0.003500) | 0.004627 / 0.011008 (-0.006381) | 0.079803 / 0.038508 (0.041295) | 0.091562 / 0.023109 (0.068453) | 0.488537 / 0.275898 (0.212639) | 0.579207 / 0.323480 (0.255728) | 0.006579 / 0.007986 (-0.001406) | 0.003946 / 0.004328 (-0.000382) | 0.080224 / 0.004250 (0.075973) | 0.074499 / 0.037052 (0.037446) | 0.488292 / 0.258489 (0.229803) | 0.569246 / 0.293841 (0.275405) | 0.039994 / 0.128546 (-0.088553) | 0.012867 / 0.075646 (-0.062780) | 0.092563 / 0.419271 (-0.326709) | 0.061656 / 0.043533 (0.018124) | 0.488271 / 0.255139 (0.233132) | 0.550651 / 0.283200 (0.267451) | 0.032078 / 0.141683 (-0.109605) | 1.874440 / 1.452155 (0.422286) | 1.973480 / 1.492716 (0.480763) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238789 / 0.018006 (0.220782) | 0.460237 / 0.000490 (0.459748) | 0.000500 / 0.000200 (0.000300) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034961 / 0.037411 (-0.002450) | 0.102696 / 0.014526 (0.088170) | 0.117772 / 0.176557 (-0.058784) | 0.183865 / 0.737135 (-0.553270) | 0.119216 / 0.296338 (-0.177122) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528894 / 0.215209 (0.313685) | 5.303954 / 2.077655 (3.226300) | 2.897505 / 1.504120 (1.393385) | 2.475898 / 1.541195 (0.934703) | 2.553479 / 1.468490 (1.084988) | 0.625847 / 4.584777 (-3.958930) | 4.656595 / 3.745712 (0.910882) | 3.745170 / 5.269862 (-1.524691) | 2.470922 / 4.565676 (-2.094755) | 0.066908 / 0.424275 (-0.357367) | 0.009172 / 0.007607 (0.001565) | 0.572695 / 0.226044 (0.346650) | 5.753428 / 2.268929 (3.484499) | 3.033226 / 55.444624 (-52.411398) | 2.677280 / 6.876477 (-4.199197) | 2.908857 / 2.142072 (0.766785) | 0.681595 / 4.805227 (-4.123632) | 0.154602 / 6.500664 (-6.346062) | 0.072608 / 0.075469 (-0.002861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.738550 / 1.841788 (-0.103237) | 25.090637 / 8.074308 (17.016329) | 18.371478 / 10.191392 (8.180086) | 0.207357 / 0.680424 (-0.473067) | 0.023396 / 0.534201 (-0.510805) | 0.505663 / 0.579283 (-0.073620) | 0.503137 / 0.434364 (0.068773) | 0.598015 / 0.540337 (0.057678) | 0.714122 / 1.386936 (-0.672814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#971e33ec81b1013654e845b1c2e33cb43cda5558 \"CML watermark\")\n",
"Just as a heads up @mariosasko, the `quickstart.ipynb` Jupyter Notebook has been built at https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb, while the URLs in here point to https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb instead, should we update that?"
] | 2023-05-26T10:25:01Z
| 2023-07-25T13:50:06Z
| 2023-07-25T13:38:33Z
|
CONTRIBUTOR
| null | 0
|
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|
## What's in this PR?
This PR solves #5887 since there was a mismatch between the tokenizer and the model used, since the tokenizer was `bert-base-cased` while the model was `distilbert-base-case` both for the PyTorch and TensorFlow alternatives. Since DistilBERT doesn't use/need the `token_type_ids`, the `**batch` was failing, as the batch contained `input_ids`, `attention_mask`, `token_type_ids`, `start_positions` and `end_positions`, and `token_type_ids` was not required.
Besides that, at the end `seqeval` was being used to evaluate the model predictions, and just `evaluate` was being installed, so I've also included the `seqeval` installation.
Finally, I've re-run everything in Google Colab, and every cell was successfully executed!
## What was done on top of the original PR?
Based on the comments from @mariosasko and @stevhliu, I've updated the contents of this PR to also review the `quickstart.mdx` and update what was needed, besides that, we may eventually move the `Overview.ipynb` dataset to `huggingface/notebooks` following @stevhliu suggestions.
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MDU6SXNzdWU4MjM4OTk5MTA=
| 2,000
|
Windows Permission Error (most recent version of datasets)
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[
"Hi @itsLuisa !\r\n\r\nCould you give us more information about the error you're getting, please?\r\nA copy-paste of the Traceback would be nice to get a better understanding of what is wrong :) ",
"Hello @SBrandeis , this is it:\r\n```\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 537, in incomplete_dir\r\n yield tmp_dir\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 578, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 656, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 982, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 297, in finalize\r\n self.write_on_file()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 230, in write_on_file\r\n pa_array = pa.array(typed_sequence)\r\n File \"pyarrow\\array.pxi\", line 222, in pyarrow.lib.array\r\n File \"pyarrow\\array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 97, in __arrow_array__\r\n out = pa.array(self.data, type=type)\r\n File \"pyarrow\\array.pxi\", line 305, in pyarrow.lib.array\r\n File \"pyarrow\\array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow\\error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow\\error.pxi\", line 107, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Expected bytes, got a 'list' object\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"C:/Users/Luisa/Documents/Uni/WS 2020,21/Neural Networks/Final_Project/NN_Project/data_loading.py\", line 122, in <module>\r\n main()\r\n File \"C:/Users/Luisa/Documents/Uni/WS 2020,21/Neural Networks/Final_Project/NN_Project/data_loading.py\", line 111, in main\r\n dataset = datasets.load_dataset(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 740, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 586, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 543, in incomplete_dir\r\n shutil.rmtree(tmp_dir)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 740, in rmtree\r\n return _rmtree_unsafe(path, onerror)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 618, in _rmtree_unsafe\r\n onerror(os.unlink, fullname, sys.exc_info())\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 616, in _rmtree_unsafe\r\n os.unlink(fullname)\r\nPermissionError: [WinError 32] Der Prozess kann nicht auf die Datei zugreifen, da sie von einem anderen Prozess verwendet wird: 'C:\\\\Users\\\\Luisa\\\\.cache\\\\huggingface\\\\datasets\\\\sample\\\\default-20ee7d51a6a9454f\\\\0.0.0\\\\5fc4c3a355ea77ab446bd31fca5082437600b8364d29b2b95264048bd1f398b1.incomplete\\\\sample-train.arrow'\r\n\r\nProcess finished with exit code 1\r\n```",
"Hi @itsLuisa, thanks for sharing the Traceback.\r\n\r\nYou are defining the \"id\" field as a `string` feature:\r\n```python\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n ...\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n features=datasets.Features(\r\n {\r\n \"id\": datasets.Value(\"string\"),\r\n # ^^ here\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"pos_tags\": datasets.Sequence(datasets.features.ClassLabel(names=[...])),\r\n[...]\r\n```\r\n\r\nBut in the `_generate_examples`, the \"id\" field is a list:\r\n```python\r\nids = list()\r\n```\r\n\r\nChanging:\r\n```python\r\n\"id\": datasets.Value(\"string\"),\r\n```\r\nInto:\r\n```python\r\n\"id\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\nShould fix your issue.\r\n\r\nLet me know if this helps!",
"It seems to be working now, thanks a lot for the help, @SBrandeis !",
"Glad to hear it!\r\nI'm closing the issue"
] | 2021-03-07T11:55:28Z
| 2021-03-09T12:42:57Z
| 2021-03-09T12:42:57Z
|
NONE
| null | null | null |
Hi everyone,
Can anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py , only I want to load the data from three local three-column tsv-files (id\ttokens\tpos_tags\n). I am using the most recent version of datasets. Thank you in advance!
Luisa
My script:
```
import datasets
import csv
logger = datasets.logging.get_logger(__name__)
class SampleConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(SampleConfig, self).__init__(**kwargs)
class Sample(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
SampleConfig(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description="Dataset with words and their POS-Tags",
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"''",
",",
"-LRB-",
"-RRB-",
".",
":",
"CC",
"CD",
"DT",
"EX",
"FW",
"HYPH",
"IN",
"JJ",
"JJR",
"JJS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WRB",
"``"
]
)
),
}
),
supervised_keys=None,
homepage="https://catalog.ldc.upenn.edu/LDC2011T03",
citation="Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.",
)
def _split_generators(self, dl_manager):
loaded_files = dl_manager.download_and_extract(self.config.data_files)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": loaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": loaded_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": loaded_files["val"]})
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
with open(filepath, encoding="cp1252") as f:
data = csv.reader(f, delimiter="\t")
ids = list()
tokens = list()
pos_tags = list()
for id_, line in enumerate(data):
#print(line)
if len(line) == 1:
if tokens:
yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags}
ids = list()
tokens = list()
pos_tags = list()
else:
ids.append(line[0])
tokens.append(line[1])
pos_tags.append(line[2])
# last example
yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags}
def main():
dataset = datasets.load_dataset(
"data_loading.py", data_files={
"train": "train.tsv",
"test": "test.tsv",
"val": "val.tsv"
}
)
#print(dataset)
if __name__=="__main__":
main()
```
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PR_kwDODunzps5AOJp7
| 5,076
|
fix: update exception throw from OSError to EnvironmentError in `push…
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-05T14:46:29Z
| 2022-10-07T14:35:57Z
| 2022-10-07T14:33:27Z
|
CONTRIBUTOR
| null | 0
|
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Status:
Ready for review
Description of Changes:
Fixes #5075
Changes proposed in this pull request:
- Throw EnvironmentError instead of OSError in `push_to_hub` when the Hub token is not present.
|
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Add cache dir for in-memory datasets
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[
"Yes, having `cache_dir` as an attribute looks cleaner.\r\n\r\n\r\n\r\n",
"Good job! Looking forward to this new feature! 🥂",
"@lhoestq Sorry for the late reply. Yes, I'll start working on tests. Thanks for the detailed explanation of the current issues with caching (like the idea of adding the `use_caching` parameter to `load_dataset`) ",
"@lhoestq Sure. I'm aware this is a high-priority issue to some extent, so feel free to take over.\r\n\r\nFew suggestions I have:\r\n* there is a slight difference between setting `use_caching` to `False` in `load_dataset` and disabling caching globally with `set_caching_enabled(False)` because the former will never execute the following code (`self._cache_dir` is always `False`): \r\nhttps://github.com/huggingface/datasets/blob/c231abdb174987419bbde3360b5b9d6a4672c736/src/datasets/arrow_dataset.py#L1807-L1824\r\n, so I'm just checking whether this is intended (if yes, maybe the docs should mention this) or not?\r\n* think we should add the `use_caching` parameter to every method that has the `keep_in_memory` (and `in_memory` 😃) parameter in its signature for better consistency, but I say let's address this in a separate PR. IMO we need one more PR that will deal exclusively with consistency in the caching logic.",
"Hi @mariosasko \r\nWe discussed internally and we think that this feature might not be the direction we're doing to take for these reasons:\r\n\r\n- it goes against our simple definition of caching: on-disk == uses file cache, and in-memory == nothing is written to disk. I think it adds too much complexity just for a minimal flexibility addition\r\n- there are a few edge cases which are really confusing:\r\n - map on an in memory dataset with a cache_file_name specified by the user -> should the result be in memory or from disk ?\r\n - it would require a special cache directory just for in memory datasets, since they don’t have a preferred directory for caching\r\n- it would break a lot of stuff and would require to rewrite significant parts of the core code and the tests\r\n\r\n\r\nSo in the end we're probably going to close this PR.\r\nLet me know what you think, and thank you anyway for your help on this !",
"Hi,\r\n\r\nI'm fine with that. I agree this adds too much complexity. Btw, I like the idea of reverting default in-memory for small datasets that led to this PR.",
"Superseded by #2460 (to close issue #2458)."
] | 2021-05-06T19:35:32Z
| 2021-06-08T19:46:48Z
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Adds the cache dir attribute to DatasetInfo as suggested by @lhoestq.
Should fix #2322
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Added fix to handle the last turn that is a user turn.
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Add Google Conceptual Captions Dataset
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| 2021-09-10T11:45:29Z
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NONE
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## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add E2E NLG
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Adding the E2E NLG dataset.
More info here : http://www.macs.hw.ac.uk/InteractionLab/E2E/
### Checkbox
- [x] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [x] Fill the `_DESCRIPTION` and `_CITATION` variables
- [x] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [x] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [x] Generate the metadata file `dataset_infos.json` for all configurations
- [x] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card `README.md` using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
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HTTPError: 404 Client Error: Not Found for url
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"FYI @lewtun ",
"Hi @km5ar, thanks for reporting.\r\n\r\nThis should be fixed in the notebook:\r\n- the filename `datasets-issues-with-hf-doc-builder.jsonl` no longer exists on the repo; instead, current filename is `datasets-issues-with-comments.jsonl`\r\n- see: https://huggingface.co/datasets/lewtun/github-issues/tree/main\r\n\r\nAnyway, depending on your version of `datasets`, you can now use:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nissues_dataset = load_dataset(\"lewtun/github-issues\")\r\nissues_dataset\r\n```\r\ninstead of:\r\n```python\r\nfrom huggingface_hub import hf_hub_url\r\n\r\ndata_files = hf_hub_url(\r\n repo_id=\"lewtun/github-issues\",\r\n filename=\"datasets-issues-with-hf-doc-builder.jsonl\",\r\n repo_type=\"dataset\",\r\n)\r\nfrom datasets import load_dataset\r\n\r\nissues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\nissues_dataset\r\n```\r\n\r\nOutput:\r\n```python\r\nIn [25]: ds = load_dataset(\"lewtun/github-issues\")\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10.5k/10.5k [00:00<00:00, 5.75MB/s]\r\nUsing custom data configuration lewtun--github-issues-cff5093ecc410ea2\r\nDownloading and preparing dataset json/lewtun--github-issues to .../.cache/huggingface/datasets/lewtun___json/lewtun--github-issues-cff5093ecc410ea2/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...\r\nDownloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12.2M/12.2M [00:00<00:00, 26.5MB/s]\r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.70s/it]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1589.96it/s]\r\nDataset json downloaded and prepared to .../.cache/huggingface/datasets/lewtun___json/lewtun--github-issues-cff5093ecc410ea2/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 133.95it/s]\r\n\r\nIn [26]: ds\r\nOut[26]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app', 'is_pull_request'],\r\n num_rows: 3019\r\n })\r\n})\r\n```",
"Thanks for reporting @km5ar and thank you @albertvillanova for the quick solution! I'll post a fix on the source too"
] | 2022-10-06T19:48:58Z
| 2022-10-07T15:12:01Z
| 2022-10-07T15:12:01Z
|
NONE
| null | null | null |
## Describe the bug
I was following chap 5 from huggingface course: https://huggingface.co/course/chapter5/6?fw=tf
However, I'm not able to download the datasets, with a 404 erros
<img width="1160" alt="iShot2022-10-06_15 54 50" src="https://user-images.githubusercontent.com/54015474/194406327-ae62c2f3-1da5-4686-8631-13d879a0edee.png">
## Steps to reproduce the bug
```python
from huggingface_hub import hf_hub_url
data_files = hf_hub_url(
repo_id="lewtun/github-issues",
filename="datasets-issues-with-hf-doc-builder.jsonl",
repo_type="dataset",
)
from datasets import load_dataset
issues_dataset = load_dataset("json", data_files=data_files, split="train")
issues_dataset
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.9.12
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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Adding the BrWaC dataset
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| 2020-12-18T15:56:56Z
| 2020-12-18T15:56:55Z
|
CONTRIBUTOR
| null | 0
|
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Adding the BrWaC dataset, a large corpus of Portuguese language texts
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billsum dataset: Checksums didn't match for dataset source files:
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[
"Hi @XingxingZhang, thanks for reporting.\r\n\r\nThis was due to a change in Google Drive service:\r\n- #3786 \r\n\r\nWe have already fixed it:\r\n- #3787\r\n\r\nYou should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```",
"thanks @albertvillanova "
] | 2022-03-16T14:52:08Z
| 2022-03-16T15:57:08Z
| 2022-03-16T15:46:44Z
|
NONE
| null | null | null |
## Describe the bug
When loading the `billsum` dataset, it throws the exception "Checksums didn't match for dataset source files"
```
File "virtualenv_projects/codex/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']
```
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
print(datasets.__version__)
load_dataset('billsum')
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: mac os
- Python version: Python 3.7.6
- PyArrow version: 3.0.0
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ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils'
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[
"update the version. fix"
] | 2023-04-14T23:28:54Z
| 2023-04-14T23:36:19Z
| 2023-04-14T23:36:19Z
|
NONE
| null | null | null |
### Describe the bug
The module moved to new place?
### Steps to reproduce the bug
in the import step,
```python
from datasets.utils.deprecation_utils import DeprecatedEnum
```
error:
```
ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils'
```
### Expected behavior
import successfully
### Environment info
python==3.9.16
datasets==1.18.3
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|
handle data alteration when trying type
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| 2020-09-21T16:13:06Z
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Fix #649
The bug came from the type inference that didn't handle a weird case in Pyarrow.
Indeed this code runs without error but alters the data in arrow:
```python
import pyarrow as pa
type = pa.struct({"a": pa.struct({"b": pa.string()})})
array_with_altered_data = pa.array([{"a": {"b": "foo", "c": "bar"}}] * 10, type=type)
print(array_with_altered_data[0].as_py())
# {'a': {'b': 'foo'}} -> the sub-field "c" is missing
```
(I don't know if this is intended in pyarrow tbh)
We didn't take this case into account during type inference. By default it was keeping old features and maybe alter data.
To fix that I added a line that checks that the first element of the array is not altered.
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| 881
|
Use GCP download url instead of tensorflow custom download for boolq
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[] | 2020-11-24T09:47:11Z
| 2020-11-24T10:12:34Z
| 2020-11-24T10:12:33Z
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BoolQ is a dataset that used tf.io.gfile.copy to download the file from a GCP bucket.
It prevented the dataset to be downloaded twice because of a FileAlreadyExistsError.
Even though the error could be fixed by providing `overwrite=True` to the tf.io.gfile.copy call, I changed the script to use GCP download urls and use regular downloads instead and remove the tensorflow dependency.
Fix #875
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| 1,041,971,117
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I_kwDODunzps4-Gzet
| 3,193
|
Update link to datasets-tagging app
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[] | 2021-11-02T07:39:59Z
| 2021-11-08T10:36:22Z
| 2021-11-08T10:36:22Z
|
MEMBER
| null | null | null |
Once datasets-tagging has been transferred to Spaces:
- huggingface/datasets-tagging#22
We should update the link in Datasets.
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Add ROPES dataset
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| 2020-12-02T10:58:36Z
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ROPES dataset
Reasoning over paragraph effects in situations - testing a system's ability to apply knowledge from a passage of text to a new situation. The task is framed into a reading comprehension task following squad-style extractive qa.
One thing to note: labels of the test set are hidden (leaderboard submission) so I encoded that as an empty list (ropes.py:L125)
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MDU6SXNzdWU2MTkwOTQ5NTQ=
| 133
|
[Question] Using/adding a local dataset
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[
"Hi @zphang,\r\n\r\nSo you can just give the local path to a dataset script file and it should work.\r\n\r\nHere is an example:\r\n- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)\r\n- then you can load it with `load_dataset('PATH/TO/YOUR/LOCAL/SCRIPT.py')`\r\n\r\nDoes it make sense?",
"Could you give a more concrete example, please? \r\n\r\nI looked up wikitext dataset script from the repo. Should I just overwrite the `data_file` on line 98 to point to the local dataset directory? Would it work for different configurations of wikitext (wikitext2, wikitext103 etc.)?\r\n\r\nOr maybe we can use DownloadManager to specify local dataset location? In that case, where do we use DownloadManager instance?\r\n\r\nThanks",
"Hi @MaveriQ , although what I am doing is to commit a new dataset, but I think looking at imdb script might help.\r\nYou may want to use `dl_manager.download_custom`, give it a url(arbitrary string), a custom_download(arbitrary function) and return a path, and finally use _get sample to fetch a sample.",
"The download manager supports local directories. You can specify a local directory instead of a url and it should work.",
"Closing this one.\r\nFeel free to re-open if you have other questions :)"
] | 2020-05-15T16:26:06Z
| 2020-07-23T16:44:09Z
| 2020-07-23T16:44:09Z
|
NONE
| null | null | null |
Users may want to either create/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.
It appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.
A notebook/example script demonstrating this would be very helpful.
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Can I load in list of list of dict format?
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[
"Thanks for reporting, @LZY-the-boys.\r\n\r\nCould you please give more details about what is your intended dataset structure? What are the names of the columns and the value of each row?\r\n\r\nCurrently, the JSON-Lines format is supported:\r\n- Each line correspond to one row of the dataset\r\n- Each line is composed of one JSON object, where the names are the names of the columns, and the values are the values for the row-column pair."
] | 2023-04-16T13:50:14Z
| 2023-04-19T12:04:36Z
| null |
NONE
| null | null | null |
### Feature request
my jsonl dataset has following format:
```
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
[{'input':xxx, 'output':xxx},{'input:xxx,'output':xxx},...]
```
I try to use `datasets.load_dataset('json', data_files=path)` or `datasets.Dataset.from_json`, it raises
```
File "site-packages/datasets/arrow_dataset.py", line 1078, in from_json
).read()
File "site-packages/datasets/io/json.py", line 59, in read
self.builder.download_and_prepare(
File "site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "site-packages/datasets/builder.py", line 1749, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "site-packages/datasets/builder.py", line 1892, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Motivation
I wanna use features like `Datasets.map` or `Datasets.shuffle`, so i need the dataset in memory to be `arrow_dataset.Datasets` format
### Your contribution
PR
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Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"
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[] | 2023-11-13T21:27:43Z
| 2023-11-13T21:27:43Z
| null |
NONE
| null | null | null |
### Describe the bug
I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error
I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs.
### Steps to reproduce the bug
```python
import s3fs
from datasets import load_dataset
from aiobotocore.session import get_session
DATA_PATH = "s3://bucket_name/path/validation.parquet"
fs = s3fs.S3FileSystem(session=get_session())
```
`fs.stat` returns the data, so we can say that fs is working and we have all permissions
```python
fs.stat(DATA_PATH)
# Returns:
# {'ETag': '"123123a-19"',
# 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()),
# 'size': 312237170,
# 'name': 'bucket_name/path/validation.parquet',
# 'type': 'file',
# 'StorageClass': 'STANDARD',
# 'VersionId': 'Abc.HtmsC9h.as',
# 'ContentType': 'binary/octet-stream'}
```
```python
fs.storage_options
# Returns:
# {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>}
```
```python
ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options)
```
<details>
<summary>Returns such error (expandable)</summary>
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[88], line 1
----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options)
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
2152 # Download and prepare data
-> 2153 builder_instance.download_and_prepare(
2154 download_config=download_config,
2155 download_mode=download_mode,
2156 verification_mode=verification_mode,
2157 try_from_hf_gcs=try_from_hf_gcs,
2158 num_proc=num_proc,
2159 storage_options=storage_options,
2160 )
2162 # Build dataset for splits
2163 keep_in_memory = (
2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2165 )
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
952 if num_proc is not None:
953 prepare_split_kwargs["num_proc"] = num_proc
--> 954 self._download_and_prepare(
955 dl_manager=dl_manager,
956 verification_mode=verification_mode,
957 **prepare_split_kwargs,
958 **download_and_prepare_kwargs,
959 )
960 # Sync info
961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1025 split_dict = SplitDict(dataset_name=self.dataset_name)
1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
-> 1027 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
1029 # Checksums verification
1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager)
32 if not self.config.data_files:
33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
---> 34 data_files = dl_manager.download_and_extract(self.config.data_files)
35 if isinstance(data_files, (str, list, tuple)):
36 files = data_files
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls)
549 def download_and_extract(self, url_or_urls):
550 """Download and extract given `url_or_urls`.
551
552 Is roughly equivalent to:
(...)
563 extracted_path(s): `str`, extracted paths of given URL(s).
564 """
--> 565 return self.extract(self.download(url_or_urls))
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls)
401 def download(self, url_or_urls):
402 """Download given URL(s).
403
404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior.
(...)
418 ```
419 """
--> 420 download_config = self.download_config.copy()
421 download_config.extract_compressed_file = False
422 if download_config.download_desc is None:
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self)
93 def copy(self) -> "DownloadConfig":
---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0)
93 def copy(self) -> "DownloadConfig":
---> 94 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
[... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)]
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
[... skipping similar frames: deepcopy at line 146 (1 times)]
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy)
204 append = y.append
205 for a in x:
--> 206 append(deepcopy(a, memo))
207 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo)
237 def _deepcopy_method(x, memo): # Copy instance methods
--> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo))
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
[... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)]
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
[... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)]
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
263 if deep and args:
264 args = (deepcopy(arg, memo) for arg in args)
--> 265 y = func(*args)
266 if deep:
267 memo[id(x)] = y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0)
262 deep = memo is not None
263 if deep and args:
--> 264 args = (deepcopy(arg, memo) for arg in args)
265 y = func(*args)
266 if deep:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy)
210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 211 y = [deepcopy(a, memo) for a in x]
212 # We're not going to put the tuple in the memo, but it's still important we
213 # check for it, in case the tuple contains recursive mutable structures.
214 try:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0)
210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 211 y = [deepcopy(a, memo) for a in x]
212 # We're not going to put the tuple in the memo, but it's still important we
213 # check for it, in case the tuple contains recursive mutable structures.
214 try:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
174 # If is its own copy, don't memoize.
175 if y is not x:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
269 if state is not None:
270 if deep:
--> 271 state = deepcopy(state, memo)
272 if hasattr(y, '__setstate__'):
273 y.__setstate__(state)
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy)
210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 211 y = [deepcopy(a, memo) for a in x]
212 # We're not going to put the tuple in the memo, but it's still important we
213 # check for it, in case the tuple contains recursive mutable structures.
214 try:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0)
210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 211 y = [deepcopy(a, memo) for a in x]
212 # We're not going to put the tuple in the memo, but it's still important we
213 # check for it, in case the tuple contains recursive mutable structures.
214 try:
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy)
229 memo[id(x)] = y
230 for key, value in x.items():
--> 231 y[deepcopy(key, memo)] = deepcopy(value, memo)
232 return y
File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil)
159 reductor = getattr(x, "__reduce_ex__", None)
160 if reductor is not None:
--> 161 rv = reductor(4)
162 else:
163 reductor = getattr(x, "__reduce__", None)
TypeError: cannot pickle '_contextvars.Context' object
```
</details>
### Expected behavior
If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well
### Environment info
- `datasets` version: 2.14.6
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.13
- Huggingface_hub version: 0.19.1
- PyArrow version: 14.0.1
- Pandas version: 1.5.3
- s3fs version: 2023.10.0
- fsspec version: 2023.10.0
- aiobotocore version: 2.7.0
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Replace `assertEqual` with `assertTupleEqual` in unit tests for verbosity
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"_The documentation is not available anymore as the PR was closed or merged._",
"FYI I used the following regex to look for the `assertEqual` statements where the assertion was being done over a Tuple: `self.assertEqual(.*, \\(.*,)(\\)\\))$`, hope this is useful!"
] | 2022-06-15T09:29:16Z
| 2022-07-07T17:06:51Z
| 2022-07-07T16:55:48Z
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As detailed in #4419 and as suggested by @mariosasko, we could replace the `assertEqual` assertions with `assertTupleEqual` when the assertion is between Tuples, in order to make the tests more verbose.
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Support parallelized downloading in load_dataset with Spark
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"Hi @lhoestq or other maintainers, this is ready for review, could you please take a look?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5807). All of your documentation changes will be reflected on that endpoint.",
"Per the discussion in #5798, will implement with `joblibspark` instead."
] | 2023-04-28T18:34:32Z
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| 2023-05-25T16:54:14Z
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As proposed in https://github.com/huggingface/datasets/issues/5798, this adds support to parallelized downloading in `load_dataset` with Spark, which can speed up the process by distributing the workload to worker nodes.
Parallelizing dataset processing is not supported in this PR.
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add Korean Sarcasm Dataset
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change udpos features structure
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[
"Let's add the tags in another PR. Thanks again !",
"Close #2061 , close #2444."
] | 2021-06-09T08:03:31Z
| 2021-06-18T11:55:09Z
| 2021-06-16T10:41:37Z
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The structure is change such that each example is a sentence
The change is done for issues:
#2061
#2444
Close #2061 , close #2444.
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I_kwDODunzps5iq8IP
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Manually dataset info made not taken into account
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[
"hi @jplu ! Did I understand you correctly that you create the dataset, push it to the Hub with `.push_to_hub` and you see a `dataset_infos.json` file there, then you edit this file, load the dataset with `load_dataset` and you don't see any changes in `.info` attribute of a dataset object? \r\n\r\nThis is actually weird that when you push your dataset to the Hub, a `dataset_infos.json` file is created, because this file is deprecated and it should create `README.md` with the `dataset_info` field instead. Some keys are also deprecated, like \"supervised_keys\" and \"task_templates\".\r\n\r\nCan you please provide a toy reproducible example of how you create and push the dataset? And also why do you want to change this file, especially the number of bytes and examples?",
"Hi @polinaeterna Yes I have created the dataset with `Dataset.from_dict` applied some updates afterward and when I pushed to the hub I had a `dataset_infos.json` file and there was a `README.md` file as well.\r\n\r\nI didn't know that the JSON file was deprecated. So I have built my dataset with `ImageBuilder` instead and now it works like a charm without having to touch anything.\r\n\r\nI haven't succeed to reproduce the creation of the JSON file with a toy example, hence, I certainly did some mistakes when I have manipulated my dataset manually at first. My bad."
] | 2023-04-05T11:15:17Z
| 2023-04-06T08:52:20Z
| 2023-04-06T08:52:19Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
Hello,
I'm manually building an image dataset with the `from_dict` approach. I also build the features with the `cast_features` methods. Once the dataset is created I push it on the hub, and a default `dataset_infos.json` file seems to have been automatically added to the repo in same time. Hence I update it manually with all the missing info, but when I download the dataset the info are never updated.
Former `dataset_infos.json` file:
```
{"default": {
"description": "",
"citation": "",
"homepage": "",
"license": "",
"features": {
"image": {
"_type": "Image"
},
"labels": {
"names": [
"Fake",
"Real"
],
"_type": "ClassLabel"
}
},
"splits": {
"validation": {
"name": "validation",
"num_bytes": 901010094.0,
"num_examples": 3200,
"dataset_name": null
},
"train": {
"name": "train",
"num_bytes": 901010094.0,
"num_examples": 3200,
"dataset_name": null
}
},
"download_size": 1802008414,
"dataset_size": 1802020188.0,
"size_in_bytes": 3604028602.0
}}
```
After I update it manually it looks like:
```
{
"bstrai--deepfake-detection":{
"description":"",
"citation":"",
"homepage":"",
"license":"",
"features":{
"image":{
"decode":true,
"id":null,
"_type":"Image"
},
"labels":{
"num_classes":2,
"names":[
"Fake",
"Real"
],
"id":null,
"_type":"ClassLabel"
}
},
"supervised_keys":{
"input":"image",
"output":"labels"
},
"task_templates":[
{
"task":"image-classification",
"image_column":"image",
"label_column":"labels"
}
],
"config_name":null,
"splits":{
"validation":{
"name":"validation",
"num_bytes":36627822,
"num_examples":123,
"dataset_name":"deepfake-detection"
},
"train":{
"name":"train",
"num_bytes":901023694,
"num_examples":3200,
"dataset_name":"deepfake-detection"
}
},
"download_checksums":null,
"download_size":937562209,
"dataset_size":937651516,
"size_in_bytes":1875213725
}
}
```
Anything I should do to have the new infos in the `dataset_infos.json` to be taken into account? Or it is not possible yet?
Thanks!
### Steps to reproduce the bug
-
### Expected behavior
-
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.10
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 2.0.0
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Create release script
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"I've aligned the release script with Transformers in #6004, so I think this issue can be closed."
] | 2021-06-11T09:38:02Z
| 2023-07-20T13:22:23Z
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MEMBER
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Create a script so that releases can be done automatically (as done in `transformers`).
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[Dataset] Tester add mock function
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need to add an empty `extract()` function to make `hansard` dataset test work.
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Support custom fingerprinting with `Dataset.from_generator`
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"The `fingerprint` parameter serves a slightly different purpose - we use it to inject a new fingerprint after transforming a `Dataset` (computed from the previous fingerprint + transform + transform args), e.g., to be able to compute the cache file for a transform. There is no concept of `fingerprint` before a `Dataset` is fully initialized, but we still need to hash the args (e.g., generator func) of the \"dataset creation methods\" (`from_generator`, `from_csv`, etc.) to compute the cache directory (to store the initial version and transformed dataset versions)\r\n\r\nI agree it should be easier to bypass the hashing mechanism in this instance, too. However, we should probably first address https://github.com/huggingface/datasets/issues/5080 before solving this (e.g., maybe exposing `hash` in `load_dataset`/`load_dataset_builder`.",
"Adding +1 here:\r\n\r\nIf the generator needs to access some external resources or state, then it's not always straightforward to make it pickle-able. So I'd like to be able to override how the default cache key derivation needs to pickle the generator (and of course, I'd accept responsibility for that part of cache consistency).\r\n\r\nAppears to be a recurrent roadbump: #6118 #5963 #5819 #5750 #4983 ",
"Silly hack incoming:\r\n\r\n```python\r\nimport uuid\r\n\r\nclass _DatasetGeneratorPickleHack:\r\n def __init__(self, generator, generator_id=None):\r\n self.generator = generator\r\n self.generator_id = (\r\n generator_id if generator_id is not None else str(uuid.uuid4())\r\n )\r\n\r\n def __call__(self, *args, **kwargs):\r\n return self.generator(*kwargs, **kwargs)\r\n\r\n def __reduce__(self):\r\n return (_DatasetGeneratorPickleHack_raise, (self.generator_id,))\r\n\r\n\r\ndef _DatasetGeneratorPickleHack_raise(*args, **kwargs):\r\n raise AssertionError(\"cannot actually unpickle _DatasetGeneratorPickleHack!\")\r\n```\r\n\r\nNow `Dataset.from_generator(_DatasetGeneratorPickleHack(gen))` works even if `gen` is unpicklable, because Dataset just pickles the shim object that avoids actually traversing `gen`. Then, one can work out how to set `generator_id` meaningfully to allow cache reuse.",
"I'd like some way to do this too. I find that sometimes the hash doesn't cover enough, and that the dataset is not regenerated even when underlying data has changed, and by supplying a custom fingerprint I could do a better job of controlling when my dataset is regenerated."
] | 2023-08-29T22:43:13Z
| 2023-09-30T16:56:51Z
| null |
NONE
| null | null | null |
### Feature request
When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`.
### Motivation
Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash.
### Your contribution
If validated, I can try to submit a PR for this.
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[Docs] How to use with PyTorch page
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"_The documentation is not available anymore as the PR was closed or merged._"
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Currently the docs about PyTorch are scattered around different pages, and we were missing a place to explain more in depth how to use and optimize a dataset for PyTorch. This PR is related to #4457 which is the TF counterpart :)
cc @Rocketknight1 we can try to align both documentations contents now I think
cc @stevhliu let me know what you think !
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Fix bug in `ImageClassifcation` task template
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[
"The CI failures are unrelated to the changes in this PR.",
"> The CI failures are unrelated to the changes in this PR.\r\n\r\nIt seems that some of the failures are due to the tests on the dataset cards (e.g. CIFAR, MNIST, FASHION_MNIST). Perhaps it's worth addressing those in this PR to avoid confusing downstream developers who branch off `master` and suddenly have a failing CI?",
"@lewtun We only run these tests against the modified datasets on the PR branch, so this will not lead to errors after merging."
] | 2022-01-10T14:09:59Z
| 2022-01-11T15:47:52Z
| 2022-01-11T15:47:52Z
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Fixes a bug in the `ImageClassification` task template which requires specifying class labels twice in dataset scripts. Additionally, this PR refactors the API around the classification task templates for cleaner `labels` handling.
CC: @lewtun @nateraw
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hindi discourse analysis dataset commit
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[
"That's a great dataset to have! We need a couple more things to be good to go:\r\n- you should `make style` and `flake8 datasets` before pushing to make the code quality check happy :) \r\n- the dataset will need some dummy data which you should be able to auto-generate and test locally: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#automatically-add-code-metadata\r\n- there's some good information in your current README, but we need the format to follow the template [here](https://github.com/huggingface/datasets/blob/master/templates/README.md) and to have YAML tags at the top, as described in the guide: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card\r\n\r\nLEt us know if you need any help!",
"Hi @yjernite \r\nI was successfully able to generate the dataset_info.json file using the command \r\npython datasets-cli test datasets/<your-dataset-folder> --save_infos --all_configs\r\n\r\nBut unfortunately, could not generate the dummy data\r\n\r\nWhile running the command \r\npython datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate\r\nI got an error as \r\n\r\nValueError: Couldn't parse columns ['0', '1', '2', '3', '4', ......, '9982']. Maybe specify which json field must be used to read the data with --json_field <my_field>.\r\n\r\nThe thing is the dataset I am trying to upload is of the format \r\n{\r\n '0': {'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'},\r\n '1': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode},\r\n .......,\r\n '9982': {'Story_no': story_no, 'Sentence': sentence, 'Discourse Mode': discourse_mode}\r\n}\r\n\r\nCan you please suggest any errors I am making in the _generate_examples method?\r\n\r\nThanks!",
"The dummy data generator doesn't support this kind of json format yet.\r\nCan you create the dummy data manually please ? You can get the instructions by running the \r\n```\r\ndatasets-cli dummy_data ./datasets/dataset_name\r\n```\r\ncommand.",
"Hi, I created the dummy data manually but the tests are still failing it seems.\r\nCan you suggest the format of JSON which is supported by dummy data generator?\r\nI will have to modify my _generate_examples method accordingly.\r\nPlease advice on the same.\r\nThanks much.\r\n",
"Can you run `make style` to format the code for the CI please ?\r\n\r\nAlso about the dummy data, here is how to generate them:\r\n\r\nWe need a dummy_data.zip file in ./datasets/hindiDiscourse/dummy/1.0.0 (or replace hindiDiscourse by hindi_discourse since we have to rename the folder anyway)\r\nTo create the zip file, first go in this directory and create a folder named dummy_data.\r\nThen inside the dummy_data folder create a file `discourse_dataset.json` and fill it with something like 5 examples.\r\nFinally zip the dummy_data folder to end up with the dummy_data.zip file\r\n\r\nOnce it's done you can check if the dummy data test passes with \r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_hindi_discourse\r\n```\r\n\r\nIf it passes you can then remove the dummy_data folder to keep only the dummy_data.zip file",
"Hi @duttahritwik did you manage to make the dummy data ?\r\nFeel free to ping me if you have questions or if we can help",
"The error `tests/test_file_utils.py::TempSeedTest::test_tensorflow` just appeared because of tensorflow's update.\r\nOnce it's fixed on master we'll be free to merge this one",
"Ci is green on master :) ",
"merging since the CI is fixed on master"
] | 2020-12-05T09:24:01Z
| 2020-12-14T19:44:48Z
| 2020-12-14T19:44:48Z
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Update multi_woz_v22 checksum
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Fix issue https://github.com/huggingface/datasets/issues/1876
The files were changed in https://github.com/budzianowski/multiwoz/pull/72
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`load_dataset` does not work with uploaded arrow file
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[
"Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)",
"cc @LysandreJik maybe we can solve this at the same time as adding `push_to_hub`"
] | 2021-10-05T20:15:10Z
| 2021-10-06T17:01:37Z
| null |
MEMBER
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## Describe the bug
I've preprocessed and uploaded a dataset here: https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed . The dataset is in `.arrow` format.
The dataset can correctly be loaded when doing:
```bash
git lfs install
git clone https://huggingface.co/datasets/ami-wav2vec2/ami_headset_single_preprocessed
```
followed by
```python
from datasets import load_from_disk
ds = load_from_disk("./ami_headset_single_preprocessed")
```
However when I try to directly download the dataset as follows:
```python
from datasets import load_dataset
ds = load_dataset("ami-wav2vec2/ami_headset_single_preprocessed")
```
the following error occurs:
```bash
/usr/local/lib/python3.7/dist-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, script_version, use_auth_token, task, streaming, **config_kwargs)
1115 ignore_verifications=ignore_verifications,
1116 try_from_hf_gcs=try_from_hf_gcs,
-> 1117 use_auth_token=use_auth_token,
1118 )
1119
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
635 if not downloaded_from_gcs:
636 self._download_and_prepare(
--> 637 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
638 )
639 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
724 try:
725 # Prepare split will record examples associated to the split
--> 726 self._prepare_split(split_generator, **prepare_split_kwargs)
727 except OSError as e:
728 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
1186 generator, unit=" tables", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)
1187 ):
-> 1188 writer.write_table(table)
1189 num_examples, num_bytes = writer.finalize()
1190
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_table(self, pa_table, writer_batch_size)
424 # reorder the arrays if necessary + cast to self._schema
425 # we can't simply use .cast here because we may need to change the order of the columns
--> 426 pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
427 batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size)
428 self._num_bytes += sum(batch.nbytes for batch in batches)
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib._sanitize_arrays()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
/usr/local/lib/python3.7/dist-packages/pyarrow/compute.py in cast(arr, target_type, safe)
279 else:
280 options = CastOptions.unsafe(target_type)
--> 281 return call_function("cast", [arr], options)
282
283
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
/usr/local/lib/python3.7/dist-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list
```
## Expected results
The dataset should be correctly loaded with `load_dataset` IMO.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.2.dev0
- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 5.0.0
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Non-deterministic tests: CI tests randomly fail
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[
"I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other hand, I guess we can investigate what causes this and discuss with the back-end team",
"Closed by:\r\n- #3982"
] | 2021-12-10T06:08:59Z
| 2022-03-31T16:38:51Z
| 2022-03-31T16:38:51Z
|
MEMBER
| null | null | null |
## Describe the bug
Some CI tests fail randomly.
1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
```
2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
- On Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
```
- On Windows:
```
=========================== short test summary info ===========================
FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
```
The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.
3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
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Nlp cli fix endpoints
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[
"LGTM 👍 "
] | 2020-06-03T14:10:39Z
| 2020-06-08T09:02:58Z
| 2020-06-08T09:02:57Z
|
MEMBER
| null | 0
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With this PR users will be able to upload their own datasets and metrics.
As mentioned in #181, I had to use the new endpoints and revert the use of dataclasses (just in case we have changes in the API in the future).
We now distinguish commands for datasets and commands for metrics:
```bash
nlp-cli upload_dataset <path/to/dataset>
nlp-cli upload_metric <path/to/metric>
nlp-cli s3_datasets {rm, ls}
nlp-cli s3_metrics {rm, ls}
```
Does it sound good to you @julien-c @thomwolf ?
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NotADirectoryError while loading the CNN/Dailymail dataset
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[
"Looks like the google drive download failed.\r\nI'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.\r\n\r\nWe should consider finding a better host than google drive for this dataset imo\r\nrelated : #873 #864 ",
"It is working now, thank you. \r\n\r\nShould I leave this issue open to address the Quota-exceeded error?",
"Yes please. It's been happening several times, we definitely need to address it",
"Any updates on this one? I'm facing a similar issue trying to add CelebA.",
"I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..\r\nPlease try to use other hosts when possible",
"The original links are google drive links. Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.",
"It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.\r\nOtherwise you can use the google drive link, but it it's not that convenient because of this quota issue.",
"Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.",
"I am getting this error as well. Is there a fix?",
"Not as long as the data is stored on GG drive unfortunately.\r\nMaybe we can ask if there's a mirror ?\r\n\r\nHi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?\r\n\r\nTo give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.",
"The following copy of CNN/DM dataset, fixed the problem for me:\r\nhttps://huggingface.co/datasets/ccdv/cnn_dailymail",
"Thanks for the link @mrazizi !\r\n\r\nApparently the original authors don't host the dataset themselves (\"for legal reasons\", source [here](https://github.com/abisee/cnn-dailymail/issues/9))."
] | 2020-12-02T11:07:56Z
| 2022-02-17T14:13:39Z
| 2022-02-17T14:13:39Z
|
NONE
| null | null | null |
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-9-cd4bf8bea840> in <module>()
22
23
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')
5 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
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Update xcopa dataset (fix checksum issues + add translated data)
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"The CI failures are unrelated to the changes (missing fields in the readme and the CER metric error fixed in #3252)."
] | 2021-11-11T20:51:33Z
| 2021-11-12T10:30:58Z
| 2021-11-12T10:30:57Z
|
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This PR updates the checksums (as reported [here](https://discuss.huggingface.co/t/how-to-load-dataset-locally/11601/2)) of the `xcopa` dataset. Additionally, it adds new configs that hold the translated data of the original set of configs. This data was not available at the time of adding this dataset to the lib.
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Using sharding to parallelize indexing
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[] | 2021-07-18T21:26:26Z
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CONTRIBUTOR
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**Is your feature request related to a problem? Please describe.**
Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding)
**Describe the solution you'd like**
When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data.
Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset.
**Describe alternatives you've considered**
Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25).
**Additional context**
The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset.
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Add Visual Genome
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Hum there seems to be some issues with tasks in test:\r\n - some tasks don't fit anything in `tasks.json`. Do I remove them in `task_categories`?\r\n - some tasks should exist, typically `visual-question-answering` (https://github.com/huggingface/datasets/blame/9f2ff14673cac1f1ad56d80221a793f5938b68c7/src/datasets/utils/resources/tasks.json#L195) yet the exception is failing on me. I'm guessing it's because my `master` is not up-to-date. However this means that the testing only tests my branch instead of the one merged with master?\r\n \r\n cc @mariosasko @lhoestq ",
"> some tasks don't fit anything in tasks.json. Do I remove them in task_categories?\r\n\r\nYou can keep them, but add `other-` as a prefix to those tasks to make the CI ignore it\r\n\r\n> some tasks should exist, typically visual-question-answering (https://github.com/huggingface/datasets/blame/9f2ff14673cac1f1ad56d80221a793f5938b68c7/src/datasets/utils/resources/tasks.json#L195) yet the exception is failing on me. I'm guessing it's because my master is not up-to-date. However this means that the testing only tests my branch instead of the one merged with master?\r\n\r\nFeel free to merge upstream/master into your branch ;)\r\n\r\nEDIT: actually I just noticed you've already done this, thanks !",
"After offline discussions: will keep that image essentially it's necessary as I have a mapping that creates a mapping between url and local path (images are downloaded via a zip file) and dummy data needs to store that dummy image. The issue is when I read an annotation, I get a url, compute the local path, and basically I assume the local path exists since I've extracted all the images ... This isn't true if dummy data doesn't have all the images, so instead I've added a script that \"fixes\" the dummy data after using the CLI, it essentially adds the dummy image in the zip corresponding to the url."
] | 2022-04-13T12:25:24Z
| 2022-04-21T15:42:49Z
| 2022-04-21T13:08:52Z
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Add Curiosity Dialogs Dataset
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"@lhoestq I tried manually creating the dummy files. But unfortunately it was raising an error during testing the dummy data (regarding JSON parsing).\r\n\r\nThe JSONs are pretty big so I cannot actually open it without crashing the text editor.\r\n\r\n Do you have any suggestions?",
"@lhoestq I have made all the changes you mentioned."
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Add Facebook [Curiosity Dialogs](https://github.com/facebookresearch/curiosity) Dataset.
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Allow for incremental cumulative metric updates in a distributed setup
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| 2021-09-26T13:42:39Z
| 2021-09-26T13:42:39Z
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CONTRIBUTOR
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Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.
If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411
If this change is ok with you, I'll open a pull request.
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[Datasets] add ted_hrlr
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@thomwolf - After looking at `xnli` I think it's better to leave the translation features and add a `translation` key to make them work in our framework.
The result looks like this:
you can see that each split has a `translation` key which value is the nlp.features.Translation object.
That's a simple change. If it's ok for you, I will add dummy data for the other configs and treat the other translation scripts in the same way.
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Release: 2.14.5
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6219). 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) | 0.009523 / 0.011353 (-0.001830) | 0.005105 / 0.011008 (-0.005903) | 0.122664 / 0.038508 (0.084156) | 0.084688 / 0.023109 (0.061579) | 0.412057 / 0.275898 (0.136159) | 0.449690 / 0.323480 (0.126210) | 0.006627 / 0.007986 (-0.001358) | 0.004150 / 0.004328 (-0.000178) | 0.082079 / 0.004250 (0.077829) | 0.065289 / 0.037052 (0.028237) | 0.432934 / 0.258489 (0.174445) | 0.492068 / 0.293841 (0.198227) | 0.048317 / 0.128546 (-0.080229) | 0.015582 / 0.075646 (-0.060064) | 0.372050 / 0.419271 (-0.047222) | 0.070649 / 0.043533 (0.027116) | 0.431754 / 0.255139 (0.176615) | 0.473349 / 0.283200 (0.190149) | 0.037293 / 0.141683 (-0.104390) | 1.807537 / 1.452155 (0.355382) | 1.923073 / 1.492716 (0.430357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271214 / 0.018006 (0.253208) | 0.592961 / 0.000490 (0.592471) | 0.004062 / 0.000200 (0.003862) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034766 / 0.037411 (-0.002645) | 0.093014 / 0.014526 (0.078488) | 0.131332 / 0.176557 (-0.045225) | 0.188110 / 0.737135 (-0.549025) | 0.117617 / 0.296338 (-0.178722) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668223 / 0.215209 (0.453013) | 6.707031 / 2.077655 (4.629376) | 3.040178 / 1.504120 (1.536058) | 2.641776 / 1.541195 (1.100581) | 2.524057 / 1.468490 (1.055567) | 0.893592 / 4.584777 (-3.691185) | 5.535848 / 3.745712 (1.790136) | 4.867067 / 5.269862 (-0.402794) | 2.999933 / 4.565676 (-1.565743) | 0.103602 / 0.424275 (-0.320673) | 0.008887 / 0.007607 (0.001280) | 0.822214 / 0.226044 (0.596169) | 8.028476 / 2.268929 (5.759547) | 3.708895 / 55.444624 (-51.735730) | 2.858314 / 6.876477 (-4.018163) | 3.101727 / 2.142072 (0.959655) | 1.083136 / 4.805227 (-3.722091) | 0.219588 / 6.500664 (-6.281076) | 0.080151 / 0.075469 (0.004682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.645819 / 1.841788 (-0.195969) | 24.407887 / 8.074308 (16.333579) | 22.371901 / 10.191392 (12.180509) | 0.219557 / 0.680424 (-0.460867) | 0.037867 / 0.534201 (-0.496334) | 0.484136 / 0.579283 (-0.095147) | 0.620546 / 0.434364 (0.186182) | 0.562272 / 0.540337 (0.021934) | 0.774256 / 1.386936 (-0.612680) |\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) | 0.009381 / 0.011353 (-0.001972) | 0.005565 / 0.011008 (-0.005444) | 0.091057 / 0.038508 (0.052549) | 0.078085 / 0.023109 (0.054975) | 0.538929 / 0.275898 (0.263031) | 0.555155 / 0.323480 (0.231675) | 0.007007 / 0.007986 (-0.000978) | 0.004268 / 0.004328 (-0.000060) | 0.086618 / 0.004250 (0.082368) | 0.064117 / 0.037052 (0.027065) | 0.523788 / 0.258489 (0.265299) | 0.586451 / 0.293841 (0.292610) | 0.050804 / 0.128546 (-0.077742) | 0.013964 / 0.075646 (-0.061682) | 0.096008 / 0.419271 (-0.323263) | 0.062242 / 0.043533 (0.018709) | 0.530398 / 0.255139 (0.275259) | 0.568527 / 0.283200 (0.285327) | 0.032456 / 0.141683 (-0.109227) | 1.894975 / 1.452155 (0.442820) | 2.084172 / 1.492716 (0.591455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295539 / 0.018006 (0.277533) | 0.588804 / 0.000490 (0.588314) | 0.006445 / 0.000200 (0.006245) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033965 / 0.037411 (-0.003447) | 0.111743 / 0.014526 (0.097217) | 0.128805 / 0.176557 (-0.047752) | 0.185013 / 0.737135 (-0.552123) | 0.129400 / 0.296338 (-0.166938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.749784 / 0.215209 (0.534575) | 7.091075 / 2.077655 (5.013420) | 3.424517 / 1.504120 (1.920397) | 3.069103 / 1.541195 (1.527908) | 3.122431 / 1.468490 (1.653941) | 0.949277 / 4.584777 (-3.635500) | 5.648731 / 3.745712 (1.903019) | 4.937684 / 5.269862 (-0.332178) | 3.198027 / 4.565676 (-1.367650) | 0.100289 / 0.424275 (-0.323987) | 0.009411 / 0.007607 (0.001803) | 0.862604 / 0.226044 (0.636559) | 8.615410 / 2.268929 (6.346482) | 4.306428 / 55.444624 (-51.138196) | 3.591404 / 6.876477 (-3.285073) | 3.823899 / 2.142072 (1.681827) | 1.108006 / 4.805227 (-3.697221) | 0.215330 / 6.500664 (-6.285334) | 0.080755 / 0.075469 (0.005286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774914 / 1.841788 (-0.066873) | 25.360983 / 8.074308 (17.286675) | 23.624044 / 10.191392 (13.432652) | 0.226887 / 0.680424 (-0.453537) | 0.032625 / 0.534201 (-0.501576) | 0.499730 / 0.579283 (-0.079553) | 0.647819 / 0.434364 (0.213455) | 0.592239 / 0.540337 (0.051901) | 0.805751 / 1.386936 (-0.581185) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0daa82428a0529478801574bcc68e1ed32051f3a \"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) | 0.008656 / 0.011353 (-0.002697) | 0.005545 / 0.011008 (-0.005463) | 0.107936 / 0.038508 (0.069428) | 0.077436 / 0.023109 (0.054327) | 0.391412 / 0.275898 (0.115514) | 0.452811 / 0.323480 (0.129331) | 0.004883 / 0.007986 (-0.003103) | 0.005125 / 0.004328 (0.000796) | 0.080006 / 0.004250 (0.075755) | 0.054425 / 0.037052 (0.017373) | 0.399667 / 0.258489 (0.141178) | 0.458099 / 0.293841 (0.164258) | 0.047302 / 0.128546 (-0.081244) | 0.014153 / 0.075646 (-0.061493) | 0.337281 / 0.419271 (-0.081991) | 0.062153 / 0.043533 (0.018620) | 0.399927 / 0.255139 (0.144788) | 0.407186 / 0.283200 (0.123987) | 0.036759 / 0.141683 (-0.104924) | 1.825935 / 1.452155 (0.373780) | 1.852238 / 1.492716 (0.359522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274163 / 0.018006 (0.256157) | 0.615624 / 0.000490 (0.615134) | 0.003782 / 0.000200 (0.003582) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011026) | 0.101151 / 0.014526 (0.086625) | 0.106115 / 0.176557 (-0.070442) | 0.161253 / 0.737135 (-0.575882) | 0.108861 / 0.296338 (-0.187478) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587079 / 0.215209 (0.371870) | 6.141743 / 2.077655 (4.064089) | 2.727199 / 1.504120 (1.223079) | 2.526827 / 1.541195 (0.985632) | 2.598321 / 1.468490 (1.129831) | 0.904706 / 4.584777 (-3.680071) | 5.227742 / 3.745712 (1.482030) | 4.621627 / 5.269862 (-0.648234) | 2.931792 / 4.565676 (-1.633885) | 0.089538 / 0.424275 (-0.334737) | 0.008281 / 0.007607 (0.000674) | 0.675773 / 0.226044 (0.449729) | 7.212869 / 2.268929 (4.943941) | 3.541569 / 55.444624 (-51.903056) | 2.804034 / 6.876477 (-4.072443) | 3.080192 / 2.142072 (0.938120) | 1.034577 / 4.805227 (-3.770650) | 0.218727 / 6.500664 (-6.281937) | 0.084548 / 0.075469 (0.009079) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.528974 / 1.841788 (-0.312814) | 21.754329 / 8.074308 (13.680021) | 20.359808 / 10.191392 (10.168416) | 0.234719 / 0.680424 (-0.445705) | 0.026182 / 0.534201 (-0.508019) | 0.448956 / 0.579283 (-0.130327) | 0.577015 / 0.434364 (0.142651) | 0.513675 / 0.540337 (-0.026662) | 0.729780 / 1.386936 (-0.657156) |\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) | 0.010427 / 0.011353 (-0.000926) | 0.005126 / 0.011008 (-0.005882) | 0.082759 / 0.038508 (0.044251) | 0.084892 / 0.023109 (0.061783) | 0.543826 / 0.275898 (0.267927) | 0.603050 / 0.323480 (0.279570) | 0.006667 / 0.007986 (-0.001319) | 0.004036 / 0.004328 (-0.000292) | 0.079534 / 0.004250 (0.075283) | 0.067523 / 0.037052 (0.030471) | 0.544845 / 0.258489 (0.286356) | 0.578823 / 0.293841 (0.284982) | 0.054786 / 0.128546 (-0.073760) | 0.014888 / 0.075646 (-0.060759) | 0.095696 / 0.419271 (-0.323576) | 0.064908 / 0.043533 (0.021375) | 0.558087 / 0.255139 (0.302948) | 0.593919 / 0.283200 (0.310719) | 0.039190 / 0.141683 (-0.102493) | 1.828680 / 1.452155 (0.376526) | 1.908891 / 1.492716 (0.416174) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298926 / 0.018006 (0.280920) | 0.589467 / 0.000490 (0.588977) | 0.005276 / 0.000200 (0.005076) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034300 / 0.037411 (-0.003111) | 0.096990 / 0.014526 (0.082464) | 0.109347 / 0.176557 (-0.067209) | 0.171312 / 0.737135 (-0.565823) | 0.121736 / 0.296338 (-0.174603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.641619 / 0.215209 (0.426410) | 6.365556 / 2.077655 (4.287901) | 2.947989 / 1.504120 (1.443869) | 2.631680 / 1.541195 (1.090485) | 2.602762 / 1.468490 (1.134272) | 0.812767 / 4.584777 (-3.772010) | 5.185753 / 3.745712 (1.440041) | 4.589897 / 5.269862 (-0.679964) | 2.833020 / 4.565676 (-1.732656) | 0.097782 / 0.424275 (-0.326493) | 0.008625 / 0.007607 (0.001018) | 0.741613 / 0.226044 (0.515568) | 7.662905 / 2.268929 (5.393976) | 3.533753 / 55.444624 (-51.910871) | 2.898929 / 6.876477 (-3.977547) | 3.042616 / 2.142072 (0.900544) | 0.933932 / 4.805227 (-3.871296) | 0.195710 / 6.500664 (-6.304954) | 0.066954 / 0.075469 (-0.008515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745353 / 1.841788 (-0.096434) | 23.820840 / 8.074308 (15.746532) | 20.892645 / 10.191392 (10.701253) | 0.234853 / 0.680424 (-0.445571) | 0.029149 / 0.534201 (-0.505051) | 0.458953 / 0.579283 (-0.120330) | 0.594278 / 0.434364 (0.159914) | 0.522929 / 0.540337 (-0.017409) | 0.753731 / 1.386936 (-0.633205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"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) | 0.005976 / 0.011353 (-0.005377) | 0.003636 / 0.011008 (-0.007372) | 0.079946 / 0.038508 (0.041437) | 0.060143 / 0.023109 (0.037034) | 0.314752 / 0.275898 (0.038854) | 0.353714 / 0.323480 (0.030234) | 0.004706 / 0.007986 (-0.003280) | 0.002862 / 0.004328 (-0.001466) | 0.061988 / 0.004250 (0.057737) | 0.045907 / 0.037052 (0.008855) | 0.316118 / 0.258489 (0.057629) | 0.358488 / 0.293841 (0.064647) | 0.027377 / 0.128546 (-0.101170) | 0.007970 / 0.075646 (-0.067677) | 0.261677 / 0.419271 (-0.157594) | 0.045289 / 0.043533 (0.001757) | 0.307931 / 0.255139 (0.052792) | 0.341364 / 0.283200 (0.058165) | 0.021021 / 0.141683 (-0.120662) | 1.440002 / 1.452155 (-0.012153) | 1.502904 / 1.492716 (0.010187) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201746 / 0.018006 (0.183740) | 0.451114 / 0.000490 (0.450624) | 0.003351 / 0.000200 (0.003151) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024233 / 0.037411 (-0.013178) | 0.075042 / 0.014526 (0.060516) | 0.085636 / 0.176557 (-0.090920) | 0.144699 / 0.737135 (-0.592436) | 0.085222 / 0.296338 (-0.211117) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389464 / 0.215209 (0.174255) | 3.889072 / 2.077655 (1.811417) | 1.908307 / 1.504120 (0.404187) | 1.738914 / 1.541195 (0.197719) | 1.866869 / 1.468490 (0.398379) | 0.500536 / 4.584777 (-4.084240) | 3.050155 / 3.745712 (-0.695557) | 2.832259 / 5.269862 (-2.437602) | 1.886657 / 4.565676 (-2.679020) | 0.059214 / 0.424275 (-0.365062) | 0.006711 / 0.007607 (-0.000896) | 0.467753 / 0.226044 (0.241709) | 4.666939 / 2.268929 (2.398011) | 2.471168 / 55.444624 (-52.973456) | 2.223508 / 6.876477 (-4.652968) | 2.176543 / 2.142072 (0.034470) | 0.593461 / 4.805227 (-4.211766) | 0.126216 / 6.500664 (-6.374448) | 0.061495 / 0.075469 (-0.013974) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301279 / 1.841788 (-0.540509) | 18.317461 / 8.074308 (10.243153) | 13.877813 / 10.191392 (3.686421) | 0.143510 / 0.680424 (-0.536914) | 0.016826 / 0.534201 (-0.517375) | 0.328735 / 0.579283 (-0.250548) | 0.342272 / 0.434364 (-0.092092) | 0.375768 / 0.540337 (-0.164570) | 0.517600 / 1.386936 (-0.869336) |\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) | 0.006215 / 0.011353 (-0.005138) | 0.003587 / 0.011008 (-0.007422) | 0.062248 / 0.038508 (0.023740) | 0.059830 / 0.023109 (0.036721) | 0.443278 / 0.275898 (0.167380) | 0.481279 / 0.323480 (0.157799) | 0.004773 / 0.007986 (-0.003213) | 0.002870 / 0.004328 (-0.001459) | 0.062730 / 0.004250 (0.058480) | 0.049422 / 0.037052 (0.012369) | 0.444196 / 0.258489 (0.185707) | 0.498614 / 0.293841 (0.204773) | 0.028477 / 0.128546 (-0.100069) | 0.008009 / 0.075646 (-0.067638) | 0.067919 / 0.419271 (-0.351352) | 0.040416 / 0.043533 (-0.003117) | 0.439460 / 0.255139 (0.184321) | 0.470529 / 0.283200 (0.187329) | 0.020767 / 0.141683 (-0.120916) | 1.478223 / 1.452155 (0.026068) | 1.538580 / 1.492716 (0.045863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271321 / 0.018006 (0.253315) | 0.456436 / 0.000490 (0.455946) | 0.011817 / 0.000200 (0.011617) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026355 / 0.037411 (-0.011056) | 0.081681 / 0.014526 (0.067155) | 0.091699 / 0.176557 (-0.084858) | 0.146115 / 0.737135 (-0.591021) | 0.094376 / 0.296338 (-0.201963) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471677 / 0.215209 (0.256468) | 4.702909 / 2.077655 (2.625254) | 2.664882 / 1.504120 (1.160762) | 2.504106 / 1.541195 (0.962911) | 2.573226 / 1.468490 (1.104736) | 0.509679 / 4.584777 (-4.075097) | 3.034970 / 3.745712 (-0.710742) | 2.894704 / 5.269862 (-2.375157) | 1.915148 / 4.565676 (-2.650528) | 0.058312 / 0.424275 (-0.365963) | 0.006615 / 0.007607 (-0.000993) | 0.545339 / 0.226044 (0.319295) | 5.462261 / 2.268929 (3.193332) | 3.101482 / 55.444624 (-52.343143) | 2.755417 / 6.876477 (-4.121060) | 2.931440 / 2.142072 (0.789368) | 0.597521 / 4.805227 (-4.207707) | 0.125676 / 6.500664 (-6.374988) | 0.061798 / 0.075469 (-0.013671) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356208 / 1.841788 (-0.485579) | 18.912492 / 8.074308 (10.838184) | 14.830128 / 10.191392 (4.638736) | 0.145992 / 0.680424 (-0.534432) | 0.019121 / 0.534201 (-0.515080) | 0.331534 / 0.579283 (-0.247749) | 0.361712 / 0.434364 (-0.072652) | 0.387532 / 0.540337 (-0.152805) | 0.536075 / 1.386936 (-0.850861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n"
] | 2023-09-06T15:17:10Z
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Add support for different configs with `push_to_hub`
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"_The documentation is not available anymore as the PR was closed or merged._",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5213). All of your documentation changes will be reflected on that endpoint.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5213). All of your documentation changes will be reflected on that endpoint.",
"Nice thanks !\r\n\r\nWould it be possible to have the new folders at the same level as \"data\" ? This way they're all separated\r\n```\r\n├─ config-v1/\r\n│ ├── train-00000-00002-...-.parquet\r\n│ └── train-00001-00002-...-.parquet\r\n└ config-v2/\r\n ├── train-00000-00002-...-.parquet\r\n └── train-00001-00002-...-.parquet\r\n```\r\nand if you don't provide a config name, it goes in a folder named \"default\" instead, that would be loaded by default.\r\n\r\nWe could also write in the YAML something like\r\n```yaml\r\nconfigs:\r\n- name: config-v1\r\n data_dir: config-v1\r\n- name: config-v2\r\n data_dir: config-v2\r\n```\r\nand loading `config-v1` would be equivalent to run `load_dataset(ds_name, \"config-v1\", data_dir=\"config-v1\")`\r\n\r\nDo you think it would make sense ?\r\n\r\nFor backward compatibility we can just keep the \"data/*\" pattern. It's ok to expect users to have an updated version of `datasets` to be able to load datasets with configurations.",
"@lhoestq thank you for the feedback! i'll reflect on this on Moday, my mind just melted because of the fever.\r\n\r\n@mariosasko @albertvillanova what do you think?",
"Thanks for addressing this, @polinaeterna. It is good:\r\n- we support configs for datasets without scripts\r\n- we align the behavior to datasets with scripts as much as possible\r\n\r\nMaybe adding some tests will help clarify what is the expected behavior...",
"After some discussion with @lhoestq we decided that it's better to rely on metadata file than on data files patterns. \r\n\r\nSo we decided to introduce a new field to yaml (like `configs` or smth like that) that would contain arbitrary configs kwargs to be passed to loader, including `data_dir` and `data_files`. \r\nThis is more aligned with datasets with custom scripts where we explicitly write all the supported configs and config parameters in the code and is extendable to all packaged modules.\r\nThis would solve https://github.com/huggingface/datasets/issues/5209\r\n\r\n(@lhoestq was right 21 days ago, this is a more general solution idk why i ignored this...)",
"closed in favor of https://github.com/huggingface/datasets/pull/5331"
] | 2022-11-08T11:45:47Z
| 2022-12-02T16:48:23Z
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will solve #5151
@lhoestq @albertvillanova @mariosasko
This is still a super draft so please ignore code issues but I want to discuss some conceptually important things.
I suggest a way to do `.push_to_hub("repo_id", "config_name")` with pushing parquet files to directories named as `config_name` (inside `data/` dir as it is now), for example:
```
data
|__config-v1
train-00000-00002-...-.parquet
train-00001-00002-...-.parquet
...
|__config-v2
....
```
When loading a dataset, I parse these configs from repository data files (only for `"data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9]*.*"` pattern that is used for parquet datasets pushed with `.push_to_hub`).
Therefore,
- when user tries to load a dataset that has configs parsed from data files dir names without providing a config (like `load_dataset("repo")` instead of `load_dataset("repo", "config-v1")`) - raise error and asks for config - to be aligned with how it works in datasets with scripts.
- for backward compatibility: if user tries to `.push_to_hub(""repo", "config_name")` to an existing parquet repo with no configurations (all parquet files are directly in `data/` dir) - raise error. My initial idea was to raise a warning and move these files to another dir with name (config) like "default" or smth but in a PR and suggest user to merge it on the Hub. But there is no support for renaming (moving) files via `HfApi` yet so it would require deleting and pushing again if I understand it right.
This parsing approach can be extended to other Hub packaged modules, and to local packaged modules and other data files patterns
(except for cases when splits are in dir names `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` because we allow for arbitrary depth of directory hierarchy).
Do you think it's reasonable? Not sure how to provide flexibility (and backward compatibility) to not parsing configs and load all the data in a single config as it is now.
I also thought about getting information about configs from Readme.md `dataset_info` ([example](https://huggingface.co/datasets/polinaeterna/test_push_two_configs/blob/main/README.md)). But that way we
are dependent on if it exists. It is created automatically with `.push_to_hub` but what if it is
accidentally deleted or smth).
Also, what I don't like is that this parsing is a part of Module/DataFiles logic, not Builder's one, which is not aligned with datasets with custom scripts. But I don't know to implement the second approach in current library's logic.
What do you think about this all? Am I missing smth?
TODO:
- [ ] save cache in the same dir for configs of the same datasets
- [ ] fix verification errors
- [ ] correctly update `dataset_infos.json` too
- [ ] ...
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Update auth when mirroring datasets on the hub
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We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3.
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Add note about loading image / audio files to docs
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4925). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the feedback @polinaeterna ! I've reworded the docs a bit to integrate your comments and this should be ready for another review :)",
"> I've just realized that there is another PR about audio documentation open: #4872\r\n> and there the more detailed description on how to use `audiofolder` is moved to another section (\"Create an audio dataset\")\r\n\r\nAh yes, let's add a comment to #4872 - that will be simpler than the alternatives :)",
"@polinaeterna @lhoestq What do you think about adding support for the metadata format from Kaggle (one metadata file for each split with the name equal to the split name) to ImageFolder/AudioFolder? I also think we can relax some requirements a bit by:\r\n* allowing `filename` as the name of the main metadata column (currently, only `file_path` is allowed)\r\n* not requiring that the features of all the given metadata files are equal. Instead, we can have a soft check by using `_check_if_features_can_be_aligned` + `_align_features`. The rationale is that train/val metadata often has extra columns compared to test metadata.\r\n\r\nThese changes would allow us to load the Kaggle dataset linked in the forum thread without any \"interventions\".\r\n\r\nPS: this metadata format for ImageFolder was also proposed by @abhishekkrthakur initially.\r\n",
"Can you give more details about the Kaggle format ? I'm down to discuss it in a separate issue if you don't mind.\r\n\r\n> allowing filename as the name of the main metadata column (currently, only file_path is allowed)\r\n\r\n`filename` refers to the name of the file, so there's no logic about relative path or directories. If I recall correctly this is what we're doing right now so why not\r\n\r\n> not requiring that the features of all the given metadata files are equal. Instead, we can have a soft check by using _check_if_features_can_be_aligned + _align_features. The rationale is that train/val metadata often has extra columns compared to test metadata.\r\n\r\n+1 and we can set to None the missing features",
"I'm not sure if this is worth opening a new issue :).\r\n\r\nWhat I mean by the Kaggle format is the structure like this one (the name of a metadata file is equal to the directory it \"references\"):\r\n```\r\n- train\r\n - img1.jpeg\r\n - img2.jpeg\r\n - ...\r\n- test\r\n - img1.jpeg\r\n - img2.jpeg\r\n - ... \r\n- train.csv\r\n- test.csv\r\n```\r\n\r\n\r\n",
"Sounds nice !",
"@mariosasko +1 to allowing different features set and metadata filenames corresponding to split names\r\n\r\nConsidering filename column - right now it's even called `file_name` now, which is not nice because in fact it's a relative file path indeed, so I think it should be `file_path` (and I don't know why I haven't thought about it before the release...)",
"@lewtun don't you mind if I close this pull request as I've integrated your changes in https://github.com/huggingface/datasets/pull/4872 ? (it doesn't have a link to a kaggle example though)"
] | 2022-09-02T10:31:58Z
| 2022-09-26T12:21:30Z
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This PR adds a small note about how to load image / audio datasets that have multiple splits in their dataset structure.
Related forum thread: https://discuss.huggingface.co/t/loading-train-and-test-splits-with-audiofolder/22447
cc @NielsRogge
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Remove `HfFileSystem` and deprecate `S3FileSystem`
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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) | 0.006658 / 0.011353 (-0.004695) | 0.004347 / 0.011008 (-0.006661) | 0.084179 / 0.038508 (0.045671) | 0.080842 / 0.023109 (0.057733) | 0.321642 / 0.275898 (0.045744) | 0.348758 / 0.323480 (0.025278) | 0.005624 / 0.007986 (-0.002362) | 0.003479 / 0.004328 (-0.000850) | 0.065125 / 0.004250 (0.060875) | 0.057624 / 0.037052 (0.020572) | 0.323643 / 0.258489 (0.065154) | 0.360939 / 0.293841 (0.067098) | 0.031005 / 0.128546 (-0.097541) | 0.008618 / 0.075646 (-0.067028) | 0.287443 / 0.419271 (-0.131828) | 0.052640 / 0.043533 (0.009107) | 0.316947 / 0.255139 (0.061808) | 0.330292 / 0.283200 (0.047093) | 0.024393 / 0.141683 (-0.117289) | 1.476734 / 1.452155 (0.024579) | 1.534505 / 1.492716 (0.041789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273808 / 0.018006 (0.255802) | 0.591146 / 0.000490 (0.590656) | 0.000322 / 0.000200 (0.000122) | 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) | 0.029992 / 0.037411 (-0.007419) | 0.086654 / 0.014526 (0.072129) | 0.098590 / 0.176557 (-0.077967) | 0.157225 / 0.737135 (-0.579910) | 0.101816 / 0.296338 (-0.194522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382578 / 0.215209 (0.167368) | 3.803576 / 2.077655 (1.725922) | 1.875136 / 1.504120 (0.371016) | 1.704207 / 1.541195 (0.163012) | 1.765146 / 1.468490 (0.296656) | 0.482802 / 4.584777 (-4.101975) | 3.571772 / 3.745712 (-0.173940) | 3.245626 / 5.269862 (-2.024235) | 2.051612 / 4.565676 (-2.514064) | 0.056539 / 0.424275 (-0.367736) | 0.007199 / 0.007607 (-0.000408) | 0.462445 / 0.226044 (0.236401) | 4.623800 / 2.268929 (2.354872) | 2.318948 / 55.444624 (-53.125677) | 1.971442 / 6.876477 (-4.905035) | 2.225444 / 2.142072 (0.083371) | 0.575205 / 4.805227 (-4.230022) | 0.129243 / 6.500664 (-6.371421) | 0.059036 / 0.075469 (-0.016433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266827 / 1.841788 (-0.574960) | 20.323419 / 8.074308 (12.249110) | 14.577603 / 10.191392 (4.386210) | 0.162131 / 0.680424 (-0.518293) | 0.018529 / 0.534201 (-0.515672) | 0.395046 / 0.579283 (-0.184237) | 0.410870 / 0.434364 (-0.023494) | 0.455782 / 0.540337 (-0.084556) | 0.662851 / 1.386936 (-0.724085) |\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) | 0.006867 / 0.011353 (-0.004486) | 0.004197 / 0.011008 (-0.006811) | 0.066060 / 0.038508 (0.027552) | 0.084145 / 0.023109 (0.061036) | 0.366740 / 0.275898 (0.090842) | 0.402362 / 0.323480 (0.078882) | 0.005785 / 0.007986 (-0.002200) | 0.003551 / 0.004328 (-0.000778) | 0.066177 / 0.004250 (0.061926) | 0.061521 / 0.037052 (0.024468) | 0.377807 / 0.258489 (0.119318) | 0.413490 / 0.293841 (0.119649) | 0.031918 / 0.128546 (-0.096628) | 0.008767 / 0.075646 (-0.066879) | 0.071437 / 0.419271 (-0.347835) | 0.049237 / 0.043533 (0.005704) | 0.365929 / 0.255139 (0.110790) | 0.393545 / 0.283200 (0.110346) | 0.024054 / 0.141683 (-0.117628) | 1.524599 / 1.452155 (0.072445) | 1.576592 / 1.492716 (0.083876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315181 / 0.018006 (0.297174) | 0.535501 / 0.000490 (0.535011) | 0.000410 / 0.000200 (0.000210) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032915 / 0.037411 (-0.004497) | 0.089310 / 0.014526 (0.074784) | 0.105136 / 0.176557 (-0.071421) | 0.158572 / 0.737135 (-0.578563) | 0.106850 / 0.296338 (-0.189489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419343 / 0.215209 (0.204134) | 4.200166 / 2.077655 (2.122511) | 2.180234 / 1.504120 (0.676114) | 2.016885 / 1.541195 (0.475690) | 2.131480 / 1.468490 (0.662990) | 0.484681 / 4.584777 (-4.100096) | 3.613535 / 3.745712 (-0.132177) | 5.762111 / 5.269862 (0.492249) | 3.190590 / 4.565676 (-1.375086) | 0.057403 / 0.424275 (-0.366872) | 0.007862 / 0.007607 (0.000255) | 0.490857 / 0.226044 (0.264813) | 4.911241 / 2.268929 (2.642313) | 2.650787 / 55.444624 (-52.793838) | 2.317060 / 6.876477 (-4.559416) | 2.579677 / 2.142072 (0.437605) | 0.587388 / 4.805227 (-4.217840) | 0.148109 / 6.500664 (-6.352555) | 0.061435 / 0.075469 (-0.014034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322181 / 1.841788 (-0.519606) | 20.647184 / 8.074308 (12.572875) | 14.907635 / 10.191392 (4.716243) | 0.156330 / 0.680424 (-0.524094) | 0.018719 / 0.534201 (-0.515482) | 0.397636 / 0.579283 (-0.181647) | 0.414107 / 0.434364 (-0.020257) | 0.460812 / 0.540337 (-0.079526) | 0.609568 / 1.386936 (-0.777368) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n",
"This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ",
"what would be the change i need to make? ",
"@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n",
"The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged",
"I just merged the other PR so you should be good now",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006303 / 0.011353 (-0.005049) | 0.003746 / 0.011008 (-0.007262) | 0.081083 / 0.038508 (0.042575) | 0.067973 / 0.023109 (0.044864) | 0.322221 / 0.275898 (0.046323) | 0.359432 / 0.323480 (0.035952) | 0.004891 / 0.007986 (-0.003095) | 0.002988 / 0.004328 (-0.001341) | 0.064068 / 0.004250 (0.059818) | 0.052042 / 0.037052 (0.014990) | 0.323387 / 0.258489 (0.064898) | 0.390416 / 0.293841 (0.096575) | 0.028090 / 0.128546 (-0.100457) | 0.008009 / 0.075646 (-0.067638) | 0.262288 / 0.419271 (-0.156984) | 0.044986 / 0.043533 (0.001453) | 0.322319 / 0.255139 (0.067180) | 0.345323 / 0.283200 (0.062123) | 0.021798 / 0.141683 (-0.119885) | 1.417259 / 1.452155 (-0.034895) | 1.490050 / 1.492716 (-0.002667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195902 / 0.018006 (0.177896) | 0.490808 / 0.000490 (0.490318) | 0.002969 / 0.000200 (0.002770) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025221 / 0.037411 (-0.012190) | 0.075341 / 0.014526 (0.060815) | 0.086703 / 0.176557 (-0.089853) | 0.146953 / 0.737135 (-0.590182) | 0.086610 / 0.296338 (-0.209728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434890 / 0.215209 (0.219681) | 4.352283 / 2.077655 (2.274629) | 2.293098 / 1.504120 (0.788979) | 2.123023 / 1.541195 (0.581829) | 2.179722 / 1.468490 (0.711232) | 0.503851 / 4.584777 (-4.080926) | 3.087991 / 3.745712 (-0.657721) | 2.898689 / 5.269862 (-2.371173) | 1.902813 / 4.565676 (-2.662864) | 0.058079 / 0.424275 (-0.366196) | 0.006600 / 0.007607 (-0.001007) | 0.509243 / 0.226044 (0.283199) | 5.080204 / 2.268929 (2.811275) | 2.753594 / 55.444624 (-52.691030) | 2.417385 / 6.876477 (-4.459091) | 2.635470 / 2.142072 (0.493398) | 0.591059 / 4.805227 (-4.214168) | 0.126360 / 6.500664 (-6.374304) | 0.062108 / 0.075469 (-0.013361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254398 / 1.841788 (-0.587390) | 18.866729 / 8.074308 (10.792420) | 14.120008 / 10.191392 (3.928616) | 0.152388 / 0.680424 (-0.528035) | 0.016997 / 0.534201 (-0.517204) | 0.336435 / 0.579283 (-0.242848) | 0.364612 / 0.434364 (-0.069752) | 0.391434 / 0.540337 (-0.148903) | 0.567180 / 1.386936 (-0.819756) |\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) | 0.006477 / 0.011353 (-0.004876) | 0.003723 / 0.011008 (-0.007285) | 0.062712 / 0.038508 (0.024204) | 0.069380 / 0.023109 (0.046271) | 0.393394 / 0.275898 (0.117496) | 0.446903 / 0.323480 (0.123423) | 0.004833 / 0.007986 (-0.003153) | 0.002946 / 0.004328 (-0.001382) | 0.062076 / 0.004250 (0.057826) | 0.051589 / 0.037052 (0.014537) | 0.388536 / 0.258489 (0.130047) | 0.451406 / 0.293841 (0.157565) | 0.027824 / 0.128546 (-0.100722) | 0.008040 / 0.075646 (-0.067606) | 0.067085 / 0.419271 (-0.352187) | 0.042269 / 0.043533 (-0.001264) | 0.363419 / 0.255139 (0.108280) | 0.435201 / 0.283200 (0.152001) | 0.021275 / 0.141683 (-0.120408) | 1.439838 / 1.452155 (-0.012316) | 1.477279 / 1.492716 (-0.015437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229667 / 0.018006 (0.211661) | 0.434101 / 0.000490 (0.433611) | 0.000652 / 0.000200 (0.000452) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026141 / 0.037411 (-0.011271) | 0.078950 / 0.014526 (0.064424) | 0.090143 / 0.176557 (-0.086413) | 0.143941 / 0.737135 (-0.593195) | 0.090465 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432042 / 0.215209 (0.216833) | 4.322134 / 2.077655 (2.244479) | 2.242897 / 1.504120 (0.738777) | 2.076351 / 1.541195 (0.535157) | 2.166739 / 1.468490 (0.698249) | 0.500833 / 4.584777 (-4.083944) | 3.140117 / 3.745712 (-0.605595) | 4.383050 / 5.269862 (-0.886812) | 2.548245 / 4.565676 (-2.017432) | 0.057521 / 0.424275 (-0.366754) | 0.006946 / 0.007607 (-0.000662) | 0.509613 / 0.226044 (0.283569) | 5.114052 / 2.268929 (2.845123) | 2.682112 / 55.444624 (-52.762512) | 2.362385 / 6.876477 (-4.514092) | 2.531787 / 2.142072 (0.389715) | 0.595085 / 4.805227 (-4.210142) | 0.130198 / 6.500664 (-6.370466) | 0.064057 / 0.075469 (-0.011412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346254 / 1.841788 (-0.495534) | 19.036911 / 8.074308 (10.962603) | 14.478689 / 10.191392 (4.287297) | 0.147541 / 0.680424 (-0.532883) | 0.016851 / 0.534201 (-0.517350) | 0.333901 / 0.579283 (-0.245382) | 0.380012 / 0.434364 (-0.054352) | 0.396021 / 0.540337 (-0.144317) | 0.540612 / 1.386936 (-0.846324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n",
"CI failure is unrelated. Merging.",
"<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) | 0.009498 / 0.011353 (-0.001855) | 0.005639 / 0.011008 (-0.005369) | 0.108731 / 0.038508 (0.070223) | 0.094052 / 0.023109 (0.070943) | 0.454375 / 0.275898 (0.178477) | 0.486852 / 0.323480 (0.163372) | 0.006627 / 0.007986 (-0.001359) | 0.004712 / 0.004328 (0.000383) | 0.082006 / 0.004250 (0.077756) | 0.079394 / 0.037052 (0.042342) | 0.450982 / 0.258489 (0.192493) | 0.502659 / 0.293841 (0.208818) | 0.049741 / 0.128546 (-0.078806) | 0.014482 / 0.075646 (-0.061164) | 0.362661 / 0.419271 (-0.056611) | 0.068225 / 0.043533 (0.024692) | 0.456219 / 0.255139 (0.201080) | 0.483919 / 0.283200 (0.200719) | 0.044490 / 0.141683 (-0.097193) | 1.809420 / 1.452155 (0.357265) | 1.908859 / 1.492716 (0.416143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267350 / 0.018006 (0.249344) | 0.600403 / 0.000490 (0.599913) | 0.003665 / 0.000200 (0.003465) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032499 / 0.037411 (-0.004912) | 0.104829 / 0.014526 (0.090303) | 0.115809 / 0.176557 (-0.060747) | 0.191561 / 0.737135 (-0.545574) | 0.113454 / 0.296338 (-0.182885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599165 / 0.215209 (0.383956) | 5.802947 / 2.077655 (3.725292) | 2.477330 / 1.504120 (0.973210) | 2.231147 / 1.541195 (0.689952) | 2.365688 / 1.468490 (0.897197) | 0.853912 / 4.584777 (-3.730865) | 5.529472 / 3.745712 (1.783760) | 6.145286 / 5.269862 (0.875424) | 3.415871 / 4.565676 (-1.149805) | 0.099889 / 0.424275 (-0.324386) | 0.008933 / 0.007607 (0.001325) | 0.704643 / 0.226044 (0.478598) | 7.178101 / 2.268929 (4.909173) | 3.367120 / 55.444624 (-52.077504) | 2.795177 / 6.876477 (-4.081300) | 2.796798 / 2.142072 (0.654726) | 1.039097 / 4.805227 (-3.766130) | 0.232784 / 6.500664 (-6.267881) | 0.083608 / 0.075469 (0.008138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646827 / 1.841788 (-0.194961) | 25.003419 / 8.074308 (16.929111) | 22.165746 / 10.191392 (11.974354) | 0.246179 / 0.680424 (-0.434245) | 0.029304 / 0.534201 (-0.504897) | 0.500767 / 0.579283 (-0.078516) | 0.606501 / 0.434364 (0.172137) | 0.564092 / 0.540337 (0.023755) | 0.857568 / 1.386936 (-0.529368) |\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) | 0.009206 / 0.011353 (-0.002146) | 0.005084 / 0.011008 (-0.005925) | 0.081402 / 0.038508 (0.042894) | 0.088028 / 0.023109 (0.064919) | 0.539509 / 0.275898 (0.263611) | 0.590759 / 0.323480 (0.267280) | 0.006527 / 0.007986 (-0.001459) | 0.004375 / 0.004328 (0.000047) | 0.082327 / 0.004250 (0.078076) | 0.065442 / 0.037052 (0.028390) | 0.548254 / 0.258489 (0.289765) | 0.598388 / 0.293841 (0.304547) | 0.049409 / 0.128546 (-0.079137) | 0.014366 / 0.075646 (-0.061280) | 0.094568 / 0.419271 (-0.324703) | 0.063685 / 0.043533 (0.020152) | 0.545359 / 0.255139 (0.290220) | 0.573358 / 0.283200 (0.290159) | 0.036864 / 0.141683 (-0.104819) | 1.817985 / 1.452155 (0.365830) | 1.925188 / 1.492716 (0.432472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303205 / 0.018006 (0.285199) | 0.620981 / 0.000490 (0.620491) | 0.004910 / 0.000200 (0.004710) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033791 / 0.037411 (-0.003620) | 0.114974 / 0.014526 (0.100448) | 0.117682 / 0.176557 (-0.058875) | 0.177188 / 0.737135 (-0.559947) | 0.126425 / 0.296338 (-0.169914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636932 / 0.215209 (0.421723) | 6.289054 / 2.077655 (4.211399) | 2.920772 / 1.504120 (1.416652) | 2.672080 / 1.541195 (1.130885) | 2.712271 / 1.468490 (1.243781) | 0.889305 / 4.584777 (-3.695472) | 5.536018 / 3.745712 (1.790306) | 4.687564 / 5.269862 (-0.582298) | 3.204239 / 4.565676 (-1.361437) | 0.095546 / 0.424275 (-0.328729) | 0.008838 / 0.007607 (0.001231) | 0.714584 / 0.226044 (0.488540) | 7.482663 / 2.268929 (5.213735) | 3.621392 / 55.444624 (-51.823232) | 2.987777 / 6.876477 (-3.888700) | 3.312636 / 2.142072 (1.170564) | 1.033721 / 4.805227 (-3.771506) | 0.206292 / 6.500664 (-6.294372) | 0.079423 / 0.075469 (0.003953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798645 / 1.841788 (-0.043143) | 25.544329 / 8.074308 (17.470021) | 23.041318 / 10.191392 (12.849926) | 0.259067 / 0.680424 (-0.421357) | 0.029839 / 0.534201 (-0.504362) | 0.495583 / 0.579283 (-0.083700) | 0.598755 / 0.434364 (0.164391) | 0.574864 / 0.540337 (0.034527) | 0.831160 / 1.386936 (-0.555776) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n"
] | 2023-07-19T15:00:01Z
| 2023-07-19T17:39:11Z
| 2023-07-19T17:27:17Z
|
CONTRIBUTOR
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Remove the legacy `HfFileSystem` and deprecate `S3FileSystem`
cc @philschmid for the SageMaker scripts/notebooks that still use `datasets`' `S3FileSystem`
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I_kwDODunzps5hz8EK
| 5,672
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Pushing dataset to hub crash
|
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[
"Hi ! It's been fixed by https://github.com/huggingface/datasets/pull/5598. We're doing a new release tomorrow with the fix and you'll be able to push your 100k images ;)\r\n\r\nBasically `push_to_hub` used to fail if the remote repository already exists and has a README.md without dataset_info in the YAML tags.\r\n\r\nIn the meantime you can install datasets from source",
"Hi @lhoestq ,\r\n\r\nWhat version of datasets library fix this case? I am using the last `v2.10.1` and I get the same error.",
"We just released 2.11 which includes a fix :)"
] | 2023-03-26T17:42:13Z
| 2023-03-30T08:11:05Z
| 2023-03-30T08:11:05Z
|
NONE
| null | null | null |
### Describe the bug
Uploading a dataset with `push_to_hub()` fails without error description.
### Steps to reproduce the bug
Hey there,
I've built a image dataset of 100k images + text pair as described here https://huggingface.co/docs/datasets/image_dataset#imagefolder
Now I'm trying to push it to the hub but I'm running into issues. First, I tried doing it via git directly, I added all the files in git lfs and pushed but I got hit with an error saying huggingface only accept up to 10k files in a folder.
So I'm now trying with the `push_to_hub()` func as follow:
```python
from datasets import load_dataset
import os
dataset = load_dataset("imagefolder", data_dir="./data", split="train")
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
```
But again, this produces an error:
```
Resolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 100212/100212 [00:00<00:00, 439108.61it/s]
Downloading and preparing dataset imagefolder/default to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 100211/100211 [00:00<00:00, 149323.73it/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 15947.92it/s]
Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 2245.34it/s]
Dataset imagefolder downloaded and prepared to /home/contact_theochampion/.cache/huggingface/datasets/imagefolder/default-20567ffc703aa314/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f. Subsequent calls will reuse this data.
Resuming upload of the dataset shards.
Pushing dataset shards to the dataset hub: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:31<00:00, 2.24s/it]
Downloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 118/118 [00:00<00:00, 225kB/s]
Traceback (most recent call last):
File "/home/contact_theochampion/organization-logos/push_to_hub.py", line 5, in <module>
dataset.push_to_hub("tzvc/organization-logos", token=os.environ.get('HF_TOKEN'))
File "/home/contact_theochampion/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 5245, in push_to_hub
repo_info = dataset_infos[next(iter(dataset_infos))]
StopIteration
```
What could be happening here ?
### Expected behavior
The dataset is pushed to the hub
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.10.0-21-cloud-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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Add snips built in intents 2016 12
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"It is not clear how to automatically add the dummy data if the source data is a more complex json format. Should I manually take a fraction of the source data and include it as dummy data?\r\n",
"Added a fraction of the real data as dummy data.",
"merging since the CI is fixed on master"
] | 2020-12-08T15:30:19Z
| 2020-12-14T09:59:07Z
| 2020-12-14T09:59:07Z
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This PR proposes to add the Snips.ai built in intents dataset. The first configuration added is for the intent labels only, but the dataset includes entity slots that may in future be added as alternate configurations.
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PR_kwDODunzps40vfra
| 3,981
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Add TER metric card
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-21T13:54:36Z
| 2022-03-29T13:57:11Z
| 2022-03-29T13:51:40Z
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Add TER metric card
This card is still missing content for the following sections:
- **Limitations & Biases**
- **Values from Papers**
If anyone has any ideas for either of the above, feel free to either add them or point me to them and I'll add them!
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Fix streaming zip files
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"Hi ! I don't fully understand this change @albertvillanova \r\nThe `_extract` method used to return the compound URL that points to the root of the inside of the archive.\r\nThis way users can use the usual os.path.join or other functions to point to the relevant files. I don't see why you're using a glob pattern ?",
"This change is to allow this:\r\n```python\r\ndata_files = f\"https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip\"\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, streaming=True)\r\nassert isinstance(ds, IterableDataset)\r\n```\r\nNote that in this case the user will not call os.path.join.\r\n\r\nBefore this PR it gave error because pointing to the root, without any subsequent join, gives error:\r\n```python\r\nfsspec.open(\"zip://::https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip\")\r\n```"
] | 2021-08-13T15:17:01Z
| 2021-08-16T14:16:50Z
| 2021-08-13T15:38:28Z
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Currently, streaming remote zip data files gives `FileNotFoundError` message:
```python
data_files = f"https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
next(iter(ds))
```
This PR fixes it by adding a glob string.
The corresponding test is implemented in PR #2786.
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MDExOlB1bGxSZXF1ZXN0NTU0NDYyNjYw
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Adjust BrWaC dataset features name
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| 2021-01-14T10:29:38Z
| 2021-01-14T10:29:38Z
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I added this dataset some days ago, and today I used it to train some models and realized that the names of the features aren't so good.
Looking at the current features hierarchy, we have "paragraphs" with a list of "sentences" with a list of "sentences?!". But the actual hierarchy is a "text" with a list of "paragraphs" with a list of "sentences".
I confused myself trying to use the dataset with these names. So I think it's better to change it.
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Simplify filesystem logic
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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) | 0.008852 / 0.011353 (-0.002501) | 0.004613 / 0.011008 (-0.006396) | 0.096153 / 0.038508 (0.057645) | 0.074945 / 0.023109 (0.051836) | 0.365960 / 0.275898 (0.090062) | 0.385450 / 0.323480 (0.061970) | 0.004757 / 0.007986 (-0.003229) | 0.003453 / 0.004328 (-0.000876) | 0.069944 / 0.004250 (0.065693) | 0.057781 / 0.037052 (0.020729) | 0.361056 / 0.258489 (0.102567) | 0.409218 / 0.293841 (0.115377) | 0.045714 / 0.128546 (-0.082833) | 0.013776 / 0.075646 (-0.061871) | 0.328797 / 0.419271 (-0.090474) | 0.063431 / 0.043533 (0.019899) | 0.370799 / 0.255139 (0.115660) | 0.370701 / 0.283200 (0.087502) | 0.034894 / 0.141683 (-0.106789) | 1.730290 / 1.452155 (0.278136) | 1.863600 / 1.492716 (0.370883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245571 / 0.018006 (0.227565) | 0.509666 / 0.000490 (0.509176) | 0.008051 / 0.000200 (0.007851) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027854 / 0.037411 (-0.009557) | 0.090735 / 0.014526 (0.076209) | 0.100100 / 0.176557 (-0.076457) | 0.158267 / 0.737135 (-0.578868) | 0.107537 / 0.296338 (-0.188801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.565455 / 0.215209 (0.350246) | 5.671436 / 2.077655 (3.593781) | 2.438078 / 1.504120 (0.933958) | 2.072403 / 1.541195 (0.531208) | 2.127830 / 1.468490 (0.659340) | 0.840101 / 4.584777 (-3.744675) | 4.945952 / 3.745712 (1.200240) | 4.840904 / 5.269862 (-0.428957) | 3.037936 / 4.565676 (-1.527740) | 0.099027 / 0.424275 (-0.325248) | 0.008448 / 0.007607 (0.000841) | 0.703315 / 0.226044 (0.477271) | 6.837550 / 2.268929 (4.568621) | 3.204232 / 55.444624 (-52.240393) | 2.492985 / 6.876477 (-4.383492) | 2.426792 / 2.142072 (0.284720) | 0.998430 / 4.805227 (-3.806797) | 0.203854 / 6.500664 (-6.296811) | 0.072386 / 0.075469 (-0.003083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606627 / 1.841788 (-0.235161) | 22.287391 / 8.074308 (14.213082) | 20.245654 / 10.191392 (10.054262) | 0.229377 / 0.680424 (-0.451046) | 0.028399 / 0.534201 (-0.505802) | 0.446567 / 0.579283 (-0.132716) | 0.565277 / 0.434364 (0.130913) | 0.502957 / 0.540337 (-0.037381) | 0.749268 / 1.386936 (-0.637668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008253 / 0.011353 (-0.003100) | 0.004432 / 0.011008 (-0.006576) | 0.081995 / 0.038508 (0.043487) | 0.075443 / 0.023109 (0.052334) | 0.442139 / 0.275898 (0.166241) | 0.507308 / 0.323480 (0.183829) | 0.007343 / 0.007986 (-0.000643) | 0.003850 / 0.004328 (-0.000478) | 0.072656 / 0.004250 (0.068406) | 0.054585 / 0.037052 (0.017533) | 0.430057 / 0.258489 (0.171568) | 0.466953 / 0.293841 (0.173112) | 0.050350 / 0.128546 (-0.078196) | 0.013682 / 0.075646 (-0.061965) | 0.088164 / 0.419271 (-0.331107) | 0.061726 / 0.043533 (0.018193) | 0.444420 / 0.255139 (0.189281) | 0.470406 / 0.283200 (0.187206) | 0.033258 / 0.141683 (-0.108425) | 1.635977 / 1.452155 (0.183823) | 1.732767 / 1.492716 (0.240051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227350 / 0.018006 (0.209344) | 0.500805 / 0.000490 (0.500316) | 0.006473 / 0.000200 (0.006273) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034456 / 0.037411 (-0.002955) | 0.094832 / 0.014526 (0.080306) | 0.118549 / 0.176557 (-0.058008) | 0.177971 / 0.737135 (-0.559164) | 0.114165 / 0.296338 (-0.182174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664805 / 0.215209 (0.449596) | 6.509756 / 2.077655 (4.432101) | 2.936840 / 1.504120 (1.432720) | 2.662645 / 1.541195 (1.121450) | 2.659957 / 1.468490 (1.191467) | 0.903019 / 4.584777 (-3.681758) | 5.237191 / 3.745712 (1.491479) | 4.791917 / 5.269862 (-0.477945) | 3.130905 / 4.565676 (-1.434772) | 0.100953 / 0.424275 (-0.323322) | 0.008388 / 0.007607 (0.000781) | 0.776393 / 0.226044 (0.550348) | 7.726230 / 2.268929 (5.457301) | 3.669223 / 55.444624 (-51.775401) | 2.904556 / 6.876477 (-3.971921) | 3.205546 / 2.142072 (1.063473) | 1.058899 / 4.805227 (-3.746329) | 0.213733 / 6.500664 (-6.286931) | 0.071374 / 0.075469 (-0.004096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.713384 / 1.841788 (-0.128403) | 23.325498 / 8.074308 (15.251190) | 20.140510 / 10.191392 (9.949118) | 0.211565 / 0.680424 (-0.468859) | 0.032916 / 0.534201 (-0.501285) | 0.460504 / 0.579283 (-0.118779) | 0.594352 / 0.434364 (0.159988) | 0.556384 / 0.540337 (0.016047) | 0.788586 / 1.386936 (-0.598350) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3c7249c11d8330ce49b1fe119c34fc6100f10774 \"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) | 0.008840 / 0.011353 (-0.002513) | 0.005045 / 0.011008 (-0.005963) | 0.110777 / 0.038508 (0.072269) | 0.100495 / 0.023109 (0.077386) | 0.420302 / 0.275898 (0.144404) | 0.456423 / 0.323480 (0.132943) | 0.006873 / 0.007986 (-0.001113) | 0.005230 / 0.004328 (0.000902) | 0.081316 / 0.004250 (0.077066) | 0.063047 / 0.037052 (0.025995) | 0.439469 / 0.258489 (0.180979) | 0.488477 / 0.293841 (0.194636) | 0.048553 / 0.128546 (-0.079994) | 0.014984 / 0.075646 (-0.060662) | 0.401317 / 0.419271 (-0.017955) | 0.074578 / 0.043533 (0.031045) | 0.435298 / 0.255139 (0.180159) | 0.464406 / 0.283200 (0.181206) | 0.048788 / 0.141683 (-0.092895) | 1.836166 / 1.452155 (0.384011) | 1.959808 / 1.492716 (0.467091) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321419 / 0.018006 (0.303412) | 0.595736 / 0.000490 (0.595246) | 0.021144 / 0.000200 (0.020944) | 0.000626 / 0.000054 (0.000571) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033033 / 0.037411 (-0.004379) | 0.112621 / 0.014526 (0.098095) | 0.118736 / 0.176557 (-0.057821) | 0.195533 / 0.737135 (-0.541602) | 0.120807 / 0.296338 (-0.175531) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616692 / 0.215209 (0.401483) | 6.033674 / 2.077655 (3.956019) | 2.630106 / 1.504120 (1.125986) | 2.316739 / 1.541195 (0.775544) | 2.387525 / 1.468490 (0.919035) | 0.863385 / 4.584777 (-3.721392) | 5.288193 / 3.745712 (1.542481) | 5.115766 / 5.269862 (-0.154096) | 3.083055 / 4.565676 (-1.482621) | 0.104885 / 0.424275 (-0.319391) | 0.012233 / 0.007607 (0.004626) | 0.739924 / 0.226044 (0.513880) | 7.422996 / 2.268929 (5.154067) | 3.403316 / 55.444624 (-52.041309) | 2.778740 / 6.876477 (-4.097736) | 2.836937 / 2.142072 (0.694864) | 1.059683 / 4.805227 (-3.745544) | 0.235838 / 6.500664 (-6.264826) | 0.083725 / 0.075469 (0.008256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.755843 / 1.841788 (-0.085944) | 25.186642 / 8.074308 (17.112334) | 24.133582 / 10.191392 (13.942190) | 0.240511 / 0.680424 (-0.439913) | 0.029563 / 0.534201 (-0.504638) | 0.486049 / 0.579283 (-0.093234) | 0.610064 / 0.434364 (0.175700) | 0.559521 / 0.540337 (0.019184) | 0.828289 / 1.386936 (-0.558647) |\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) | 0.012134 / 0.011353 (0.000781) | 0.005133 / 0.011008 (-0.005875) | 0.084521 / 0.038508 (0.046013) | 0.095172 / 0.023109 (0.072063) | 0.527298 / 0.275898 (0.251400) | 0.558915 / 0.323480 (0.235435) | 0.006996 / 0.007986 (-0.000989) | 0.004283 / 0.004328 (-0.000045) | 0.082975 / 0.004250 (0.078725) | 0.067976 / 0.037052 (0.030924) | 0.534020 / 0.258489 (0.275531) | 0.560810 / 0.293841 (0.266969) | 0.051603 / 0.128546 (-0.076943) | 0.013330 / 0.075646 (-0.062316) | 0.094093 / 0.419271 (-0.325178) | 0.068967 / 0.043533 (0.025434) | 0.512527 / 0.255139 (0.257388) | 0.542182 / 0.283200 (0.258982) | 0.039159 / 0.141683 (-0.102524) | 1.858841 / 1.452155 (0.406686) | 1.915450 / 1.492716 (0.422734) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269013 / 0.018006 (0.251007) | 0.601711 / 0.000490 (0.601222) | 0.013950 / 0.000200 (0.013750) | 0.000166 / 0.000054 (0.000112) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038817 / 0.037411 (0.001405) | 0.138528 / 0.014526 (0.124002) | 0.130691 / 0.176557 (-0.045865) | 0.192825 / 0.737135 (-0.544310) | 0.128337 / 0.296338 (-0.168002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678725 / 0.215209 (0.463516) | 6.869763 / 2.077655 (4.792108) | 3.416224 / 1.504120 (1.912104) | 3.106971 / 1.541195 (1.565776) | 3.117248 / 1.468490 (1.648757) | 0.895004 / 4.584777 (-3.689773) | 5.551618 / 3.745712 (1.805906) | 4.964811 / 5.269862 (-0.305051) | 3.239555 / 4.565676 (-1.326121) | 0.099776 / 0.424275 (-0.324500) | 0.008723 / 0.007607 (0.001116) | 0.818554 / 0.226044 (0.592510) | 8.015976 / 2.268929 (5.747047) | 4.200392 / 55.444624 (-51.244232) | 3.566942 / 6.876477 (-3.309535) | 3.766249 / 2.142072 (1.624177) | 1.083428 / 4.805227 (-3.721799) | 0.214614 / 6.500664 (-6.286050) | 0.081951 / 0.075469 (0.006482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.854400 / 1.841788 (0.012612) | 26.002556 / 8.074308 (17.928248) | 24.315194 / 10.191392 (14.123802) | 0.249012 / 0.680424 (-0.431412) | 0.032681 / 0.534201 (-0.501520) | 0.502360 / 0.579283 (-0.076923) | 0.606014 / 0.434364 (0.171650) | 0.616852 / 0.540337 (0.076514) | 0.861785 / 1.386936 (-0.525151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53c5c01583153cc112a507082aff4679433a1cce \"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) | 0.006723 / 0.011353 (-0.004630) | 0.004135 / 0.011008 (-0.006873) | 0.079241 / 0.038508 (0.040733) | 0.065484 / 0.023109 (0.042374) | 0.302831 / 0.275898 (0.026933) | 0.343747 / 0.323480 (0.020268) | 0.005910 / 0.007986 (-0.002076) | 0.006028 / 0.004328 (0.001699) | 0.064000 / 0.004250 (0.059750) | 0.047872 / 0.037052 (0.010820) | 0.336928 / 0.258489 (0.078439) | 0.357726 / 0.293841 (0.063885) | 0.039375 / 0.128546 (-0.089171) | 0.010439 / 0.075646 (-0.065207) | 0.310453 / 0.419271 (-0.108819) | 0.055320 / 0.043533 (0.011787) | 0.294722 / 0.255139 (0.039583) | 0.314649 / 0.283200 (0.031450) | 0.033223 / 0.141683 (-0.108460) | 1.386705 / 1.452155 (-0.065450) | 1.420546 / 1.492716 (-0.072170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262649 / 0.018006 (0.244643) | 0.536764 / 0.000490 (0.536274) | 0.011090 / 0.000200 (0.010891) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023822 / 0.037411 (-0.013590) | 0.074279 / 0.014526 (0.059753) | 0.081295 / 0.176557 (-0.095262) | 0.135853 / 0.737135 (-0.601282) | 0.080193 / 0.296338 (-0.216146) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468577 / 0.215209 (0.253368) | 4.615975 / 2.077655 (2.538321) | 2.059232 / 1.504120 (0.555112) | 1.798578 / 1.541195 (0.257383) | 1.801436 / 1.468490 (0.332946) | 0.660489 / 4.584777 (-3.924288) | 4.394652 / 3.745712 (0.648940) | 3.956277 / 5.269862 (-1.313585) | 2.406700 / 4.565676 (-2.158976) | 0.077174 / 0.424275 (-0.347101) | 0.007121 / 0.007607 (-0.000486) | 0.568213 / 0.226044 (0.342168) | 5.721217 / 2.268929 (3.452289) | 2.662741 / 55.444624 (-52.781883) | 2.207333 / 6.876477 (-4.669144) | 2.165279 / 2.142072 (0.023206) | 0.772566 / 4.805227 (-4.032661) | 0.162845 / 6.500664 (-6.337819) | 0.057515 / 0.075469 (-0.017954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313565 / 1.841788 (-0.528223) | 19.298926 / 8.074308 (11.224618) | 17.194320 / 10.191392 (7.002928) | 0.223404 / 0.680424 (-0.457020) | 0.024735 / 0.534201 (-0.509466) | 0.388452 / 0.579283 (-0.190831) | 0.489354 / 0.434364 (0.054990) | 0.427962 / 0.540337 (-0.112375) | 0.629483 / 1.386936 (-0.757453) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007404 / 0.011353 (-0.003949) | 0.004434 / 0.011008 (-0.006574) | 0.061633 / 0.038508 (0.023125) | 0.058446 / 0.023109 (0.035336) | 0.386107 / 0.275898 (0.110209) | 0.397676 / 0.323480 (0.074197) | 0.005463 / 0.007986 (-0.002523) | 0.003797 / 0.004328 (-0.000531) | 0.067323 / 0.004250 (0.063072) | 0.053826 / 0.037052 (0.016774) | 0.387910 / 0.258489 (0.129421) | 0.409364 / 0.293841 (0.115523) | 0.039836 / 0.128546 (-0.088710) | 0.011940 / 0.075646 (-0.063706) | 0.071812 / 0.419271 (-0.347459) | 0.047952 / 0.043533 (0.004419) | 0.386826 / 0.255139 (0.131687) | 0.392845 / 0.283200 (0.109645) | 0.029430 / 0.141683 (-0.112253) | 1.390961 / 1.452155 (-0.061194) | 1.482744 / 1.492716 (-0.009972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258814 / 0.018006 (0.240807) | 0.535505 / 0.000490 (0.535015) | 0.006097 / 0.000200 (0.005897) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028046 / 0.037411 (-0.009365) | 0.078077 / 0.014526 (0.063552) | 0.087713 / 0.176557 (-0.088843) | 0.140856 / 0.737135 (-0.596279) | 0.090565 / 0.296338 (-0.205773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504375 / 0.215209 (0.289165) | 5.133472 / 2.077655 (3.055817) | 2.368968 / 1.504120 (0.864848) | 2.176939 / 1.541195 (0.635744) | 2.151976 / 1.468490 (0.683486) | 0.720566 / 4.584777 (-3.864211) | 5.050505 / 3.745712 (1.304793) | 3.993614 / 5.269862 (-1.276248) | 2.492234 / 4.565676 (-2.073443) | 0.089629 / 0.424275 (-0.334646) | 0.008074 / 0.007607 (0.000467) | 0.677706 / 0.226044 (0.451661) | 6.208332 / 2.268929 (3.939403) | 3.058299 / 55.444624 (-52.386325) | 2.461078 / 6.876477 (-4.415399) | 2.622681 / 2.142072 (0.480609) | 0.873573 / 4.805227 (-3.931654) | 0.176321 / 6.500664 (-6.324343) | 0.062410 / 0.075469 (-0.013059) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.454767 / 1.841788 (-0.387021) | 19.544225 / 8.074308 (11.469917) | 17.365997 / 10.191392 (7.174605) | 0.225461 / 0.680424 (-0.454963) | 0.027679 / 0.534201 (-0.506522) | 0.396419 / 0.579283 (-0.182864) | 0.513244 / 0.434364 (0.078880) | 0.469054 / 0.540337 (-0.071283) | 0.676458 / 1.386936 (-0.710478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d44d8a649b541cd0b10ea99fbfe7a02c3ba50a63 \"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) | 0.007606 / 0.011353 (-0.003747) | 0.004692 / 0.011008 (-0.006317) | 0.100525 / 0.038508 (0.062017) | 0.085426 / 0.023109 (0.062317) | 0.378568 / 0.275898 (0.102670) | 0.412268 / 0.323480 (0.088788) | 0.004756 / 0.007986 (-0.003230) | 0.003871 / 0.004328 (-0.000457) | 0.075244 / 0.004250 (0.070994) | 0.064969 / 0.037052 (0.027916) | 0.385569 / 0.258489 (0.127079) | 0.429117 / 0.293841 (0.135276) | 0.035798 / 0.128546 (-0.092749) | 0.009999 / 0.075646 (-0.065647) | 0.351380 / 0.419271 (-0.067891) | 0.060850 / 0.043533 (0.017317) | 0.381327 / 0.255139 (0.126188) | 0.403663 / 0.283200 (0.120464) | 0.028103 / 0.141683 (-0.113580) | 1.814143 / 1.452155 (0.361988) | 1.895062 / 1.492716 (0.402346) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263581 / 0.018006 (0.245575) | 0.506988 / 0.000490 (0.506499) | 0.012775 / 0.000200 (0.012575) | 0.000456 / 0.000054 (0.000402) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033452 / 0.037411 (-0.003959) | 0.104950 / 0.014526 (0.090425) | 0.114803 / 0.176557 (-0.061754) | 0.182465 / 0.737135 (-0.554671) | 0.116156 / 0.296338 (-0.180183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441574 / 0.215209 (0.226365) | 4.394601 / 2.077655 (2.316946) | 2.170797 / 1.504120 (0.666677) | 1.926675 / 1.541195 (0.385480) | 1.974867 / 1.468490 (0.506377) | 0.546777 / 4.584777 (-4.038000) | 4.053612 / 3.745712 (0.307900) | 3.934278 / 5.269862 (-1.335583) | 2.354660 / 4.565676 (-2.211017) | 0.067706 / 0.424275 (-0.356569) | 0.009217 / 0.007607 (0.001610) | 0.539261 / 0.226044 (0.313217) | 5.409552 / 2.268929 (3.140623) | 2.835739 / 55.444624 (-52.608886) | 2.282246 / 6.876477 (-4.594230) | 2.359930 / 2.142072 (0.217858) | 0.696363 / 4.805227 (-4.108864) | 0.155947 / 6.500664 (-6.344717) | 0.071293 / 0.075469 (-0.004176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.495512 / 1.841788 (-0.346275) | 22.027128 / 8.074308 (13.952820) | 16.226068 / 10.191392 (6.034676) | 0.180281 / 0.680424 (-0.500142) | 0.021839 / 0.534201 (-0.512362) | 0.446151 / 0.579283 (-0.133132) | 0.476872 / 0.434364 (0.042508) | 0.515171 / 0.540337 (-0.025166) | 0.731372 / 1.386936 (-0.655564) |\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) | 0.006843 / 0.011353 (-0.004510) | 0.004286 / 0.011008 (-0.006722) | 0.074104 / 0.038508 (0.035596) | 0.076789 / 0.023109 (0.053680) | 0.441506 / 0.275898 (0.165608) | 0.500999 / 0.323480 (0.177519) | 0.006041 / 0.007986 (-0.001945) | 0.003718 / 0.004328 (-0.000610) | 0.074189 / 0.004250 (0.069938) | 0.060513 / 0.037052 (0.023461) | 0.460812 / 0.258489 (0.202323) | 0.503631 / 0.293841 (0.209790) | 0.037026 / 0.128546 (-0.091520) | 0.009611 / 0.075646 (-0.066035) | 0.077037 / 0.419271 (-0.342234) | 0.052191 / 0.043533 (0.008658) | 0.444567 / 0.255139 (0.189428) | 0.486730 / 0.283200 (0.203530) | 0.023846 / 0.141683 (-0.117837) | 1.692422 / 1.452155 (0.240267) | 1.809648 / 1.492716 (0.316932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222001) | 0.481980 / 0.000490 (0.481490) | 0.006945 / 0.000200 (0.006746) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037198 / 0.037411 (-0.000213) | 0.119413 / 0.014526 (0.104887) | 0.137409 / 0.176557 (-0.039148) | 0.199130 / 0.737135 (-0.538005) | 0.133137 / 0.296338 (-0.163202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521747 / 0.215209 (0.306538) | 4.955653 / 2.077655 (2.877999) | 2.694323 / 1.504120 (1.190203) | 2.496629 / 1.541195 (0.955434) | 2.661151 / 1.468490 (1.192660) | 0.576687 / 4.584777 (-4.008089) | 4.251437 / 3.745712 (0.505725) | 3.683020 / 5.269862 (-1.586842) | 2.363951 / 4.565676 (-2.201726) | 0.064631 / 0.424275 (-0.359644) | 0.007958 / 0.007607 (0.000351) | 0.616498 / 0.226044 (0.390454) | 5.919424 / 2.268929 (3.650496) | 3.255936 / 55.444624 (-52.188689) | 2.866167 / 6.876477 (-4.010309) | 3.007272 / 2.142072 (0.865199) | 0.660259 / 4.805227 (-4.144968) | 0.152469 / 6.500664 (-6.348195) | 0.065254 / 0.075469 (-0.010215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.547912 / 1.841788 (-0.293876) | 22.494611 / 8.074308 (14.420303) | 16.400746 / 10.191392 (6.209354) | 0.184137 / 0.680424 (-0.496287) | 0.023615 / 0.534201 (-0.510586) | 0.473923 / 0.579283 (-0.105360) | 0.473030 / 0.434364 (0.038666) | 0.534264 / 0.540337 (-0.006073) | 0.770178 / 1.386936 (-0.616758) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#565efb7f43839072ef01247681645ca404ba0b94 \"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) | 0.006812 / 0.011353 (-0.004541) | 0.004254 / 0.011008 (-0.006754) | 0.084271 / 0.038508 (0.045763) | 0.084299 / 0.023109 (0.061189) | 0.317437 / 0.275898 (0.041539) | 0.350855 / 0.323480 (0.027375) | 0.004296 / 0.007986 (-0.003690) | 0.003610 / 0.004328 (-0.000718) | 0.065205 / 0.004250 (0.060955) | 0.057734 / 0.037052 (0.020682) | 0.324049 / 0.258489 (0.065560) | 0.365042 / 0.293841 (0.071201) | 0.031454 / 0.128546 (-0.097092) | 0.008703 / 0.075646 (-0.066943) | 0.286603 / 0.419271 (-0.132668) | 0.052251 / 0.043533 (0.008719) | 0.312404 / 0.255139 (0.057265) | 0.335902 / 0.283200 (0.052703) | 0.025087 / 0.141683 (-0.116595) | 1.478573 / 1.452155 (0.026418) | 1.559548 / 1.492716 (0.066831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307637 / 0.018006 (0.289631) | 0.567169 / 0.000490 (0.566679) | 0.006782 / 0.000200 (0.006582) | 0.000235 / 0.000054 (0.000180) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030979 / 0.037411 (-0.006433) | 0.089972 / 0.014526 (0.075446) | 0.101689 / 0.176557 (-0.074868) | 0.162038 / 0.737135 (-0.575097) | 0.103107 / 0.296338 (-0.193232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382458 / 0.215209 (0.167248) | 3.813105 / 2.077655 (1.735450) | 1.855198 / 1.504120 (0.351078) | 1.699850 / 1.541195 (0.158656) | 1.902818 / 1.468490 (0.434328) | 0.478654 / 4.584777 (-4.106123) | 3.536926 / 3.745712 (-0.208786) | 3.558557 / 5.269862 (-1.711304) | 2.121098 / 4.565676 (-2.444579) | 0.056584 / 0.424275 (-0.367691) | 0.007693 / 0.007607 (0.000086) | 0.471157 / 0.226044 (0.245112) | 4.717742 / 2.268929 (2.448813) | 2.389033 / 55.444624 (-53.055591) | 2.102898 / 6.876477 (-4.773579) | 2.233404 / 2.142072 (0.091332) | 0.585829 / 4.805227 (-4.219398) | 0.133784 / 6.500664 (-6.366880) | 0.063963 / 0.075469 (-0.011506) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272234 / 1.841788 (-0.569554) | 19.897647 / 8.074308 (11.823339) | 14.808090 / 10.191392 (4.616698) | 0.167199 / 0.680424 (-0.513224) | 0.018357 / 0.534201 (-0.515844) | 0.391635 / 0.579283 (-0.187648) | 0.409603 / 0.434364 (-0.024761) | 0.467670 / 0.540337 (-0.072668) | 0.639763 / 1.386936 (-0.747173) |\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) | 0.006794 / 0.011353 (-0.004559) | 0.004317 / 0.011008 (-0.006692) | 0.065434 / 0.038508 (0.026926) | 0.079066 / 0.023109 (0.055957) | 0.415486 / 0.275898 (0.139588) | 0.448072 / 0.323480 (0.124593) | 0.005705 / 0.007986 (-0.002281) | 0.003589 / 0.004328 (-0.000739) | 0.065195 / 0.004250 (0.060945) | 0.058951 / 0.037052 (0.021899) | 0.414466 / 0.258489 (0.155977) | 0.453844 / 0.293841 (0.160003) | 0.032437 / 0.128546 (-0.096110) | 0.008805 / 0.075646 (-0.066841) | 0.071741 / 0.419271 (-0.347530) | 0.048051 / 0.043533 (0.004518) | 0.413197 / 0.255139 (0.158058) | 0.430071 / 0.283200 (0.146872) | 0.023144 / 0.141683 (-0.118539) | 1.507756 / 1.452155 (0.055601) | 1.572180 / 1.492716 (0.079464) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326556 / 0.018006 (0.308550) | 0.533664 / 0.000490 (0.533174) | 0.007400 / 0.000200 (0.007200) | 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) | 0.033397 / 0.037411 (-0.004014) | 0.092486 / 0.014526 (0.077960) | 0.108454 / 0.176557 (-0.068103) | 0.163885 / 0.737135 (-0.573250) | 0.109682 / 0.296338 (-0.186657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429283 / 0.215209 (0.214074) | 4.285774 / 2.077655 (2.208119) | 2.245646 / 1.504120 (0.741526) | 2.088460 / 1.541195 (0.547265) | 2.217908 / 1.468490 (0.749418) | 0.500126 / 4.584777 (-4.084651) | 3.640253 / 3.745712 (-0.105459) | 3.435069 / 5.269862 (-1.834793) | 2.158015 / 4.565676 (-2.407662) | 0.059087 / 0.424275 (-0.365188) | 0.007479 / 0.007607 (-0.000128) | 0.518067 / 0.226044 (0.292023) | 5.181891 / 2.268929 (2.912963) | 2.759156 / 55.444624 (-52.685468) | 2.452164 / 6.876477 (-4.424313) | 2.712764 / 2.142072 (0.570692) | 0.604871 / 4.805227 (-4.200356) | 0.137810 / 6.500664 (-6.362854) | 0.061999 / 0.075469 (-0.013470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338081 / 1.841788 (-0.503706) | 19.934668 / 8.074308 (11.860360) | 14.482526 / 10.191392 (4.291134) | 0.167615 / 0.680424 (-0.512809) | 0.020257 / 0.534201 (-0.513944) | 0.399103 / 0.579283 (-0.180180) | 0.431785 / 0.434364 (-0.002579) | 0.475470 / 0.540337 (-0.064868) | 0.648003 / 1.386936 (-0.738933) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#153a6c0b0897fc30203ded5a6d6c358c53aa3a0e \"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) | 0.011916 / 0.011353 (0.000563) | 0.004696 / 0.011008 (-0.006313) | 0.101061 / 0.038508 (0.062553) | 0.093383 / 0.023109 (0.070274) | 0.391517 / 0.275898 (0.115619) | 0.434374 / 0.323480 (0.110894) | 0.006193 / 0.007986 (-0.001792) | 0.003840 / 0.004328 (-0.000489) | 0.077946 / 0.004250 (0.073696) | 0.066332 / 0.037052 (0.029280) | 0.413103 / 0.258489 (0.154614) | 0.452988 / 0.293841 (0.159148) | 0.044899 / 0.128546 (-0.083647) | 0.009969 / 0.075646 (-0.065677) | 0.344569 / 0.419271 (-0.074703) | 0.064688 / 0.043533 (0.021155) | 0.388042 / 0.255139 (0.132903) | 0.417615 / 0.283200 (0.134416) | 0.032899 / 0.141683 (-0.108784) | 1.738834 / 1.452155 (0.286679) | 1.837562 / 1.492716 (0.344845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255265 / 0.018006 (0.237259) | 0.547550 / 0.000490 (0.547061) | 0.009018 / 0.000200 (0.008818) | 0.001232 / 0.000054 (0.001178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033171 / 0.037411 (-0.004241) | 0.102569 / 0.014526 (0.088043) | 0.113611 / 0.176557 (-0.062946) | 0.181805 / 0.737135 (-0.555330) | 0.115015 / 0.296338 (-0.181323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456430 / 0.215209 (0.241221) | 4.536000 / 2.077655 (2.458346) | 2.220554 / 1.504120 (0.716434) | 2.037965 / 1.541195 (0.496770) | 2.223780 / 1.468490 (0.755290) | 0.565732 / 4.584777 (-4.019045) | 4.574917 / 3.745712 (0.829205) | 4.085683 / 5.269862 (-1.184178) | 2.529052 / 4.565676 (-2.036624) | 0.067061 / 0.424275 (-0.357214) | 0.009161 / 0.007607 (0.001554) | 0.551377 / 0.226044 (0.325332) | 5.510422 / 2.268929 (3.241493) | 2.788264 / 55.444624 (-52.656360) | 2.432821 / 6.876477 (-4.443656) | 2.500835 / 2.142072 (0.358762) | 0.683645 / 4.805227 (-4.121582) | 0.155595 / 6.500664 (-6.345069) | 0.072265 / 0.075469 (-0.003204) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512571 / 1.841788 (-0.329217) | 23.752582 / 8.074308 (15.678273) | 16.798834 / 10.191392 (6.607442) | 0.210325 / 0.680424 (-0.470099) | 0.023446 / 0.534201 (-0.510755) | 0.472964 / 0.579283 (-0.106319) | 0.518003 / 0.434364 (0.083639) | 0.588422 / 0.540337 (0.048085) | 0.830762 / 1.386936 (-0.556174) |\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) | 0.008075 / 0.011353 (-0.003278) | 0.004569 / 0.011008 (-0.006439) | 0.079786 / 0.038508 (0.041278) | 0.092741 / 0.023109 (0.069632) | 0.500732 / 0.275898 (0.224834) | 0.544108 / 0.323480 (0.220628) | 0.006305 / 0.007986 (-0.001680) | 0.003843 / 0.004328 (-0.000486) | 0.078347 / 0.004250 (0.074096) | 0.066969 / 0.037052 (0.029916) | 0.504116 / 0.258489 (0.245627) | 0.548109 / 0.293841 (0.254268) | 0.038263 / 0.128546 (-0.090283) | 0.010006 / 0.075646 (-0.065640) | 0.085582 / 0.419271 (-0.333690) | 0.056937 / 0.043533 (0.013404) | 0.502861 / 0.255139 (0.247722) | 0.532002 / 0.283200 (0.248802) | 0.027003 / 0.141683 (-0.114679) | 1.811658 / 1.452155 (0.359503) | 1.878863 / 1.492716 (0.386147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242297 / 0.018006 (0.224291) | 0.489060 / 0.000490 (0.488570) | 0.005770 / 0.000200 (0.005570) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040368 / 0.037411 (0.002956) | 0.116221 / 0.014526 (0.101695) | 0.125195 / 0.176557 (-0.051361) | 0.188616 / 0.737135 (-0.548519) | 0.126473 / 0.296338 (-0.169866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513975 / 0.215209 (0.298766) | 5.122407 / 2.077655 (3.044752) | 2.854024 / 1.504120 (1.349904) | 2.611101 / 1.541195 (1.069906) | 2.704880 / 1.468490 (1.236390) | 0.581568 / 4.584777 (-4.003209) | 4.628965 / 3.745712 (0.883253) | 4.069359 / 5.269862 (-1.200503) | 2.433793 / 4.565676 (-2.131883) | 0.068624 / 0.424275 (-0.355651) | 0.008843 / 0.007607 (0.001235) | 0.609147 / 0.226044 (0.383102) | 6.096923 / 2.268929 (3.827995) | 3.411687 / 55.444624 (-52.032937) | 2.972037 / 6.876477 (-3.904440) | 3.210266 / 2.142072 (1.068194) | 0.697935 / 4.805227 (-4.107292) | 0.156855 / 6.500664 (-6.343809) | 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) | 1.673126 / 1.841788 (-0.168661) | 24.782231 / 8.074308 (16.707923) | 17.945937 / 10.191392 (7.754545) | 0.229063 / 0.680424 (-0.451361) | 0.024264 / 0.534201 (-0.509937) | 0.474904 / 0.579283 (-0.104379) | 0.616602 / 0.434364 (0.182238) | 0.587687 / 0.540337 (0.047350) | 0.875600 / 1.386936 (-0.511336) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ae63ad72c0733c947fa0f2996fa739d80e1ef \"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) | 0.004866 / 0.011353 (-0.006487) | 0.002877 / 0.011008 (-0.008132) | 0.061786 / 0.038508 (0.023277) | 0.051555 / 0.023109 (0.028446) | 0.262182 / 0.275898 (-0.013716) | 0.288908 / 0.323480 (-0.034572) | 0.002929 / 0.007986 (-0.005057) | 0.002358 / 0.004328 (-0.001971) | 0.048246 / 0.004250 (0.043995) | 0.040391 / 0.037052 (0.003339) | 0.268165 / 0.258489 (0.009675) | 0.304844 / 0.293841 (0.011003) | 0.023280 / 0.128546 (-0.105266) | 0.007274 / 0.075646 (-0.068372) | 0.200698 / 0.419271 (-0.218574) | 0.036181 / 0.043533 (-0.007352) | 0.267292 / 0.255139 (0.012153) | 0.286981 / 0.283200 (0.003781) | 0.018686 / 0.141683 (-0.122996) | 1.131903 / 1.452155 (-0.320251) | 1.196631 / 1.492716 (-0.296086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092158 / 0.018006 (0.074152) | 0.300621 / 0.000490 (0.300132) | 0.000205 / 0.000200 (0.000006) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018101 / 0.037411 (-0.019310) | 0.062478 / 0.014526 (0.047952) | 0.073092 / 0.176557 (-0.103464) | 0.119397 / 0.737135 (-0.617738) | 0.073768 / 0.296338 (-0.222570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286711 / 0.215209 (0.071502) | 2.766663 / 2.077655 (0.689008) | 1.431238 / 1.504120 (-0.072882) | 1.308312 / 1.541195 (-0.232883) | 1.344886 / 1.468490 (-0.123605) | 0.396719 / 4.584777 (-4.188058) | 2.371154 / 3.745712 (-1.374558) | 2.626471 / 5.269862 (-2.643391) | 1.574837 / 4.565676 (-2.990840) | 0.046344 / 0.424275 (-0.377931) | 0.005108 / 0.007607 (-0.002499) | 0.334200 / 0.226044 (0.108156) | 3.277034 / 2.268929 (1.008106) | 1.789338 / 55.444624 (-53.655286) | 1.527584 / 6.876477 (-5.348892) | 1.570417 / 2.142072 (-0.571656) | 0.472663 / 4.805227 (-4.332564) | 0.100825 / 6.500664 (-6.399839) | 0.042270 / 0.075469 (-0.033199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965416 / 1.841788 (-0.876372) | 11.827406 / 8.074308 (3.753098) | 10.820703 / 10.191392 (0.629311) | 0.128636 / 0.680424 (-0.551788) | 0.014696 / 0.534201 (-0.519505) | 0.271019 / 0.579283 (-0.308264) | 0.270077 / 0.434364 (-0.164287) | 0.313054 / 0.540337 (-0.227284) | 0.402941 / 1.386936 (-0.983995) |\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) | 0.005204 / 0.011353 (-0.006149) | 0.002976 / 0.011008 (-0.008032) | 0.047723 / 0.038508 (0.009215) | 0.056180 / 0.023109 (0.033071) | 0.277751 / 0.275898 (0.001853) | 0.304109 / 0.323480 (-0.019371) | 0.004254 / 0.007986 (-0.003732) | 0.002386 / 0.004328 (-0.001943) | 0.047815 / 0.004250 (0.043564) | 0.041553 / 0.037052 (0.004501) | 0.280958 / 0.258489 (0.022469) | 0.308639 / 0.293841 (0.014799) | 0.023549 / 0.128546 (-0.104997) | 0.007846 / 0.075646 (-0.067800) | 0.053762 / 0.419271 (-0.365509) | 0.031763 / 0.043533 (-0.011770) | 0.278208 / 0.255139 (0.023069) | 0.294024 / 0.283200 (0.010825) | 0.018648 / 0.141683 (-0.123035) | 1.140664 / 1.452155 (-0.311490) | 1.206706 / 1.492716 (-0.286010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093211 / 0.018006 (0.075205) | 0.303067 / 0.000490 (0.302577) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021745 / 0.037411 (-0.015666) | 0.070400 / 0.014526 (0.055874) | 0.083250 / 0.176557 (-0.093307) | 0.119745 / 0.737135 (-0.617391) | 0.083004 / 0.296338 (-0.213335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305841 / 0.215209 (0.090632) | 2.958171 / 2.077655 (0.880516) | 1.596990 / 1.504120 (0.092870) | 1.466522 / 1.541195 (-0.074673) | 1.487050 / 1.468490 (0.018560) | 0.402866 / 4.584777 (-4.181911) | 2.425415 / 3.745712 (-1.320297) | 2.545245 / 5.269862 (-2.724617) | 1.569719 / 4.565676 (-2.995958) | 0.046344 / 0.424275 (-0.377931) | 0.005275 / 0.007607 (-0.002332) | 0.362024 / 0.226044 (0.135980) | 3.556721 / 2.268929 (1.287792) | 1.961359 / 55.444624 (-53.483266) | 1.672835 / 6.876477 (-5.203641) | 1.814036 / 2.142072 (-0.328036) | 0.482012 / 4.805227 (-4.323215) | 0.099275 / 6.500664 (-6.401389) | 0.040988 / 0.075469 (-0.034481) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984368 / 1.841788 (-0.857420) | 12.251555 / 8.074308 (4.177247) | 10.645975 / 10.191392 (0.454583) | 0.128955 / 0.680424 (-0.551468) | 0.015355 / 0.534201 (-0.518846) | 0.272498 / 0.579283 (-0.306785) | 0.279342 / 0.434364 (-0.155022) | 0.303055 / 0.540337 (-0.237282) | 0.392437 / 1.386936 (-0.994499) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56458fa98d2670ff6bf47a782b6f418785c017fd \"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) | 0.009502 / 0.011353 (-0.001851) | 0.004957 / 0.011008 (-0.006052) | 0.111062 / 0.038508 (0.072553) | 0.100012 / 0.023109 (0.076903) | 0.415747 / 0.275898 (0.139849) | 0.453910 / 0.323480 (0.130430) | 0.006030 / 0.007986 (-0.001956) | 0.004271 / 0.004328 (-0.000057) | 0.088694 / 0.004250 (0.084444) | 0.064529 / 0.037052 (0.027477) | 0.414999 / 0.258489 (0.156510) | 0.477115 / 0.293841 (0.183274) | 0.047565 / 0.128546 (-0.080982) | 0.013352 / 0.075646 (-0.062294) | 0.367948 / 0.419271 (-0.051324) | 0.067577 / 0.043533 (0.024044) | 0.405107 / 0.255139 (0.149968) | 0.430281 / 0.283200 (0.147081) | 0.041629 / 0.141683 (-0.100054) | 1.784746 / 1.452155 (0.332591) | 1.901539 / 1.492716 (0.408822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308456 / 0.018006 (0.290450) | 0.623253 / 0.000490 (0.622763) | 0.014966 / 0.000200 (0.014766) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031538 / 0.037411 (-0.005873) | 0.100321 / 0.014526 (0.085796) | 0.112788 / 0.176557 (-0.063769) | 0.180998 / 0.737135 (-0.556138) | 0.111589 / 0.296338 (-0.184750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.603121 / 0.215209 (0.387912) | 5.769795 / 2.077655 (3.692140) | 2.501168 / 1.504120 (0.997048) | 2.240982 / 1.541195 (0.699787) | 2.333123 / 1.468490 (0.864633) | 0.799246 / 4.584777 (-3.785531) | 5.148529 / 3.745712 (1.402817) | 4.737782 / 5.269862 (-0.532080) | 3.003032 / 4.565676 (-1.562644) | 0.087457 / 0.424275 (-0.336818) | 0.008777 / 0.007607 (0.001170) | 0.692961 / 0.226044 (0.466916) | 7.235537 / 2.268929 (4.966608) | 3.464074 / 55.444624 (-51.980551) | 2.817360 / 6.876477 (-4.059116) | 2.903121 / 2.142072 (0.761049) | 1.026150 / 4.805227 (-3.779077) | 0.231814 / 6.500664 (-6.268850) | 0.088358 / 0.075469 (0.012888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.527889 / 1.841788 (-0.313898) | 24.374770 / 8.074308 (16.300462) | 21.720415 / 10.191392 (11.529023) | 0.209357 / 0.680424 (-0.471067) | 0.027587 / 0.534201 (-0.506614) | 0.479136 / 0.579283 (-0.100147) | 0.573005 / 0.434364 (0.138641) | 0.537713 / 0.540337 (-0.002625) | 0.753628 / 1.386936 (-0.633308) |\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) | 0.009724 / 0.011353 (-0.001629) | 0.004798 / 0.011008 (-0.006210) | 0.076423 / 0.038508 (0.037915) | 0.085693 / 0.023109 (0.062584) | 0.446864 / 0.275898 (0.170966) | 0.482700 / 0.323480 (0.159220) | 0.006448 / 0.007986 (-0.001537) | 0.004451 / 0.004328 (0.000122) | 0.078295 / 0.004250 (0.074045) | 0.061940 / 0.037052 (0.024888) | 0.446091 / 0.258489 (0.187601) | 0.478567 / 0.293841 (0.184726) | 0.047206 / 0.128546 (-0.081340) | 0.012608 / 0.075646 (-0.063038) | 0.089719 / 0.419271 (-0.329552) | 0.057791 / 0.043533 (0.014258) | 0.438357 / 0.255139 (0.183218) | 0.475060 / 0.283200 (0.191860) | 0.035466 / 0.141683 (-0.106216) | 1.691982 / 1.452155 (0.239827) | 1.773834 / 1.492716 (0.281118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290053 / 0.018006 (0.272047) | 0.595465 / 0.000490 (0.594976) | 0.007531 / 0.000200 (0.007331) | 0.000179 / 0.000054 (0.000124) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034625 / 0.037411 (-0.002786) | 0.098725 / 0.014526 (0.084200) | 0.111248 / 0.176557 (-0.065308) | 0.172113 / 0.737135 (-0.565022) | 0.111299 / 0.296338 (-0.185040) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.581773 / 0.215209 (0.366564) | 6.150993 / 2.077655 (4.073338) | 2.761099 / 1.504120 (1.256980) | 2.431459 / 1.541195 (0.890264) | 2.501471 / 1.468490 (1.032981) | 0.805751 / 4.584777 (-3.779026) | 5.375406 / 3.745712 (1.629693) | 4.829323 / 5.269862 (-0.440538) | 3.095235 / 4.565676 (-1.470442) | 0.103336 / 0.424275 (-0.320939) | 0.012678 / 0.007607 (0.005071) | 0.730121 / 0.226044 (0.504077) | 7.272025 / 2.268929 (5.003097) | 3.607889 / 55.444624 (-51.836735) | 2.904797 / 6.876477 (-3.971680) | 3.179139 / 2.142072 (1.037067) | 0.997510 / 4.805227 (-3.807717) | 0.219023 / 6.500664 (-6.281641) | 0.076680 / 0.075469 (0.001211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712838 / 1.841788 (-0.128950) | 24.242240 / 8.074308 (16.167932) | 19.746825 / 10.191392 (9.555433) | 0.234590 / 0.680424 (-0.445833) | 0.032015 / 0.534201 (-0.502186) | 0.462554 / 0.579283 (-0.116729) | 0.604529 / 0.434364 (0.170165) | 0.537779 / 0.540337 (-0.002558) | 0.777386 / 1.386936 (-0.609550) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89c1b13d85b8971925440deb84f558e23c224a47 \"CML watermark\")\n",
"Cool ! Nice to simplify this",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004659 / 0.011353 (-0.006693) | 0.002672 / 0.011008 (-0.008337) | 0.062385 / 0.038508 (0.023877) | 0.030581 / 0.023109 (0.007471) | 0.243210 / 0.275898 (-0.032688) | 0.271441 / 0.323480 (-0.052039) | 0.002909 / 0.007986 (-0.005076) | 0.002371 / 0.004328 (-0.001957) | 0.049213 / 0.004250 (0.044962) | 0.043952 / 0.037052 (0.006900) | 0.250257 / 0.258489 (-0.008232) | 0.280470 / 0.293841 (-0.013371) | 0.023048 / 0.128546 (-0.105499) | 0.006893 / 0.075646 (-0.068754) | 0.204026 / 0.419271 (-0.215245) | 0.054067 / 0.043533 (0.010534) | 0.248730 / 0.255139 (-0.006409) | 0.272325 / 0.283200 (-0.010874) | 0.019028 / 0.141683 (-0.122655) | 1.103477 / 1.452155 (-0.348678) | 1.185775 / 1.492716 (-0.306942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097295 / 0.018006 (0.079289) | 0.302997 / 0.000490 (0.302507) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018653 / 0.037411 (-0.018759) | 0.062604 / 0.014526 (0.048079) | 0.075652 / 0.176557 (-0.100904) | 0.121298 / 0.737135 (-0.615838) | 0.074129 / 0.296338 (-0.222209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283315 / 0.215209 (0.068106) | 2.833975 / 2.077655 (0.756320) | 1.463877 / 1.504120 (-0.040243) | 1.352197 / 1.541195 (-0.188998) | 1.337623 / 1.468490 (-0.130867) | 0.405282 / 4.584777 (-4.179495) | 2.371381 / 3.745712 (-1.374331) | 2.584853 / 5.269862 (-2.685009) | 1.565902 / 4.565676 (-2.999775) | 0.046398 / 0.424275 (-0.377877) | 0.004795 / 0.007607 (-0.002812) | 0.345949 / 0.226044 (0.119905) | 3.326662 / 2.268929 (1.057733) | 1.778394 / 55.444624 (-53.666230) | 1.520788 / 6.876477 (-5.355688) | 1.526517 / 2.142072 (-0.615556) | 0.471788 / 4.805227 (-4.333439) | 0.099236 / 6.500664 (-6.401428) | 0.041886 / 0.075469 (-0.033583) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958183 / 1.841788 (-0.883605) | 11.474476 / 8.074308 (3.400168) | 10.547550 / 10.191392 (0.356158) | 0.129316 / 0.680424 (-0.551108) | 0.013969 / 0.534201 (-0.520232) | 0.272028 / 0.579283 (-0.307255) | 0.271027 / 0.434364 (-0.163337) | 0.312124 / 0.540337 (-0.228214) | 0.423879 / 1.386936 (-0.963057) |\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) | 0.004743 / 0.011353 (-0.006610) | 0.002724 / 0.011008 (-0.008284) | 0.049526 / 0.038508 (0.011018) | 0.051429 / 0.023109 (0.028319) | 0.265202 / 0.275898 (-0.010696) | 0.287498 / 0.323480 (-0.035981) | 0.004034 / 0.007986 (-0.003951) | 0.002460 / 0.004328 (-0.001868) | 0.049367 / 0.004250 (0.045116) | 0.038526 / 0.037052 (0.001474) | 0.271496 / 0.258489 (0.013007) | 0.300969 / 0.293841 (0.007128) | 0.024159 / 0.128546 (-0.104387) | 0.006959 / 0.075646 (-0.068687) | 0.055316 / 0.419271 (-0.363955) | 0.032409 / 0.043533 (-0.011124) | 0.267524 / 0.255139 (0.012385) | 0.284667 / 0.283200 (0.001467) | 0.017305 / 0.141683 (-0.124378) | 1.127560 / 1.452155 (-0.324595) | 1.188271 / 1.492716 (-0.304445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093587 / 0.018006 (0.075581) | 0.301834 / 0.000490 (0.301344) | 0.000211 / 0.000200 (0.000011) | 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) | 0.020899 / 0.037411 (-0.016512) | 0.069999 / 0.014526 (0.055473) | 0.081434 / 0.176557 (-0.095123) | 0.120538 / 0.737135 (-0.616598) | 0.082708 / 0.296338 (-0.213630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291845 / 0.215209 (0.076636) | 2.872476 / 2.077655 (0.794822) | 1.579330 / 1.504120 (0.075210) | 1.453083 / 1.541195 (-0.088112) | 1.496675 / 1.468490 (0.028185) | 0.406178 / 4.584777 (-4.178599) | 2.434121 / 3.745712 (-1.311592) | 2.519760 / 5.269862 (-2.750101) | 1.535781 / 4.565676 (-3.029895) | 0.046331 / 0.424275 (-0.377944) | 0.004749 / 0.007607 (-0.002858) | 0.340862 / 0.226044 (0.114817) | 3.362750 / 2.268929 (1.093822) | 1.924707 / 55.444624 (-53.519917) | 1.646820 / 6.876477 (-5.229657) | 1.630885 / 2.142072 (-0.511188) | 0.478623 / 4.805227 (-4.326605) | 0.098235 / 6.500664 (-6.402429) | 0.040741 / 0.075469 (-0.034728) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989858 / 1.841788 (-0.851929) | 12.111035 / 8.074308 (4.036727) | 11.065284 / 10.191392 (0.873892) | 0.143443 / 0.680424 (-0.536981) | 0.015873 / 0.534201 (-0.518328) | 0.271932 / 0.579283 (-0.307351) | 0.281440 / 0.434364 (-0.152924) | 0.309518 / 0.540337 (-0.230819) | 0.414701 / 1.386936 (-0.972235) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#322ee4bd7d460a5789f9991a45453b9fb5f5aed1 \"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) | 0.005840 / 0.011353 (-0.005513) | 0.003580 / 0.011008 (-0.007428) | 0.079921 / 0.038508 (0.041413) | 0.036316 / 0.023109 (0.013206) | 0.321065 / 0.275898 (0.045167) | 0.348594 / 0.323480 (0.025115) | 0.004662 / 0.007986 (-0.003324) | 0.002884 / 0.004328 (-0.001444) | 0.062964 / 0.004250 (0.058714) | 0.052856 / 0.037052 (0.015804) | 0.322087 / 0.258489 (0.063598) | 0.355546 / 0.293841 (0.061705) | 0.027025 / 0.128546 (-0.101521) | 0.007969 / 0.075646 (-0.067678) | 0.261416 / 0.419271 (-0.157855) | 0.066612 / 0.043533 (0.023079) | 0.314631 / 0.255139 (0.059492) | 0.340939 / 0.283200 (0.057739) | 0.019710 / 0.141683 (-0.121972) | 1.446068 / 1.452155 (-0.006086) | 1.510342 / 1.492716 (0.017625) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219742 / 0.018006 (0.201736) | 0.431794 / 0.000490 (0.431304) | 0.005717 / 0.000200 (0.005517) | 0.000195 / 0.000054 (0.000141) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024486 / 0.037411 (-0.012926) | 0.073231 / 0.014526 (0.058706) | 0.084053 / 0.176557 (-0.092503) | 0.145857 / 0.737135 (-0.591279) | 0.083050 / 0.296338 (-0.213289) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400532 / 0.215209 (0.185323) | 3.989293 / 2.077655 (1.911638) | 1.935520 / 1.504120 (0.431400) | 1.754146 / 1.541195 (0.212951) | 1.821060 / 1.468490 (0.352570) | 0.512603 / 4.584777 (-4.072173) | 3.070974 / 3.745712 (-0.674738) | 2.984617 / 5.269862 (-2.285245) | 1.875790 / 4.565676 (-2.689886) | 0.057881 / 0.424275 (-0.366394) | 0.006403 / 0.007607 (-0.001204) | 0.465542 / 0.226044 (0.239498) | 4.659589 / 2.268929 (2.390661) | 2.349637 / 55.444624 (-53.094987) | 2.011511 / 6.876477 (-4.864965) | 2.071893 / 2.142072 (-0.070179) | 0.591113 / 4.805227 (-4.214114) | 0.125000 / 6.500664 (-6.375664) | 0.061372 / 0.075469 (-0.014097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237068 / 1.841788 (-0.604720) | 17.493192 / 8.074308 (9.418884) | 13.600688 / 10.191392 (3.409296) | 0.142508 / 0.680424 (-0.537916) | 0.017305 / 0.534201 (-0.516896) | 0.333352 / 0.579283 (-0.245931) | 0.366699 / 0.434364 (-0.067665) | 0.381104 / 0.540337 (-0.159233) | 0.562645 / 1.386936 (-0.824291) |\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) | 0.006337 / 0.011353 (-0.005016) | 0.003584 / 0.011008 (-0.007424) | 0.063351 / 0.038508 (0.024843) | 0.061351 / 0.023109 (0.038242) | 0.430690 / 0.275898 (0.154792) | 0.462158 / 0.323480 (0.138678) | 0.004922 / 0.007986 (-0.003064) | 0.002898 / 0.004328 (-0.001430) | 0.063722 / 0.004250 (0.059472) | 0.046970 / 0.037052 (0.009918) | 0.436340 / 0.258489 (0.177851) | 0.472842 / 0.293841 (0.179001) | 0.029238 / 0.128546 (-0.099309) | 0.008079 / 0.075646 (-0.067568) | 0.068425 / 0.419271 (-0.350846) | 0.041272 / 0.043533 (-0.002261) | 0.429150 / 0.255139 (0.174011) | 0.451859 / 0.283200 (0.168659) | 0.020135 / 0.141683 (-0.121547) | 1.440388 / 1.452155 (-0.011767) | 1.506784 / 1.492716 (0.014068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225810 / 0.018006 (0.207804) | 0.408447 / 0.000490 (0.407957) | 0.002484 / 0.000200 (0.002284) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026162 / 0.037411 (-0.011250) | 0.079292 / 0.014526 (0.064766) | 0.091126 / 0.176557 (-0.085431) | 0.141607 / 0.737135 (-0.595528) | 0.090073 / 0.296338 (-0.206266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420689 / 0.215209 (0.205479) | 4.207631 / 2.077655 (2.129976) | 2.163469 / 1.504120 (0.659350) | 2.098208 / 1.541195 (0.557013) | 2.217340 / 1.468490 (0.748850) | 0.502599 / 4.584777 (-4.082178) | 3.128151 / 3.745712 (-0.617561) | 2.921041 / 5.269862 (-2.348820) | 1.808352 / 4.565676 (-2.757325) | 0.057724 / 0.424275 (-0.366551) | 0.006423 / 0.007607 (-0.001184) | 0.490631 / 0.226044 (0.264587) | 4.878761 / 2.268929 (2.609833) | 2.614831 / 55.444624 (-52.829793) | 2.214611 / 6.876477 (-4.661866) | 2.253313 / 2.142072 (0.111241) | 0.585643 / 4.805227 (-4.219584) | 0.122436 / 6.500664 (-6.378228) | 0.057974 / 0.075469 (-0.017495) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334290 / 1.841788 (-0.507498) | 17.778981 / 8.074308 (9.704672) | 14.982837 / 10.191392 (4.791445) | 0.135731 / 0.680424 (-0.544693) | 0.018314 / 0.534201 (-0.515887) | 0.332318 / 0.579283 (-0.246966) | 0.380185 / 0.434364 (-0.054179) | 0.391430 / 0.540337 (-0.148907) | 0.554577 / 1.386936 (-0.832359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1715b61a096cafb20caeb136111522432aba04f5 \"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) | 0.005248 / 0.011353 (-0.006105) | 0.003188 / 0.011008 (-0.007820) | 0.063045 / 0.038508 (0.024537) | 0.033620 / 0.023109 (0.010511) | 0.244725 / 0.275898 (-0.031173) | 0.283259 / 0.323480 (-0.040220) | 0.003013 / 0.007986 (-0.004973) | 0.002486 / 0.004328 (-0.001842) | 0.048873 / 0.004250 (0.044623) | 0.049431 / 0.037052 (0.012379) | 0.245297 / 0.258489 (-0.013192) | 0.283127 / 0.293841 (-0.010714) | 0.024204 / 0.128546 (-0.104342) | 0.007542 / 0.075646 (-0.068104) | 0.204831 / 0.419271 (-0.214440) | 0.067487 / 0.043533 (0.023954) | 0.251477 / 0.255139 (-0.003662) | 0.273108 / 0.283200 (-0.010091) | 0.021035 / 0.141683 (-0.120648) | 1.108361 / 1.452155 (-0.343793) | 1.172923 / 1.492716 (-0.319793) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094729 / 0.018006 (0.076722) | 0.301877 / 0.000490 (0.301388) | 0.000223 / 0.000200 (0.000023) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019901 / 0.037411 (-0.017511) | 0.068059 / 0.014526 (0.053534) | 0.075333 / 0.176557 (-0.101224) | 0.123276 / 0.737135 (-0.613859) | 0.076810 / 0.296338 (-0.219528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283421 / 0.215209 (0.068211) | 2.775511 / 2.077655 (0.697857) | 1.430927 / 1.504120 (-0.073193) | 1.317334 / 1.541195 (-0.223860) | 1.359483 / 1.468490 (-0.109007) | 0.403186 / 4.584777 (-4.181591) | 2.405789 / 3.745712 (-1.339923) | 2.773039 / 5.269862 (-2.496823) | 1.666722 / 4.565676 (-2.898954) | 0.047937 / 0.424275 (-0.376338) | 0.004879 / 0.007607 (-0.002728) | 0.347225 / 0.226044 (0.121180) | 3.380860 / 2.268929 (1.111931) | 1.838532 / 55.444624 (-53.606092) | 1.597681 / 6.876477 (-5.278796) | 1.600123 / 2.142072 (-0.541949) | 0.478836 / 4.805227 (-4.326391) | 0.100332 / 6.500664 (-6.400332) | 0.043334 / 0.075469 (-0.032135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942591 / 1.841788 (-0.899196) | 12.588886 / 8.074308 (4.514578) | 11.375666 / 10.191392 (1.184274) | 0.143460 / 0.680424 (-0.536964) | 0.014990 / 0.534201 (-0.519211) | 0.271068 / 0.579283 (-0.308216) | 0.265478 / 0.434364 (-0.168885) | 0.310914 / 0.540337 (-0.229423) | 0.428310 / 1.386936 (-0.958626) |\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) | 0.004986 / 0.011353 (-0.006367) | 0.003263 / 0.011008 (-0.007745) | 0.049076 / 0.038508 (0.010567) | 0.063665 / 0.023109 (0.040556) | 0.270352 / 0.275898 (-0.005546) | 0.298849 / 0.323480 (-0.024631) | 0.004083 / 0.007986 (-0.003903) | 0.002503 / 0.004328 (-0.001826) | 0.048586 / 0.004250 (0.044335) | 0.040701 / 0.037052 (0.003648) | 0.274082 / 0.258489 (0.015593) | 0.308279 / 0.293841 (0.014438) | 0.024734 / 0.128546 (-0.103812) | 0.007535 / 0.075646 (-0.068111) | 0.054670 / 0.419271 (-0.364602) | 0.032828 / 0.043533 (-0.010705) | 0.276226 / 0.255139 (0.021087) | 0.289322 / 0.283200 (0.006122) | 0.018789 / 0.141683 (-0.122893) | 1.279837 / 1.452155 (-0.172318) | 1.203010 / 1.492716 (-0.289706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095674 / 0.018006 (0.077667) | 0.309754 / 0.000490 (0.309265) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021733 / 0.037411 (-0.015678) | 0.074858 / 0.014526 (0.060332) | 0.081845 / 0.176557 (-0.094711) | 0.121991 / 0.737135 (-0.615145) | 0.084057 / 0.296338 (-0.212281) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298456 / 0.215209 (0.083246) | 2.884930 / 2.077655 (0.807276) | 1.574875 / 1.504120 (0.070755) | 1.451598 / 1.541195 (-0.089597) | 1.548106 / 1.468490 (0.079616) | 0.408662 / 4.584777 (-4.176115) | 2.444306 / 3.745712 (-1.301406) | 2.737027 / 5.269862 (-2.532835) | 1.633085 / 4.565676 (-2.932592) | 0.047349 / 0.424275 (-0.376926) | 0.004864 / 0.007607 (-0.002744) | 0.355434 / 0.226044 (0.129389) | 3.495531 / 2.268929 (1.226603) | 1.972737 / 55.444624 (-53.471888) | 1.706973 / 6.876477 (-5.169504) | 1.798985 / 2.142072 (-0.343087) | 0.490353 / 4.805227 (-4.314874) | 0.099533 / 6.500664 (-6.401131) | 0.042397 / 0.075469 (-0.033073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978092 / 1.841788 (-0.863696) | 13.166220 / 8.074308 (5.091912) | 11.673518 / 10.191392 (1.482126) | 0.134253 / 0.680424 (-0.546171) | 0.016478 / 0.534201 (-0.517723) | 0.271629 / 0.579283 (-0.307654) | 0.284082 / 0.434364 (-0.150282) | 0.313352 / 0.540337 (-0.226986) | 0.416913 / 1.386936 (-0.970023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e7144a02825fea3418872c51a8ca93950f3080 \"CML watermark\")\n"
] | 2023-10-27T15:54:18Z
| 2023-11-15T14:08:29Z
| 2023-11-15T14:02:02Z
|
CONTRIBUTOR
| null | 0
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Simplifies the existing filesystem logic (e.g., to avoid unnecessary if-else as mentioned in https://github.com/huggingface/datasets/pull/6098#issue-1827655071)
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| 1,294,405,251
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I_kwDODunzps5NJw6D
| 4,634
|
Can't load the Hausa audio dataset
|
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[
"Could you provide the error details. It is difficult to debug otherwise. Also try other config. `ha` is not a valid."
] | 2022-07-05T14:47:36Z
| 2022-09-13T14:07:32Z
| 2022-09-13T14:07:32Z
|
NONE
| null | null | null |
common_voice_train = load_dataset("common_voice", "ha", split="train+validation")
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I_kwDODunzps55kx7n
| 6,494
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Image Data loaded Twice
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[] | 2023-12-13T13:11:42Z
| 2023-12-13T13:11:42Z
| null |
NONE
| null | null | null |
### Describe the bug
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images
### Steps to reproduce the bug
from datasets import Dataset, load_dataset
dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])
print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))
### Expected behavior
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8
})
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
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I_kwDODunzps5tinBu
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|
Inaccurate Bounding Boxes in "wildreceipt" Dataset
|
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[
"Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it."
] | 2023-08-05T14:34:13Z
| 2023-08-17T14:25:27Z
| 2023-08-17T14:25:26Z
|
NONE
| null | null | null |
### Describe the bug
I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset.
To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face:
**Example 1:**
**Example 2:**
It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar).
This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated.
### Steps to reproduce the bug
```python
import matplotlib.pyplot as plt
from datasets import load_dataset
# Define functions to convert bounding box formats
def convert_format1(box):
x, y, w, h = box
x2, y2 = x + w, y + h
return [x, y, x2, y2]
def convert_format2(box):
x1, y1, x2, y2 = box
return [x1, y1, x2, y2]
def plot_cropped_image(image, box, title):
cropped_image = image.crop(box)
plt.imshow(cropped_image)
plt.title(title)
plt.axis('off')
plt.savefig(title+'.png')
plt.show()
doc_index = 1
word_index = 3
dataset = load_dataset("Theivaprakasham/wildreceipt")['train']
bbox_hugging_face = dataset[doc_index]['bboxes'][word_index]
text_unit_face = dataset[doc_index]['words'][word_index]
common_box_hugface_1 = convert_format1(bbox_hugging_face)
common_box_hugface_2 = convert_format2(bbox_hugging_face)
plot_cropped_image(image_hugging, common_box_hugface_1,
f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}')
plot_cropped_image(image_hugging, common_box_hugface_2,
f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}')
```
### Expected behavior
The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset.
### Environment info
- Python version: 3.8
- Hugging Face datasets version: 2.14.2
- Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar
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Add spider dataset
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| 2020-12-10T15:12:31Z
| 2020-12-10T15:12:31Z
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This PR adds the Spider dataset, a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students. The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.
Dataset website: https://yale-lily.github.io/spider
Paper link: https://www.aclweb.org/anthology/D18-1425/
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Add modules cache
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"All the tests pass on my side. Not sure if it is a cache issue or a pytest issue or a circleci issue.\r\nEDIT: I have the same error on google colab. Trying to fix that",
"I think I fixed it (sorry didn't notice you were on it as well)"
] | 2020-09-04T16:30:03Z
| 2020-09-22T10:27:08Z
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As discusses in #554 , we should use a module cache directory outside of the python packages directory since we may not have write permissions.
I added a new HF_MODULES_PATH directory that is added to the python path when doing `import nlp`.
In this directory, a module `nlp_modules` is created so that datasets can be added to `nlp_modules.datasets` and metrics to `nlp_modules.metrics`. `nlp_modules` doesn't exist on Pypi.
If someone using cloudpickle still wants to have the downloaded dataset/metrics scripts to be inside the nlp directory, it is still possible to change the environment variable HF_MODULES_CACHE to be a path inside the nlp lib.
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Created dataset card snli.md
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"Adding a direct link to the rendered markdown:\r\nhttps://github.com/mcmillanmajora/datasets/blob/add_dataset_documentation/datasets/snli/README.md\r\n",
"It would be amazing if we ended up with this much information on all of our datasets :) \r\n\r\nI don't think there's too much repetition, everything that is in here is relevant. The main challenge will be to figure out how to structure the sheet so that all of the information can be presented without overwhelming the reader. We'll also want to have as much of it as possible in structured form so it can be easily navigated.",
"@mcmillanmajora for now can you remove the prompts / quoted blocks so we can see what the datasheet would look like on its own?\r\n\r\nWould also love to hear if @sgugger has some first impressions",
"I removed the prompts. It's definitely a little easier to read without them!",
"Should we name the file `README.md` for consistency with models?",
"Asked @sleepinyourhat for some insights too :) ",
"Thank you for taking the time to look through the card and for all your comments @sleepinyourhat ! I've incorporated them in the latest update. ",
"Be careful to keep the ‘sa’ term in the license. It’s something we\ninherited from the Flickr captions.\n\nOn Thu, Oct 1, 2020 at 10:09 AM Julien Chaumond <notifications@github.com>\nwrote:\n\n> *@julien-c* commented on this pull request.\n> ------------------------------\n>\n> In datasets/snli/README.md\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23discussion-5Fr498273172&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=WbEkKXCbL6j5Ui3sox_WqvzrbShbJn2WW-51SENL2ZQ&e=>\n> :\n>\n> > +---\n> +language:\n> +- en\n> +task:\n> +- text-classification\n> +purpose:\n> +- NLI\n> +size:\n> +- \">100k\"\n> +language producers:\n> +- crowdsourced\n> +annotation:\n> +- crowdsourced\n> +tags:\n> +- extended-from-other-datasets\n> +license: \"CC BY-SA 4.0\"\n>\n> ⬇️ Suggested change\n>\n> -license: \"CC BY-SA 4.0\"\n> +license: cc-by-4.0\n>\n> For models (documented at\n> https://huggingface.co/docs#what-metadata-can-i-add-to-my-model-card\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__huggingface.co_docs-23what-2Dmetadata-2Dcan-2Di-2Dadd-2Dto-2Dmy-2Dmodel-2Dcard&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=ck3x8c_ujrwKReDTSGuWWgD9W6REHEPbZaO7S4GFRd4&e=>)\n> we use the License keywords listed by GitHub at\n> https://docs.github.com/en/free-pro-team@latest/github/creating-cloning-and-archiving-repositories/licensing-a-repository#searching-github-by-license-type\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.github.com_en_free-2Dpro-2Dteam-40latest_github_creating-2Dcloning-2Dand-2Darchiving-2Drepositories_licensing-2Da-2Drepository-23searching-2Dgithub-2Dby-2Dlicense-2Dtype&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=dWBP-ZvtMErD-egoBiBTCKA4500mjDXVSk03oW1g16U&e=>\n>\n> (Hopefully we'll plug some sort of form validation for users at some point)\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23pullrequestreview-2D500386385&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=HU2Hwi7HH9W2NtMoCIiQlhXxxEULLi8L9gnWU5PBAPY&e=>,\n> or unsubscribe\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AAJZSWL63W2LB7SBICA2GMTSISEPZANCNFSM4RWKAZRA&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=086__lKQLxTanHfjE8kOIpaJbaWPzBB9gGIt_prWeH8&e=>\n> .\n>\n",
"@sleepinyourhat You're right, wrong copy/paste",
"Question: Where does this standard come from? It looks similar to both\n'Data Statements' and 'Datasheets for Datasets', but it doesn't look quite\nlike either.\n\nOn Mon, Oct 12, 2020 at 4:27 PM Yacine Jernite <notifications@github.com>\nwrote:\n\n> Merged #663\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=s4_X-BSEnTKgGg9rPLBt3cyVptyMX_iWD5Ql3UMBi-I&e=>\n> into master.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23event-2D3868180429&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=elcM4umqReQfIrgHhpey9W_wPaq5QRgq7xNlubM47QI&e=>,\n> or unsubscribe\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AAJZSWJVGQRCR4OTTV27VTTSKNRBXANCNFSM4RWKAZRA&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=NB6nEROnTPgwNyF3ZklOmHnvP7kOkOm7sEa740KbVCs&e=>\n> .\n>\n",
"@sleepinyourhat The schema is definitely drawing from Data Statements and Datasheets for Datasets but we also wanted to include some more general information to introduce the dataset to new users. If you have any suggestions for changes to the schema itself, please let us know!"
] | 2020-09-22T22:29:37Z
| 2020-10-13T17:05:20Z
| 2020-10-12T20:26:52Z
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First draft of a dataset card using the SNLI corpus as an example.
This is mostly based on the [Google Doc draft](https://docs.google.com/document/d/1dKPGP-dA2W0QoTRGfqQ5eBp0CeSsTy7g2yM8RseHtos/edit), but I added a few sections and moved some things around.
- I moved **Who Was Involved** to follow **Language**, both because I thought the authors should be presented more towards the front and because I think it makes sense to present the speakers close to the language so it doesn't have to be repeated.
- I created a section I called **Data Characteristics** by pulling some things out of the other sections. I was thinking that this would be more about the language use in context of the specific task construction. That name isn't very descriptive though and could probably be improved.
-- Domain and language type out of **Language**. I particularly wanted to keep the Language section as simple and as abstracted from the task as possible.
-- 'How was the data collected' out of **Who Was Involved**
-- Normalization out of **Features/Dataset Structure**
-- I also added an annotation process section.
- I kept the **Features** section mostly the same as the Google Doc, but I renamed it **Dataset Structure** to more clearly separate it from the language use, and added some links to the documentation pages.
- I also kept **Tasks Supported**, **Known Limitations**, and **Licensing Information** mostly the same. Looking at it again though, maybe **Tasks Supported** should come before **Data Characteristics**?
The trickiest part about writing a dataset card for the SNLI corpus specifically is that it's built on datasets which are themselves built on datasets so I had to dig in a lot of places to find information. I think this will be easier with other datasets and once there is more uptake of dataset cards so they can just link to each other. (Maybe that needs to be an added section?)
I also made an effort not to repeat information across the sections or to refer to a previous section if the information was relevant in a later one. Is there too much repetition still?
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[Cached Path] Better error message
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IMO returning `None` in this function only leads to confusion and is never helpful.
|
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|
adding bprec
|
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[
"@lhoestq I think this is ready for review, I assume the errors (connection) are unrelated to the PR :) ",
"merging since the CI is fixed on master"
] | 2020-12-09T08:02:45Z
| 2020-12-16T17:04:44Z
| 2020-12-16T17:04:44Z
|
CONTRIBUTOR
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Brand-Product Relation Extraction Corpora in Polish
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MDExOlB1bGxSZXF1ZXN0NDk2MjM4NzIw
| 692
|
Update README.md
|
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[
"Hacktoberfest spam",
"To enhance its readability.....not Hacktoberfest spam",
"How is adding a punctuation to the end of a sentence justified as \"To enhance its readability\". \r\nConsidering that this is not your first \"README enhancement '' please don't spam the open source community with useless PR to get a free T-Shirt it just hurts the maintainers.\r\n\r\n//Joey",
"closed as spam"
] | 2020-10-01T12:57:22Z
| 2020-10-02T11:01:59Z
| 2020-10-02T11:01:59Z
|
NONE
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|
Better error message for uninitialized metric
|
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When calling `metric.compute()` without having called `metric.add` or `metric.add_batch` at least once, the error was quite cryptic. I added a better error message
Fix #729
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MDExOlB1bGxSZXF1ZXN0NDc1MzU3MjY2
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|
[logging] Add centralized logging - Bump-up cache loads to warnings
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Add a `nlp.logging` module to set the global logging level easily. The verbosity level also controls the tqdm bars (disabled when set higher than INFO).
You can use:
```
nlp.logging.set_verbosity(verbosity: int)
nlp.logging.set_verbosity_info()
nlp.logging.set_verbosity_warning()
nlp.logging.set_verbosity_debug()
nlp.logging.set_verbosity_error()
nlp.logging.get_verbosity() -> int
```
And use the levels:
```
nlp.logging.CRITICAL
nlp.logging.DEBUG
nlp.logging.ERROR
nlp.logging.FATAL
nlp.logging.INFO
nlp.logging.NOTSET
nlp.logging.WARN
nlp.logging.WARNING
```
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PR_kwDODunzps5Zxd5c
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|
Update README.md
|
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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) | 0.006757 / 0.011353 (-0.004596) | 0.004233 / 0.011008 (-0.006775) | 0.084123 / 0.038508 (0.045614) | 0.077513 / 0.023109 (0.054404) | 0.357024 / 0.275898 (0.081126) | 0.392956 / 0.323480 (0.069476) | 0.005408 / 0.007986 (-0.002577) | 0.003363 / 0.004328 (-0.000966) | 0.064395 / 0.004250 (0.060145) | 0.054711 / 0.037052 (0.017659) | 0.367287 / 0.258489 (0.108798) | 0.402934 / 0.293841 (0.109093) | 0.031845 / 0.128546 (-0.096701) | 0.008646 / 0.075646 (-0.067000) | 0.288740 / 0.419271 (-0.130531) | 0.053171 / 0.043533 (0.009638) | 0.360711 / 0.255139 (0.105572) | 0.388707 / 0.283200 (0.105507) | 0.025321 / 0.141683 (-0.116361) | 1.500684 / 1.452155 (0.048529) | 1.585747 / 1.492716 (0.093030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207329 / 0.018006 (0.189323) | 0.465304 / 0.000490 (0.464814) | 0.003229 / 0.000200 (0.003029) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028752 / 0.037411 (-0.008659) | 0.085327 / 0.014526 (0.070802) | 0.332210 / 0.176557 (0.155653) | 0.178779 / 0.737135 (-0.558356) | 0.097765 / 0.296338 (-0.198573) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403710 / 0.215209 (0.188501) | 4.027069 / 2.077655 (1.949414) | 2.053451 / 1.504120 (0.549331) | 1.906647 / 1.541195 (0.365452) | 1.992507 / 1.468490 (0.524017) | 0.490203 / 4.584777 (-4.094574) | 3.696569 / 3.745712 (-0.049143) | 3.319919 / 5.269862 (-1.949943) | 2.072794 / 4.565676 (-2.492883) | 0.057893 / 0.424275 (-0.366383) | 0.007723 / 0.007607 (0.000116) | 0.485400 / 0.226044 (0.259355) | 4.842891 / 2.268929 (2.573963) | 2.578949 / 55.444624 (-52.865675) | 2.229217 / 6.876477 (-4.647259) | 2.468017 / 2.142072 (0.325945) | 0.595236 / 4.805227 (-4.209992) | 0.135641 / 6.500664 (-6.365023) | 0.061232 / 0.075469 (-0.014237) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307059 / 1.841788 (-0.534729) | 20.108581 / 8.074308 (12.034273) | 14.438985 / 10.191392 (4.247593) | 0.168878 / 0.680424 (-0.511545) | 0.018208 / 0.534201 (-0.515993) | 0.395986 / 0.579283 (-0.183297) | 0.427440 / 0.434364 (-0.006924) | 0.459917 / 0.540337 (-0.080421) | 0.631379 / 1.386936 (-0.755557) |\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) | 0.007002 / 0.011353 (-0.004351) | 0.004120 / 0.011008 (-0.006888) | 0.064817 / 0.038508 (0.026309) | 0.081297 / 0.023109 (0.058188) | 0.405598 / 0.275898 (0.129700) | 0.442360 / 0.323480 (0.118880) | 0.005475 / 0.007986 (-0.002511) | 0.003483 / 0.004328 (-0.000845) | 0.064750 / 0.004250 (0.060499) | 0.058111 / 0.037052 (0.021059) | 0.410154 / 0.258489 (0.151665) | 0.445137 / 0.293841 (0.151296) | 0.033314 / 0.128546 (-0.095232) | 0.008747 / 0.075646 (-0.066899) | 0.071595 / 0.419271 (-0.347676) | 0.048894 / 0.043533 (0.005361) | 0.409162 / 0.255139 (0.154023) | 0.428877 / 0.283200 (0.145677) | 0.024127 / 0.141683 (-0.117556) | 1.521369 / 1.452155 (0.069214) | 1.573505 / 1.492716 (0.080789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233199 / 0.018006 (0.215193) | 0.455619 / 0.000490 (0.455129) | 0.003688 / 0.000200 (0.003488) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033186 / 0.037411 (-0.004225) | 0.100528 / 0.014526 (0.086003) | 0.105617 / 0.176557 (-0.070940) | 0.159437 / 0.737135 (-0.577698) | 0.108064 / 0.296338 (-0.188274) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435509 / 0.215209 (0.220300) | 4.339920 / 2.077655 (2.262265) | 2.368983 / 1.504120 (0.864863) | 2.211761 / 1.541195 (0.670566) | 2.301701 / 1.468490 (0.833211) | 0.495144 / 4.584777 (-4.089633) | 3.768882 / 3.745712 (0.023170) | 3.348940 / 5.269862 (-1.920922) | 2.081142 / 4.565676 (-2.484534) | 0.058184 / 0.424275 (-0.366091) | 0.007597 / 0.007607 (-0.000010) | 0.508806 / 0.226044 (0.282762) | 5.089226 / 2.268929 (2.820297) | 2.851930 / 55.444624 (-52.592694) | 2.512144 / 6.876477 (-4.364332) | 2.724461 / 2.142072 (0.582388) | 0.593446 / 4.805227 (-4.211781) | 0.134908 / 6.500664 (-6.365756) | 0.060811 / 0.075469 (-0.014658) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362279 / 1.841788 (-0.479508) | 20.548216 / 8.074308 (12.473908) | 15.179181 / 10.191392 (4.987789) | 0.170249 / 0.680424 (-0.510175) | 0.020772 / 0.534201 (-0.513429) | 0.398737 / 0.579283 (-0.180546) | 0.441487 / 0.434364 (0.007124) | 0.480096 / 0.540337 (-0.060242) | 0.645825 / 1.386936 (-0.741111) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6fb8b9a833afb25311da395c6e0d9bf770ca2c7 \"CML watermark\")\n"
] | 2023-09-07T11:33:20Z
| 2023-09-13T22:32:31Z
| 2023-09-13T22:23:42Z
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fixed a few typos
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Add Lahnda language tag
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-05T14:34:20Z
| 2022-05-10T12:10:04Z
| 2022-05-10T12:02:38Z
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This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset.
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Fix Windows paths in CommonLanguage dataset
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| 2021-10-04T09:07:58Z
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Minor fix in CommonLanguage dataset for Windows pathname component separator.
Related to #2989.
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YAML integer keys are not preserved Hub server-side
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[
"@huggingface/datasets if you agree, I can make the bulk edit on the Hub to fix integer keys into strings.",
"Ok for me, and we can merge (internal) https://github.com/huggingface/moon-landing/pull/4609",
"FYI there are still 2k+ weekly users on `datasets` 2.6.1 which doesn't support the string label format for class labels. And among those, some are using datasets with class labels like imdb (60 users), conllpp (40), msra_ner (40), peoples_daily_enr (40), weibo_ner (30), conll2003 (20), etc. And renaming to string would break these users code.",
"but isn't `datasets 2.6.1` downloading files from the Hub with the corresponding tag? I thought we had something like this before",
"We're using `main` as models do. Some datasets need to be updated from time to time, e.g. when a link to download the data is dead.\r\n\r\nBut yea a year ago we had those tags, we just ended up not using them",
"I opened https://github.com/huggingface/datasets/issues/5406 to communicate on this. Let me know what you think, and if it sounds good to you I can pin this issue",
"So, is it OK to make the bulk edit on the Hub now or should we wait longer? If the latter, how long?",
"I think we can do it. If you want to be extra cautious you can do it for all datasets except imdb and conllpp for now which are actively used by 2.6.1 users. For those two we can keep the YAML like this for some more time, or alternatively use the old dataset_infos.json file",
"The bulk edit of canonical datasets (except imdb and conllpp) is running. \r\n\r\nSee e.g.: https://huggingface.co/datasets/acronym_identification/discussions/3\r\n\r\nEDITED: \r\nDone, except for \"universal_morphologies\", where I get\r\n```\r\nHTTPError: 413 Client Error: Payload Too Large for url: https://huggingface.co/api/validate-yaml\r\n```\r\n\r\nAlso not done for the datasets missing matadata \"dataset_info\":\r\n- mc4: https://huggingface.co/datasets/mc4/discussions/3\r\n- the_pile: https://huggingface.co/datasets/the_pile/discussions/6\r\n- timit_asr: https://huggingface.co/datasets/timit_asr/discussions/1",
"Thank you !",
"@lhoestq, there are 6 community datasets with YAML integer keys in their `dataset_info` `class_label`:\r\n- indonlp/indonlu\r\n- rcds/swiss_judgment_prediction\r\n- Jean-Baptiste/wikiner_fr\r\n- Bingsu/Cat_and_Dog\r\n- taskydata/tasky_or_not\r\n- RCC-MSU/collection3\r\n\r\nMaybe we could open a PR on them as well?",
"Let's do this then:\r\n\r\n- [x] [indonlp/indonlu](https://huggingface.co/datasets/indonlp/indonlu/discussions/3)\r\n- [x] rcds/swiss_judgment_prediction\r\n- [x] Jean-Baptiste/wikiner_fr\r\n- [x] Bingsu/Cat_and_Dog -> merged\r\n- [x] taskydata/tasky_or_not (was already using quotes)\r\n- [x] RCC-MSU/collection3\r\n\r\nEDIT: all done :)",
"@lhoestq I was not asking you to do it, but asking if you agree me to do it... :man_facepalming: \r\nAs I self-assigned this issue... :sweat_smile: "
] | 2022-11-22T08:14:47Z
| 2023-01-26T10:52:35Z
| 2023-01-26T10:40:21Z
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After an internal discussion (https://github.com/huggingface/moon-landing/issues/4563):
- YAML integer keys are not preserved server-side: they are transformed to strings
- See for example this Hub PR: https://huggingface.co/datasets/acronym_identification/discussions/1/files
- Original:
```yaml
class_label:
names:
0: B-long
1: B-short
```
- Returned by the server:
```yaml
class_label:
names:
'0': B-long
'1': B-short
```
- They are planning to enforce only string keys
- Other projects already use interger-transformed-to string keys: e.g. `transformers` models `id2label`: https://huggingface.co/roberta-large-mnli/blob/main/config.json
```yaml
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
}
```
On the other hand, at `datasets` we are currently using YAML integer keys for `dataset_info` `class_label`.
Please note (thanks @lhoestq for pointing out) that previous versions (2.6 and 2.7) of `datasets` need being patched:
```python
In [18]: Features._from_yaml_list([{'dtype': {'class_label': {'names': {'0': 'neg', '1': 'pos'}}}, 'name': 'label'}])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-974f07eea526> in <module>
----> 1 Features._from_yaml_list(ry)
~/Desktop/hf/nlp/src/datasets/features/features.py in _from_yaml_list(cls, yaml_data)
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
1744
-> 1745 return cls.from_dict(from_yaml_inner(yaml_data))
1746
1747 def encode_example(self, example):
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in <dictcomp>(.0)
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
-> 1741 return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
1742 else:
1743 raise TypeError(f"Expected a dict or a list but got {type(obj)}: {obj}")
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1734 return {"_type": snakecase_to_camelcase(obj["dtype"])}
1735 else:
-> 1736 return from_yaml_inner(obj["dtype"])
1737 else:
1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
~/Desktop/hf/nlp/src/datasets/features/features.py in from_yaml_inner(obj)
1736 return from_yaml_inner(obj["dtype"])
1737 else:
-> 1738 return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
1739 elif isinstance(obj, list):
1740 names = [_feature.pop("name") for _feature in obj]
~/Desktop/hf/nlp/src/datasets/features/features.py in unsimplify(feature)
1704 if isinstance(feature.get("class_label"), dict) and isinstance(feature["class_label"].get("names"), dict):
1705 label_ids = sorted(feature["class_label"]["names"])
-> 1706 if label_ids and label_ids != list(range(label_ids[-1] + 1)):
1707 raise ValueError(
1708 f"ClassLabel expected a value for all label ids [0:{label_ids[-1] + 1}] but some ids are missing."
TypeError: can only concatenate str (not "int") to str
```
TODO:
- [x] Remove YAML integer keys from `dataset_info` metadata
- [x] Make a patch release for affected `datasets` versions: 2.6 and 2.7
- [x] Communicate on the fix
- [x] Wait for adoption
- [x] Bulk edit the Hub to fix this in all canonical datasets
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Allow setting the environment variable for the lock file path
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[] | 2023-05-17T07:10:02Z
| 2023-05-17T07:11:05Z
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NONE
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### Feature request
Add an environment variable to replace the default lock file path.
### Motivation
Usually, dataset path is a read-only path while the lock file needs to be modified each time. It would be convenient if the path can be reset individually.
### Your contribution
```/src/datasets/utils/filelock.py
class UnixFileLock(BaseFileLock):
def __init__(self, lock_file, timeout=-1, max_filename_length=None):
#-------------------
if os.getenv('DS_TMP_PATH'):
file_name = str(lock_file).split('/')[-1]
dataset_tmp_path = os.getenv('DS_TMP_PATH')
lock_file = os.path.join(dataset_tmp_path, file_name)
#-------------------
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
super().__init__(lock_file, timeout=timeout, max_filename_length=max_filename_length)
```
A simple demo is as upper. Thanks.
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Enable label alignment for token classification datasets
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"Hmm, not sure why the Windows tests are failing with:\r\n\r\n```\r\nDid not find path entry C:\\tools\\miniconda3\\bin\r\nC:\\tools\\miniconda3\\envs\\py37\\python.exe: No module named pytest\r\n```\r\n\r\nEdit: running the CI again fixed the problem 🙃 ",
"> One last nit and we can merge then\r\n\r\nThanks, done!"
] | 2022-05-04T07:15:16Z
| 2022-05-06T15:42:15Z
| 2022-05-06T15:36:31Z
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This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur
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Consider renaming to nld
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"I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.\r\n",
"Chiming in to second everything @honnibal said, and to add that I think the current name is going to impact the discoverability of this library. People who are looking for \"NLP Datasets\" through a search engine are going to see a library called `nlp` and think it's too broad. People who are looking to do NLP in python are going to search \"Python NLP\" and end up here, confused that this is a collection of datasets.\r\n\r\nThe names of the other huggingface libraries work because they're the only game in town: there are not very many robust, distinct libraries for `tokenizers` or `transformers` in python, for example. But there are several options for NLP in python, and adding this as a possible search result for \"python nlp\" when datasets are likely not what someone is searching for adds noise and frustrates potential users.",
"I'm also not sure whether the naming of `nlp` is the problem itself, as long as it comes with the appropriate identifier, so maybe something like `huggingface_nlp`? This is analogous to what @honnibal and spacy are doing for `spacy-transformers`. Of course, this is a \"step back\" from the recent changes/renaming of transformers, but may be some middle ground between a complete rebranding, and keeping it identifiable.",
"Interesting, thanks for sharing your thoughts.\r\n\r\nAs we’ll move toward a first non-beta release, we will pool the community of contributors/users of the library for their opinions on a good final name (like when we renamed the beautifully (?) named `pytorch-pretrained-bert`)\r\n\r\nIn the meantime, using `from nlp import load_dataset, load_metric` should work 😉",
"I feel like we are conflating two distinct subjects here:\r\n\r\n1. @honnibal's point is that using `nlp` as a package name might break existing code and bring developer usability issues in the future\r\n2. @pmbaumgartner's point is that the `nlp` package name is too broad and shouldn't be used by a package that exposes only datasets and metrics\r\n\r\n(let me know if I mischaracterize your point)\r\n\r\nI'll chime in to say that the first point is a bit silly IMO. As Python developers due to the limitations of the import system we already have to share:\r\n- a single flat namespace for packages\r\n- which also conflicts with local modules i.e. local files\r\n\r\nIf we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI also think all Python software developers/ML engineers/scientists are capable of at least a subset of:\r\n- importing only the methods that they need like @thomwolf suggested\r\n- aliasing their import\r\n- renaming a local variable",
"By the way, `nlp` will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nI see it as a laboratory for testing several long-term ideas about how we could do NLP in terms of research as well as open-source and community sharing, most of these ideas being too experimental/big to fit in `transformers`.\r\n\r\nSome of the directions we would like to explore are about sharing, traceability and more experimental models, as well as seeing a model as the community-based process of creating a composite entity from data, optimization, and code.\r\n\r\nWe'll see how these ideas end up being implemented and we'll better know how we should define the library when we start to dive into these topics. I'll try to get the `nlp` team to draft a roadmap on these topics at some point.",
"> If we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI'm sort of confused by your point here. The namespace *is* shared by variable names. You should not use local variables that are named the same as modules, because then you cannot use the module within the scope of your function.\r\n\r\nFor instance,\r\n\r\n```python\r\n\r\nimport nlp\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n```\r\n\r\nThis is a bug: you've just overwritten the module, so now you can't use it. Or instead:\r\n\r\n```python\r\n\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n# (Later, e.g. in a notebook)\r\nimport nlp\r\n```\r\n\r\nThis is also a bug: you've overwritten your variable with an import.\r\n\r\nIf you have a module named `nlp`, you should avoid using `nlp` as a variable, or you'll have bugs in some contexts and inconsistencies in other contexts. You'll have situations where you need to import differently in one module vs another, or name variables differently in one context vs another, which is bad.\r\n\r\n> importing only the methods that they need like @thomwolf suggested\r\n\r\nOkay but the same logic applies to naming the module *literally anything else*. There's absolutely no point in having a module name that's 3 letters if you always plan to do `import from`! It would be entirely better to name it `nlp_datasets` if you don't want people to do `import nlp`.\r\n\r\nAnd finally:\r\n\r\n> By the way, nlp will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nSo...it isn't a datasets library? https://twitter.com/Thom_Wolf/status/1261282491622731781\r\n\r\nI'm confused 😕 ",
"Dropping by as I noticed that the library has been renamed `datasets` so I wonder if the conversation above is settled (`nlp` not used anymore) :) ",
"I guess indeed",
"I'd argue that `datasets` is worse than `nlp`. Datasets should be a user specific decision and not encapsulate all of python (`pip install datasets`). If this package contained every dataset in the world (NLP / vision / etc) then it would make sense =/",
"I can't speak for the HF team @jramapuram, but as member of the community it looks to me that HF wanted to avoid the past path of changing names as scope broadened over time:\r\n\r\nRemember\r\nhttps://github.com/huggingface/pytorch-openai-transformer-lm\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT\r\nhttps://github.com/huggingface/pytorch-transformers\r\nand now\r\nhttps://github.com/huggingface/transformers\r\n\r\n;) \r\n\r\nJokes aside, seems that the library is growing in a multi-modal direction (https://github.com/huggingface/datasets/pull/363) so the current name is not that implausible. Possibly HF ambition is really to grow its community and bring here a large chunk of datasets of the world (including tabular / vision / audio?).",
"Yea I see your point. However, wouldn't scoping solve the entire problem? \r\n\r\n```python\r\nimport huggingface.datasets as D\r\nimport huggingface.transformers as T\r\n```\r\n\r\nCalling something `datasets` is akin to saying I'm going to name my package `python` --> `import python` ",
"Sorry to reply to an old thread, but the name issue really makes troubles recently in my project.\r\n\r\nI'd never known in advance there's a package called \"datasets\". My first thought is that such a general term may be safe to arbitrarily use. Avoiding such a common name because of its ambiguity is quite weird.\r\n\r\nAs we know in python it's not easy to differentiate system-wide and project-wide import like in C and C++.\r\n\r\nOn the contrary I fully understand the challenge to rename a popular library. So it seems to provide a \"huggingface\" wrapper library as suggested above by @jramapuram may be a happy medium for both developers and users.\r\n\r\nBest Regards."
] | 2020-05-15T20:23:27Z
| 2022-09-16T05:18:22Z
| 2020-09-28T00:08:10Z
|
NONE
| null | null | null |
Hey :)
Just making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.
The issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.
If you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.
If people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.
I don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.
I suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.
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JSON script
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[
"The tests for the Wikipedia dataset do not pass anymore with the error:\r\n```\r\nTo be able to use this dataset, you need to install the following dependencies ['mwparserfromhell'] using 'pip install mwparserfromhell' for instance'\r\n```",
"This was an issue on master. You can just rebase from master.",
"Perfect! Indeed, it worked^^ Thanks @lhoestq ",
"Currently the dummy_data tests are always green because in a PR the dataset is not yet synchronized with aws. This PR fixes this: https://github.com/huggingface/nlp/pull/140 . \r\n\r\nCould you test `json` locally or wait until the PR: https://github.com/huggingface/nlp/pull/140 is merged ? :-) ",
"Ok, I will wait #140 to be merged and then rebase :) "
] | 2020-05-12T07:11:22Z
| 2020-05-18T06:50:37Z
| 2020-05-18T06:50:36Z
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CONTRIBUTOR
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Add a JSONS script to read JSON datasets from files.
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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_5979). 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) | 0.008087 / 0.011353 (-0.003266) | 0.004691 / 0.011008 (-0.006317) | 0.121545 / 0.038508 (0.083037) | 0.057436 / 0.023109 (0.034326) | 0.368864 / 0.275898 (0.092966) | 0.457199 / 0.323480 (0.133719) | 0.006745 / 0.007986 (-0.001241) | 0.003689 / 0.004328 (-0.000640) | 0.090480 / 0.004250 (0.086229) | 0.071368 / 0.037052 (0.034316) | 0.372788 / 0.258489 (0.114299) | 0.429894 / 0.293841 (0.136053) | 0.037544 / 0.128546 (-0.091002) | 0.010142 / 0.075646 (-0.065505) | 0.420467 / 0.419271 (0.001196) | 0.064359 / 0.043533 (0.020826) | 0.370345 / 0.255139 (0.115206) | 0.405220 / 0.283200 (0.122020) | 0.028410 / 0.141683 (-0.113273) | 1.824845 / 1.452155 (0.372690) | 1.888109 / 1.492716 (0.395392) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234585 / 0.018006 (0.216578) | 0.499965 / 0.000490 (0.499476) | 0.000461 / 0.000200 (0.000261) | 0.000064 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032294 / 0.037411 (-0.005117) | 0.131769 / 0.014526 (0.117243) | 0.146472 / 0.176557 (-0.030085) | 0.210035 / 0.737135 (-0.527100) | 0.145600 / 0.296338 (-0.150739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507455 / 0.215209 (0.292246) | 5.080090 / 2.077655 (3.002435) | 2.506104 / 1.504120 (1.001984) | 2.297655 / 1.541195 (0.756460) | 2.324920 / 1.468490 (0.856430) | 0.645003 / 4.584777 (-3.939774) | 4.677856 / 3.745712 (0.932144) | 2.254179 / 5.269862 (-3.015683) | 1.280663 / 4.565676 (-3.285013) | 0.078809 / 0.424275 (-0.345466) | 0.014059 / 0.007607 (0.006452) | 0.628053 / 0.226044 (0.402009) | 6.327289 / 2.268929 (4.058360) | 2.957918 / 55.444624 (-52.486706) | 2.571568 / 6.876477 (-4.304909) | 2.708766 / 2.142072 (0.566694) | 0.772868 / 4.805227 (-4.032360) | 0.164835 / 6.500664 (-6.335829) | 0.075334 / 0.075469 (-0.000135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471930 / 1.841788 (-0.369858) | 17.917340 / 8.074308 (9.843032) | 15.719327 / 10.191392 (5.527935) | 0.191999 / 0.680424 (-0.488424) | 0.022464 / 0.534201 (-0.511737) | 0.511038 / 0.579283 (-0.068245) | 0.512050 / 0.434364 (0.077686) | 0.608711 / 0.540337 (0.068373) | 0.749660 / 1.386936 (-0.637276) |\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) | 0.008028 / 0.011353 (-0.003325) | 0.004908 / 0.011008 (-0.006100) | 0.092294 / 0.038508 (0.053786) | 0.053051 / 0.023109 (0.029942) | 0.453862 / 0.275898 (0.177964) | 0.512548 / 0.323480 (0.189068) | 0.004817 / 0.007986 (-0.003168) | 0.005330 / 0.004328 (0.001002) | 0.095600 / 0.004250 (0.091350) | 0.068763 / 0.037052 (0.031710) | 0.453654 / 0.258489 (0.195165) | 0.504995 / 0.293841 (0.211154) | 0.038123 / 0.128546 (-0.090423) | 0.010650 / 0.075646 (-0.064996) | 0.102854 / 0.419271 (-0.316417) | 0.062973 / 0.043533 (0.019440) | 0.430420 / 0.255139 (0.175281) | 0.465448 / 0.283200 (0.182248) | 0.029736 / 0.141683 (-0.111947) | 1.844225 / 1.452155 (0.392070) | 1.934685 / 1.492716 (0.441968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227797 / 0.018006 (0.209791) | 0.467868 / 0.000490 (0.467378) | 0.004531 / 0.000200 (0.004331) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035632 / 0.037411 (-0.001780) | 0.145943 / 0.014526 (0.131417) | 0.151944 / 0.176557 (-0.024613) | 0.220519 / 0.737135 (-0.516616) | 0.159732 / 0.296338 (-0.136606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520641 / 0.215209 (0.305432) | 5.184740 / 2.077655 (3.107086) | 2.538751 / 1.504120 (1.034631) | 2.316571 / 1.541195 (0.775377) | 2.387898 / 1.468490 (0.919408) | 0.614515 / 4.584777 (-3.970262) | 4.573142 / 3.745712 (0.827430) | 4.657052 / 5.269862 (-0.612809) | 2.159664 / 4.565676 (-2.406013) | 0.079713 / 0.424275 (-0.344562) | 0.014462 / 0.007607 (0.006855) | 0.656611 / 0.226044 (0.430566) | 6.481630 / 2.268929 (4.212702) | 3.135047 / 55.444624 (-52.309577) | 2.757502 / 6.876477 (-4.118975) | 2.851488 / 2.142072 (0.709415) | 0.790795 / 4.805227 (-4.014432) | 0.172358 / 6.500664 (-6.328306) | 0.080255 / 0.075469 (0.004786) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.571391 / 1.841788 (-0.270396) | 19.025224 / 8.074308 (10.950916) | 17.079230 / 10.191392 (6.887838) | 0.172823 / 0.680424 (-0.507601) | 0.021845 / 0.534201 (-0.512356) | 0.522286 / 0.579283 (-0.056998) | 0.510406 / 0.434364 (0.076042) | 0.604830 / 0.540337 (0.064493) | 0.735466 / 1.386936 (-0.651471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4084609bdc40d173d1daa74ad2fe98f3ead72f8e \"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) | 0.010025 / 0.011353 (-0.001328) | 0.005699 / 0.011008 (-0.005310) | 0.134194 / 0.038508 (0.095686) | 0.056154 / 0.023109 (0.033045) | 0.470091 / 0.275898 (0.194193) | 0.539225 / 0.323480 (0.215745) | 0.006659 / 0.007986 (-0.001326) | 0.004468 / 0.004328 (0.000140) | 0.110040 / 0.004250 (0.105790) | 0.074172 / 0.037052 (0.037119) | 0.497450 / 0.258489 (0.238961) | 0.535048 / 0.293841 (0.241207) | 0.051195 / 0.128546 (-0.077352) | 0.014926 / 0.075646 (-0.060721) | 0.461334 / 0.419271 (0.042062) | 0.073773 / 0.043533 (0.030240) | 0.450741 / 0.255139 (0.195602) | 0.474853 / 0.283200 (0.191653) | 0.036372 / 0.141683 (-0.105311) | 1.982873 / 1.452155 (0.530719) | 1.989912 / 1.492716 (0.497196) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287817 / 0.018006 (0.269811) | 0.613415 / 0.000490 (0.612926) | 0.007082 / 0.000200 (0.006882) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031119 / 0.037411 (-0.006292) | 0.129886 / 0.014526 (0.115361) | 0.143492 / 0.176557 (-0.033065) | 0.208536 / 0.737135 (-0.528600) | 0.147081 / 0.296338 (-0.149257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668312 / 0.215209 (0.453103) | 6.568609 / 2.077655 (4.490955) | 2.708788 / 1.504120 (1.204668) | 2.366737 / 1.541195 (0.825542) | 2.392598 / 1.468490 (0.924108) | 0.967582 / 4.584777 (-3.617195) | 5.582743 / 3.745712 (1.837031) | 3.021607 / 5.269862 (-2.248255) | 1.866402 / 4.565676 (-2.699275) | 0.115998 / 0.424275 (-0.308277) | 0.015571 / 0.007607 (0.007964) | 0.820069 / 0.226044 (0.594025) | 8.229725 / 2.268929 (5.960797) | 3.437068 / 55.444624 (-52.007557) | 2.902312 / 6.876477 (-3.974164) | 3.025874 / 2.142072 (0.883802) | 1.230359 / 4.805227 (-3.574868) | 0.237341 / 6.500664 (-6.263323) | 0.089923 / 0.075469 (0.014453) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670970 / 1.841788 (-0.170818) | 19.667167 / 8.074308 (11.592859) | 21.624423 / 10.191392 (11.433031) | 0.231683 / 0.680424 (-0.448741) | 0.029145 / 0.534201 (-0.505056) | 0.543441 / 0.579283 (-0.035842) | 0.617510 / 0.434364 (0.183146) | 0.612662 / 0.540337 (0.072324) | 0.790589 / 1.386936 (-0.596347) |\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) | 0.010324 / 0.011353 (-0.001029) | 0.005339 / 0.011008 (-0.005669) | 0.104762 / 0.038508 (0.066254) | 0.052631 / 0.023109 (0.029522) | 0.485864 / 0.275898 (0.209966) | 0.595768 / 0.323480 (0.272288) | 0.007417 / 0.007986 (-0.000569) | 0.005229 / 0.004328 (0.000900) | 0.100775 / 0.004250 (0.096524) | 0.067144 / 0.037052 (0.030092) | 0.522269 / 0.258489 (0.263780) | 0.592597 / 0.293841 (0.298756) | 0.051101 / 0.128546 (-0.077446) | 0.015277 / 0.075646 (-0.060369) | 0.115530 / 0.419271 (-0.303741) | 0.071922 / 0.043533 (0.028390) | 0.490208 / 0.255139 (0.235069) | 0.578936 / 0.283200 (0.295736) | 0.040382 / 0.141683 (-0.101301) | 1.986059 / 1.452155 (0.533904) | 2.040600 / 1.492716 (0.547883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300399 / 0.018006 (0.282393) | 0.624702 / 0.000490 (0.624212) | 0.004908 / 0.000200 (0.004708) | 0.000155 / 0.000054 (0.000100) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038031 / 0.037411 (0.000619) | 0.140353 / 0.014526 (0.125828) | 0.152600 / 0.176557 (-0.023956) | 0.219165 / 0.737135 (-0.517970) | 0.154232 / 0.296338 (-0.142106) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.698855 / 0.215209 (0.483646) | 7.125543 / 2.077655 (5.047889) | 3.251222 / 1.504120 (1.747102) | 2.953404 / 1.541195 (1.412209) | 3.051108 / 1.468490 (1.582618) | 0.962068 / 4.584777 (-3.622709) | 5.789579 / 3.745712 (2.043867) | 5.193271 / 5.269862 (-0.076591) | 2.757886 / 4.565676 (-1.807790) | 0.111865 / 0.424275 (-0.312410) | 0.014684 / 0.007607 (0.007077) | 0.875967 / 0.226044 (0.649923) | 8.818359 / 2.268929 (6.549430) | 4.165216 / 55.444624 (-51.279408) | 3.372059 / 6.876477 (-3.504418) | 3.486886 / 2.142072 (1.344813) | 1.232276 / 4.805227 (-3.572951) | 0.238967 / 6.500664 (-6.261697) | 0.091584 / 0.075469 (0.016115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.850755 / 1.841788 (0.008968) | 20.058756 / 8.074308 (11.984448) | 23.761271 / 10.191392 (13.569879) | 0.231826 / 0.680424 (-0.448598) | 0.030119 / 0.534201 (-0.504082) | 0.532614 / 0.579283 (-0.046669) | 0.628968 / 0.434364 (0.194604) | 0.628403 / 0.540337 (0.088066) | 0.745648 / 1.386936 (-0.641288) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8a797cc92e860c8d0df71e0aa826f4d2690713e \"CML watermark\")\n"
] | 2023-06-22T18:32:14Z
| 2023-06-22T18:42:22Z
| 2023-06-22T18:32:22Z
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An example or recommendations for creating large image datasets?
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[
"The beam utilities allow to prepare a dataset as parquet in your cloud storage. From my perspective this CLI is not super easy to use, but we've been working on a new python API to prepare a dataset in your cloud storage:\r\n```python\r\nfrom datasets import load_dataset_builder\r\n\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepapre(\"s3://my-bucket/c4\", file_format=\"parquet\")\r\n```\r\n\r\nAnd to use Beam you can do:\r\n```python\r\nbeam_runner = ... # one of \"SparkRunner\", \"DataFlowRunner\", \"DirectRunner\", etc.\r\nbeam_options = ...\r\n\r\nbuilder.download_and_prepapre(\r\n \"s3://my-bucket/c4\",\r\n file_format=\"parquet\",\r\n beam_runner=beam_runner,\r\n beam_options=beam_options\r\n)\r\n```\r\n\r\nThough Beam can be used ONLY if there is a dataset script based on the `BeamBasedBuilder` right now - it doesn't work on an arbitrary dataset (see [wikipedia.py](https://huggingface.co/datasets/wikipedia/blob/main/wikipedia.py) for example).",
"Thanks! \r\n\r\nWould be nice to have something similar for creating large image datasets. "
] | 2022-11-01T07:38:38Z
| 2022-11-02T10:17:11Z
| null |
MEMBER
| null | null | null |
I know that Apache Beam and `datasets` have [some connector utilities](https://huggingface.co/docs/datasets/beam). But it's a little unclear what we mean by "But if you want to run your own Beam pipeline with Dataflow, here is how:". What does that pipeline do?
As a user, I was wondering if we have this support for creating large image datasets. If so, we should mention that [here](https://huggingface.co/docs/datasets/image_dataset).
Cc @lhoestq
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Fix xtreme panx directory
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"great, I think I did not download the data the way you do, but yours is more reasonable."
] | 2020-07-20T10:09:17Z
| 2020-07-21T08:15:46Z
| 2020-07-21T08:15:44Z
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Fix #412
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I_kwDODunzps5PX8YT
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`with_format` behavior is inconsistent on different datasets
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"Hi! You can get a `torch.Tensor` if you do the following:\r\n```python\r\nraw = load_dataset(\"beans\", split=\"train\")\r\nraw = raw.select(range(100))\r\n\r\npreprocessor = AutoFeatureExtractor.from_pretrained(\"nateraw/vit-base-beans\")\r\n\r\nfrom datasets import Array3D\r\nfeatures = raw.features.copy()\r\nfeatures[\"pixel_values\"] = datasets.Array3D(shape=(3, 224, 224), dtype=\"float32\")\r\n\r\ndef preprocess_func(examples):\r\n imgs = [img.convert(\"RGB\") for img in examples[\"image\"]]\r\n return preprocessor(imgs)\r\n\r\ndata = raw.map(preprocess_func, batched=True, features=features)\r\n\r\nprint(type(data[0][\"pixel_values\"]))\r\n\r\ndata = data.with_format(\"torch\", columns=[\"pixel_values\"])\r\n\r\nprint(type(data[0][\"pixel_values\"]))\r\n```\r\n\r\nThe reason for this \"inconsistency\" in the default case is the way PyArrow infers the type of multi-dim arrays (in this case, the `pixel_values` column). If the type is not specified manually, PyArrow assumes it is a dynamic-length sequence (it needs to know the type before writing the first batch to a cache file, and it can't be sure the array is fixed ahead of time; `ArrayXD` is our way of telling that the dims are fixed), so it already fails to convert the corresponding array to NumPy properly (you get an array of `np.object` arrays). And `with_format(\"torch\")` replaces NumPy arrays with Torch tensors, so this bad formatting propagates."
] | 2022-08-08T10:41:34Z
| 2022-08-09T16:49:09Z
| null |
CONTRIBUTOR
| null | null | null |
## Describe the bug
I found a case where `with_format` does not transform the dataset to the requested format.
## Steps to reproduce the bug
Run:
```python
from transformers import AutoTokenizer, AutoFeatureExtractor
from datasets import load_dataset
raw = load_dataset("glue", "sst2", split="train")
raw = raw.select(range(100))
tokenizer = AutoTokenizer.from_pretrained("philschmid/tiny-bert-sst2-distilled")
def preprocess_func(examples):
return tokenizer(examples["sentence"], padding=True, max_length=256, truncation=True)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["input_ids"]))
data = data.with_format("torch", columns=["input_ids"])
print(type(data[0]["input_ids"]))
```
printing as expected:
```python
<class 'list'>
<class 'torch.Tensor'>
```
Then run:
```python
raw = load_dataset("beans", split="train")
raw = raw.select(range(100))
preprocessor = AutoFeatureExtractor.from_pretrained("nateraw/vit-base-beans")
def preprocess_func(examples):
imgs = [img.convert("RGB") for img in examples["image"]]
return preprocessor(imgs)
data = raw.map(preprocess_func, batched=True)
print(type(data[0]["pixel_values"]))
data = data.with_format("torch", columns=["pixel_values"])
print(type(data[0]["pixel_values"]))
```
Printing, unexpectedly
```python
<class 'list'>
<class 'list'>
```
## Expected results
`with_format` should transform into the requested format; it's not the case.
## Actual results
`type(data[0]["pixel_values"])` should be `torch.Tensor` in the example above
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: dev version, commit 44af3fafb527302282f6b6507b952de7435f0979
- Platform: Linux
- Python version: 3.9.12
- PyArrow version: 7.0.0
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Fix inconsistent caching behaviour in Dataset.map() with multiprocessing #3044
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[
"Hi ! Thanks for noticing this inconsistence and suggesting a fix :)\r\n\r\nIf I understand correctly you try to pass the same fingerprint to each processed shard of the dataset. This can be an issue since each shard is actually a different dataset with different data: they shouldn't have the same fingerprint.\r\n\r\nIdeally we want the result after `map` to have this fingerprint. The result after `map` is the concatenation of all the processed shards. In this case what we can do is add the `fingerprint` parameter to `concatenate_datasets` to overwrite the fingerprint here if needed:\r\nhttps://github.com/huggingface/datasets/blob/03b7f123cc17afc517c0aa2f912bbd90cb266185/src/datasets/arrow_dataset.py#L3588-L3590\r\n\r\nthen you can pass the fingerprint to `concatenate_datasets` here:\r\nhttps://github.com/huggingface/datasets/blob/03b7f123cc17afc517c0aa2f912bbd90cb266185/src/datasets/arrow_dataset.py#L2044-L2044",
"Hi @lhoestq, thanks for the pointers! Not having a unique fingerprint per shard was indeed was indeed a problem. \r\n\r\nLet me look into this. I'll be back with a fix soon.",
"Alright, to clarify about my problem. I using am using `datasets` with large datasets, and want to cache a heavy and non-deterministically fingerprintable function (using `datasets.fingerprint.Hasher`). Using `Dataset.map()` as it is would cause generating a random fingerprint. To circumvent this, I am generating custom deterministic fingerprints, which I pass as an argument to `Dataset.map()`. In that way, a deterministic fingerprint is set, and caching can be used. \r\n\r\nThis approach works well when using `num_proc==1`, but not so well when using `num_proc>1`. In both cases, `dataset._fingerprint` is effectively set to `new_fingerprint` at the end of the `.map()` call. However, caching is not used when `num_proc>1`, a non deterministically fingerprintable function and `new_fingerprint != null. The reason is that caching operates within `Dataset._map_single` and `new_fingerprint` is not passed here. \r\n\r\nThis pull request implements a quick fix (+unit test) by passing `new_fingerprint=f\"{new_fingerprint}-part{rank+1}-{num_proc}\"` to each `_map_single` call. Using a separate name for each call makes sure that each worker uses a different cache file (as you mentioned above).\r\n\r\nHowever, this solution still means that using a different value for `num_proc` will require computing new partial cache files. In the long run, performing the caching within `map()` instead of within `_map_single()` would be a cleaner solution.",
"Hi @vlievin,\r\n\r\nIf I understand your example correctly, you are trying to use the `new_fingerprint` param to have a deterministic fingerprint of the transform, which is not hashable due to randomness. Any particular reason why you are not using the `cache_file_name` param instead? I did run your example with the `cache_file_name` specified, and it behaves as expected based on the logs. Internally, `new_fingerprint` is needed to inject the calculated fingerprint into a method by the `fingerprint_transform` decorator, which is then used to compute the cache file name in `Dataset._get_cache_file_path` if the user hasn't specified one. ",
"Hi @lhoestq, I have cleaned up the unit test (incl. styling). It should be ready to merge as such. I am using this branch in my project and everything works fine. \r\n\r\nHi @mariosasko, the argument `new_fingerprint` allowed me to deterministically cache my transformation when using `num_proc=1`, so I assumed that was the right way to go. But maybe I have misinterpreted how `new_fingerprint` should be used.\r\n\r\nBut in any case, `map()` should perform consistently with regards to `num_proc`. In my opinion, the behaviour of `Dataset.map()` should perform the same, and this without requiring the user to input `cache_file_name` when `num_proc>1` is set.\r\nBut maybe there is a more elegant way to fix this using `cache_file_name` internally for each `_single_map()` call.\r\n\r\nSo, I think this is a more high level design decision and I will leave it to the maintainers :) ",
"Hi @vlievin,\r\n\r\nI appreciate your effort, but `new_fingerprint` behaves as described in the `Dataset.map` docs, and we don't have to follow some artificial consistency in regards to `num_proc`:\r\nhttps://github.com/huggingface/datasets/blob/adc5cec58dd15ee672016086fefdea34b3143e4f/src/datasets/arrow_dataset.py#L1962-L1963\r\n\r\nAdditionally, to compute the cache file name, you are using a private method (`dset._get_cache_file_path(new_fingerprint)`); prefixed with `_`), so this is a sign you may be doing something wrong because you are relying on the internals. I suggest you use cache_file_name instead and follow the suffix template docs, which explain how to compute file paths of the created cache files when `num_proc > 1`.",
"Hi @mariosasko, thanks for the pointer regarding the use of the private method in then unit tests. \r\n\r\nYes, `new_fingerprint` behaves as documented. If you don't think this is an issue, feel free to close this pull request. \r\n",
"Allowing the users to pass the fingerprint themselves for functions that can't be hashed would be a nice improvements. However I agree that as @mariosasko mentioned this is currently not how we want the API to behave for now - since it has to do with the internals of the library.\r\n\r\nThough we can discuss what could be the right way of doing it in https://github.com/huggingface/datasets/issues/3044 if you don't mind !"
] | 2021-10-08T10:59:21Z
| 2021-10-21T16:58:32Z
| 2021-10-21T14:22:44Z
|
NONE
| null | 0
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Fix #3044
1. A rough unit test that fails without the fix. It probably doesn't comply with your code standards, but that just to draft the idea.
2. A one liner fix
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imdb dataset cannot be loaded
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[
"It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n```\r\nto make sure it's not a corrupted file issue ?",
"I was using version 1.1.2 and this resolved with version 1.1.3, thanks. ",
"Hello,\r\nI have the same pb with 1.8.0",
"Hi ! I just tried in 1.8.0 and it worked fine. Can you try again ? Maybe the dataset host had some issues that are fixed now",
"Hello,\r\nIt works fine now :) !\r\nThanks !"
] | 2020-11-22T08:24:43Z
| 2021-11-26T11:07:16Z
| 2020-12-24T17:38:47Z
|
CONTRIBUTOR
| null | null | null |
Hi
I am trying to load the imdb train dataset
`dataset = datasets.load_dataset("imdb", split="train")`
getting following errors, thanks for your help
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
>>> dataset = datasets.load_dataset("imdb", split="train")
```
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Added NewsPH Raw Dataset
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"looks like this PR has changes to many files other than the ones for `NewsPH`\r\n\r\nCan you create another branch and another PR please ?"
] | 2020-12-07T15:17:53Z
| 2020-12-08T16:27:15Z
| 2020-12-08T16:27:15Z
|
NONE
| null | 0
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Added the raw version of the NewsPH dataset, which was used to automatically generate the NewsPH-NLI corpus. Dataset of news articles in Filipino from mainstream Philippine news sites on the internet. Can be used as a language modeling dataset or to reproduce the NewsPH-NLI dataset.
Paper: https://arxiv.org/abs/2010.11574
Repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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MDU6SXNzdWU2NTMyNTA2MTE=
| 353
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[Dataset requests] New datasets for Text Classification
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[
"Pinging @mariamabarham as well",
"- `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification dataset",
"Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n",
"This is the homepage for the Amazon dataset: https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!",
"Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq",
"Pretty sure the quora dataset is the same one I implemented here: https://github.com/huggingface/nlp/pull/366",
"Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https://arxiv.org/abs/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos/Neg/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https://arxiv.org/abs/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer ",
"On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps://jmcauley.ucsd.edu/data/amazon/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer "
] | 2020-07-08T12:17:58Z
| 2022-08-04T12:08:47Z
| null |
MEMBER
| null | null | null |
We are missing a few datasets for Text Classification which is an important field.
Namely, it would be really nice to add:
- [x] TREC-6 dataset (see here for instance: https://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html#torchnlp.datasets.trec_dataset) **[done]**
- #386
- [x] Yelp-5
- #1315
- [x] Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**
- [x] SST (Stanford Sentiment Treebank) **[include in glue]**
- #1934
- [ ] Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**
- [x] Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification
- #791
- #1389
- [x] 20 Newsgroups. The 20 Newsgroups dataset **[done]**
- #410
- [x] Sogou News dataset **[done]**
- #450
- [x] Reuters news. The Reuters-21578 dataset [165] **[done]**
- #471
- [x] DBpedia. The DBpedia dataset [170]
- #1116
- [ ] Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database
- [ ] EUR-Lex. The EUR-Lex dataset
- [x] WOS. The Web Of Science (WOS) dataset **[done]**
- #424
- [ ] PubMed. PubMed [173]
- [x] TREC-QA: TREC-6 + TREC-50
- See above: TREC-6 dataset
- [x] Quora. The Quora dataset [180]
- #366
All these datasets are cited in https://arxiv.org/abs/2004.03705
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MDExOlB1bGxSZXF1ZXN0NTMzMDk0NTIx
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Add KorQuAD v1 Dataset
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[] | 2020-12-05T21:25:46Z
| 2020-12-07T13:41:37Z
| 2020-12-07T13:41:37Z
|
CONTRIBUTOR
| null | 0
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# The Korean Question Answering Dataset
Adding the [KorQuAD](https://korquad.github.io/KorQuad%201.0/) v1 dataset as part of the sprint 🎉
This dataset is very similar to SQuAD which is why I added it as `squad_kor_v1`. There is also a v2 which I added [here](https://github.com/huggingface/datasets/pull/1180).
- Crowd generated questions and answer (1-answer per question) for Wikipedia articles.
- [x] All tests passed
- [x] Added dummy data
- [x] Added data card (as much as I could)
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| 405
|
Make select() faster by batching reads
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[] | 2020-07-16T21:19:45Z
| 2020-07-17T17:05:44Z
| 2020-07-17T16:51:26Z
|
CONTRIBUTOR
| null | 0
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Here's a benchmark:
```
dataset = nlp.load_dataset('bookcorpus', split='train')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1, load_from_cache_file=False)
end = time.time()
print(f'{end - start}')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1000, load_from_cache_file=False)
end = time.time()
print(f'{end - start}')
```
Without batching, select takes around 1.27 seconds. With batching, it takes around 0.01 seconds. The slowness was upsetting me because dataset.shuffle() was supposed to take ~27 hours for bookcorpus. Now with the fix it takes ~2.5 hours (which still is pretty slow, but I'll open a separate issue for that).
|
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I_kwDODunzps5MxcTB
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Issue with jalFaizy/detect_chess_pieces when running datasets-cli test
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[
"Hi @faizankshaikh\r\n\r\nPlease note that we have recently launched the Community feature, specifically targeted to create Discussions (about issues/questions/asking-for-help) on each Dataset on the Hub:\r\n- Blog post: https://huggingface.co/blog/community-update\r\n- Docs: https://huggingface.co/docs/hub/repositories-pull-requests-discussions\r\n\r\nThe Discussion tab for your \"jalFaizy/detect_chess_pieces\" dataset is here: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions\r\nYou can use it to ask for help by pinging the Datasets maintainers: see our docs here: https://huggingface.co/docs/datasets/master/en/share#ask-for-a-help-and-reviews\r\n\r\nI'm transferring this discussion to your Discussion tab and trying to address it: https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/discussions/1",
"Thank you @albertvillanova , I will keep that in mind.\r\n\r\nJust a quick note - I posted the issue on Github because the dataset viewer suggested me to \"open an issue for direct support\". Maybe it can be updated with your suggestion\r\n\r\n\r\n\r\n\r\n",
"Thank you pointing this out: yes, definitely, we should fix the error message. We are working on this."
] | 2022-06-29T00:15:54Z
| 2022-06-29T10:30:03Z
| 2022-06-29T07:49:27Z
|
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/jalFaizy/detect_chess_pieces
### Description
I am trying to write a appropriate data loader for [a custom dataset](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces) using [this script](https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/blob/main/detect_chess_pieces.py)
When I run the command
`$ datasets-cli test "D:\workspace\HF\detect_chess_pieces" --save_infos --all_configs`
It gives the following error
```
Using custom data configuration default
Traceback (most recent call last):
File "c:\users\faiza\anaconda3\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "c:\users\faiza\anaconda3\lib\runpy.py", line 87, in _run_code
exec(code, run_globals)
File "C:\Users\faiza\anaconda3\Scripts\datasets-cli.exe\__main__.py", line 7, in <module>
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\datasets_cli.py", line 39, in main
service.run()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 132, in run
for j, builder in enumerate(get_builders()):
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\commands\test.py", line 125, in get_builders
yield builder_cls(
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 1148, in __init__
super().__init__(*args, **kwargs)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 306, in __init__
info = self.get_exported_dataset_info()
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 405, in get_exported_dataset_info
return self.get_all_exported_dataset_infos().get(self.config.name, DatasetInfo())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\builder.py", line 390, in get_all_exported_dataset_infos
return DatasetInfosDict.from_directory(cls.get_imported_module_dir())
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 309, in from_directory
dataset_infos_dict = {
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 310, in <dictcomp>
config_name: DatasetInfo.from_dict(dataset_info_dict)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 272, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 160, in __post_init__
templates = [
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\info.py", line 161, in <listcomp>
template if isinstance(template, TaskTemplate) else task_template_from_dict(template)
File "c:\users\faiza\anaconda3\lib\site-packages\datasets\tasks\__init__.py", line 43, in task_template_from_dict
return template.from_dict(task_template_dict)
AttributeError: 'NoneType' object has no attribute 'from_dict'
```
My assumption is that there is some kind of issue in how the "task_templates" are read, because even if I keep them as None, or not include the argument at all, the same error occurs
### Owner
Yes
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PR_kwDODunzps45E1Qb
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Add missing kwargs to docstrings
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remove int documentation from logging docs
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| 2022-05-23T15:16:55Z
| 2022-05-23T15:08:32Z
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Removes the `int` documentation from the [logging section](https://huggingface.co/docs/datasets/package_reference/logging_methods#levels) of the docs.
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MDExOlB1bGxSZXF1ZXN0NjYzODUyODU1
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|
Rename config and environment variable for in memory max size
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[
"Thank you for the rename, @albertvillanova!"
] | 2021-06-07T19:21:08Z
| 2021-06-07T20:43:46Z
| 2021-06-07T20:43:46Z
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As discussed in #2409, both config and environment variable have been renamed.
cc: @stas00, huggingface/transformers#12056
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I_kwDODunzps5mDW56
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Behaviour difference between datasets.map and IterableDatasets.map
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"PS - some work is definitely needed for 'special cases' docs, not explanations, just usages of 'functions' under mixture of special cases, like a combination of custom databuilder + iterable dataset for large size + dynamic .map() application."
] | 2023-05-16T14:32:57Z
| 2023-05-16T14:36:05Z
| null |
NONE
| null | null | null |
### Describe the bug
All the examples in all the docs mentioned throughout huggingface datasets correspond to datasets object, and not IterableDatasets object. At one point of time, they might have been in sync, but the code for datasets version >=2.9.0 is very different as compared to the docs.
I basically need to .map() a transform on images in an iterable dataset, which was made using a custom databuilder config.
This works very good in map-styles datasets, but the .map() fails in IterableDatasets, show behvaiour as such:
"pixel_values" key not found, KeyError in examples object/dict passed into transform function for map, which works fine with map style, even as batch.
In iterable style, the object/dict passed into map() paramter callable function is completely different as what is mentioned in all examples.
Please look into this. Thank you
My databuilder class is inherited as such:
def _info(self):
print ("Config: ",self.config.__dict__.keys())
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"labels": datasets.Sequence(datasets.Value("uint16")),
# "labels_name": datasets.Value("string"),
# "pixel_values": datasets.Array3D(shape=(3, 1280, 960), dtype="float32"),
"pixel_values": datasets.Array3D(shape=(1280, 960, 3), dtype="uint8"),
"image_s3_path": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="none",
citation="",
)
def _split_generators(self, dl_manager):
records_train = list(db.mini_set.find({'split':'train'},{'image_s3_path':1, 'ocwen_template_name':1}))[:10000]
records_val = list(db.mini_set.find({'split':'val'},{'image_s3_path':1, 'ocwen_template_name':1}))[:1000]
# print (len(records),self.config.num_shards)
# shard_size_train = len(records_train)//self.config.num_shards
# sharded_records_train = [records_train[i:i+shard_size_train] for i in range(0,len(records_train),shard_size_train)]
# shard_size_val = len(records_val)//self.config.num_shards
# sharded_records_val = [records_val[i:i+shard_size_val] for i in range(0,len(records_val),shard_size_val)]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"records":records_train} # passing list of records, for sharding to take over
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"records":records_val} # passing list of records, for sharding to take over
),
]
def _generate_examples(self, records):
# print ("Generating examples for [{}] shards".format(len(shards)))
# initiate_db_connection()
# records = list(db.mini_set.find({'split':split},{'image_s3_path':1, 'ocwen_template_name':1}))[:10]
id_ = 0
# for records in shards:
for i,rec in enumerate(records):
img_local_path = fetch_file(rec['image_s3_path'],self.config.buffer_dir)
# t = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.squeeze()
# print (t.shape, type(t),type(t[0][0][0]))
# sys.exit()
pvs = np.array(Image.open(img_local_path).resize((1280,960))) # image object is wxh, so resize as per that, numpy array of it is hxwxc, transposing to cxwxh
# pvs = self.config.processor(Image.open(img_local_path), random_padding=True, return_tensors="np").pixel_values.astype(np.float16).squeeze()
# print (type(pvs[0][0][0]))
lblids = self.config.processor.tokenizer('<s_class>'+rec['ocwen_template_name']+'</s_class>'+'</s>', add_special_tokens=False, padding=False, truncation=False, return_tensors="np")["input_ids"].squeeze(0) # take padding later, as per batch collating
# print (len(lblids),type(lblids[0]))
# print (type(pvs),pvs.shape,type(pvs[0][0][0]), type(lblids))
yield id_, {"labels":lblids,"pixel_values":pvs,"image_s3_path":rec['image_s3_path']}
id_+=1
os.remove(img_local_path)
and I load it inside my trainer script as such
`ds = load_dataset("/tmp/DonutDS/dataset/", split="train", streaming=True) # iterable dataset, where .map() falls`
or also as
`ds = load_from_disk('/tmp/DonutDS/dataset/') #map style dataset`
Thank you to the team for having such a great library, and for this bug fix in advance!
### Steps to reproduce the bug
Above config can allow one to reproduce the said bug
### Expected behavior
.map() should show some consistency b/w map-style and iterable-style datasets, or atleast the docs should address iterable-style datasets behaviour and examples. I honestly do not figure the use of such docs.
### Environment info
datasets==2.9.0
transformers==4.26.0
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Temporarily pin fsspec < 2023.9.0
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[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==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) | 0.006494 / 0.011353 (-0.004859) | 0.003896 / 0.011008 (-0.007112) | 0.083940 / 0.038508 (0.045432) | 0.068335 / 0.023109 (0.045225) | 0.365770 / 0.275898 (0.089872) | 0.403702 / 0.323480 (0.080222) | 0.004005 / 0.007986 (-0.003981) | 0.003276 / 0.004328 (-0.001052) | 0.064877 / 0.004250 (0.060626) | 0.053524 / 0.037052 (0.016472) | 0.372951 / 0.258489 (0.114462) | 0.420935 / 0.293841 (0.127094) | 0.030656 / 0.128546 (-0.097890) | 0.009048 / 0.075646 (-0.066599) | 0.287607 / 0.419271 (-0.131665) | 0.052042 / 0.043533 (0.008509) | 0.371446 / 0.255139 (0.116307) | 0.408781 / 0.283200 (0.125581) | 0.024228 / 0.141683 (-0.117455) | 1.483325 / 1.452155 (0.031170) | 1.544321 / 1.492716 (0.051605) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212355 / 0.018006 (0.194349) | 0.463298 / 0.000490 (0.462808) | 0.005170 / 0.000200 (0.004970) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027824 / 0.037411 (-0.009587) | 0.081880 / 0.014526 (0.067354) | 0.094886 / 0.176557 (-0.081670) | 0.150024 / 0.737135 (-0.587111) | 0.096643 / 0.296338 (-0.199696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388521 / 0.215209 (0.173312) | 3.877251 / 2.077655 (1.799596) | 1.931085 / 1.504120 (0.426965) | 1.766525 / 1.541195 (0.225330) | 1.814802 / 1.468490 (0.346312) | 0.489478 / 4.584777 (-4.095299) | 3.570973 / 3.745712 (-0.174739) | 3.190211 / 5.269862 (-2.079651) | 2.015670 / 4.565676 (-2.550006) | 0.057773 / 0.424275 (-0.366503) | 0.007611 / 0.007607 (0.000004) | 0.462162 / 0.226044 (0.236117) | 4.616173 / 2.268929 (2.347244) | 2.360531 / 55.444624 (-53.084094) | 2.053680 / 6.876477 (-4.822797) | 2.228057 / 2.142072 (0.085985) | 0.584921 / 4.805227 (-4.220306) | 0.132470 / 6.500664 (-6.368194) | 0.060482 / 0.075469 (-0.014987) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263393 / 1.841788 (-0.578394) | 19.416841 / 8.074308 (11.342532) | 14.049032 / 10.191392 (3.857640) | 0.162822 / 0.680424 (-0.517602) | 0.018189 / 0.534201 (-0.516012) | 0.391142 / 0.579283 (-0.188141) | 0.409367 / 0.434364 (-0.024997) | 0.454589 / 0.540337 (-0.085748) | 0.632946 / 1.386936 (-0.753990) |\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) | 0.006568 / 0.011353 (-0.004785) | 0.004026 / 0.011008 (-0.006982) | 0.064522 / 0.038508 (0.026014) | 0.071738 / 0.023109 (0.048629) | 0.395771 / 0.275898 (0.119873) | 0.421553 / 0.323480 (0.098073) | 0.005291 / 0.007986 (-0.002694) | 0.003266 / 0.004328 (-0.001063) | 0.064464 / 0.004250 (0.060214) | 0.054622 / 0.037052 (0.017569) | 0.395010 / 0.258489 (0.136521) | 0.433895 / 0.293841 (0.140054) | 0.031670 / 0.128546 (-0.096876) | 0.008536 / 0.075646 (-0.067111) | 0.071059 / 0.419271 (-0.348212) | 0.047117 / 0.043533 (0.003584) | 0.391210 / 0.255139 (0.136071) | 0.411685 / 0.283200 (0.128486) | 0.022779 / 0.141683 (-0.118904) | 1.479900 / 1.452155 (0.027746) | 1.551853 / 1.492716 (0.059137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332814 / 0.018006 (0.314807) | 0.460654 / 0.000490 (0.460164) | 0.062257 / 0.000200 (0.062057) | 0.000374 / 0.000054 (0.000319) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005610) | 0.090730 / 0.014526 (0.076204) | 0.102955 / 0.176557 (-0.073602) | 0.155928 / 0.737135 (-0.581207) | 0.103028 / 0.296338 (-0.193310) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434460 / 0.215209 (0.219251) | 4.331550 / 2.077655 (2.253895) | 2.335990 / 1.504120 (0.831870) | 2.183985 / 1.541195 (0.642790) | 2.233086 / 1.468490 (0.764595) | 0.488484 / 4.584777 (-4.096293) | 3.603856 / 3.745712 (-0.141856) | 3.229833 / 5.269862 (-2.040029) | 2.007366 / 4.565676 (-2.558311) | 0.057658 / 0.424275 (-0.366617) | 0.007339 / 0.007607 (-0.000268) | 0.512812 / 0.226044 (0.286768) | 5.141497 / 2.268929 (2.872569) | 2.847383 / 55.444624 (-52.597241) | 2.467010 / 6.876477 (-4.409467) | 2.644995 / 2.142072 (0.502923) | 0.581385 / 4.805227 (-4.223842) | 0.130755 / 6.500664 (-6.369909) | 0.058834 / 0.075469 (-0.016635) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350162 / 1.841788 (-0.491626) | 19.768412 / 8.074308 (11.694104) | 15.079196 / 10.191392 (4.887804) | 0.167083 / 0.680424 (-0.513341) | 0.020372 / 0.534201 (-0.513829) | 0.402685 / 0.579283 (-0.176598) | 0.408338 / 0.434364 (-0.026026) | 0.476788 / 0.540337 (-0.063550) | 0.654765 / 1.386936 (-0.732171) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ff803c7e9f256c5a137c25c090e18d844f9fc6e4 \"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) | 0.008047 / 0.011353 (-0.003305) | 0.004662 / 0.011008 (-0.006346) | 0.102487 / 0.038508 (0.063978) | 0.096832 / 0.023109 (0.073723) | 0.375298 / 0.275898 (0.099400) | 0.420604 / 0.323480 (0.097124) | 0.004655 / 0.007986 (-0.003330) | 0.005699 / 0.004328 (0.001370) | 0.077681 / 0.004250 (0.073430) | 0.065987 / 0.037052 (0.028935) | 0.393146 / 0.258489 (0.134657) | 0.436324 / 0.293841 (0.142483) | 0.036168 / 0.128546 (-0.092378) | 0.010398 / 0.075646 (-0.065248) | 0.347579 / 0.419271 (-0.071693) | 0.061723 / 0.043533 (0.018190) | 0.377439 / 0.255139 (0.122300) | 0.416666 / 0.283200 (0.133467) | 0.031874 / 0.141683 (-0.109809) | 1.818885 / 1.452155 (0.366730) | 1.904749 / 1.492716 (0.412032) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240497 / 0.018006 (0.222491) | 0.507907 / 0.000490 (0.507417) | 0.004574 / 0.000200 (0.004374) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033504 / 0.037411 (-0.003907) | 0.102919 / 0.014526 (0.088393) | 0.113014 / 0.176557 (-0.063543) | 0.181111 / 0.737135 (-0.556024) | 0.115047 / 0.296338 (-0.181291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453640 / 0.215209 (0.238431) | 4.514604 / 2.077655 (2.436949) | 2.219758 / 1.504120 (0.715638) | 2.004735 / 1.541195 (0.463541) | 2.112817 / 1.468490 (0.644327) | 0.579534 / 4.584777 (-4.005243) | 4.095994 / 3.745712 (0.350282) | 3.887204 / 5.269862 (-1.382658) | 2.461755 / 4.565676 (-2.103921) | 0.068930 / 0.424275 (-0.355345) | 0.009102 / 0.007607 (0.001495) | 0.540031 / 0.226044 (0.313987) | 5.394324 / 2.268929 (3.125396) | 2.738906 / 55.444624 (-52.705719) | 2.332041 / 6.876477 (-4.544436) | 2.600764 / 2.142072 (0.458692) | 0.697859 / 4.805227 (-4.107368) | 0.159247 / 6.500664 (-6.341417) | 0.073339 / 0.075469 (-0.002130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561082 / 1.841788 (-0.280706) | 23.581031 / 8.074308 (15.506723) | 17.011085 / 10.191392 (6.819693) | 0.196115 / 0.680424 (-0.484308) | 0.022050 / 0.534201 (-0.512151) | 0.470865 / 0.579283 (-0.108418) | 0.480539 / 0.434364 (0.046175) | 0.546458 / 0.540337 (0.006120) | 0.744353 / 1.386936 (-0.642583) |\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) | 0.007884 / 0.011353 (-0.003468) | 0.004723 / 0.011008 (-0.006286) | 0.076431 / 0.038508 (0.037923) | 0.087016 / 0.023109 (0.063907) | 0.501880 / 0.275898 (0.225982) | 0.546286 / 0.323480 (0.222806) | 0.006224 / 0.007986 (-0.001762) | 0.003858 / 0.004328 (-0.000471) | 0.076485 / 0.004250 (0.072234) | 0.066758 / 0.037052 (0.029706) | 0.510090 / 0.258489 (0.251601) | 0.553935 / 0.293841 (0.260094) | 0.037785 / 0.128546 (-0.090761) | 0.009946 / 0.075646 (-0.065700) | 0.084001 / 0.419271 (-0.335270) | 0.056732 / 0.043533 (0.013199) | 0.490724 / 0.255139 (0.235585) | 0.528367 / 0.283200 (0.245168) | 0.026082 / 0.141683 (-0.115601) | 1.769200 / 1.452155 (0.317045) | 1.847559 / 1.492716 (0.354843) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306752 / 0.018006 (0.288745) | 0.481215 / 0.000490 (0.480725) | 0.048231 / 0.000200 (0.048031) | 0.000249 / 0.000054 (0.000194) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039517 / 0.037411 (0.002106) | 0.112884 / 0.014526 (0.098359) | 0.123858 / 0.176557 (-0.052698) | 0.188260 / 0.737135 (-0.548875) | 0.125819 / 0.296338 (-0.170520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515260 / 0.215209 (0.300051) | 5.125038 / 2.077655 (3.047383) | 2.785122 / 1.504120 (1.281003) | 2.590753 / 1.541195 (1.049558) | 2.682084 / 1.468490 (1.213594) | 0.581162 / 4.584777 (-4.003615) | 4.241776 / 3.745712 (0.496063) | 3.860979 / 5.269862 (-1.408883) | 2.434203 / 4.565676 (-2.131473) | 0.068580 / 0.424275 (-0.355695) | 0.008700 / 0.007607 (0.001093) | 0.604712 / 0.226044 (0.378667) | 6.044240 / 2.268929 (3.775311) | 3.379734 / 55.444624 (-52.064890) | 2.968906 / 6.876477 (-3.907571) | 3.195775 / 2.142072 (1.053703) | 0.702431 / 4.805227 (-4.102796) | 0.158752 / 6.500664 (-6.341912) | 0.072795 / 0.075469 (-0.002674) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.616354 / 1.841788 (-0.225434) | 24.258731 / 8.074308 (16.184423) | 17.505483 / 10.191392 (7.314091) | 0.173445 / 0.680424 (-0.506979) | 0.023215 / 0.534201 (-0.510986) | 0.472975 / 0.579283 (-0.106308) | 0.478425 / 0.434364 (0.044061) | 0.566950 / 0.540337 (0.026612) | 0.767648 / 1.386936 (-0.619288) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1d520a5226268f2c6f0303de3e8bfd72198b082 \"CML watermark\")\n"
] | 2023-09-04T07:07:07Z
| 2023-09-04T07:40:23Z
| 2023-09-04T07:30:00Z
|
MEMBER
| null | 0
|
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Temporarily pin fsspec < 2023.9.0 until permanent solution is found.
Hot fix #6209.
|
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https://api.github.com/repos/huggingface/datasets/issues/6210/timeline
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https://api.github.com/repos/huggingface/datasets/issues/3329
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https://github.com/huggingface/datasets/issues/3329
| 1,065,096,971
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I_kwDODunzps4_fBcL
| 3,329
|
Map function: Type error on iter #999
|
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[
"Hi, thanks for reporting.\r\n\r\nIt would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.",
"```\r\ndef text_numbers_to_int(text, column=\"\"):\r\n \"\"\"\r\n Convert text numbers to int.\r\n\r\n :param text: text numbers\r\n :return: int\r\n \"\"\"\r\n try:\r\n numbers = find_numbers(text)\r\n if not numbers:\r\n return text\r\n result = \"\"\r\n i, j = 0, 0\r\n while i < len(text):\r\n if j < len(numbers) and i == numbers[j][1]:\r\n n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])\r\n result += str(n)\r\n i = numbers[j][2] #end\r\n j += 1\r\n else:\r\n result += text[i]\r\n i += 1\r\n if column:\r\n return{column: result}\r\n else:\r\n return {column: result}\r\n except Exception as e:\r\n print(e)\r\n return {column: result}\r\n```",
"Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string",
"Yes that was it, good catch! Thanks"
] | 2021-11-27T17:53:05Z
| 2021-11-29T20:40:15Z
| 2021-11-29T20:40:15Z
|
NONE
| null | null | null |
## Describe the bug
Using the map function, it throws a type error on iter #999
Here is the code I am calling:
```
dataset = datasets.load_dataset('squad')
dataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})
```
text_numbers_to_int returns the input text with numbers replaced in the format {'context': text}
It happens at
`
File "C:\Users\lonek\anaconda3\envs\ai\Lib\site-packages\datasets\arrow_writer.py", line 289, in <listcomp>
[row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col
`
The issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)
Here is an example of what self.current_examples should be
({'context': 'Super Bowl 50 was an...merals 50.'}, '')
Here is an example of what self.current_examples are when it throws the error:
('The Panthers used th... Marriott.', '')
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I_kwDODunzps5IMohd
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Embed image and audio files in `save_to_disk`
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[] | 2022-04-21T16:25:18Z
| 2022-12-14T18:22:59Z
| 2022-12-14T18:22:59Z
|
MEMBER
| null | null | null |
Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files.
Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice:
- the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else
- users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset
- consistency with push_to_hub
This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files.
cc @mariosasko
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Update to simplify some datasets conversion
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[
"Awesome! ",
"Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob` into `glob.glob` (will need to add `import glob`)",
"> Also we should convert `tf.io.gfile.exists` into `os.path.exists` , `tf.io.gfile.listdir`into `os.listdir` and `tf.io.gfile.glob` into `glob.glob` (will need to add `import glob`)\r\n\r\nWe should probably open a new PR about this",
"I think it might be a good idea to both change the supervised keys to a named tuple and also handle the translation features specifically.",
"Just noticed that `pyarrow` apparently does not have a `is_boolean` function. Or do I have the wrong `pyarrow` version? ",
"Ah, it was a typo `pa.types.is_boolean` is the correct name. Will fix in: https://github.com/huggingface/nlp/pull/59"
] | 2020-05-07T22:02:24Z
| 2020-05-08T10:38:32Z
| 2020-05-08T10:18:24Z
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This PR updates the encoding of `Values` like `integers`, `boolean` and `float` to use python casting and avoid having to cast in the dataset scripts, as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r420176626
We could also change (not included in this PR yet):
- `supervized_keys` to make them a NamedTuple instead of a dataclass, and
- handle specifically the `Translation` features.
as mentioned here: https://github.com/huggingface/nlp/pull/37#discussion_r421740236
@patrickvonplaten @mariamabarham tell me if you want these two last changes as well.
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Stored XSS
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[
"Hi! You can contact `security@huggingface.co` to report this vulnerability."
] | 2023-03-26T20:55:58Z
| 2023-03-27T21:01:55Z
| 2023-03-27T21:01:55Z
|
NONE
| null | null | null |
### Describe the bug
I found a Stored XSS on a page that can be publicly accessible to all visitors. But I didn't find a suitable place to report.
Please guide me on this.
### Steps to reproduce the bug
Due to security restrictions, I don't want to publish it publicly.
### Expected behavior
User inputs must be sanitized before rendering.
### Environment info
https://huggingface.co/ Web UI
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Adds APPS dataset to the hub [WIP]
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"Thanks for your contribution, @arampacha. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help."
] | 2021-07-17T13:13:17Z
| 2022-10-03T09:38:10Z
| 2022-10-03T09:38:10Z
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NONE
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A loading script for [APPS dataset](https://github.com/hendrycks/apps)
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Fix lc_quad download checksum
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[
"hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`",
"I just re-ran the CI, hopefully it's fixed",
"_The documentation is not available anymore as the PR was closed or merged._",
"> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it",
"I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq Hi sorry to bother you, the CI check_code_quality failed and it said `would reformat /home/runner/work/datasets/datasets/src/datasets/download/streaming_download_manager.py` but I cant see any changes when I run `python3 -m black --check tests src benchmarks metrics` and `python3 -m ruff tests src benchmarks metrics` on my own computer, is there any version requirements on the tools? I didn't specific the version.",
"I just ran `make style` and pushed the changes.\r\nYou can install the right versions of black and ruff using `pip install -e .[quality]` ;)",
"I am working on this issue right now https://github.com/huggingface/datasets/issues/6017 which is strongly connected to your PR, and I might end up cherry-picking some of your commits (keeping attribution of course !). Would you be ok with that ?",
"it's totally ok for me, I just wish the S3 File system could support streaming too.\r\n",
"\r\nI already adjust the code and test on my local Mac, you can check it now, and you can make any changes to it.",
"Closing this PR in favor of https://github.com/huggingface/datasets/pull/6028 which includes your contribution :)"
] | 2023-06-01T05:52:32Z
| 2023-07-18T06:14:32Z
| 2023-07-17T17:02:00Z
|
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| null | 0
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to solve the issue in #5880
1. add s3 support in the link check step, previous we only check `http` and `https`,
2. change the parameter of `use_auth_token` to `download_config` to support both `storage_options` and `use_auth_token` parameter when trying to handle(list, open, read, etc,.) the remote files.
3. integrate the check part's duplicate code to make adding or deleting other sources easier.
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Fast start up
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Currently if optional dependencies such as tensorflow, torch, apache_beam, faiss and elasticsearch are installed, then it takes a long time to do `import datasets` since it imports all of these heavy dependencies.
To make a fast start up for `datasets` I changed that so that they are not imported when `datasets` is being imported. On my side it changed the import time of `datasets` from 5sec to 0.5sec, which is enjoyable.
To be able to check if optional dependencies are available without importing them I'm using `importlib_metadata`, which is part of the standard lib in python>=3.8 and was backported. The difference with `importlib` is that it also enables to get the versions of the libraries without importing them.
I added this dependency in `setup.py`.
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use_custom_baseline still produces errors for bertscore
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[
"Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)",
"This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ",
"Thanks for the heads up @pvl and for the PR as well :)",
"Hello everyone,\r\n\r\nI think the problem is not solved: \r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric=load_metric('bertscore')\r\nmetric.compute(\r\n predictions=predictions,\r\n references=references,\r\n lang='fr',\r\n rescale_with_baseline=True\r\n)\r\nTypeError: get_hash() missing 2 required positional arguments: 'use_custom_baseline' and 'use_fast_tokenizer'\r\n```\r\nThis code is produced using `Python 3.6.9 datasets==1.1.2 and bert_score==0.3.10`",
"Hi ! This has been fixed by https://github.com/huggingface/datasets/pull/2770, we'll do a new release soon to make the fix available :)\r\n\r\nIn the meantime please use an older version of `bert_score`"
] | 2020-11-12T11:44:32Z
| 2021-08-31T10:06:44Z
| 2021-02-09T14:21:48Z
|
NONE
| null | null | null |
`metric = load_metric('bertscore')`
`a1 = "random sentences"`
`b1 = "random sentences"`
`metric.compute(predictions = [a1], references = [b1], lang = 'en')`
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/stephen_chan/.cache/huggingface/modules/datasets_modules/metrics/bertscore/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363/bertscore.py", line 108, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`
Adding 'use_custom_baseline = False' as an argument produces this error
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
TypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`
This is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2
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OpenWebText: NonMatchingSplitsSizesError
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[
"Thanks for reporting, I'm updating the verifications metadata",
"I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~/.cache/huggingface/datasets`) and try again please ?\r\nAlso, on which platform are you (linux/macos/windows) ?",
"I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)",
"Hi @thomasw21 , are you still having this issue after clearing your cache ?",
"Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !"
] | 2021-08-26T13:50:26Z
| 2021-09-21T14:12:40Z
| 2021-09-21T14:09:43Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
When downloading `openwebtext`, I'm getting:
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]
```
I suspect that the file we download from has changed since the size doesn't look like to match with documentation
`Downloading: 0%| | 0.00/12.9G [00:00<?, ?B/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("openwebtext", download_mode="force_redownload")
```
## Expected results
Loading is successful
## Actual results
Loading throws above error.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: linux (Redhat version 8.1)
- Python version: 3.8
- PyArrow version: 4.0.1
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Fix docstring rendering of Dataset/DatasetDict.from_csv args
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Fix the docstring rendering of Dataset/DatasetDict.from_csv args.
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