Datasets:

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Francesco-A

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github-issues_huggingface-datasets

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)", "Have raised the PR [here](https://github.com/huggingface/datasets/pull/2717)", "Fixed by #2717." ]

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[ "@lhoestq note that the segfault also occurs on Linux.", "Created the ticket at\r\nhttps://issues.apache.org/jira/browse/ARROW-12568", "@lhoestq the ticket you mentioned is now in state resolved. Pyarrow supports AArch64 after version 4.0.0. Because of this restriction `datasets` is not installing in AArch64 systems." ]

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[ "I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n\r\n```", "Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `/home/XXX/.cache/lib/python3.7/site-packages/nlp/datasets/json/(...)/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.", "I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path/to/your/new/script', data_files={nlp.Split.TRAIN: \"./datasets/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.", "This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. ", "Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n", "Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```", "Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps://huggingface.co/nlp/add_dataset.html", "@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 558, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 498, in _getitem\r\n outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n File \"pyarrow/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n File \"pyarrow/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 766, in table_to_blockmanager\r\n blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 1101, in _table_to_blocks\r\n list(extension_columns.keys()))\r\n File \"pyarrow/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n File \"pyarrow/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n", "Very happy to see some extended documentation! ", "#376 seems to be reporting the same issue as mentioned above. ", "This issue helped me a lot, thanks.\r\nHope this issue will be fixed soon." ]

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[ "@lhoestq \r\nI address all the comments, I think. Thanks! \r\n", "The five test fails are unrelated to this PR and fixed on master so we can ignore them" ]

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[ "Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name.\r\n\r\nedit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the \"official\" list :)", "@lhoestq hmm - ok thanks for the answer.\r\nTo be honest I am not sure if this issue can be closed now.\r\nI just wanted to point out that this should either be documented or linked in the documentation.\r\nIf you feel like it is (will be) please just close this.", "We're still working on the validation+documentation in this.\r\nFeel free to keep this issue open till we've added them", "@lhoestq what is the status on this? Did you add documentation?", "Hi ! There's the tagging app at https://huggingface.co/datasets/tagging/ that you can use.\r\nIt shows the list of all the tags you can use.\r\n\r\nIt is based on all the tag sets defined in this folder:\r\nhttps://github.com/huggingface/datasets/tree/master/src/datasets/utils/resources", "@lhoestq is there something like this form Models?", "I don't think so. Feel free to take a look at the tags of other models (example [here](https://huggingface.co/bert-base-uncased/blob/main/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this", "When modifying a README file, the Hub now displays a special UI with allowed values (see https://huggingface.co/docs/datasets/main/en/upload_dataset#create-a-dataset-card)." ]

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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) | 0.007633 / 0.011353 (-0.003720) | 0.004666 / 0.011008 (-0.006343) | 0.097768 / 0.038508 (0.059260) | 0.085153 / 0.023109 (0.062044) | 0.400315 / 0.275898 (0.124417) | 0.452903 / 0.323480 (0.129423) | 0.006227 / 0.007986 (-0.001759) | 0.003814 / 0.004328 (-0.000515) | 0.074586 / 0.004250 (0.070336) | 0.064295 / 0.037052 (0.027242) | 0.408082 / 0.258489 (0.149593) | 0.446921 / 0.293841 (0.153080) | 0.034593 / 0.128546 (-0.093953) | 0.009191 / 0.075646 (-0.066456) | 0.337099 / 0.419271 (-0.082173) | 0.075320 / 0.043533 (0.031787) | 0.403488 / 0.255139 (0.148349) | 0.435309 / 0.283200 (0.152109) | 0.035675 / 0.141683 (-0.106008) | 1.732642 / 1.452155 (0.280487) | 1.770238 / 1.492716 (0.277522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235879 / 0.018006 (0.217873) | 0.500330 / 0.000490 (0.499841) | 0.005221 / 0.000200 (0.005021) | 0.000150 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032479 / 0.037411 (-0.004933) | 0.095873 / 0.014526 (0.081348) | 0.107118 / 0.176557 (-0.069438) | 0.173809 / 0.737135 (-0.563326) | 0.109832 / 0.296338 (-0.186507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444342 / 0.215209 (0.229133) | 4.459010 / 2.077655 (2.381355) | 2.209687 / 1.504120 (0.705567) | 2.007556 / 1.541195 (0.466362) | 2.113683 / 1.468490 (0.645193) | 0.544281 / 4.584777 (-4.040496) | 4.037151 / 3.745712 (0.291439) | 4.852644 / 5.269862 (-0.417217) | 3.134126 / 4.565676 (-1.431550) | 0.066815 / 0.424275 (-0.357460) | 0.008836 / 0.007607 (0.001229) | 0.560904 / 0.226044 (0.334859) | 5.302760 / 2.268929 (3.033832) | 2.750182 / 55.444624 (-52.694442) | 2.322595 / 6.876477 (-4.553882) | 2.547486 / 2.142072 (0.405414) | 0.665766 / 4.805227 (-4.139461) | 0.151613 / 6.500664 (-6.349051) | 0.071155 / 0.075469 (-0.004314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473717 / 1.841788 (-0.368071) | 22.584179 / 8.074308 (14.509871) | 15.888001 / 10.191392 (5.696609) | 0.181073 / 0.680424 (-0.499351) | 0.021395 / 0.534201 (-0.512806) | 0.452693 / 0.579283 (-0.126590) | 0.447709 / 0.434364 (0.013345) | 0.529599 / 0.540337 (-0.010738) | 0.699241 / 1.386936 (-0.687695) |\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) | 0.007917 / 0.011353 (-0.003436) | 0.004544 / 0.011008 (-0.006464) | 0.074566 / 0.038508 (0.036058) | 0.087530 / 0.023109 (0.064421) | 0.419753 / 0.275898 (0.143854) | 0.452352 / 0.323480 (0.128872) | 0.005882 / 0.007986 (-0.002104) | 0.003904 / 0.004328 (-0.000425) | 0.073539 / 0.004250 (0.069289) | 0.071320 / 0.037052 (0.034267) | 0.432899 / 0.258489 (0.174409) | 0.470365 / 0.293841 (0.176524) | 0.036198 / 0.128546 (-0.092348) | 0.009342 / 0.075646 (-0.066304) | 0.080970 / 0.419271 (-0.338301) | 0.058769 / 0.043533 (0.015236) | 0.413397 / 0.255139 (0.158258) | 0.448362 / 0.283200 (0.165162) | 0.034177 / 0.141683 (-0.107506) | 1.706217 / 1.452155 (0.254063) | 1.776743 / 1.492716 (0.284026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198779 / 0.018006 (0.180773) | 0.499862 / 0.000490 (0.499372) | 0.003891 / 0.000200 (0.003692) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034671 / 0.037411 (-0.002740) | 0.103165 / 0.014526 (0.088639) | 0.115813 / 0.176557 (-0.060744) | 0.177407 / 0.737135 (-0.559728) | 0.117733 / 0.296338 (-0.178606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476859 / 0.215209 (0.261650) | 4.823063 / 2.077655 (2.745409) | 2.524133 / 1.504120 (1.020013) | 2.374482 / 1.541195 (0.833288) | 2.518047 / 1.468490 (1.049557) | 0.559131 / 4.584777 (-4.025646) | 4.126213 / 3.745712 (0.380501) | 6.488570 / 5.269862 (1.218708) | 3.816540 / 4.565676 (-0.749137) | 0.064742 / 0.424275 (-0.359533) | 0.008476 / 0.007607 (0.000869) | 0.576432 / 0.226044 (0.350387) | 5.835133 / 2.268929 (3.566205) | 3.237833 / 55.444624 (-52.206791) | 2.726596 / 6.876477 (-4.149880) | 2.799212 / 2.142072 (0.657139) | 0.661628 / 4.805227 (-4.143599) | 0.153997 / 6.500664 (-6.346667) | 0.070621 / 0.075469 (-0.004848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648505 / 1.841788 (-0.193282) | 22.454019 / 8.074308 (14.379711) | 16.077098 / 10.191392 (5.885706) | 0.217875 / 0.680424 (-0.462549) | 0.021285 / 0.534201 (-0.512916) | 0.459837 / 0.579283 (-0.119446) | 0.476211 / 0.434364 (0.041847) | 0.525903 / 0.540337 (-0.014435) | 0.717224 / 1.386936 (-0.669712) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"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) | 0.008929 / 0.011353 (-0.002424) | 0.004188 / 0.011008 (-0.006820) | 0.097030 / 0.038508 (0.058522) | 0.071363 / 0.023109 (0.048254) | 0.333116 / 0.275898 (0.057218) | 0.371272 / 0.323480 (0.047792) | 0.006430 / 0.007986 (-0.001555) | 0.003689 / 0.004328 (-0.000639) | 0.068666 / 0.004250 (0.064416) | 0.057562 / 0.037052 (0.020510) | 0.347208 / 0.258489 (0.088719) | 0.390514 / 0.293841 (0.096673) | 0.050560 / 0.128546 (-0.077987) | 0.013372 / 0.075646 (-0.062275) | 0.311345 / 0.419271 (-0.107927) | 0.068990 / 0.043533 (0.025457) | 0.363026 / 0.255139 (0.107887) | 0.379793 / 0.283200 (0.096593) | 0.036891 / 0.141683 (-0.104792) | 1.583481 / 1.452155 (0.131327) | 1.688727 / 1.492716 (0.196011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209777 / 0.018006 (0.191771) | 0.507267 / 0.000490 (0.506777) | 0.003637 / 0.000200 (0.003438) | 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) | 0.029309 / 0.037411 (-0.008102) | 0.088386 / 0.014526 (0.073861) | 0.104974 / 0.176557 (-0.071582) | 0.171999 / 0.737135 (-0.565137) | 0.110797 / 0.296338 (-0.185542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543465 / 0.215209 (0.328256) | 5.361491 / 2.077655 (3.283836) | 2.348712 / 1.504120 (0.844592) | 2.012527 / 1.541195 (0.471332) | 2.069776 / 1.468490 (0.601286) | 0.874262 / 4.584777 (-3.710515) | 4.877317 / 3.745712 (1.131605) | 5.327459 / 5.269862 (0.057597) | 3.336823 / 4.565676 (-1.228854) | 0.100456 / 0.424275 (-0.323819) | 0.008503 / 0.007607 (0.000895) | 0.692009 / 0.226044 (0.465965) | 6.912731 / 2.268929 (4.643802) | 3.110548 / 55.444624 (-52.334076) | 2.443665 / 6.876477 (-4.432811) | 2.528713 / 2.142072 (0.386641) | 1.076358 / 4.805227 (-3.728869) | 0.220352 / 6.500664 (-6.280312) | 0.080293 / 0.075469 (0.004824) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538444 / 1.841788 (-0.303344) | 21.121221 / 8.074308 (13.046913) | 19.810609 / 10.191392 (9.619216) | 0.225406 / 0.680424 (-0.455018) | 0.026652 / 0.534201 (-0.507549) | 0.430372 / 0.579283 (-0.148911) | 0.510722 / 0.434364 (0.076358) | 0.514347 / 0.540337 (-0.025991) | 0.686050 / 1.386936 (-0.700886) |\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) | 0.007675 / 0.011353 (-0.003678) | 0.004542 / 0.011008 (-0.006466) | 0.069655 / 0.038508 (0.031147) | 0.069338 / 0.023109 (0.046229) | 0.436505 / 0.275898 (0.160607) | 0.481806 / 0.323480 (0.158326) | 0.005315 / 0.007986 (-0.002670) | 0.004455 / 0.004328 (0.000127) | 0.072674 / 0.004250 (0.068424) | 0.058088 / 0.037052 (0.021035) | 0.445825 / 0.258489 (0.187336) | 0.501706 / 0.293841 (0.207865) | 0.047123 / 0.128546 (-0.081424) | 0.012943 / 0.075646 (-0.062703) | 0.093491 / 0.419271 (-0.325780) | 0.060169 / 0.043533 (0.016637) | 0.436530 / 0.255139 (0.181391) | 0.466873 / 0.283200 (0.183674) | 0.040453 / 0.141683 (-0.101230) | 1.586438 / 1.452155 (0.134283) | 1.671081 / 1.492716 (0.178365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180607 / 0.018006 (0.162601) | 0.520145 / 0.000490 (0.519655) | 0.004824 / 0.000200 (0.004624) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029308 / 0.037411 (-0.008103) | 0.093652 / 0.014526 (0.079126) | 0.102332 / 0.176557 (-0.074224) | 0.162414 / 0.737135 (-0.574721) | 0.098017 / 0.296338 (-0.198321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583949 / 0.215209 (0.368740) | 6.035191 / 2.077655 (3.957536) | 2.801274 / 1.504120 (1.297155) | 2.566150 / 1.541195 (1.024955) | 2.437122 / 1.468490 (0.968632) | 0.865038 / 4.584777 (-3.719739) | 4.841727 / 3.745712 (1.096015) | 4.683919 / 5.269862 (-0.585943) | 2.941240 / 4.565676 (-1.624437) | 0.104888 / 0.424275 (-0.319387) | 0.007747 / 0.007607 (0.000140) | 0.780041 / 0.226044 (0.553997) | 7.771314 / 2.268929 (5.502385) | 3.680814 / 55.444624 (-51.763811) | 2.938472 / 6.876477 (-3.938004) | 2.981740 / 2.142072 (0.839668) | 1.065411 / 4.805227 (-3.739816) | 0.222265 / 6.500664 (-6.278399) | 0.082428 / 0.075469 (0.006959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.626774 / 1.841788 (-0.215014) | 21.618284 / 8.074308 (13.543976) | 20.596743 / 10.191392 (10.405351) | 0.240969 / 0.680424 (-0.439454) | 0.025630 / 0.534201 (-0.508570) | 0.481981 / 0.579283 (-0.097302) | 0.547914 / 0.434364 (0.113550) | 0.522296 / 0.540337 (-0.018041) | 0.729174 / 1.386936 (-0.657762) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]

[]

[ "awesome - so no manual downloaded needed here? ", "Yes exactly. It works like a standard download" ]

[]

[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006998 / 0.011353 (-0.004355) | 0.005093 / 0.011008 (-0.005916) | 0.100490 / 0.038508 (0.061982) | 0.032736 / 0.023109 (0.009627) | 0.297738 / 0.275898 (0.021840) | 0.322255 / 0.323480 (-0.001225) | 0.005583 / 0.007986 (-0.002402) | 0.004007 / 0.004328 (-0.000321) | 0.075863 / 0.004250 (0.071613) | 0.044212 / 0.037052 (0.007159) | 0.300033 / 0.258489 (0.041544) | 0.341997 / 0.293841 (0.048156) | 0.036172 / 0.128546 (-0.092374) | 0.012176 / 0.075646 (-0.063471) | 0.356052 / 0.419271 (-0.063220) | 0.050438 / 0.043533 (0.006905) | 0.294677 / 0.255139 (0.039538) | 0.318050 / 0.283200 (0.034850) | 0.104733 / 0.141683 (-0.036950) | 1.435681 / 1.452155 (-0.016474) | 1.534793 / 1.492716 (0.042076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242815 / 0.018006 (0.224809) | 0.565983 / 0.000490 (0.565494) | 0.006800 / 0.000200 (0.006600) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026548 / 0.037411 (-0.010863) | 0.104816 / 0.014526 (0.090290) | 0.116222 / 0.176557 (-0.060335) | 0.172143 / 0.737135 (-0.564992) | 0.121631 / 0.296338 (-0.174707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400126 / 0.215209 (0.184917) | 4.004538 / 2.077655 (1.926883) | 1.798822 / 1.504120 (0.294702) | 1.595191 / 1.541195 (0.053996) | 1.645777 / 1.468490 (0.177287) | 0.705643 / 4.584777 (-3.879134) | 3.750887 / 3.745712 (0.005175) | 2.136547 / 5.269862 (-3.133315) | 1.475881 / 4.565676 (-3.089795) | 0.086921 / 0.424275 (-0.337354) | 0.012379 / 0.007607 (0.004771) | 0.505824 / 0.226044 (0.279779) | 5.052364 / 2.268929 (2.783435) | 2.279983 / 55.444624 (-53.164641) | 1.932253 / 6.876477 (-4.944224) | 2.051359 / 2.142072 (-0.090714) | 0.851906 / 4.805227 (-3.953321) | 0.169566 / 6.500664 (-6.331098) | 0.064600 / 0.075469 (-0.010869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.165859 / 1.841788 (-0.675929) | 15.049950 / 8.074308 (6.975642) | 14.095981 / 10.191392 (3.904589) | 0.151779 / 0.680424 (-0.528645) | 0.017537 / 0.534201 (-0.516664) | 0.420164 / 0.579283 (-0.159119) | 0.418932 / 0.434364 (-0.015432) | 0.488749 / 0.540337 (-0.051588) | 0.582359 / 1.386936 (-0.804577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007426 / 0.011353 (-0.003927) | 0.005248 / 0.011008 (-0.005761) | 0.074118 / 0.038508 (0.035610) | 0.034223 / 0.023109 (0.011114) | 0.337780 / 0.275898 (0.061882) | 0.376300 / 0.323480 (0.052820) | 0.006142 / 0.007986 (-0.001843) | 0.004246 / 0.004328 (-0.000083) | 0.074177 / 0.004250 (0.069926) | 0.052698 / 0.037052 (0.015646) | 0.340229 / 0.258489 (0.081740) | 0.396172 / 0.293841 (0.102331) | 0.037293 / 0.128546 (-0.091253) | 0.012514 / 0.075646 (-0.063132) | 0.087144 / 0.419271 (-0.332128) | 0.051922 / 0.043533 (0.008390) | 0.333188 / 0.255139 (0.078049) | 0.355420 / 0.283200 (0.072220) | 0.110273 / 0.141683 (-0.031410) | 1.447826 / 1.452155 (-0.004329) | 1.561135 / 1.492716 (0.068419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269203 / 0.018006 (0.251197) | 0.551997 / 0.000490 (0.551508) | 0.001558 / 0.000200 (0.001359) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029511 / 0.037411 (-0.007900) | 0.108614 / 0.014526 (0.094089) | 0.123438 / 0.176557 (-0.053118) | 0.171596 / 0.737135 (-0.565539) | 0.126828 / 0.296338 (-0.169511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420520 / 0.215209 (0.205310) | 4.175672 / 2.077655 (2.098017) | 1.982220 / 1.504120 (0.478101) | 1.788575 / 1.541195 (0.247381) | 1.860840 / 1.468490 (0.392349) | 0.706730 / 4.584777 (-3.878047) | 3.858718 / 3.745712 (0.113005) | 3.069389 / 5.269862 (-2.200472) | 1.827603 / 4.565676 (-2.738073) | 0.087893 / 0.424275 (-0.336382) | 0.012613 / 0.007607 (0.005006) | 0.524177 / 0.226044 (0.298132) | 5.177077 / 2.268929 (2.908148) | 2.494397 / 55.444624 (-52.950227) | 2.189484 / 6.876477 (-4.686992) | 2.217626 / 2.142072 (0.075554) | 0.846326 / 4.805227 (-3.958901) | 0.176558 / 6.500664 (-6.324106) | 0.065018 / 0.075469 (-0.010451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268618 / 1.841788 (-0.573170) | 15.132711 / 8.074308 (7.058403) | 14.585530 / 10.191392 (4.394138) | 0.163454 / 0.680424 (-0.516970) | 0.017442 / 0.534201 (-0.516759) | 0.421746 / 0.579283 (-0.157537) | 0.425412 / 0.434364 (-0.008952) | 0.499178 / 0.540337 (-0.041159) | 0.595458 / 1.386936 (-0.791478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab77e58cd32413f4ef4828134a2470ebd53bb542 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007980 / 0.011353 (-0.003373) | 0.005414 / 0.011008 (-0.005594) | 0.099226 / 0.038508 (0.060718) | 0.035442 / 0.023109 (0.012332) | 0.304851 / 0.275898 (0.028952) | 0.337144 / 0.323480 (0.013664) | 0.006162 / 0.007986 (-0.001823) | 0.004151 / 0.004328 (-0.000177) | 0.074708 / 0.004250 (0.070458) | 0.049690 / 0.037052 (0.012638) | 0.307658 / 0.258489 (0.049168) | 0.358472 / 0.293841 (0.064631) | 0.037181 / 0.128546 (-0.091365) | 0.012259 / 0.075646 (-0.063387) | 0.335426 / 0.419271 (-0.083846) | 0.050790 / 0.043533 (0.007257) | 0.301715 / 0.255139 (0.046576) | 0.320834 / 0.283200 (0.037634) | 0.102357 / 0.141683 (-0.039326) | 1.454750 / 1.452155 (0.002596) | 1.571994 / 1.492716 (0.079278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218708 / 0.018006 (0.200702) | 0.444391 / 0.000490 (0.443901) | 0.005717 / 0.000200 (0.005517) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028017 / 0.037411 (-0.009395) | 0.112753 / 0.014526 (0.098227) | 0.121003 / 0.176557 (-0.055554) | 0.181085 / 0.737135 (-0.556050) | 0.127211 / 0.296338 (-0.169127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400803 / 0.215209 (0.185594) | 4.007315 / 2.077655 (1.929660) | 1.826911 / 1.504120 (0.322791) | 1.637799 / 1.541195 (0.096605) | 1.699754 / 1.468490 (0.231264) | 0.709413 / 4.584777 (-3.875364) | 4.008904 / 3.745712 (0.263192) | 3.916540 / 5.269862 (-1.353322) | 1.902102 / 4.565676 (-2.663575) | 0.089048 / 0.424275 (-0.335227) | 0.012763 / 0.007607 (0.005155) | 0.498957 / 0.226044 (0.272913) | 4.979865 / 2.268929 (2.710937) | 2.301987 / 55.444624 (-53.142637) | 1.929404 / 6.876477 (-4.947073) | 2.107839 / 2.142072 (-0.034233) | 0.857253 / 4.805227 (-3.947974) | 0.171935 / 6.500664 (-6.328729) | 0.066753 / 0.075469 (-0.008716) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186811 / 1.841788 (-0.654977) | 15.866319 / 8.074308 (7.792011) | 14.738555 / 10.191392 (4.547163) | 0.142879 / 0.680424 (-0.537544) | 0.017679 / 0.534201 (-0.516522) | 0.422840 / 0.579283 (-0.156443) | 0.450307 / 0.434364 (0.015943) | 0.491802 / 0.540337 (-0.048536) | 0.588837 / 1.386936 (-0.798099) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007659 / 0.011353 (-0.003694) | 0.005331 / 0.011008 (-0.005678) | 0.075360 / 0.038508 (0.036852) | 0.034011 / 0.023109 (0.010902) | 0.354488 / 0.275898 (0.078590) | 0.401781 / 0.323480 (0.078301) | 0.005806 / 0.007986 (-0.002179) | 0.004029 / 0.004328 (-0.000300) | 0.073822 / 0.004250 (0.069572) | 0.049067 / 0.037052 (0.012015) | 0.364483 / 0.258489 (0.105994) | 0.405637 / 0.293841 (0.111796) | 0.037166 / 0.128546 (-0.091380) | 0.012397 / 0.075646 (-0.063249) | 0.087346 / 0.419271 (-0.331926) | 0.050888 / 0.043533 (0.007355) | 0.334796 / 0.255139 (0.079657) | 0.387681 / 0.283200 (0.104481) | 0.105056 / 0.141683 (-0.036627) | 1.471630 / 1.452155 (0.019475) | 1.554764 / 1.492716 (0.062047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231825 / 0.018006 (0.213819) | 0.449746 / 0.000490 (0.449256) | 0.000888 / 0.000200 (0.000688) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030363 / 0.037411 (-0.007049) | 0.115234 / 0.014526 (0.100708) | 0.123005 / 0.176557 (-0.053551) | 0.172772 / 0.737135 (-0.564363) | 0.127818 / 0.296338 (-0.168520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425761 / 0.215209 (0.210552) | 4.237950 / 2.077655 (2.160295) | 1.992045 / 1.504120 (0.487925) | 1.801622 / 1.541195 (0.260427) | 1.918477 / 1.468490 (0.449987) | 0.722730 / 4.584777 (-3.862047) | 4.015968 / 3.745712 (0.270256) | 3.720412 / 5.269862 (-1.549450) | 1.763111 / 4.565676 (-2.802566) | 0.089041 / 0.424275 (-0.335234) | 0.012608 / 0.007607 (0.005001) | 0.522645 / 0.226044 (0.296601) | 5.227108 / 2.268929 (2.958180) | 2.444714 / 55.444624 (-52.999910) | 2.109745 / 6.876477 (-4.766732) | 2.194042 / 2.142072 (0.051969) | 0.871781 / 4.805227 (-3.933447) | 0.173149 / 6.500664 (-6.327515) | 0.066192 / 0.075469 (-0.009277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312051 / 1.841788 (-0.529737) | 16.024315 / 8.074308 (7.950007) | 15.123823 / 10.191392 (4.932431) | 0.163997 / 0.680424 (-0.516427) | 0.017595 / 0.534201 (-0.516606) | 0.426379 / 0.579283 (-0.152904) | 0.467709 / 0.434364 (0.033345) | 0.498308 / 0.540337 (-0.042030) | 0.591426 / 1.386936 (-0.795510) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13488cc110b67090289794f48d5c84a4fd0c063a \"CML watermark\")\n", "CI is failing due to unrelated issues, hopefully https://github.com/huggingface/datasets/pull/5642 fixes it", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006478 / 0.011353 (-0.004875) | 0.004347 / 0.011008 (-0.006661) | 0.097103 / 0.038508 (0.058595) | 0.027650 / 0.023109 (0.004541) | 0.372355 / 0.275898 (0.096457) | 0.408794 / 0.323480 (0.085314) | 0.005034 / 0.007986 (-0.002952) | 0.003252 / 0.004328 (-0.001076) | 0.074068 / 0.004250 (0.069818) | 0.035542 / 0.037052 (-0.001510) | 0.367392 / 0.258489 (0.108903) | 0.409644 / 0.293841 (0.115803) | 0.031745 / 0.128546 (-0.096801) | 0.011501 / 0.075646 (-0.064145) | 0.323355 / 0.419271 (-0.095917) | 0.043065 / 0.043533 (-0.000467) | 0.377313 / 0.255139 (0.122174) | 0.395326 / 0.283200 (0.112127) | 0.087101 / 0.141683 (-0.054582) | 1.461228 / 1.452155 (0.009073) | 1.529413 / 1.492716 (0.036696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199245 / 0.018006 (0.181239) | 0.409978 / 0.000490 (0.409488) | 0.002655 / 0.000200 (0.002455) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023903 / 0.037411 (-0.013508) | 0.097855 / 0.014526 (0.083330) | 0.106405 / 0.176557 (-0.070152) | 0.166889 / 0.737135 (-0.570247) | 0.110256 / 0.296338 (-0.186082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440351 / 0.215209 (0.225142) | 4.382848 / 2.077655 (2.305194) | 2.049602 / 1.504120 (0.545482) | 1.824638 / 1.541195 (0.283443) | 1.850519 / 1.468490 (0.382029) | 0.702652 / 4.584777 (-3.882125) | 3.394571 / 3.745712 (-0.351141) | 1.940608 / 5.269862 (-3.329254) | 1.263961 / 4.565676 (-3.301716) | 0.083985 / 0.424275 (-0.340290) | 0.013046 / 0.007607 (0.005439) | 0.538272 / 0.226044 (0.312228) | 5.407563 / 2.268929 (3.138634) | 2.519207 / 55.444624 (-52.925418) | 2.153379 / 6.876477 (-4.723098) | 2.394512 / 2.142072 (0.252439) | 0.812840 / 4.805227 (-3.992387) | 0.152868 / 6.500664 (-6.347796) | 0.067823 / 0.075469 (-0.007646) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220031 / 1.841788 (-0.621757) | 13.781237 / 8.074308 (5.706929) | 14.203975 / 10.191392 (4.012583) | 0.141077 / 0.680424 (-0.539347) | 0.016518 / 0.534201 (-0.517682) | 0.379079 / 0.579283 (-0.200204) | 0.378916 / 0.434364 (-0.055448) | 0.434589 / 0.540337 (-0.105749) | 0.521129 / 1.386936 (-0.865807) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006997 / 0.011353 (-0.004356) | 0.004599 / 0.011008 (-0.006410) | 0.078700 / 0.038508 (0.040192) | 0.027902 / 0.023109 (0.004793) | 0.344406 / 0.275898 (0.068508) | 0.392918 / 0.323480 (0.069438) | 0.005175 / 0.007986 (-0.002811) | 0.004755 / 0.004328 (0.000427) | 0.077707 / 0.004250 (0.073457) | 0.039409 / 0.037052 (0.002357) | 0.343250 / 0.258489 (0.084761) | 0.405544 / 0.293841 (0.111703) | 0.032286 / 0.128546 (-0.096260) | 0.011674 / 0.075646 (-0.063972) | 0.087633 / 0.419271 (-0.331639) | 0.043346 / 0.043533 (-0.000186) | 0.355076 / 0.255139 (0.099937) | 0.382155 / 0.283200 (0.098955) | 0.090914 / 0.141683 (-0.050769) | 1.518369 / 1.452155 (0.066215) | 1.583530 / 1.492716 (0.090813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.160369 / 0.018006 (0.142362) | 0.406844 / 0.000490 (0.406354) | 0.002651 / 0.000200 (0.002451) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025295 / 0.037411 (-0.012116) | 0.101490 / 0.014526 (0.086964) | 0.108825 / 0.176557 (-0.067732) | 0.161673 / 0.737135 (-0.575462) | 0.113610 / 0.296338 (-0.182729) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443514 / 0.215209 (0.228305) | 4.436722 / 2.077655 (2.359067) | 2.144008 / 1.504120 (0.639888) | 2.005324 / 1.541195 (0.464129) | 2.123356 / 1.468490 (0.654866) | 0.697217 / 4.584777 (-3.887560) | 3.401105 / 3.745712 (-0.344607) | 1.874621 / 5.269862 (-3.395240) | 1.165069 / 4.565676 (-3.400608) | 0.082799 / 0.424275 (-0.341476) | 0.012806 / 0.007607 (0.005199) | 0.542688 / 0.226044 (0.316644) | 5.420963 / 2.268929 (3.152034) | 2.579034 / 55.444624 (-52.865590) | 2.240201 / 6.876477 (-4.636276) | 2.261309 / 2.142072 (0.119237) | 0.800246 / 4.805227 (-4.004981) | 0.150380 / 6.500664 (-6.350285) | 0.066880 / 0.075469 (-0.008589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281721 / 1.841788 (-0.560067) | 13.906361 / 8.074308 (5.832053) | 14.135336 / 10.191392 (3.943944) | 0.128865 / 0.680424 (-0.551559) | 0.016452 / 0.534201 (-0.517749) | 0.373563 / 0.579283 (-0.205720) | 0.385321 / 0.434364 (-0.049043) | 0.437198 / 0.540337 (-0.103139) | 0.530720 / 1.386936 (-0.856216) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2f8e17f3c8f8d0cb77a4c566a78e31fab47108c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008099 / 0.011353 (-0.003254) | 0.005093 / 0.011008 (-0.005916) | 0.106258 / 0.038508 (0.067750) | 0.037051 / 0.023109 (0.013942) | 0.347960 / 0.275898 (0.072062) | 0.370849 / 0.323480 (0.047369) | 0.006122 / 0.007986 (-0.001863) | 0.004094 / 0.004328 (-0.000235) | 0.079549 / 0.004250 (0.075299) | 0.046563 / 0.037052 (0.009510) | 0.332735 / 0.258489 (0.074246) | 0.417061 / 0.293841 (0.123220) | 0.038105 / 0.128546 (-0.090441) | 0.011886 / 0.075646 (-0.063760) | 0.342103 / 0.419271 (-0.077169) | 0.053233 / 0.043533 (0.009700) | 0.344754 / 0.255139 (0.089615) | 0.355354 / 0.283200 (0.072155) | 0.101059 / 0.141683 (-0.040624) | 1.518561 / 1.452155 (0.066406) | 1.558652 / 1.492716 (0.065935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225919 / 0.018006 (0.207913) | 0.518539 / 0.000490 (0.518049) | 0.006230 / 0.000200 (0.006030) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026782 / 0.037411 (-0.010629) | 0.108457 / 0.014526 (0.093931) | 0.125203 / 0.176557 (-0.051353) | 0.175726 / 0.737135 (-0.561409) | 0.127051 / 0.296338 (-0.169287) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416427 / 0.215209 (0.201217) | 4.168851 / 2.077655 (2.091196) | 1.962238 / 1.504120 (0.458118) | 1.825224 / 1.541195 (0.284029) | 1.831200 / 1.468490 (0.362710) | 0.765526 / 4.584777 (-3.819250) | 4.303957 / 3.745712 (0.558245) | 2.193467 / 5.269862 (-3.076395) | 1.654605 / 4.565676 (-2.911071) | 0.096709 / 0.424275 (-0.327566) | 0.013792 / 0.007607 (0.006185) | 0.537862 / 0.226044 (0.311818) | 5.152230 / 2.268929 (2.883302) | 2.520938 / 55.444624 (-52.923686) | 2.108422 / 6.876477 (-4.768054) | 2.214220 / 2.142072 (0.072147) | 0.834320 / 4.805227 (-3.970907) | 0.170635 / 6.500664 (-6.330029) | 0.063131 / 0.075469 (-0.012338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215767 / 1.841788 (-0.626020) | 15.254781 / 8.074308 (7.180473) | 14.360764 / 10.191392 (4.169372) | 0.172511 / 0.680424 (-0.507913) | 0.020161 / 0.534201 (-0.514040) | 0.426936 / 0.579283 (-0.152347) | 0.438771 / 0.434364 (0.004407) | 0.486973 / 0.540337 (-0.053364) | 0.584238 / 1.386936 (-0.802698) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006777 / 0.011353 (-0.004576) | 0.005304 / 0.011008 (-0.005704) | 0.073717 / 0.038508 (0.035209) | 0.033604 / 0.023109 (0.010494) | 0.340448 / 0.275898 (0.064550) | 0.351861 / 0.323480 (0.028381) | 0.005786 / 0.007986 (-0.002199) | 0.005013 / 0.004328 (0.000685) | 0.071263 / 0.004250 (0.067012) | 0.048189 / 0.037052 (0.011137) | 0.339457 / 0.258489 (0.080968) | 0.384383 / 0.293841 (0.090542) | 0.035563 / 0.128546 (-0.092983) | 0.011509 / 0.075646 (-0.064137) | 0.083722 / 0.419271 (-0.335550) | 0.048886 / 0.043533 (0.005353) | 0.350184 / 0.255139 (0.095045) | 0.361037 / 0.283200 (0.077837) | 0.105191 / 0.141683 (-0.036492) | 1.503247 / 1.452155 (0.051093) | 1.582298 / 1.492716 (0.089581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221687 / 0.018006 (0.203681) | 0.466489 / 0.000490 (0.465999) | 0.000484 / 0.000200 (0.000284) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027978 / 0.037411 (-0.009434) | 0.119572 / 0.014526 (0.105047) | 0.133530 / 0.176557 (-0.043026) | 0.177892 / 0.737135 (-0.559243) | 0.127045 / 0.296338 (-0.169294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430198 / 0.215209 (0.214989) | 4.435512 / 2.077655 (2.357858) | 2.007183 / 1.504120 (0.503063) | 1.799230 / 1.541195 (0.258036) | 1.884750 / 1.468490 (0.416260) | 0.745232 / 4.584777 (-3.839545) | 4.088069 / 3.745712 (0.342357) | 4.114669 / 5.269862 (-1.155193) | 2.374086 / 4.565676 (-2.191590) | 0.089154 / 0.424275 (-0.335121) | 0.012938 / 0.007607 (0.005331) | 0.505954 / 0.226044 (0.279909) | 5.194226 / 2.268929 (2.925298) | 2.487230 / 55.444624 (-52.957394) | 2.163353 / 6.876477 (-4.713124) | 2.177879 / 2.142072 (0.035807) | 0.828728 / 4.805227 (-3.976499) | 0.171157 / 6.500664 (-6.329507) | 0.062883 / 0.075469 (-0.012586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275906 / 1.841788 (-0.565882) | 15.235484 / 8.074308 (7.161176) | 14.467396 / 10.191392 (4.276004) | 0.198994 / 0.680424 (-0.481430) | 0.020203 / 0.534201 (-0.513998) | 0.447904 / 0.579283 (-0.131380) | 0.454210 / 0.434364 (0.019846) | 0.528062 / 0.540337 (-0.012275) | 0.619311 / 1.386936 (-0.767625) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11cd0f73acbce1d16174f2555e56fda511d5a08b \"CML watermark\")\n" ]

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[ "Thanks Quentin !" ]

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[ "great work @TheophileBlard ", "LGTM, thanks a lot for adding dummy data tests :-) Was it difficult to create the correct dummy data folder? ", "It was pretty easy actually. Documentation is on point !" ]

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[ "@lhoestq Thanks, Quentin. Removed the .ipynb_checkpoints and edited the README.md. The tests are failing because of other dataets. I'm figuring out why since the commits only have changes on `wisesight_sentiment`\r\n\r\n```\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_class_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_builder_configs_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_flue\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_norwegian_ner\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xglue\r\n```", "@cstorm125 I really like the dataset and dataset card but there seems to have been a rebase issue at some point since it's now changing 140 files :D \r\n\r\nCould you rebase from master?", "I think it might be faster to close and reopen.", "To be continued on: https://github.com/huggingface/datasets/pull/981" ]

[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]

[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the feedback!\r\n\r\nI agree that adding it to `load_hub.mdx` is probably a bit too specific, especially for beginners reading the tutorials. Since this clarification is closely related to loading from the Hub (the only difference being the presence/absence of a loading script), I think it makes the most sense to keep it somewhere in `loading.mdx`. What do you think about adding a Warning in Loading >>> Hugging Face Hub that explains the difference between relative/absolute paths when there is a script?", "What about updating the section about \"manual download\" ? I think it goes there no ?\r\n\r\nhttps://huggingface.co/docs/datasets/v2.4.0/en/loading#manual-download", "Updated the manual download section :)", "Thanks ! Pinging @albertvillanova to review this change, and then I think we're good to merge" ]

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[ "@lhoestq Refactored into new branch, please review :) ", "The `RemoteDatasetTest ` errors in the CI are fixed on master so it's fine", "merging since the CI is fixed on master" ]

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[ "@lhoestq here's the fix!" ]

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[ "Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of 🤗`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https://github.com/huggingface/datasets/issues/1641#issuecomment-751571471).\r\nLet me know if this helps !", "Thanks @SBrandeis ,\r\nProblem solved by downloading and installing the latest version datasets." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi again @lhoestq this is ready for review! Not sure I have permission to add people to the reviewers list...", "Cool ! I think you can define `IterableDataset.from_spark` instead of adding `streaming=` in `Dataset.from_spark`, it can be more intuitive IMO :)", "Thanks for reviewing! I moved the streaming behavior to IterableDataset.from_spark", "Thanks Quentin! I'll flesh out the docs in a follow-up PR", "Friendly ping @lhoestq ", "Thanks @lhoestq ! I fixed the partition order thing and added more unit tests.", "<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) | 0.006165 / 0.011353 (-0.005188) | 0.004497 / 0.011008 (-0.006511) | 0.099142 / 0.038508 (0.060634) | 0.027479 / 0.023109 (0.004369) | 0.352491 / 0.275898 (0.076593) | 0.402993 / 0.323480 (0.079513) | 0.004885 / 0.007986 (-0.003100) | 0.003315 / 0.004328 (-0.001013) | 0.075787 / 0.004250 (0.071537) | 0.035320 / 0.037052 (-0.001732) | 0.368401 / 0.258489 (0.109912) | 0.409090 / 0.293841 (0.115249) | 0.030125 / 0.128546 (-0.098421) | 0.011670 / 0.075646 (-0.063976) | 0.324381 / 0.419271 (-0.094890) | 0.050815 / 0.043533 (0.007283) | 0.352598 / 0.255139 (0.097460) | 0.389189 / 0.283200 (0.105989) | 0.092873 / 0.141683 (-0.048810) | 1.485140 / 1.452155 (0.032986) | 1.545586 / 1.492716 (0.052869) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199522 / 0.018006 (0.181516) | 0.404576 / 0.000490 (0.404087) | 0.003322 / 0.000200 (0.003122) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022945 / 0.037411 (-0.014466) | 0.095512 / 0.014526 (0.080987) | 0.103077 / 0.176557 (-0.073480) | 0.163918 / 0.737135 (-0.573217) | 0.105560 / 0.296338 (-0.190779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417360 / 0.215209 (0.202151) | 4.161693 / 2.077655 (2.084039) | 1.851941 / 1.504120 (0.347821) | 1.649872 / 1.541195 (0.108677) | 1.682099 / 1.468490 (0.213609) | 0.693187 / 4.584777 (-3.891590) | 3.462528 / 3.745712 (-0.283184) | 1.839893 / 5.269862 (-3.429968) | 1.155945 / 4.565676 (-3.409731) | 0.082611 / 0.424275 (-0.341664) | 0.012076 / 0.007607 (0.004469) | 0.514325 / 0.226044 (0.288280) | 5.155052 / 2.268929 (2.886123) | 2.307280 / 55.444624 (-53.137345) | 1.966483 / 6.876477 (-4.909994) | 2.018892 / 2.142072 (-0.123181) | 0.803068 / 4.805227 (-4.002159) | 0.152213 / 6.500664 (-6.348451) | 0.066320 / 0.075469 (-0.009149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218578 / 1.841788 (-0.623209) | 13.563869 / 8.074308 (5.489561) | 13.954596 / 10.191392 (3.763204) | 0.151527 / 0.680424 (-0.528897) | 0.016655 / 0.534201 (-0.517546) | 0.380637 / 0.579283 (-0.198646) | 0.395854 / 0.434364 (-0.038509) | 0.459111 / 0.540337 (-0.081226) | 0.560219 / 1.386936 (-0.826717) |\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) | 0.006427 / 0.011353 (-0.004926) | 0.004728 / 0.011008 (-0.006280) | 0.080525 / 0.038508 (0.042017) | 0.027294 / 0.023109 (0.004185) | 0.414688 / 0.275898 (0.138790) | 0.449882 / 0.323480 (0.126402) | 0.004771 / 0.007986 (-0.003214) | 0.003402 / 0.004328 (-0.000926) | 0.078748 / 0.004250 (0.074497) | 0.037046 / 0.037052 (-0.000007) | 0.417398 / 0.258489 (0.158909) | 0.462921 / 0.293841 (0.169080) | 0.030364 / 0.128546 (-0.098182) | 0.011810 / 0.075646 (-0.063837) | 0.089787 / 0.419271 (-0.329485) | 0.039806 / 0.043533 (-0.003727) | 0.403401 / 0.255139 (0.148262) | 0.439477 / 0.283200 (0.156278) | 0.088431 / 0.141683 (-0.053252) | 1.534373 / 1.452155 (0.082219) | 1.592316 / 1.492716 (0.099600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217701 / 0.018006 (0.199695) | 0.384770 / 0.000490 (0.384280) | 0.000437 / 0.000200 (0.000237) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024952 / 0.037411 (-0.012459) | 0.098728 / 0.014526 (0.084202) | 0.106324 / 0.176557 (-0.070233) | 0.155484 / 0.737135 (-0.581651) | 0.109503 / 0.296338 (-0.186836) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450639 / 0.215209 (0.235430) | 4.523110 / 2.077655 (2.445455) | 2.224810 / 1.504120 (0.720690) | 2.119516 / 1.541195 (0.578321) | 2.225192 / 1.468490 (0.756702) | 0.695397 / 4.584777 (-3.889380) | 3.433559 / 3.745712 (-0.312153) | 2.633127 / 5.269862 (-2.636735) | 1.448471 / 4.565676 (-3.117206) | 0.082262 / 0.424275 (-0.342013) | 0.012246 / 0.007607 (0.004639) | 0.561243 / 0.226044 (0.335199) | 5.652711 / 2.268929 (3.383782) | 2.689771 / 55.444624 (-52.754853) | 2.359512 / 6.876477 (-4.516965) | 2.471098 / 2.142072 (0.329026) | 0.802955 / 4.805227 (-4.002272) | 0.151142 / 6.500664 (-6.349522) | 0.067494 / 0.075469 (-0.007975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306879 / 1.841788 (-0.534909) | 14.030775 / 8.074308 (5.956467) | 12.917790 / 10.191392 (2.726398) | 0.141269 / 0.680424 (-0.539155) | 0.016264 / 0.534201 (-0.517937) | 0.411957 / 0.579283 (-0.167326) | 0.393235 / 0.434364 (-0.041129) | 0.505144 / 0.540337 (-0.035193) | 0.590660 / 1.386936 (-0.796276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7790ebd7072eafff755fb575b392f3efa74069e4 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "the `RemoteDatasetTest ` error pf the CI is fixed on master so it's fine", "merging since the CI is fixed on master" ]

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[ "Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>()\r\n 1 import datasets\r\n 2 \r\n----> 3 train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n\r\n5 frames\r\n[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n 1705 ignore_verifications=ignore_verifications,\r\n 1706 try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707 use_auth_token=use_auth_token,\r\n 1708 )\r\n 1709 \r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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)\r\n 593 if not downloaded_from_gcs:\r\n 594 self._download_and_prepare(\r\n--> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 596 )\r\n 597 # Sync info\r\n\r\n[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 659 split_dict = SplitDict(dataset_name=self.name)\r\n 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 662 \r\n 663 # Checksums verification\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _split_generators(self, dl_manager)\r\n 253 def _split_generators(self, dl_manager):\r\n 254 dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 255 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n 256 # Generate shared vocabulary\r\n 257 \r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _subset_filenames(dl_paths, split)\r\n 154 else:\r\n 155 logger.fatal(\"Unsupported split: %s\", split)\r\n--> 156 cnn = _find_files(dl_paths, \"cnn\", urls)\r\n 157 dm = _find_files(dl_paths, \"dm\", urls)\r\n 158 return cnn + dm\r\n\r\n[/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _find_files(dl_paths, publisher, url_dict)\r\n 133 else:\r\n 134 logger.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 135 files = sorted(os.listdir(top_dir))\r\n 136 \r\n 137 ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'\r\n```", "Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. \r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today (indeed, we were planning to do it last Friday).\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nCC: @lhoestq " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "This has been fixed in 1.15, let me know if you still have this issue" ]

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[ "Nice thanks ! I think there are other places with\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nCould you replace them as well ?", "Sure @lhoestq! I was not sure if this change should only be circumscribed to `http_get`..." ]

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[ "Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it ?", "Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.", "Could you trying reinstalling pyarrow with pip ?\r\nI'm not sure why it would check in your multicurtural-sc directory for source files.", "Sure! I tried reinstalling to get latest. pip was mad because it looks like Datasets currently wants <4.0.0 (which is interesting, because apparently I ended up with 4.0.0 already?), but I gave it a shot anyway:\r\n\r\n```bash\r\n$ pip install --upgrade --force-reinstall pyarrow\r\nCollecting pyarrow\r\n Downloading pyarrow-4.0.1-cp39-cp39-manylinux2014_x86_64.whl (21.9 MB)\r\n |████████████████████████████████| 21.9 MB 23.8 MB/s\r\nCollecting numpy>=1.16.6\r\n Using cached numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB)\r\nInstalling collected packages: numpy, pyarrow\r\n Attempting uninstall: numpy\r\n Found existing installation: numpy 1.20.3\r\n Uninstalling numpy-1.20.3:\r\n Successfully uninstalled numpy-1.20.3\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 3.0.0\r\n Uninstalling pyarrow-3.0.0:\r\n Successfully uninstalled pyarrow-3.0.0\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ndatasets 1.8.0 requires pyarrow<4.0.0,>=1.0.0, but you have pyarrow 4.0.1 which is incompatible.\r\nSuccessfully installed numpy-1.20.3 pyarrow-4.0.1\r\n```\r\n\r\nTrying it, the same issue:\r\n\r\n\r\n\r\nI tried installing `\"pyarrow<4.0.0\"`, which gave me 3.0.0. Running, still, same issue.\r\n\r\nI agree it's weird that pyarrow is checking the source code directory for its files. (There is no `pyarrow/` directory there.) To me, that makes it seem like an issue with how pyarrow is called.\r\n\r\nOut of curiosity, I tried running this with fewer workers to see when the error arises:\r\n\r\n- 1: ✅\r\n- 2: ✅\r\n- 4: ✅\r\n- 8: ✅\r\n- 10: ✅\r\n- 11: ❌ 🤔\r\n- 12: ❌\r\n- 16: ❌\r\n- 32: ❌\r\n\r\nchecking my datasets:\r\n\r\n```python\r\n>>> datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text'],\r\n num_rows: 389290\r\n })\r\n validation.sc: Dataset({\r\n features: ['text'],\r\n num_rows: 10 # 🤔\r\n })\r\n validation.wvs: Dataset({\r\n features: ['text'],\r\n num_rows: 93928\r\n })\r\n})\r\n```\r\n\r\nNew hypothesis: crash if `num_proc` > length of a dataset? 😅\r\n\r\nIf so, this might be totally my fault, as the caller. Could be a docs fix, or maybe this library could do a check to limit `num_proc` for this case?", "Good catch ! Not sure why it could raise such a weird issue from pyarrow though\r\nWe should definitely reduce num_proc to the length of the dataset if needed and log a warning.", "This has been fixed in #2566, thanks @connor-mccarthy !\r\nWe'll make a new release soon that includes the fix ;)" ]

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[ "We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !" ]

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[ "> Awesome !\r\n> Good job on parsing these files :O\r\n> \r\n> Do you think it would be hard to get the two other split configurations ?\r\n\r\nIt shouldn't be that hard, I think I can consider different config names for each split ", "> > Awesome !\r\n> > Good job on parsing these files :O\r\n> > Do you think it would be hard to get the two other split configurations ?\r\n> \r\n> It shouldn't be that hard, I think I can consider different config names for each split\r\n\r\nYes that would be perfect", "closing this PR and opening a new one to fix the circle CI problems" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks @thomas-happify for reporting and thanks @mariosasko for the fix." ]

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[ "1) Changed the language in a few places apart from those you mentioned in README\r\n2) Reduced the size of dummy data folder by removing all other entries except the first\r\n3) Updated YAML tags by using to the past version of `datasets-tagging` app. Will update the quick fix on that repository too in a while", "@bhavitvyamalik Thanks for adding the dataset on huggingface! Can you please add a metric also for the dataset using the squad_v2 metric file? ", "@MohammedRakib you can check [#2257](https://github.com/huggingface/datasets/pull/2257)" ]

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[ "Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?", "Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, in the link you sent I see split of {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train/test do not have en indeed. thanks a lot for the great explanations", "I close the ticket, since I do not see any en existing, they have trained on \"SQuAD V1.1\" instead. Thanks. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3856). All of your documentation changes will be reflected on that endpoint." ]

[ { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]

[ "This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n" ]

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[ "`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.", "@lhoestq Thanks for the info ", "@lhoestq I tried installing transformer from the master branch. Python imports for DPR again didnt' work. Anyways, Looking forward to trying it in the next release of nlp ", "@nsankar have you tried with the latest version of the library?", "@yjernite it worked. Thanks" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "To make it warn only once, feel free to use a global counter in python - and if the warning has already been done, you don't do it again", "> Added the same formatting for the error message :)\r\n\r\nnice!! thank you! \r\n\r\n> Oh and regarding the warning counter, you can do it in another PR maybe ?\r\n\r\nYes, more warnings is better then no warnings.... I'll merge when the CI passes" ]

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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) | 0.006722 / 0.011353 (-0.004631) | 0.004425 / 0.011008 (-0.006583) | 0.100850 / 0.038508 (0.062341) | 0.040816 / 0.023109 (0.017707) | 0.348823 / 0.275898 (0.072925) | 0.446285 / 0.323480 (0.122805) | 0.005738 / 0.007986 (-0.002247) | 0.003517 / 0.004328 (-0.000811) | 0.078824 / 0.004250 (0.074574) | 0.064695 / 0.037052 (0.027643) | 0.389894 / 0.258489 (0.131405) | 0.416107 / 0.293841 (0.122266) | 0.028850 / 0.128546 (-0.099696) | 0.009011 / 0.075646 (-0.066635) | 0.323117 / 0.419271 (-0.096154) | 0.049162 / 0.043533 (0.005629) | 0.340144 / 0.255139 (0.085005) | 0.382072 / 0.283200 (0.098872) | 0.023160 / 0.141683 (-0.118523) | 1.549218 / 1.452155 (0.097063) | 1.581266 / 1.492716 (0.088550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293360 / 0.018006 (0.275353) | 0.602189 / 0.000490 (0.601700) | 0.004608 / 0.000200 (0.004408) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.107088 / 0.014526 (0.092562) | 0.112188 / 0.176557 (-0.064369) | 0.174669 / 0.737135 (-0.562466) | 0.116359 / 0.296338 (-0.179980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422911 / 0.215209 (0.207702) | 4.231524 / 2.077655 (2.153869) | 1.906711 / 1.504120 (0.402591) | 1.706841 / 1.541195 (0.165646) | 1.792066 / 1.468490 (0.323576) | 0.559221 / 4.584777 (-4.025556) | 3.434280 / 3.745712 (-0.311433) | 1.918714 / 5.269862 (-3.351148) | 1.073070 / 4.565676 (-3.492606) | 0.067891 / 0.424275 (-0.356384) | 0.011927 / 0.007607 (0.004320) | 0.530843 / 0.226044 (0.304799) | 5.309213 / 2.268929 (3.040285) | 2.439246 / 55.444624 (-53.005378) | 2.101245 / 6.876477 (-4.775231) | 2.177436 / 2.142072 (0.035363) | 0.672150 / 4.805227 (-4.133077) | 0.137571 / 6.500664 (-6.363093) | 0.068343 / 0.075469 (-0.007126) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265262 / 1.841788 (-0.576525) | 14.988021 / 8.074308 (6.913713) | 13.611677 / 10.191392 (3.420285) | 0.171389 / 0.680424 (-0.509035) | 0.017681 / 0.534201 (-0.516520) | 0.377542 / 0.579283 (-0.201741) | 0.399475 / 0.434364 (-0.034889) | 0.469553 / 0.540337 (-0.070785) | 0.561888 / 1.386936 (-0.825048) |\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) | 0.006782 / 0.011353 (-0.004571) | 0.004412 / 0.011008 (-0.006597) | 0.078594 / 0.038508 (0.040086) | 0.039930 / 0.023109 (0.016820) | 0.371879 / 0.275898 (0.095981) | 0.444910 / 0.323480 (0.121430) | 0.005707 / 0.007986 (-0.002279) | 0.003901 / 0.004328 (-0.000427) | 0.080125 / 0.004250 (0.075875) | 0.063977 / 0.037052 (0.026925) | 0.382781 / 0.258489 (0.124292) | 0.441791 / 0.293841 (0.147950) | 0.030428 / 0.128546 (-0.098118) | 0.009008 / 0.075646 (-0.066638) | 0.084447 / 0.419271 (-0.334824) | 0.044432 / 0.043533 (0.000899) | 0.365686 / 0.255139 (0.110547) | 0.394312 / 0.283200 (0.111113) | 0.024508 / 0.141683 (-0.117175) | 1.577020 / 1.452155 (0.124865) | 1.630259 / 1.492716 (0.137543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307960 / 0.018006 (0.289953) | 0.591473 / 0.000490 (0.590983) | 0.008098 / 0.000200 (0.007898) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029567 / 0.037411 (-0.007845) | 0.112773 / 0.014526 (0.098247) | 0.117362 / 0.176557 (-0.059194) | 0.174293 / 0.737135 (-0.562843) | 0.123156 / 0.296338 (-0.173182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457475 / 0.215209 (0.242266) | 4.599067 / 2.077655 (2.521412) | 2.262638 / 1.504120 (0.758518) | 2.124943 / 1.541195 (0.583748) | 2.339912 / 1.468490 (0.871422) | 0.566264 / 4.584777 (-4.018513) | 3.489261 / 3.745712 (-0.256451) | 1.925151 / 5.269862 (-3.344711) | 1.099389 / 4.565676 (-3.466287) | 0.068232 / 0.424275 (-0.356043) | 0.011660 / 0.007607 (0.004052) | 0.571227 / 0.226044 (0.345183) | 5.702059 / 2.268929 (3.433130) | 2.837701 / 55.444624 (-52.606924) | 2.605468 / 6.876477 (-4.271008) | 2.818396 / 2.142072 (0.676323) | 0.681856 / 4.805227 (-4.123371) | 0.141401 / 6.500664 (-6.359263) | 0.069728 / 0.075469 (-0.005741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354935 / 1.841788 (-0.486853) | 15.437404 / 8.074308 (7.363095) | 15.415193 / 10.191392 (5.223801) | 0.153459 / 0.680424 (-0.526964) | 0.017190 / 0.534201 (-0.517011) | 0.367256 / 0.579283 (-0.212027) | 0.392709 / 0.434364 (-0.041655) | 0.426125 / 0.540337 (-0.114213) | 0.522612 / 1.386936 (-0.864324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"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) | 0.009183 / 0.011353 (-0.002170) | 0.005232 / 0.011008 (-0.005776) | 0.120349 / 0.038508 (0.081841) | 0.044715 / 0.023109 (0.021606) | 0.361519 / 0.275898 (0.085621) | 0.463702 / 0.323480 (0.140223) | 0.005842 / 0.007986 (-0.002144) | 0.004041 / 0.004328 (-0.000288) | 0.096953 / 0.004250 (0.092703) | 0.070593 / 0.037052 (0.033540) | 0.409790 / 0.258489 (0.151301) | 0.477452 / 0.293841 (0.183611) | 0.045827 / 0.128546 (-0.082719) | 0.014038 / 0.075646 (-0.061608) | 0.421317 / 0.419271 (0.002045) | 0.065276 / 0.043533 (0.021743) | 0.360074 / 0.255139 (0.104935) | 0.409147 / 0.283200 (0.125947) | 0.032444 / 0.141683 (-0.109238) | 1.739257 / 1.452155 (0.287102) | 1.831408 / 1.492716 (0.338692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274852 / 0.018006 (0.256846) | 0.596320 / 0.000490 (0.595830) | 0.006399 / 0.000200 (0.006199) | 0.000133 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031400 / 0.037411 (-0.006012) | 0.127052 / 0.014526 (0.112526) | 0.134269 / 0.176557 (-0.042288) | 0.225998 / 0.737135 (-0.511137) | 0.150019 / 0.296338 (-0.146319) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654202 / 0.215209 (0.438993) | 6.216735 / 2.077655 (4.139081) | 2.440214 / 1.504120 (0.936094) | 2.150575 / 1.541195 (0.609380) | 2.124790 / 1.468490 (0.656300) | 0.923514 / 4.584777 (-3.661263) | 5.556924 / 3.745712 (1.811212) | 2.843886 / 5.269862 (-2.425975) | 1.834232 / 4.565676 (-2.731444) | 0.111735 / 0.424275 (-0.312540) | 0.014823 / 0.007607 (0.007216) | 0.820503 / 0.226044 (0.594459) | 7.887737 / 2.268929 (5.618809) | 3.120307 / 55.444624 (-52.324317) | 2.405856 / 6.876477 (-4.470621) | 2.411239 / 2.142072 (0.269167) | 1.071283 / 4.805227 (-3.733944) | 0.227738 / 6.500664 (-6.272926) | 0.073516 / 0.075469 (-0.001953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.531806 / 1.841788 (-0.309982) | 18.547661 / 8.074308 (10.473353) | 21.083922 / 10.191392 (10.892530) | 0.241706 / 0.680424 (-0.438718) | 0.034169 / 0.534201 (-0.500032) | 0.497514 / 0.579283 (-0.081769) | 0.599801 / 0.434364 (0.165437) | 0.576465 / 0.540337 (0.036127) | 0.673509 / 1.386936 (-0.713427) |\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) | 0.007558 / 0.011353 (-0.003795) | 0.005001 / 0.011008 (-0.006008) | 0.093809 / 0.038508 (0.055301) | 0.039792 / 0.023109 (0.016683) | 0.456869 / 0.275898 (0.180971) | 0.493370 / 0.323480 (0.169891) | 0.005561 / 0.007986 (-0.002424) | 0.003982 / 0.004328 (-0.000346) | 0.085421 / 0.004250 (0.081170) | 0.059817 / 0.037052 (0.022765) | 0.468040 / 0.258489 (0.209550) | 0.514853 / 0.293841 (0.221012) | 0.044267 / 0.128546 (-0.084279) | 0.012674 / 0.075646 (-0.062972) | 0.098324 / 0.419271 (-0.320948) | 0.056604 / 0.043533 (0.013071) | 0.432200 / 0.255139 (0.177061) | 0.459812 / 0.283200 (0.176612) | 0.033872 / 0.141683 (-0.107811) | 1.618576 / 1.452155 (0.166421) | 1.676562 / 1.492716 (0.183846) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230625 / 0.018006 (0.212619) | 0.600558 / 0.000490 (0.600068) | 0.003419 / 0.000200 (0.003219) | 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) | 0.026916 / 0.037411 (-0.010496) | 0.103003 / 0.014526 (0.088478) | 0.117078 / 0.176557 (-0.059478) | 0.169359 / 0.737135 (-0.567776) | 0.120305 / 0.296338 (-0.176034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616877 / 0.215209 (0.401668) | 6.157232 / 2.077655 (4.079577) | 2.869219 / 1.504120 (1.365099) | 2.381410 / 1.541195 (0.840216) | 2.417357 / 1.468490 (0.948867) | 0.914947 / 4.584777 (-3.669830) | 5.718526 / 3.745712 (1.972814) | 2.757253 / 5.269862 (-2.512609) | 1.794122 / 4.565676 (-2.771554) | 0.108423 / 0.424275 (-0.315852) | 0.013378 / 0.007607 (0.005771) | 0.831067 / 0.226044 (0.605023) | 8.478946 / 2.268929 (6.210018) | 3.685937 / 55.444624 (-51.758687) | 2.867472 / 6.876477 (-4.009005) | 2.895975 / 2.142072 (0.753903) | 1.137547 / 4.805227 (-3.667681) | 0.213891 / 6.500664 (-6.286773) | 0.075825 / 0.075469 (0.000356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621193 / 1.841788 (-0.220594) | 17.322110 / 8.074308 (9.247802) | 21.804016 / 10.191392 (11.612624) | 0.243692 / 0.680424 (-0.436732) | 0.030331 / 0.534201 (-0.503870) | 0.492186 / 0.579283 (-0.087097) | 0.632583 / 0.434364 (0.198219) | 0.576265 / 0.540337 (0.035927) | 0.713165 / 1.386936 (-0.673771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) | 0.008916 / 0.011353 (-0.002437) | 0.004737 / 0.011008 (-0.006271) | 0.134271 / 0.038508 (0.095763) | 0.054472 / 0.023109 (0.031363) | 0.380942 / 0.275898 (0.105044) | 0.474138 / 0.323480 (0.150658) | 0.007917 / 0.007986 (-0.000068) | 0.003748 / 0.004328 (-0.000580) | 0.092765 / 0.004250 (0.088515) | 0.077873 / 0.037052 (0.040821) | 0.397533 / 0.258489 (0.139043) | 0.454737 / 0.293841 (0.160896) | 0.039901 / 0.128546 (-0.088645) | 0.010188 / 0.075646 (-0.065458) | 0.447312 / 0.419271 (0.028040) | 0.068684 / 0.043533 (0.025151) | 0.371554 / 0.255139 (0.116415) | 0.459655 / 0.283200 (0.176455) | 0.027157 / 0.141683 (-0.114526) | 1.874643 / 1.452155 (0.422488) | 2.014800 / 1.492716 (0.522083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227079 / 0.018006 (0.209073) | 0.483241 / 0.000490 (0.482751) | 0.012404 / 0.000200 (0.012204) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033135 / 0.037411 (-0.004277) | 0.137782 / 0.014526 (0.123257) | 0.142951 / 0.176557 (-0.033605) | 0.209825 / 0.737135 (-0.527311) | 0.152438 / 0.296338 (-0.143900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513066 / 0.215209 (0.297857) | 5.122776 / 2.077655 (3.045121) | 2.399270 / 1.504120 (0.895150) | 2.180143 / 1.541195 (0.638949) | 2.286395 / 1.468490 (0.817905) | 0.641866 / 4.584777 (-3.942911) | 4.694922 / 3.745712 (0.949210) | 2.543390 / 5.269862 (-2.726472) | 1.398592 / 4.565676 (-3.167084) | 0.088662 / 0.424275 (-0.335613) | 0.015854 / 0.007607 (0.008247) | 0.688891 / 0.226044 (0.462847) | 6.370148 / 2.268929 (4.101220) | 2.949974 / 55.444624 (-52.494650) | 2.538049 / 6.876477 (-4.338428) | 2.699380 / 2.142072 (0.557308) | 0.792670 / 4.805227 (-4.012557) | 0.169126 / 6.500664 (-6.331538) | 0.078511 / 0.075469 (0.003042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609119 / 1.841788 (-0.232669) | 18.785069 / 8.074308 (10.710761) | 16.670783 / 10.191392 (6.479391) | 0.213081 / 0.680424 (-0.467343) | 0.023904 / 0.534201 (-0.510296) | 0.567720 / 0.579283 (-0.011564) | 0.505806 / 0.434364 (0.071442) | 0.649466 / 0.540337 (0.109129) | 0.773174 / 1.386936 (-0.613762) |\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) | 0.008036 / 0.011353 (-0.003317) | 0.004808 / 0.011008 (-0.006201) | 0.094316 / 0.038508 (0.055808) | 0.056174 / 0.023109 (0.033065) | 0.481618 / 0.275898 (0.205720) | 0.565300 / 0.323480 (0.241820) | 0.006339 / 0.007986 (-0.001646) | 0.003950 / 0.004328 (-0.000379) | 0.093389 / 0.004250 (0.089139) | 0.076163 / 0.037052 (0.039111) | 0.489013 / 0.258489 (0.230524) | 0.565451 / 0.293841 (0.271611) | 0.039392 / 0.128546 (-0.089155) | 0.010553 / 0.075646 (-0.065093) | 0.101406 / 0.419271 (-0.317865) | 0.062355 / 0.043533 (0.018822) | 0.470461 / 0.255139 (0.215322) | 0.502574 / 0.283200 (0.219375) | 0.030196 / 0.141683 (-0.111486) | 1.893926 / 1.452155 (0.441771) | 1.958902 / 1.492716 (0.466185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198074 / 0.018006 (0.180068) | 0.476828 / 0.000490 (0.476338) | 0.003457 / 0.000200 (0.003257) | 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) | 0.037576 / 0.037411 (0.000165) | 0.146663 / 0.014526 (0.132138) | 0.152969 / 0.176557 (-0.023588) | 0.218683 / 0.737135 (-0.518452) | 0.161552 / 0.296338 (-0.134786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.525988 / 0.215209 (0.310779) | 5.234673 / 2.077655 (3.157018) | 2.571668 / 1.504120 (1.067548) | 2.339760 / 1.541195 (0.798565) | 2.422886 / 1.468490 (0.954395) | 0.651537 / 4.584777 (-3.933240) | 4.811148 / 3.745712 (1.065436) | 4.451165 / 5.269862 (-0.818697) | 2.016283 / 4.565676 (-2.549394) | 0.096393 / 0.424275 (-0.327882) | 0.015222 / 0.007607 (0.007615) | 0.739132 / 0.226044 (0.513087) | 6.813327 / 2.268929 (4.544399) | 3.169018 / 55.444624 (-52.275606) | 2.783120 / 6.876477 (-4.093356) | 2.918979 / 2.142072 (0.776907) | 0.797476 / 4.805227 (-4.007751) | 0.171038 / 6.500664 (-6.329626) | 0.079878 / 0.075469 (0.004409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595082 / 1.841788 (-0.246705) | 19.685844 / 8.074308 (11.611536) | 17.518989 / 10.191392 (7.327597) | 0.220015 / 0.680424 (-0.460409) | 0.026351 / 0.534201 (-0.507850) | 0.578977 / 0.579283 (-0.000306) | 0.549564 / 0.434364 (0.115200) | 0.667564 / 0.540337 (0.127227) | 0.802121 / 1.386936 (-0.584815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]

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[ "Thanks for the changes !\r\n\r\nSorry if I wasn't clear about the suggestion of adding the `raw` dataset as well.\r\nBy `raw` I meant the dataset with its original features, i.e. not tokenized to follow the conll format for NER.\r\nThe `raw` dataset has data fields `doc_text`, `doc_id` and `entities`.", "Alright, @lhoestq, now I understand your suggestion, but the JSON files the script downloads aren't actually the \"raw\" HAREM dataset, the [real raw version](https://www.linguateca.pt/primeiroHAREM/harem/ColeccaoDouradaHAREM.zip) of it is on XML format that needs a lot of preprocessing. Those JSON are just pre-processed versions of the dataset.\r\n\r\nI can make the config of the raw version of the pre-processed dataset, but I'll leave a comment on the dataset summary about those details.", "Oh I see ! Sorry I thought there really were the raw (i.e. original/unprocessed) files", "The original xml format doesn't seem very practical to use :/ \r\nWe can ignore that and only keep the default and selective configs then.\r\nCan you revert to the old configs ?\r\n\r\nThanks again for adding this dataset !", "Hi @lhoestq, I've reverted the changes and put more details on README about the dataset." ]

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[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[ "Is there any update on this? \r\n\r\nThanks!", "Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch?", "We currently provide a wrapper on the TensorFlow implementation: https://huggingface.co/metrics/bleurt\r\n\r\nWe have long term plans to better handle model-based metrics, but they probably won't be implemented right away\r\n\r\n@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're interested, but that would best be discussed there :) \r\n\r\nclosing for now", "Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes: \r\n\r\n1. Transposing 'kernel' layers instead of 'dense' ones when copying params from the original model;\r\n2. Taking pooler_output as a cls_state in forward function of the BleurtModel class.\r\n\r\nPlus few minor syntactical fixes for the outdated parts. The result is still not exactly the same, but is very close to the expected one (1.0483 vs 1.0474).\r\n\r\nFind the fixed version here (fixes are commented): https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing \r\n", "I created a new model based on `transformers` that can load every BLEURT checkpoints released so far. https://github.com/lucadiliello/bleurt-pytorch" ]

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[ "The CI can be fixed by merging `main` into your branch. Can you do that before we merge ?", "_The documentation is not available anymore as the PR was closed or merged._", "Done.\n\nOn Thu, Apr 20, 2023 at 6:01 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> The CI can be fixed by merging main into your branch. Can you do that\n> before we merge ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/5758#issuecomment-1516488124>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73QPLA735AMN4PFDYRTXCFFTJANCNFSM6AAAAAAXACBUQU>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "Nice thanks !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007161 / 0.011353 (-0.004192) | 0.005099 / 0.011008 (-0.005909) | 0.099301 / 0.038508 (0.060793) | 0.034144 / 0.023109 (0.011034) | 0.298273 / 0.275898 (0.022375) | 0.329009 / 0.323480 (0.005529) | 0.005486 / 0.007986 (-0.002500) | 0.003887 / 0.004328 (-0.000441) | 0.074769 / 0.004250 (0.070518) | 0.047505 / 0.037052 (0.010453) | 0.306550 / 0.258489 (0.048061) | 0.335380 / 0.293841 (0.041540) | 0.034796 / 0.128546 (-0.093750) | 0.012152 / 0.075646 (-0.063495) | 0.332194 / 0.419271 (-0.087077) | 0.049661 / 0.043533 (0.006128) | 0.296832 / 0.255139 (0.041693) | 0.316417 / 0.283200 (0.033218) | 0.098234 / 0.141683 (-0.043449) | 1.494114 / 1.452155 (0.041959) | 1.566468 / 1.492716 (0.073751) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221309 / 0.018006 (0.203303) | 0.440855 / 0.000490 (0.440365) | 0.003025 / 0.000200 (0.002825) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026594 / 0.037411 (-0.010817) | 0.110406 / 0.014526 (0.095880) | 0.116117 / 0.176557 (-0.060439) | 0.173502 / 0.737135 (-0.563633) | 0.121988 / 0.296338 (-0.174351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403307 / 0.215209 (0.188098) | 4.034146 / 2.077655 (1.956492) | 1.852162 / 1.504120 (0.348042) | 1.675643 / 1.541195 (0.134448) | 1.748851 / 1.468490 (0.280360) | 0.703458 / 4.584777 (-3.881319) | 3.809055 / 3.745712 (0.063343) | 2.118060 / 5.269862 (-3.151801) | 1.338394 / 4.565676 (-3.227282) | 0.086319 / 0.424275 (-0.337956) | 0.012195 / 0.007607 (0.004588) | 0.520814 / 0.226044 (0.294769) | 5.201074 / 2.268929 (2.932145) | 2.418384 / 55.444624 (-53.026240) | 2.085496 / 6.876477 (-4.790980) | 2.245638 / 2.142072 (0.103565) | 0.849042 / 4.805227 (-3.956185) | 0.171912 / 6.500664 (-6.328752) | 0.065691 / 0.075469 (-0.009778) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.159985 / 1.841788 (-0.681803) | 14.910867 / 8.074308 (6.836559) | 14.473926 / 10.191392 (4.282534) | 0.181532 / 0.680424 (-0.498891) | 0.017203 / 0.534201 (-0.516998) | 0.420805 / 0.579283 (-0.158479) | 0.426455 / 0.434364 (-0.007909) | 0.497086 / 0.540337 (-0.043251) | 0.593909 / 1.386936 (-0.793027) |\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) | 0.007688 / 0.011353 (-0.003665) | 0.005353 / 0.011008 (-0.005656) | 0.076869 / 0.038508 (0.038361) | 0.035030 / 0.023109 (0.011921) | 0.344649 / 0.275898 (0.068751) | 0.387669 / 0.323480 (0.064190) | 0.005913 / 0.007986 (-0.002072) | 0.004107 / 0.004328 (-0.000221) | 0.074111 / 0.004250 (0.069860) | 0.049351 / 0.037052 (0.012299) | 0.346061 / 0.258489 (0.087572) | 0.395499 / 0.293841 (0.101658) | 0.035549 / 0.128546 (-0.092997) | 0.012340 / 0.075646 (-0.063307) | 0.087031 / 0.419271 (-0.332241) | 0.049088 / 0.043533 (0.005556) | 0.342774 / 0.255139 (0.087635) | 0.362037 / 0.283200 (0.078837) | 0.100329 / 0.141683 (-0.041354) | 1.442349 / 1.452155 (-0.009806) | 1.551079 / 1.492716 (0.058363) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228458 / 0.018006 (0.210452) | 0.446190 / 0.000490 (0.445701) | 0.000413 / 0.000200 (0.000213) | 0.000056 / 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) | 0.029884 / 0.037411 (-0.007527) | 0.117527 / 0.014526 (0.103002) | 0.123221 / 0.176557 (-0.053335) | 0.172290 / 0.737135 (-0.564845) | 0.128682 / 0.296338 (-0.167657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420905 / 0.215209 (0.205696) | 4.199342 / 2.077655 (2.121687) | 2.007327 / 1.504120 (0.503207) | 1.814732 / 1.541195 (0.273537) | 1.893999 / 1.468490 (0.425509) | 0.712259 / 4.584777 (-3.872518) | 3.843402 / 3.745712 (0.097690) | 3.198514 / 5.269862 (-2.071348) | 1.678732 / 4.565676 (-2.886945) | 0.086435 / 0.424275 (-0.337840) | 0.012233 / 0.007607 (0.004626) | 0.526121 / 0.226044 (0.300077) | 5.190578 / 2.268929 (2.921650) | 2.473259 / 55.444624 (-52.971366) | 2.142795 / 6.876477 (-4.733682) | 2.277594 / 2.142072 (0.135521) | 0.846117 / 4.805227 (-3.959110) | 0.169458 / 6.500664 (-6.331206) | 0.065017 / 0.075469 (-0.010452) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272479 / 1.841788 (-0.569309) | 15.086473 / 8.074308 (7.012165) | 14.659728 / 10.191392 (4.468336) | 0.163915 / 0.680424 (-0.516509) | 0.017561 / 0.534201 (-0.516640) | 0.422074 / 0.579283 (-0.157209) | 0.421963 / 0.434364 (-0.012401) | 0.490321 / 0.540337 (-0.050016) | 0.586854 / 1.386936 (-0.800083) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7ce0ac60c7efc10886471932854903a7c19f172 \"CML watermark\")\n" ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[ "@lhoestq I am going to implement the consolidation step you mentioned in #1870.", "@lhoestq I was thinking that the order of the TableBlocks is not relevant, isn't it?\r\n\r\nI mean, in order to consolidate _consecutive_ in-memory table blocks, in this case:\r\n```\r\nblocks = [in_memory_1, memory_mapped, in_memory_2]\r\n```\r\nI could reorder the list:\r\n```\r\nblocks = [in_memory_1, in_memory_2, memory_mapped]\r\n```\r\nso that the first 2 can be consolidated into a single one:\r\n```\r\nblocks = [in_memory_3, memory_mapped]\r\n```", "I think the order is important, users won't expect the dataset to be \"shuffled\" when they add a new item", "> I think the order is important, users won't expect the dataset to be \"shuffled\" when they add a new item\r\n\r\nOK, therefore I leave `_consolidate_blocks` as it is, which currently keeps the order of the blocks (no shuffling).", "Thank you guys for implementing this. Minor thing I noticed in the [documentation](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.concatenate_datasets): it says \"Converts a list of Dataset with **the same schema** into a single Dataset\". With the addition of the axis parameter, perhaps this should be reworded, no?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008823 / 0.011353 (-0.002529) | 0.004738 / 0.011008 (-0.006270) | 0.102338 / 0.038508 (0.063830) | 0.030603 / 0.023109 (0.007494) | 0.302995 / 0.275898 (0.027097) | 0.362080 / 0.323480 (0.038600) | 0.007096 / 0.007986 (-0.000889) | 0.003493 / 0.004328 (-0.000835) | 0.079129 / 0.004250 (0.074878) | 0.037966 / 0.037052 (0.000914) | 0.310412 / 0.258489 (0.051923) | 0.346740 / 0.293841 (0.052899) | 0.033795 / 0.128546 (-0.094751) | 0.011595 / 0.075646 (-0.064051) | 0.325189 / 0.419271 (-0.094083) | 0.041679 / 0.043533 (-0.001854) | 0.302339 / 0.255139 (0.047200) | 0.322519 / 0.283200 (0.039319) | 0.089058 / 0.141683 (-0.052625) | 1.496223 / 1.452155 (0.044068) | 1.512562 / 1.492716 (0.019845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009298 / 0.018006 (-0.008709) | 0.406726 / 0.000490 (0.406236) | 0.003753 / 0.000200 (0.003553) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023327 / 0.037411 (-0.014084) | 0.098175 / 0.014526 (0.083649) | 0.106040 / 0.176557 (-0.070516) | 0.151934 / 0.737135 (-0.585201) | 0.108465 / 0.296338 (-0.187873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419073 / 0.215209 (0.203864) | 4.188012 / 2.077655 (2.110358) | 1.857667 / 1.504120 (0.353547) | 1.664124 / 1.541195 (0.122929) | 1.704341 / 1.468490 (0.235851) | 0.699671 / 4.584777 (-3.885106) | 3.391110 / 3.745712 (-0.354602) | 1.871136 / 5.269862 (-3.398725) | 1.176794 / 4.565676 (-3.388882) | 0.083322 / 0.424275 (-0.340953) | 0.012450 / 0.007607 (0.004843) | 0.525058 / 0.226044 (0.299014) | 5.265425 / 2.268929 (2.996497) | 2.320672 / 55.444624 (-53.123952) | 1.964806 / 6.876477 (-4.911671) | 2.027055 / 2.142072 (-0.115017) | 0.819768 / 4.805227 (-3.985459) | 0.149638 / 6.500664 (-6.351026) | 0.064774 / 0.075469 (-0.010695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204575 / 1.841788 (-0.637212) | 13.651878 / 8.074308 (5.577570) | 13.751973 / 10.191392 (3.560581) | 0.154781 / 0.680424 (-0.525643) | 0.028887 / 0.534201 (-0.505314) | 0.404905 / 0.579283 (-0.174379) | 0.411320 / 0.434364 (-0.023043) | 0.485026 / 0.540337 (-0.055311) | 0.579690 / 1.386936 (-0.807246) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006615 / 0.011353 (-0.004737) | 0.004606 / 0.011008 (-0.006402) | 0.076099 / 0.038508 (0.037591) | 0.027247 / 0.023109 (0.004137) | 0.360731 / 0.275898 (0.084833) | 0.393688 / 0.323480 (0.070208) | 0.005079 / 0.007986 (-0.002906) | 0.003345 / 0.004328 (-0.000984) | 0.077184 / 0.004250 (0.072934) | 0.037850 / 0.037052 (0.000797) | 0.379738 / 0.258489 (0.121249) | 0.400474 / 0.293841 (0.106633) | 0.031581 / 0.128546 (-0.096966) | 0.011508 / 0.075646 (-0.064138) | 0.084966 / 0.419271 (-0.334306) | 0.041740 / 0.043533 (-0.001793) | 0.349887 / 0.255139 (0.094748) | 0.384405 / 0.283200 (0.101205) | 0.089022 / 0.141683 (-0.052661) | 1.503448 / 1.452155 (0.051293) | 1.564870 / 1.492716 (0.072154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233581 / 0.018006 (0.215574) | 0.413819 / 0.000490 (0.413330) | 0.000398 / 0.000200 (0.000198) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024805 / 0.037411 (-0.012607) | 0.101348 / 0.014526 (0.086822) | 0.108701 / 0.176557 (-0.067856) | 0.160011 / 0.737135 (-0.577124) | 0.111696 / 0.296338 (-0.184642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436303 / 0.215209 (0.221094) | 4.368684 / 2.077655 (2.291029) | 2.082366 / 1.504120 (0.578247) | 1.888108 / 1.541195 (0.346913) | 1.958295 / 1.468490 (0.489804) | 0.700858 / 4.584777 (-3.883919) | 3.408321 / 3.745712 (-0.337391) | 1.872960 / 5.269862 (-3.396902) | 1.165116 / 4.565676 (-3.400560) | 0.083556 / 0.424275 (-0.340719) | 0.012348 / 0.007607 (0.004741) | 0.536551 / 0.226044 (0.310506) | 5.359974 / 2.268929 (3.091045) | 2.539043 / 55.444624 (-52.905581) | 2.200314 / 6.876477 (-4.676162) | 2.222051 / 2.142072 (0.079979) | 0.808567 / 4.805227 (-3.996661) | 0.151222 / 6.500664 (-6.349442) | 0.066351 / 0.075469 (-0.009118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265502 / 1.841788 (-0.576286) | 13.692066 / 8.074308 (5.617758) | 13.124507 / 10.191392 (2.933115) | 0.129545 / 0.680424 (-0.550879) | 0.016827 / 0.534201 (-0.517374) | 0.380326 / 0.579283 (-0.198957) | 0.387268 / 0.434364 (-0.047096) | 0.463722 / 0.540337 (-0.076616) | 0.553681 / 1.386936 (-0.833255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6569014a9948eab7d031a3587405e64ba92d6c59 \"CML watermark\")\n" ]

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[ "Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !", "Closing since this has been fixed by #1923" ]

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[ "Looks good to me. I have not really looked too much into the Beam Datasets yet though - so I think you can merge whenever you think is good for Beam datasets :-) " ]

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[ "Hi @rabeehk , I am unable to reproduce your problem locally.\r\nCan you try emptying the cache (removing the content of `/idiap/temp/rkarimi/cache_home_1/datasets`) and retry ?", "Hi,\r\nthanks, I did remove the cache and still the same error here\r\n\r\n```\r\n>>> a = datasets.load_dataset(\"imdb\", split=\"train\")\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\nDownloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets\r\ncahce dir /idiap/temp/rkarimi/cache_home_1/datasets/downloads\r\nTraceback (most recent call last): \r\n File \"<stdin>\", line 1, in <module>\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 476, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py\", line 558, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py\", line 73, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=4902716, num_examples=3680, dataset_name='imdb')}]\r\n```\r\n\r\ndatasets version\r\n```\r\ndatasets 1.1.2 <pip>\r\ntensorflow-datasets 4.1.0 <pip>\r\n\r\n```", "resolved with moving to version 1.1.3" ]

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Regarding the license issue, I think we should allow it since it starts with `other-`. Cc @gchhablani what do you think ?", "@lhoestq I agree, I'll look into it." ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it." ]

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