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[ "same issue here. @albertvillanova @lhoestq ", "Also impacted by this issue in many of my datasets (though not all) - in my case, this also seems to affect datasets that have been updated recently. Git cloning and the web interface still work:\r\n- https://huggingface.co/api/datasets/acmc/cheat_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_reuter_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_wp_reduced\r\n- https://huggingface.co/api/datasets/acmc/ghostbuster_essay_reduced\r\n\r\nOddly enough, the system status looks good: https://status.huggingface.co/", "Hey how to download these datasets using git cloning?", "Also reported here\r\nhttps://github.com/huggingface/huggingface_hub/issues/2425", "I have been getting the same error for the past 8 hours as well", "Same error since yesterday, fails on any new dataset created", "Same here. I cannot download the HelpSteer2 dataset: https://huggingface.co/datasets/nvidia/HelpSteer2 which has been uploaded about a month ago", "> Hey how to download these datasets using git cloning?\n\nYou'll find a guide [here](https://huggingface.co/docs/hub/en/datasets-downloading) 👍🏻", "Same here for imdb dataset", "It also happens with this dataset: https://huggingface.co/datasets/ylacombe/jenny-tts-6h-tagged", "same here for all datsets in the sentence-tramsformers repo and related collections.\r\n\r\nsame issue with dataset that i recently uploaded on my repo.\r\nseems that the upload date of the datset is not relevat (getting this issue with both old datasets and newer ones)\r\n\r\nfor some reason, i was able to get the dataset by turning it private and accessing it with the id token (accessing it as public while providing the token doesn not work)..... but i can say if that is just a random coincidence.\r\n\r\nseems not much deterministic, for a specific dataset (sentence-transformer nq ) , that was \"down\" since some hours , worked for like 5-10 minutes, then stopped again\r\n\r\nnow even this dataset (that worked since some min ago, and that i'm in the middle of processing steps) stopped working: _https://huggingface.co/datasets/bobox/msmarco-bm25-EduScore/_\r\n\r\nas already pointed out, there are no updates on **_https://status.huggingface.co/_**\r\n\r\n\\n\r\n\\n\r\n\r\nan example of the whole error message:\r\n``` \r\nHfHubHTTPError \r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\r\n 2592 \r\n 2593 # Create a dataset builder\r\n-> 2594 builder_instance = load_dataset_builder(\r\n 2595 path=path,\r\n 2596 name=name,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\r\n 2264 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 2265 download_config.storage_options.update(storage_options)\r\n-> 2266 dataset_module = dataset_module_factory(\r\n 2267 path,\r\n 2268 revision=revision,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\r\n 1912 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1913 ) from None\r\n-> 1914 raise e1 from None\r\n 1915 else:\r\n 1916 raise FileNotFoundError(\r\n\r\n[/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\r\n 1832 hf_api = HfApi(config.HF_ENDPOINT)\r\n 1833 try:\r\n-> 1834 dataset_info = hf_api.dataset_info(\r\n 1835 repo_id=path,\r\n 1836 revision=revision,\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py](https://localhost:8080/#) in _inner_fn(*args, **kwargs)\r\n 112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)\r\n 113 \r\n--> 114 return fn(*args, **kwargs)\r\n 115 \r\n 116 return _inner_fn # type: ignore\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py](https://localhost:8080/#) in dataset_info(self, repo_id, revision, timeout, files_metadata, token)\r\n 2362 \r\n 2363 r = get_session().get(path, headers=headers, timeout=timeout, params=params)\r\n-> 2364 hf_raise_for_status(r)\r\n 2365 data = r.json()\r\n 2366 return DatasetInfo(**data)\r\n\r\n[/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py](https://localhost:8080/#) in hf_raise_for_status(response, endpoint_name)\r\n 369 # Convert `HTTPError` into a `HfHubHTTPError` to display request information\r\n 370 # as well (request id and/or server error message)\r\n--> 371 raise HfHubHTTPError(str(e), response=response) from e\r\n 372 \r\n 373 \r\n\r\nHfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/bobox/xSum-processed (Request ID: Root=1-66a527f0-756cfbc35cc466f075382289;7d5dc06a-37e9-4c22-874d-92b0b1023276)\r\n\r\nInternal Error - We're working hard to fix this as soon as possible!\r\n``` ", "we're working on a fix !", "We fixed the issue, you can load datasets again, sorry for the inconvenience !", "I can confirm, it's working now. I can load the dataset, yay. Thank you @lhoestq ", "@lhoestq thank you so much! " ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7078). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005262 / 0.011353 (-0.006090) | 0.003733 / 0.011008 (-0.007275) | 0.062619 / 0.038508 (0.024111) | 0.029491 / 0.023109 (0.006382) | 0.248947 / 0.275898 (-0.026951) | 0.278741 / 0.323480 (-0.044739) | 0.003173 / 0.007986 (-0.004813) | 0.002777 / 0.004328 (-0.001551) | 0.049344 / 0.004250 (0.045094) | 0.043103 / 0.037052 (0.006051) | 0.252402 / 0.258489 (-0.006087) | 0.288030 / 0.293841 (-0.005811) | 0.029425 / 0.128546 (-0.099121) | 0.012058 / 0.075646 (-0.063589) | 0.204509 / 0.419271 (-0.214762) | 0.035721 / 0.043533 (-0.007812) | 0.249121 / 0.255139 (-0.006018) | 0.272171 / 0.283200 (-0.011029) | 0.019515 / 0.141683 (-0.122168) | 1.130088 / 1.452155 (-0.322067) | 1.148856 / 1.492716 (-0.343860) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093613 / 0.018006 (0.075607) | 0.300830 / 0.000490 (0.300340) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018381 / 0.037411 (-0.019030) | 0.061515 / 0.014526 (0.046989) | 0.074370 / 0.176557 (-0.102186) | 0.120751 / 0.737135 (-0.616384) | 0.074971 / 0.296338 (-0.221367) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280499 / 0.215209 (0.065290) | 2.763114 / 2.077655 (0.685459) | 1.458696 / 1.504120 (-0.045424) | 1.331214 / 1.541195 (-0.209981) | 1.343157 / 1.468490 (-0.125333) | 0.732775 / 4.584777 (-3.852002) | 2.381485 / 3.745712 (-1.364227) | 2.930117 / 5.269862 (-2.339745) | 1.887617 / 4.565676 (-2.678059) | 0.080543 / 0.424275 (-0.343732) | 0.005136 / 0.007607 (-0.002471) | 0.336924 / 0.226044 (0.110879) | 3.343071 / 2.268929 (1.074142) | 1.823677 / 55.444624 (-53.620948) | 1.572300 / 6.876477 (-5.304176) | 1.564040 / 2.142072 (-0.578032) | 0.802369 / 4.805227 (-4.002858) | 0.135198 / 6.500664 (-6.365466) | 0.041499 / 0.075469 (-0.033970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961202 / 1.841788 (-0.880585) | 11.275695 / 8.074308 (3.201387) | 9.508052 / 10.191392 (-0.683340) | 0.136921 / 0.680424 (-0.543503) | 0.014055 / 0.534201 (-0.520146) | 0.300076 / 0.579283 (-0.279208) | 0.263403 / 0.434364 (-0.170961) | 0.340871 / 0.540337 (-0.199466) | 0.433452 / 1.386936 (-0.953484) |\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) | 0.005683 / 0.011353 (-0.005670) | 0.003596 / 0.011008 (-0.007412) | 0.049913 / 0.038508 (0.011405) | 0.033275 / 0.023109 (0.010166) | 0.266011 / 0.275898 (-0.009887) | 0.295182 / 0.323480 (-0.028298) | 0.004336 / 0.007986 (-0.003649) | 0.002787 / 0.004328 (-0.001541) | 0.049035 / 0.004250 (0.044784) | 0.039833 / 0.037052 (0.002781) | 0.283520 / 0.258489 (0.025031) | 0.317437 / 0.293841 (0.023596) | 0.032578 / 0.128546 (-0.095968) | 0.011744 / 0.075646 (-0.063902) | 0.060174 / 0.419271 (-0.359097) | 0.034182 / 0.043533 (-0.009351) | 0.271821 / 0.255139 (0.016682) | 0.292189 / 0.283200 (0.008989) | 0.017045 / 0.141683 (-0.124638) | 1.127742 / 1.452155 (-0.324413) | 1.180621 / 1.492716 (-0.312095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093798 / 0.018006 (0.075792) | 0.310715 / 0.000490 (0.310226) | 0.000213 / 0.000200 (0.000013) | 0.000046 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022379 / 0.037411 (-0.015032) | 0.076823 / 0.014526 (0.062298) | 0.088086 / 0.176557 (-0.088471) | 0.128926 / 0.737135 (-0.608210) | 0.089187 / 0.296338 (-0.207151) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293982 / 0.215209 (0.078773) | 2.930932 / 2.077655 (0.853277) | 1.576425 / 1.504120 (0.072305) | 1.445163 / 1.541195 (-0.096031) | 1.462118 / 1.468490 (-0.006372) | 0.725816 / 4.584777 (-3.858961) | 0.949767 / 3.745712 (-2.795945) | 2.832821 / 5.269862 (-2.437041) | 1.897064 / 4.565676 (-2.668612) | 0.079853 / 0.424275 (-0.344423) | 0.005352 / 0.007607 (-0.002255) | 0.344551 / 0.226044 (0.118507) | 3.442506 / 2.268929 (1.173578) | 1.938700 / 55.444624 (-53.505925) | 1.662205 / 6.876477 (-5.214272) | 1.769061 / 2.142072 (-0.373011) | 0.818089 / 4.805227 (-3.987139) | 0.134612 / 6.500664 (-6.366052) | 0.040419 / 0.075469 (-0.035050) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032267 / 1.841788 (-0.809521) | 11.902598 / 8.074308 (3.828290) | 10.342229 / 10.191392 (0.150837) | 0.140509 / 0.680424 (-0.539915) | 0.015593 / 0.534201 (-0.518608) | 0.303326 / 0.579283 (-0.275957) | 0.127391 / 0.434364 (-0.306973) | 0.342095 / 0.540337 (-0.198243) | 0.438978 / 1.386936 (-0.947958) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#30000fb6ca53126917ee17e1b1987f94f07a1569 \"CML watermark\")\n" ]
[]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7076). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7075). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005717 / 0.011353 (-0.005636) | 0.004102 / 0.011008 (-0.006906) | 0.064343 / 0.038508 (0.025835) | 0.031510 / 0.023109 (0.008400) | 0.254534 / 0.275898 (-0.021364) | 0.275080 / 0.323480 (-0.048400) | 0.004243 / 0.007986 (-0.003742) | 0.002782 / 0.004328 (-0.001546) | 0.049554 / 0.004250 (0.045303) | 0.045291 / 0.037052 (0.008239) | 0.264118 / 0.258489 (0.005629) | 0.296476 / 0.293841 (0.002635) | 0.030298 / 0.128546 (-0.098248) | 0.012646 / 0.075646 (-0.063000) | 0.208403 / 0.419271 (-0.210869) | 0.036365 / 0.043533 (-0.007168) | 0.250294 / 0.255139 (-0.004845) | 0.276057 / 0.283200 (-0.007143) | 0.018687 / 0.141683 (-0.122996) | 1.128970 / 1.452155 (-0.323184) | 1.170923 / 1.492716 (-0.321793) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.134953 / 0.018006 (0.116947) | 0.301722 / 0.000490 (0.301232) | 0.000242 / 0.000200 (0.000042) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019650 / 0.037411 (-0.017761) | 0.063404 / 0.014526 (0.048878) | 0.074883 / 0.176557 (-0.101674) | 0.122846 / 0.737135 (-0.614289) | 0.077410 / 0.296338 (-0.218928) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287710 / 0.215209 (0.072501) | 2.813834 / 2.077655 (0.736179) | 1.454710 / 1.504120 (-0.049410) | 1.327303 / 1.541195 (-0.213891) | 1.375064 / 1.468490 (-0.093426) | 0.746831 / 4.584777 (-3.837946) | 2.361008 / 3.745712 (-1.384705) | 3.080869 / 5.269862 (-2.188993) | 1.969927 / 4.565676 (-2.595749) | 0.081045 / 0.424275 (-0.343230) | 0.005168 / 0.007607 (-0.002440) | 0.342657 / 0.226044 (0.116613) | 3.404883 / 2.268929 (1.135955) | 1.840761 / 55.444624 (-53.603863) | 1.535400 / 6.876477 (-5.341076) | 1.584613 / 2.142072 (-0.557460) | 0.828003 / 4.805227 (-3.977224) | 0.135564 / 6.500664 (-6.365100) | 0.042717 / 0.075469 (-0.032752) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985301 / 1.841788 (-0.856487) | 11.945913 / 8.074308 (3.871605) | 9.887577 / 10.191392 (-0.303815) | 0.141261 / 0.680424 (-0.539163) | 0.014961 / 0.534201 (-0.519240) | 0.304134 / 0.579283 (-0.275150) | 0.264733 / 0.434364 (-0.169631) | 0.349993 / 0.540337 (-0.190345) | 0.440390 / 1.386936 (-0.946546) |\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) | 0.006145 / 0.011353 (-0.005207) | 0.004259 / 0.011008 (-0.006749) | 0.051245 / 0.038508 (0.012737) | 0.034873 / 0.023109 (0.011764) | 0.274149 / 0.275898 (-0.001749) | 0.299761 / 0.323480 (-0.023719) | 0.004457 / 0.007986 (-0.003529) | 0.002938 / 0.004328 (-0.001390) | 0.049547 / 0.004250 (0.045297) | 0.042441 / 0.037052 (0.005389) | 0.284961 / 0.258489 (0.026472) | 0.322197 / 0.293841 (0.028356) | 0.033850 / 0.128546 (-0.094696) | 0.012615 / 0.075646 (-0.063031) | 0.061967 / 0.419271 (-0.357304) | 0.035229 / 0.043533 (-0.008304) | 0.273941 / 0.255139 (0.018802) | 0.293395 / 0.283200 (0.010195) | 0.020566 / 0.141683 (-0.121117) | 1.173423 / 1.452155 (-0.278732) | 1.219948 / 1.492716 (-0.272768) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096131 / 0.018006 (0.078125) | 0.305548 / 0.000490 (0.305059) | 0.000217 / 0.000200 (0.000017) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023847 / 0.037411 (-0.013564) | 0.079536 / 0.014526 (0.065010) | 0.088889 / 0.176557 (-0.087667) | 0.129181 / 0.737135 (-0.607954) | 0.090879 / 0.296338 (-0.205460) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.299315 / 0.215209 (0.084106) | 2.952656 / 2.077655 (0.875001) | 1.587354 / 1.504120 (0.083234) | 1.453420 / 1.541195 (-0.087774) | 1.501784 / 1.468490 (0.033294) | 0.711481 / 4.584777 (-3.873296) | 0.971790 / 3.745712 (-2.773922) | 2.897636 / 5.269862 (-2.372226) | 1.947086 / 4.565676 (-2.618591) | 0.079700 / 0.424275 (-0.344575) | 0.005395 / 0.007607 (-0.002212) | 0.351340 / 0.226044 (0.125296) | 3.416472 / 2.268929 (1.147543) | 2.007559 / 55.444624 (-53.437066) | 1.660401 / 6.876477 (-5.216076) | 1.837049 / 2.142072 (-0.305024) | 0.817306 / 4.805227 (-3.987921) | 0.135176 / 6.500664 (-6.365488) | 0.041477 / 0.075469 (-0.033992) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030033 / 1.841788 (-0.811755) | 12.528661 / 8.074308 (4.454353) | 10.603212 / 10.191392 (0.411820) | 0.142434 / 0.680424 (-0.537989) | 0.015603 / 0.534201 (-0.518598) | 0.304516 / 0.579283 (-0.274767) | 0.125324 / 0.434364 (-0.309040) | 0.343092 / 0.540337 (-0.197245) | 0.443359 / 1.386936 (-0.943577) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45c5b3daacd7af212cae4c848a56e14d3cac291f \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7074). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005168 / 0.011353 (-0.006185) | 0.003572 / 0.011008 (-0.007436) | 0.062755 / 0.038508 (0.024247) | 0.030371 / 0.023109 (0.007262) | 0.250240 / 0.275898 (-0.025658) | 0.268091 / 0.323480 (-0.055389) | 0.003260 / 0.007986 (-0.004726) | 0.002706 / 0.004328 (-0.001622) | 0.048957 / 0.004250 (0.044706) | 0.044441 / 0.037052 (0.007389) | 0.251801 / 0.258489 (-0.006688) | 0.289401 / 0.293841 (-0.004440) | 0.028991 / 0.128546 (-0.099555) | 0.011871 / 0.075646 (-0.063775) | 0.203722 / 0.419271 (-0.215549) | 0.035911 / 0.043533 (-0.007622) | 0.248070 / 0.255139 (-0.007069) | 0.266480 / 0.283200 (-0.016720) | 0.019831 / 0.141683 (-0.121852) | 1.143429 / 1.452155 (-0.308726) | 1.160102 / 1.492716 (-0.332614) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096740 / 0.018006 (0.078734) | 0.302473 / 0.000490 (0.301983) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018367 / 0.037411 (-0.019045) | 0.062346 / 0.014526 (0.047820) | 0.074416 / 0.176557 (-0.102140) | 0.120507 / 0.737135 (-0.616628) | 0.076536 / 0.296338 (-0.219802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284093 / 0.215209 (0.068884) | 2.738805 / 2.077655 (0.661150) | 1.469263 / 1.504120 (-0.034856) | 1.349122 / 1.541195 (-0.192073) | 1.355578 / 1.468490 (-0.112912) | 0.720364 / 4.584777 (-3.864413) | 2.360339 / 3.745712 (-1.385373) | 2.941134 / 5.269862 (-2.328728) | 1.888692 / 4.565676 (-2.676984) | 0.077111 / 0.424275 (-0.347164) | 0.005070 / 0.007607 (-0.002537) | 0.334122 / 0.226044 (0.108078) | 3.298378 / 2.268929 (1.029450) | 1.868514 / 55.444624 (-53.576111) | 1.528561 / 6.876477 (-5.347916) | 1.535319 / 2.142072 (-0.606754) | 0.778591 / 4.805227 (-4.026636) | 0.131364 / 6.500664 (-6.369300) | 0.041697 / 0.075469 (-0.033773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970243 / 1.841788 (-0.871544) | 11.324752 / 8.074308 (3.250443) | 9.612381 / 10.191392 (-0.579011) | 0.138842 / 0.680424 (-0.541582) | 0.014479 / 0.534201 (-0.519722) | 0.309415 / 0.579283 (-0.269868) | 0.264654 / 0.434364 (-0.169710) | 0.343695 / 0.540337 (-0.196642) | 0.435323 / 1.386936 (-0.951613) |\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) | 0.005680 / 0.011353 (-0.005673) | 0.003614 / 0.011008 (-0.007394) | 0.060575 / 0.038508 (0.022067) | 0.031103 / 0.023109 (0.007994) | 0.269083 / 0.275898 (-0.006815) | 0.291556 / 0.323480 (-0.031923) | 0.004354 / 0.007986 (-0.003632) | 0.002739 / 0.004328 (-0.001589) | 0.049056 / 0.004250 (0.044806) | 0.039759 / 0.037052 (0.002707) | 0.280608 / 0.258489 (0.022119) | 0.324798 / 0.293841 (0.030957) | 0.032030 / 0.128546 (-0.096516) | 0.011862 / 0.075646 (-0.063784) | 0.060011 / 0.419271 (-0.359261) | 0.033960 / 0.043533 (-0.009573) | 0.271114 / 0.255139 (0.015975) | 0.293922 / 0.283200 (0.010722) | 0.019497 / 0.141683 (-0.122185) | 1.137871 / 1.452155 (-0.314284) | 1.180656 / 1.492716 (-0.312061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094201 / 0.018006 (0.076194) | 0.306657 / 0.000490 (0.306167) | 0.000215 / 0.000200 (0.000015) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022562 / 0.037411 (-0.014850) | 0.077170 / 0.014526 (0.062644) | 0.088915 / 0.176557 (-0.087642) | 0.129455 / 0.737135 (-0.607680) | 0.091571 / 0.296338 (-0.204767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300753 / 0.215209 (0.085544) | 2.941929 / 2.077655 (0.864274) | 1.613451 / 1.504120 (0.109331) | 1.498365 / 1.541195 (-0.042830) | 1.517124 / 1.468490 (0.048634) | 0.709209 / 4.584777 (-3.875568) | 0.950478 / 3.745712 (-2.795235) | 2.799328 / 5.269862 (-2.470533) | 1.872895 / 4.565676 (-2.692782) | 0.078233 / 0.424275 (-0.346042) | 0.005613 / 0.007607 (-0.001994) | 0.349590 / 0.226044 (0.123545) | 3.500213 / 2.268929 (1.231284) | 2.001155 / 55.444624 (-53.443469) | 1.704845 / 6.876477 (-5.171632) | 1.810722 / 2.142072 (-0.331350) | 0.795326 / 4.805227 (-4.009901) | 0.132913 / 6.500664 (-6.367751) | 0.041209 / 0.075469 (-0.034260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.029513 / 1.841788 (-0.812274) | 12.005617 / 8.074308 (3.931309) | 10.119379 / 10.191392 (-0.072013) | 0.139767 / 0.680424 (-0.540657) | 0.015241 / 0.534201 (-0.518960) | 0.301164 / 0.579283 (-0.278119) | 0.121563 / 0.434364 (-0.312801) | 0.336672 / 0.540337 (-0.203666) | 0.431526 / 1.386936 (-0.955410) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92bdab56a3c7d5ded10e8ae4134c943e32d3bc86 \"CML watermark\")\n" ]
[ "Any recent change in the API backend rejecting parameter `revision=\"refs/pr/1\"` to `HfApi.preupload_lfs_files`?\r\n```\r\nf\"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}\"\r\n\r\nhttps://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.\r\nInvalid rev id: refs/pr/1\r\n```\r\n@Wauplin @huggingface/datasets @huggingface/moon-landing @huggingface/moon-landing-back ", "I have temporarily fixed the CI with:\r\n- #7074\r\n\r\nHowever, the underlying issue must be fixed and #7074 must be reverted.", "Hmm does it do the preupload call before creating the ref cc @Wauplin ?\r\n\r\n(in that case it should do a preupload call on the base branch with `?create_pr=1`)", "@coyotte508, the CI test was implemented 2 months ago and it was working OK until yesterday. See the CI status of the commits in the main branch of `datasets`: https://github.com/huggingface/datasets/commits/main/", "Yes i get that\r\n\r\nWe changed the preupload response to return the commit id in https://github.com/huggingface-internal/moon-landing/pull/10756\r\n\r\nThis line is probably causing the error: https://github.com/huggingface-internal/moon-landing/pull/10756/files#diff-558f6f9865e30bfa091b94d6a4a900138103ddb4eb0bec96b6deec5bf5626fa0R2322\r\n\r\nIt's weird the error is returned, it means that maybe a ref with 0 history (not even the first commit) was created\r\n\r\nDoes this change have any impact in production, or just the CI test? If it's just the CI test it should be fixed on your side, if it impacts production we can look at a solution", "@coyotte508 it impacts production: `convert_to_parquet` raises the above error when the dataset has more that one configs/subsets:\r\n- First subset calls `push_to_hub` with `create_pr=True`\r\n- Second subset uses the `refs/pr/#` returned by the call above, and calls `push_to_hub` with `revision=\"refs/pr/#\"`", "I tried removing the `mock_commit` call: https://github.com/huggingface/datasets/pull/7076\r\n\r\nAnd the tests seem to work.\r\n\r\nSo it's probably because the commit is not actually called, it doesn't actually create the pull request on the remote (and the associated `refs/pr/1`). But the `preupload` call is not mocked.\r\n\r\nAnyway it shouldn't impact production, since production isn't mocked", "@coyotte508 thanks a lot for the investigation and sorry for the noise. \r\nI promise not trying to fix things when I have a slight fever: my head does not work well.\r\n\r\nWe need indeed to mock `preupload_lfs_files`: before it was not necessary, but now it is.", "I fixed the test in:\r\n- #7078\r\n\r\nThanks again, @coyotte508." ]
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7069). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "cc @Wauplin maybe it's a `huggingface_hub` bug ?\r\n\r\nEDIT: ah actually the issue is opened at https://github.com/huggingface/huggingface_hub/issues/2419" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7068). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
[ "Many users have encountered the same issue, which has caused inconvenience.\r\n\r\nhttps://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733\r\n", "Thanks for reporting.\r\n\r\nI will make the code more robust.", "I have opened an issue in the huggingface-hub repo:\r\n- https://github.com/huggingface/huggingface_hub/issues/2419\r\n\r\nI am opening a PR to avoid calling `create_branch` if the branch already exists." ]
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[]
[ "Looks good to me ! :)\r\n\r\nyou might want to add the `map` num_proc argument as well, for people who want to make it run faster", "Thanks for the feedback @lhoestq! The last commits include:\r\n- Adding the `num_proc` parameter to `batch`\r\n- Adding tests similar to the one done for `IterableDataset.batch()`\r\n- Updated the documentation -> I think they are actually misplaced in the `Stream` page. But could not find a better place atm. Where would you put this documentation?\r\n\r\nWDYT?", "You can put the documentation in process.mdx :)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7064). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I reset the head to the commit before I added the `Dataset.batch()` documentation to `stream.mdx` and instead added the documentation to `process.mdx`. ", "<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) | 0.005736 / 0.011353 (-0.005617) | 0.003959 / 0.011008 (-0.007049) | 0.063259 / 0.038508 (0.024751) | 0.030705 / 0.023109 (0.007596) | 0.245706 / 0.275898 (-0.030192) | 0.278766 / 0.323480 (-0.044714) | 0.003354 / 0.007986 (-0.004632) | 0.004246 / 0.004328 (-0.000082) | 0.049346 / 0.004250 (0.045095) | 0.046439 / 0.037052 (0.009386) | 0.257930 / 0.258489 (-0.000559) | 0.295562 / 0.293841 (0.001722) | 0.030529 / 0.128546 (-0.098017) | 0.012465 / 0.075646 (-0.063182) | 0.205595 / 0.419271 (-0.213677) | 0.036319 / 0.043533 (-0.007214) | 0.243872 / 0.255139 (-0.011267) | 0.275834 / 0.283200 (-0.007366) | 0.020330 / 0.141683 (-0.121353) | 1.108337 / 1.452155 (-0.343817) | 1.150406 / 1.492716 (-0.342310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.113498 / 0.018006 (0.095491) | 0.306654 / 0.000490 (0.306164) | 0.000238 / 0.000200 (0.000038) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019092 / 0.037411 (-0.018319) | 0.063180 / 0.014526 (0.048654) | 0.078244 / 0.176557 (-0.098313) | 0.126106 / 0.737135 (-0.611030) | 0.078651 / 0.296338 (-0.217687) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284132 / 0.215209 (0.068923) | 2.781250 / 2.077655 (0.703595) | 1.471864 / 1.504120 (-0.032256) | 1.354661 / 1.541195 (-0.186534) | 1.362839 / 1.468490 (-0.105651) | 0.719126 / 4.584777 (-3.865651) | 2.396969 / 3.745712 (-1.348743) | 2.987924 / 5.269862 (-2.281938) | 1.910555 / 4.565676 (-2.655121) | 0.078612 / 0.424275 (-0.345663) | 0.005170 / 0.007607 (-0.002437) | 0.333876 / 0.226044 (0.107832) | 3.298340 / 2.268929 (1.029412) | 1.853332 / 55.444624 (-53.591292) | 1.551919 / 6.876477 (-5.324557) | 1.585677 / 2.142072 (-0.556395) | 0.802487 / 4.805227 (-4.002741) | 0.134828 / 6.500664 (-6.365837) | 0.041966 / 0.075469 (-0.033503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992277 / 1.841788 (-0.849511) | 11.626887 / 8.074308 (3.552578) | 9.715623 / 10.191392 (-0.475769) | 0.140306 / 0.680424 (-0.540117) | 0.014528 / 0.534201 (-0.519673) | 0.306247 / 0.579283 (-0.273036) | 0.263067 / 0.434364 (-0.171297) | 0.342325 / 0.540337 (-0.198013) | 0.432299 / 1.386936 (-0.954637) |\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) | 0.006004 / 0.011353 (-0.005349) | 0.003890 / 0.011008 (-0.007118) | 0.050408 / 0.038508 (0.011900) | 0.031880 / 0.023109 (0.008771) | 0.273114 / 0.275898 (-0.002784) | 0.296653 / 0.323480 (-0.026826) | 0.004569 / 0.007986 (-0.003416) | 0.002831 / 0.004328 (-0.001497) | 0.050032 / 0.004250 (0.045782) | 0.040468 / 0.037052 (0.003415) | 0.284718 / 0.258489 (0.026229) | 0.321754 / 0.293841 (0.027913) | 0.033863 / 0.128546 (-0.094684) | 0.012183 / 0.075646 (-0.063463) | 0.060805 / 0.419271 (-0.358466) | 0.034919 / 0.043533 (-0.008614) | 0.274354 / 0.255139 (0.019215) | 0.293477 / 0.283200 (0.010277) | 0.019418 / 0.141683 (-0.122265) | 1.151571 / 1.452155 (-0.300584) | 1.217174 / 1.492716 (-0.275542) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097326 / 0.018006 (0.079320) | 0.316277 / 0.000490 (0.315787) | 0.000225 / 0.000200 (0.000025) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022932 / 0.037411 (-0.014479) | 0.077455 / 0.014526 (0.062929) | 0.088949 / 0.176557 (-0.087608) | 0.129447 / 0.737135 (-0.607688) | 0.093705 / 0.296338 (-0.202634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303918 / 0.215209 (0.088709) | 2.973866 / 2.077655 (0.896211) | 1.593165 / 1.504120 (0.089045) | 1.465312 / 1.541195 (-0.075883) | 1.484503 / 1.468490 (0.016013) | 0.731849 / 4.584777 (-3.852928) | 0.953337 / 3.745712 (-2.792375) | 2.887815 / 5.269862 (-2.382047) | 1.923618 / 4.565676 (-2.642058) | 0.080073 / 0.424275 (-0.344202) | 0.005460 / 0.007607 (-0.002148) | 0.359876 / 0.226044 (0.133832) | 3.532251 / 2.268929 (1.263323) | 1.987778 / 55.444624 (-53.456846) | 1.685572 / 6.876477 (-5.190905) | 1.827141 / 2.142072 (-0.314932) | 0.815953 / 4.805227 (-3.989274) | 0.136698 / 6.500664 (-6.363967) | 0.042185 / 0.075469 (-0.033285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032508 / 1.841788 (-0.809280) | 12.526918 / 8.074308 (4.452610) | 10.202942 / 10.191392 (0.011550) | 0.145920 / 0.680424 (-0.534504) | 0.015643 / 0.534201 (-0.518558) | 0.300465 / 0.579283 (-0.278818) | 0.126786 / 0.434364 (-0.307578) | 0.342885 / 0.540337 (-0.197453) | 0.438139 / 1.386936 (-0.948797) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c98e069b47d40a219b6f27e62ed85a5bb17449e \"CML watermark\")\n" ]
[]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7062). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005591 / 0.011353 (-0.005761) | 0.003992 / 0.011008 (-0.007016) | 0.063932 / 0.038508 (0.025424) | 0.034572 / 0.023109 (0.011463) | 0.252532 / 0.275898 (-0.023366) | 0.271233 / 0.323480 (-0.052247) | 0.005146 / 0.007986 (-0.002840) | 0.002844 / 0.004328 (-0.001484) | 0.049555 / 0.004250 (0.045305) | 0.044111 / 0.037052 (0.007059) | 0.270131 / 0.258489 (0.011642) | 0.318109 / 0.293841 (0.024269) | 0.030247 / 0.128546 (-0.098300) | 0.012438 / 0.075646 (-0.063209) | 0.205160 / 0.419271 (-0.214112) | 0.036228 / 0.043533 (-0.007305) | 0.250664 / 0.255139 (-0.004475) | 0.263884 / 0.283200 (-0.019315) | 0.018141 / 0.141683 (-0.123541) | 1.128504 / 1.452155 (-0.323650) | 1.182543 / 1.492716 (-0.310173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094576 / 0.018006 (0.076570) | 0.301153 / 0.000490 (0.300664) | 0.000246 / 0.000200 (0.000046) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019143 / 0.037411 (-0.018268) | 0.062788 / 0.014526 (0.048262) | 0.074688 / 0.176557 (-0.101869) | 0.121799 / 0.737135 (-0.615336) | 0.076200 / 0.296338 (-0.220138) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277002 / 0.215209 (0.061793) | 2.735738 / 2.077655 (0.658083) | 1.430408 / 1.504120 (-0.073712) | 1.309795 / 1.541195 (-0.231400) | 1.339083 / 1.468490 (-0.129407) | 0.702540 / 4.584777 (-3.882237) | 2.352468 / 3.745712 (-1.393244) | 2.913698 / 5.269862 (-2.356164) | 1.871739 / 4.565676 (-2.693938) | 0.077054 / 0.424275 (-0.347221) | 0.005055 / 0.007607 (-0.002552) | 0.330550 / 0.226044 (0.104505) | 3.272556 / 2.268929 (1.003627) | 1.805268 / 55.444624 (-53.639356) | 1.504791 / 6.876477 (-5.371686) | 1.511361 / 2.142072 (-0.630712) | 0.784451 / 4.805227 (-4.020776) | 0.132182 / 6.500664 (-6.368482) | 0.042516 / 0.075469 (-0.032954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946939 / 1.841788 (-0.894849) | 11.369607 / 8.074308 (3.295299) | 9.667350 / 10.191392 (-0.524042) | 0.138689 / 0.680424 (-0.541735) | 0.014416 / 0.534201 (-0.519785) | 0.300685 / 0.579283 (-0.278598) | 0.259709 / 0.434364 (-0.174655) | 0.341271 / 0.540337 (-0.199066) | 0.435609 / 1.386936 (-0.951327) |\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) | 0.005726 / 0.011353 (-0.005627) | 0.004071 / 0.011008 (-0.006937) | 0.050837 / 0.038508 (0.012329) | 0.047000 / 0.023109 (0.023890) | 0.278543 / 0.275898 (0.002645) | 0.300526 / 0.323480 (-0.022954) | 0.004483 / 0.007986 (-0.003503) | 0.002835 / 0.004328 (-0.001494) | 0.050925 / 0.004250 (0.046675) | 0.041834 / 0.037052 (0.004782) | 0.285059 / 0.258489 (0.026570) | 0.324557 / 0.293841 (0.030716) | 0.038949 / 0.128546 (-0.089597) | 0.012145 / 0.075646 (-0.063501) | 0.061791 / 0.419271 (-0.357481) | 0.034493 / 0.043533 (-0.009040) | 0.274034 / 0.255139 (0.018895) | 0.295886 / 0.283200 (0.012686) | 0.018524 / 0.141683 (-0.123159) | 1.148766 / 1.452155 (-0.303388) | 1.207966 / 1.492716 (-0.284750) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094078 / 0.018006 (0.076071) | 0.307850 / 0.000490 (0.307361) | 0.000224 / 0.000200 (0.000024) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023502 / 0.037411 (-0.013910) | 0.077321 / 0.014526 (0.062795) | 0.091147 / 0.176557 (-0.085410) | 0.131111 / 0.737135 (-0.606025) | 0.090906 / 0.296338 (-0.205432) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290700 / 0.215209 (0.075491) | 2.833655 / 2.077655 (0.756001) | 1.546371 / 1.504120 (0.042251) | 1.415337 / 1.541195 (-0.125858) | 1.445752 / 1.468490 (-0.022738) | 0.737880 / 4.584777 (-3.846897) | 0.961549 / 3.745712 (-2.784164) | 2.844021 / 5.269862 (-2.425841) | 2.023547 / 4.565676 (-2.542130) | 0.079791 / 0.424275 (-0.344484) | 0.005449 / 0.007607 (-0.002158) | 0.356381 / 0.226044 (0.130337) | 3.515555 / 2.268929 (1.246627) | 1.920407 / 55.444624 (-53.524217) | 1.628637 / 6.876477 (-5.247839) | 1.752995 / 2.142072 (-0.389077) | 0.807264 / 4.805227 (-3.997963) | 0.133627 / 6.500664 (-6.367037) | 0.041861 / 0.075469 (-0.033609) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.035643 / 1.841788 (-0.806144) | 12.114792 / 8.074308 (4.040484) | 10.185844 / 10.191392 (-0.005548) | 0.142354 / 0.680424 (-0.538070) | 0.015466 / 0.534201 (-0.518734) | 0.304681 / 0.579283 (-0.274603) | 0.124297 / 0.434364 (-0.310067) | 0.339907 / 0.540337 (-0.200430) | 0.436266 / 1.386936 (-0.950670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#856eb84569006ab9389ddbcce8b7141befeab9cc \"CML watermark\")\n" ]
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7060). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]
[]
[]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7057). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005617 / 0.011353 (-0.005736) | 0.003994 / 0.011008 (-0.007014) | 0.064188 / 0.038508 (0.025680) | 0.030939 / 0.023109 (0.007829) | 0.248712 / 0.275898 (-0.027186) | 0.273417 / 0.323480 (-0.050063) | 0.003340 / 0.007986 (-0.004646) | 0.002823 / 0.004328 (-0.001506) | 0.049985 / 0.004250 (0.045734) | 0.046872 / 0.037052 (0.009820) | 0.254554 / 0.258489 (-0.003935) | 0.288142 / 0.293841 (-0.005699) | 0.030540 / 0.128546 (-0.098006) | 0.012295 / 0.075646 (-0.063352) | 0.204589 / 0.419271 (-0.214683) | 0.036383 / 0.043533 (-0.007150) | 0.254277 / 0.255139 (-0.000862) | 0.267962 / 0.283200 (-0.015237) | 0.021173 / 0.141683 (-0.120510) | 1.126933 / 1.452155 (-0.325221) | 1.190841 / 1.492716 (-0.301875) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093622 / 0.018006 (0.075616) | 0.297967 / 0.000490 (0.297477) | 0.000241 / 0.000200 (0.000041) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018623 / 0.037411 (-0.018789) | 0.062210 / 0.014526 (0.047684) | 0.074369 / 0.176557 (-0.102187) | 0.120585 / 0.737135 (-0.616550) | 0.075966 / 0.296338 (-0.220372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285440 / 0.215209 (0.070231) | 2.804275 / 2.077655 (0.726620) | 1.484539 / 1.504120 (-0.019580) | 1.366587 / 1.541195 (-0.174607) | 1.355269 / 1.468490 (-0.113221) | 0.722289 / 4.584777 (-3.862488) | 2.344567 / 3.745712 (-1.401145) | 2.831779 / 5.269862 (-2.438083) | 1.899800 / 4.565676 (-2.665876) | 0.078657 / 0.424275 (-0.345619) | 0.005188 / 0.007607 (-0.002420) | 0.340150 / 0.226044 (0.114106) | 3.390915 / 2.268929 (1.121986) | 1.836473 / 55.444624 (-53.608152) | 1.520718 / 6.876477 (-5.355759) | 1.723448 / 2.142072 (-0.418624) | 0.810281 / 4.805227 (-3.994946) | 0.136008 / 6.500664 (-6.364657) | 0.044005 / 0.075469 (-0.031465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989982 / 1.841788 (-0.851806) | 11.671075 / 8.074308 (3.596767) | 9.805471 / 10.191392 (-0.385921) | 0.141637 / 0.680424 (-0.538787) | 0.014551 / 0.534201 (-0.519650) | 0.310077 / 0.579283 (-0.269206) | 0.266838 / 0.434364 (-0.167526) | 0.348894 / 0.540337 (-0.191444) | 0.451530 / 1.386936 (-0.935406) |\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) | 0.005639 / 0.011353 (-0.005713) | 0.003935 / 0.011008 (-0.007074) | 0.050147 / 0.038508 (0.011639) | 0.031023 / 0.023109 (0.007914) | 0.268361 / 0.275898 (-0.007537) | 0.295774 / 0.323480 (-0.027706) | 0.005029 / 0.007986 (-0.002956) | 0.002832 / 0.004328 (-0.001496) | 0.049806 / 0.004250 (0.045556) | 0.040515 / 0.037052 (0.003463) | 0.283298 / 0.258489 (0.024809) | 0.321946 / 0.293841 (0.028105) | 0.031833 / 0.128546 (-0.096714) | 0.012137 / 0.075646 (-0.063510) | 0.060510 / 0.419271 (-0.358761) | 0.033754 / 0.043533 (-0.009779) | 0.268079 / 0.255139 (0.012940) | 0.292468 / 0.283200 (0.009268) | 0.017268 / 0.141683 (-0.124414) | 1.159922 / 1.452155 (-0.292233) | 1.188961 / 1.492716 (-0.303755) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096930 / 0.018006 (0.078923) | 0.306921 / 0.000490 (0.306431) | 0.000226 / 0.000200 (0.000026) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022811 / 0.037411 (-0.014600) | 0.077298 / 0.014526 (0.062772) | 0.088949 / 0.176557 (-0.087608) | 0.130763 / 0.737135 (-0.606372) | 0.090429 / 0.296338 (-0.205909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300866 / 0.215209 (0.085657) | 2.963375 / 2.077655 (0.885720) | 1.595753 / 1.504120 (0.091633) | 1.463091 / 1.541195 (-0.078104) | 1.481182 / 1.468490 (0.012692) | 0.712939 / 4.584777 (-3.871838) | 0.956694 / 3.745712 (-2.789018) | 2.802890 / 5.269862 (-2.466971) | 1.891092 / 4.565676 (-2.674585) | 0.077570 / 0.424275 (-0.346706) | 0.005536 / 0.007607 (-0.002072) | 0.351958 / 0.226044 (0.125914) | 3.459114 / 2.268929 (1.190185) | 1.989488 / 55.444624 (-53.455137) | 1.676271 / 6.876477 (-5.200205) | 1.808073 / 2.142072 (-0.334000) | 0.786920 / 4.805227 (-4.018307) | 0.132220 / 6.500664 (-6.368444) | 0.041602 / 0.075469 (-0.033867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031759 / 1.841788 (-0.810029) | 12.007776 / 8.074308 (3.933467) | 10.568254 / 10.191392 (0.376862) | 0.143176 / 0.680424 (-0.537248) | 0.015556 / 0.534201 (-0.518645) | 0.304484 / 0.579283 (-0.274799) | 0.125508 / 0.434364 (-0.308855) | 0.340017 / 0.540337 (-0.200320) | 0.434285 / 1.386936 (-0.952651) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16fa4421f44b22bbbc607f379a93f45af468d1fc \"CML watermark\")\n" ]
[ "Oh cool !\r\n\r\nThe time it takes to resume depends on the expected maximum distance in this case right ? Do you know its relationship with $B$ ?\r\n\r\nIn your test it already as high as 15k for $B=1024$, which is ok for text datasets but is maybe not ideal for datasets with heavy samples like audio/image/video ? Though for heavy samples datasets the buffer size is generally much smaller to avoid memory issues.\r\n\r\nMaybe we could just add a warning message on resuming to tell the user that it might take some time to recover the shuffle buffer (with a progress bar maybe ?), and have the option to stop + re-run with an env variable to disable shuffle buffer recovering ? WDYT ?", "> The time it takes to resume depends on the expected maximum distance in this case right ? Do you know its relationship with $B$\r\n\r\nHi, I created a histogram to visualize the distances in the simulation exp.\r\n![](https://github.com/user-attachments/assets/464f7a86-051c-412f-b48a-461f7e7c9f20)\r\nI think there is no guarantee as to when the oldest example will be yielded. It could stay in the buffer until the entire shard is consumed. However, this can be rare, and in most cases, the pushed examples will be yielded very quickly. In the figure above, most examples are yielded within $2B$ steps. Things will improve if the dataset is split into enough shards and each shard is not too large.\r\n\r\nI agree that we may need to add some warnings or provide some options to allow users to make their own choices.", "Maybe there's a middle ground between rebuilding the buffer from scratch and storing the entire buffer, but the logic is a bit complicated and takes time to implement. At least for now, we have a way to make shuffled `IterableDataset` resumable :)", "@lhoestq I'm not sure if it's ok to use progress bar when having multiple workers. \r\nHow about passing an arg `resumable=True` to `IterableDataset.shuffle` to allow for controling of the behaviors?", "I feel like the default behavior should ideally be fast and perfect resuming.\r\n\r\nLoading from disk is a good option for this (although it's not always possible to serialize the content of the buffer, in that case the buffer would restart empty and we can show a warning). \r\n\r\nThe state_dict() would be part of the training state_dict that is saved to disk along with the model and optimizer anyway. Cc @muellerzr from that worked on storing training state_dicts for the `accelerate` lib, in case you have an opinion.\r\n\r\nI also feel like it is simpler and more intuitive to users. It doesn't require to explain why we need to stream a lot of data just to recover a buffer.\r\n\r\n> Maybe there's a middle ground between rebuilding the buffer from scratch and storing the entire buffer, but the logic is a bit complicated and takes time to implement.\r\n\r\ndefinitely, and it would also make things even harder to understand to users", "@lhoestq \r\n> Loading from disk is a good option for this (although it's not always possible to serialize the content of the buffer, in that case the buffer would restart empty and we can show a warning).\r\nThe state_dict() would be part of the training state_dict that is saved to disk along with the model and optimizer anyway. Cc @muellerzr from that worked on storing training state_dicts for the accelerate lib, in case you have an opinion.\r\nI also feel like it is simpler and more intuitive to users. It doesn't require to explain why we need to stream a lot of data just to recover a buffer.\r\n\r\nYea, agree with you. But here's the thing: saving buffers as state dict can get pretty tricky. When it comes to tokenized text data, working with multi-worker shuffle can take around x hundreds GB of memories in my case. That's just not feasible for most machine envs out there, and can be more severe for audio/video data.\r\n\r\nAlso, serializing the buffer does take a major toll on performance, and in my experience, I've had to lean heavily on numpy/torch tensor operations to manage those tokenized text data efficiently, which isn't easily transferable to other scenarios—it's kind of a custom fix that works for now, but it's not a one-size-fits-all solution. So, for me it's not that ideal to directly serialize the buffer content with those limitations.\r\n\r\n", "> When it comes to tokenized text data, working with multi-worker shuffle can taken around x hundreds GB memories in my case.\r\n\r\nit's kinda close to the size of a model + optimizer no ?\r\n\r\nAnyway that makes sense and adding the feature to recover a buffer shuffle (at least as an opt-in for now, we can decide on the default later based on users feedback and experience).\r\n\r\nAre you ok with adding `buffer_resuming_mode=` to `.shuffle()` to enable buffer recovering using your method with `buffer_resuming_mode=\"recover_from_source\"` ? (feel free to suggest other names for the parameter and value)", "@lhoestq \r\n> Are you ok with adding buffer_resuming_mode= to .shuffle() to enable buffer recovering using your method with buffer_resuming_mode=\"recover_from_source\" ? (feel free to suggest other names for the parameter and value)\r\n\r\nOf course, appreciate your feedbacks." ]
[ "Since `datasets` uses is built on Arrow to store the data, it requires each sample to have the same columns.\r\n\r\nThis can be fixed by specifyign in advance the name of all the possible columns in the `dataset_info` in YAML, and missing values will be `None`", "Thanks. This currently doesn't work for WebDataset because there's no `BuilderConfig` with `features` and in turn `_info` is missing `features=self.config.features`. I'll prepare a PR to fix this.\r\n\r\nNote it may be useful to add the [expected format of `features`](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [`Builder Parameters`](https://huggingface.co/docs/datasets/repository_structure#builder-parameters).\r\n", "Oh good catch ! thanks\r\n\r\n> Note it may be useful to add the [expected format of features](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [Buil](https://huggingface.co/docs/datasets/repository_structure#builder-parameters)\r\n\r\nGood idea, let me open a PR", "#7060 ", "Actually I just tried with `datasets` on the `main` branch and having `features` defined in `dataset_info` worked for me\r\n\r\n```python\r\n>>> list(load_dataset(\"/Users/quentinlhoest/tmp\", streaming=True, split=\"train\"))\r\n[{'txt': 'hello there\\n', 'other': None}]\r\n```\r\nwhere `tmp` contains data.tar with \"hello there\\n\" in a text file and the README.md:\r\n```\r\n---\r\ndataset_info:\r\n features:\r\n - name: txt\r\n dtype: string\r\n - name: other\r\n dtype: string\r\n---\r\n\r\nThis is a dataset card\r\n```\r\n\r\nWhat error did you get when you tried to specify the columns in `dataset_info` ?", "If you review the changes in #7060 you'll note that `features` are not passed to `DatasetInfo`.\r\n\r\nIn your case the features are being extracted by [this code](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L72-L98).\r\n\r\nTry with the `Steps to reproduce the bug`. It's the same error mentioned in `Describe the bug` because `features` are not passed to `DatasetInfo`.\r\n\r\n`features` are [not used](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L365-L366) when the `BuilderConfig` has no `features` attribute. `WebDataset` uses the default [`BuilderConfig`](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L101-L124).\r\n\r\nThere is a [warning](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/load.py#L640-L648) that `features` are ignored.\r\n\r\nNote that as mentioned in `Describe the bug` this could also be resolved by removing the check [here](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) because Arrow actually handles this itself, Arrow sets any missing fields to `None`, at least in my case.", "Note for anyone else who encounters this issue, every dataset type except folder-based types supported features in the [documented](https://huggingface.co/docs/datasets/repository_structure#builder-parameters) manner; [Arrow](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/arrow/arrow.py#L15-L21), [csv](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/csv/csv.py#L25-L68), [generator](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/generator/generator.py#L8-L19), [json](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/json/json.py#L42-L52), [pandas](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/pandas/pandas.py#L14-L20), [parquet](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/parquet/parquet.py#L16-L24), [spark](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/spark/spark.py#L31-L37), [sql](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/sql/sql.py#L24-L35) and [text](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/text/text.py#L18-L27). `WebDataset` is different and requires [`dataset_info` which is vaguely documented](https://huggingface.co/docs/datasets/dataset_script#optional-generate-dataset-metadata) under dataset loading scripts.", "Thanks for explaining. I see the Dataset Viewer is still failing - I'll update `datasets` in the Viewer to fix this" ]
[ "Cool ! Thanks for diving into it :)\r\n\r\nYour implementation is great and indeed supports shuffling and batching, you just need to additionally account for state_dict (for dataset [checkpointing+resuming](https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume))\r\n\r\nThat being said, I believe the implementation can be made simpler by relying on `IterableDataset.map()` which already implements all this. Maybe something like\r\n\r\n```python\r\n\r\ndef batch(self, batch_size: int, drop_last_batch: bool = False) -> \"IterableDataset\":\r\n def batch(unbatched: dict[str, list]) -> dict[str, list]:\r\n return {k: [v] for k, v in unbatched}\r\n\r\n return self.map(batch, batched=True, batch_size=batch_size, drop_last_batch=drop_last_batch)\r\n```\r\n\r\nAnd this way no need to reimplement everything !\r\n\r\n(my only small concern is that it's not an Arrow-optimized function so it requires the examples to be manipulated as python objects even if the original data is in Arrow format (e.g. when streaming Parquet files) but it's not a big deal and we can see later if we need to optimize this)", "Thanks a lot for the feedback @lhoestq! I definitely could have saved some time looking into it properly first. 😅 \r\n\r\nImplemented the `.batch()` method, added a proper docsrtring for documentation, and added tests.\r\n\r\nLet me know what you think and if this needs some update.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7054). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for the feedbak @lhoestq!\r\n\r\nApplied it and referenced the `batched=True` option in the `map` function and highlighted the difference. Hope i got this right.", "<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) | 0.005181 / 0.011353 (-0.006172) | 0.003714 / 0.011008 (-0.007294) | 0.063060 / 0.038508 (0.024552) | 0.030885 / 0.023109 (0.007776) | 0.239060 / 0.275898 (-0.036838) | 0.262480 / 0.323480 (-0.061000) | 0.004103 / 0.007986 (-0.003883) | 0.002696 / 0.004328 (-0.001632) | 0.048706 / 0.004250 (0.044456) | 0.042577 / 0.037052 (0.005525) | 0.249928 / 0.258489 (-0.008561) | 0.283252 / 0.293841 (-0.010589) | 0.029304 / 0.128546 (-0.099242) | 0.012001 / 0.075646 (-0.063646) | 0.204467 / 0.419271 (-0.214804) | 0.035639 / 0.043533 (-0.007894) | 0.243850 / 0.255139 (-0.011289) | 0.261609 / 0.283200 (-0.021590) | 0.018302 / 0.141683 (-0.123381) | 1.096040 / 1.452155 (-0.356115) | 1.135917 / 1.492716 (-0.356800) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091976 / 0.018006 (0.073970) | 0.296396 / 0.000490 (0.295906) | 0.000203 / 0.000200 (0.000003) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018405 / 0.037411 (-0.019007) | 0.062470 / 0.014526 (0.047944) | 0.073340 / 0.176557 (-0.103216) | 0.119474 / 0.737135 (-0.617661) | 0.075750 / 0.296338 (-0.220588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279586 / 0.215209 (0.064377) | 2.768542 / 2.077655 (0.690887) | 1.449158 / 1.504120 (-0.054962) | 1.328760 / 1.541195 (-0.212435) | 1.336338 / 1.468490 (-0.132152) | 0.732582 / 4.584777 (-3.852195) | 2.325558 / 3.745712 (-1.420154) | 2.898077 / 5.269862 (-2.371784) | 1.893107 / 4.565676 (-2.672569) | 0.078788 / 0.424275 (-0.345487) | 0.005273 / 0.007607 (-0.002335) | 0.334887 / 0.226044 (0.108842) | 3.304173 / 2.268929 (1.035244) | 1.834743 / 55.444624 (-53.609882) | 1.527463 / 6.876477 (-5.349014) | 1.538824 / 2.142072 (-0.603249) | 0.785646 / 4.805227 (-4.019581) | 0.134876 / 6.500664 (-6.365788) | 0.042894 / 0.075469 (-0.032575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976635 / 1.841788 (-0.865152) | 11.217156 / 8.074308 (3.142848) | 9.616971 / 10.191392 (-0.574421) | 0.127276 / 0.680424 (-0.553148) | 0.014344 / 0.534201 (-0.519857) | 0.301896 / 0.579283 (-0.277387) | 0.259615 / 0.434364 (-0.174749) | 0.340693 / 0.540337 (-0.199645) | 0.429145 / 1.386936 (-0.957791) |\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) | 0.005534 / 0.011353 (-0.005819) | 0.003795 / 0.011008 (-0.007213) | 0.049761 / 0.038508 (0.011253) | 0.031311 / 0.023109 (0.008202) | 0.276032 / 0.275898 (0.000134) | 0.297316 / 0.323480 (-0.026164) | 0.004396 / 0.007986 (-0.003590) | 0.002693 / 0.004328 (-0.001635) | 0.049025 / 0.004250 (0.044775) | 0.039707 / 0.037052 (0.002654) | 0.284264 / 0.258489 (0.025775) | 0.319962 / 0.293841 (0.026121) | 0.031842 / 0.128546 (-0.096705) | 0.012192 / 0.075646 (-0.063454) | 0.059895 / 0.419271 (-0.359376) | 0.033676 / 0.043533 (-0.009856) | 0.275917 / 0.255139 (0.020778) | 0.292637 / 0.283200 (0.009437) | 0.017992 / 0.141683 (-0.123691) | 1.199329 / 1.452155 (-0.252826) | 1.259083 / 1.492716 (-0.233633) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092770 / 0.018006 (0.074764) | 0.313363 / 0.000490 (0.312873) | 0.000212 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022977 / 0.037411 (-0.014434) | 0.076839 / 0.014526 (0.062314) | 0.088289 / 0.176557 (-0.088267) | 0.128625 / 0.737135 (-0.608510) | 0.089348 / 0.296338 (-0.206990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300881 / 0.215209 (0.085672) | 2.946499 / 2.077655 (0.868845) | 1.599686 / 1.504120 (0.095566) | 1.479332 / 1.541195 (-0.061862) | 1.476910 / 1.468490 (0.008420) | 0.720536 / 4.584777 (-3.864241) | 0.944822 / 3.745712 (-2.800890) | 2.771864 / 5.269862 (-2.497998) | 1.886573 / 4.565676 (-2.679103) | 0.078462 / 0.424275 (-0.345813) | 0.005392 / 0.007607 (-0.002215) | 0.354984 / 0.226044 (0.128939) | 3.516449 / 2.268929 (1.247520) | 1.977033 / 55.444624 (-53.467592) | 1.671922 / 6.876477 (-5.204555) | 1.785755 / 2.142072 (-0.356318) | 0.795330 / 4.805227 (-4.009897) | 0.132895 / 6.500664 (-6.367769) | 0.041178 / 0.075469 (-0.034291) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031780 / 1.841788 (-0.810008) | 11.855600 / 8.074308 (3.781292) | 10.245599 / 10.191392 (0.054207) | 0.140649 / 0.680424 (-0.539775) | 0.015332 / 0.534201 (-0.518869) | 0.299402 / 0.579283 (-0.279881) | 0.120007 / 0.434364 (-0.314357) | 0.337770 / 0.540337 (-0.202568) | 0.433679 / 1.386936 (-0.953257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e83d6fa574710fcb44e341087239d2687183f62b \"CML watermark\")\n" ]
[ "Hi,\r\n\r\nThis issue was fixed in `datasets` 2.15.0:\r\n- #6105\r\n\r\nYou will need to update your `datasets`:\r\n```\r\npip install -U datasets\r\n```", "Duplicate of:\r\n- #6100" ]
[]
[ "This is not possible right now afaik :/\r\n\r\nMaybe we could have something like this ? wdyt ?\r\n\r\n```python\r\nds = interleave_datasets(\r\n [shuffled_dataset_a, dataset_b],\r\n probabilities=probabilities,\r\n stopping_strategy='all_exhausted',\r\n reshuffle_each_iteration=True,\r\n)", "That would be helpful for this case! \r\n\r\nIf there was some way for from_generator to iterate over just a single shard of some dataset that would probably be more ideal. Maybe something like\r\n\r\n```\r\ndef from_dataset_generator(dataset, generator_fn, gen_kwargs):\r\n # calls generator_fn(dataset=dataset_shard, **gen_kwargs)\r\n```\r\n\r\nAnother transform I was trying to implement is an input bucketing transform. Essentially you need to iterate through a dataset and reorder the examples in them, which is not really possible with a `map()` call. But using `from_generator()` causes the final dataset to be a single shard and loses speed gains from multiple dataloader workers", "I see, there are some internal functions to get a single shard already but the public `.shard()` method hasn't been implemented yet for `IterableDataset` :/\r\n\r\n(see the use of `ex_iterable.shard_data_sources` in `IterableDataset._prepare_ex_iterable_for_iteration` for example)", "Would that be something planned on the roadmap for the near future, or do you suggest hacking through with internal APIs for now?", "Ok this turned out to be not too difficult. Are there any obvious issues with my implementation?\r\n\r\n```\r\nclass ShuffleEveryEpochIterable(iterable_dataset._BaseExamplesIterable):\r\n \"\"\"ExamplesIterable that reshuffles the dataset every epoch.\"\"\"\r\n\r\n def __init__(\r\n self,\r\n ex_iterable: iterable_dataset._BaseExamplesIterable,\r\n generator: np.random.Generator,\r\n ):\r\n \"\"\"Constructor.\"\"\"\r\n super().__init__()\r\n self.ex_iterable = ex_iterable\r\n self.generator = generator\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = {\r\n 'ex_iterable': self.ex_iterable._init_state_dict(),\r\n 'epoch': 0,\r\n }\r\n return self._state_dict\r\n\r\n @typing.override\r\n def __iter__(self):\r\n epoch = self._state_dict['epoch'] if self._state_dict else 0\r\n for i in itertools.count(epoch):\r\n # Create effective seed using i (subtract in order to avoir overflow in long_scalars)\r\n effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - i\r\n effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed\r\n generator = np.random.default_rng(effective_seed)\r\n self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n if self._state_dict:\r\n self._state_dict['epoch'] = i\r\n self._state_dict['ex_iterable'] = self.ex_iterable._init_state_dict()\r\n it = iter(self.ex_iterable)\r\n yield from it\r\n\r\n @typing.override\r\n def shuffle_data_sources(self, generator):\r\n ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\n\r\n @typing.override\r\n def shard_data_sources(self, worker_id: int, num_workers: int):\r\n ex_iterable = self.ex_iterable.shard_data_sources(worker_id, num_workers)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=self.generator)\r\n\r\n @typing.override\r\n @property\r\n def n_shards(self) -> int:\r\n return self.ex_iterable.n_shards\r\n \r\ngenerator = np.random.default_rng(seed)\r\nshuffling = iterable_dataset.ShufflingConfig(generator=generator, _original_seed=seed)\r\nex_iterable = iterable_dataset.BufferShuffledExamplesIterable(\r\n dataset._ex_iterable, buffer_size=buffer_size, generator=generator\r\n)\r\nex_iterable = ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\ndataset = datasets.IterableDataset(\r\n ex_iterable=ex_iterable,\r\n info=dataset._info.copy(),\r\n split=dataset._split,\r\n formatting=dataset._formatting,\r\n shuffling=shuffling,\r\n distributed=copy.deepcopy(dataset._distributed),\r\n token_per_repo_id=dataset._token_per_repo_id,\r\n)\r\n```\r\n", "Nice ! This iterable is infinite though no ? How would `interleave_dataset` know when to stop ?\r\n\r\nMaybe the re-shuffling can be implemented directly in `RandomlyCyclingMultiSourcesExamplesIterable` (which is the iterable used by `interleave_dataset`) ?", "Infinite is fine for my usecases fortunately." ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7050). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005707 / 0.011353 (-0.005646) | 0.004381 / 0.011008 (-0.006627) | 0.063711 / 0.038508 (0.025202) | 0.031882 / 0.023109 (0.008772) | 0.250056 / 0.275898 (-0.025842) | 0.287616 / 0.323480 (-0.035863) | 0.003327 / 0.007986 (-0.004658) | 0.003717 / 0.004328 (-0.000611) | 0.049103 / 0.004250 (0.044853) | 0.048821 / 0.037052 (0.011769) | 0.259688 / 0.258489 (0.001199) | 0.311469 / 0.293841 (0.017628) | 0.030667 / 0.128546 (-0.097879) | 0.013091 / 0.075646 (-0.062555) | 0.204737 / 0.419271 (-0.214534) | 0.038312 / 0.043533 (-0.005221) | 0.250055 / 0.255139 (-0.005084) | 0.272199 / 0.283200 (-0.011001) | 0.021161 / 0.141683 (-0.120522) | 1.116095 / 1.452155 (-0.336060) | 1.153588 / 1.492716 (-0.339129) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107828 / 0.018006 (0.089822) | 0.315898 / 0.000490 (0.315408) | 0.000228 / 0.000200 (0.000028) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018873 / 0.037411 (-0.018539) | 0.063374 / 0.014526 (0.048848) | 0.076424 / 0.176557 (-0.100133) | 0.123468 / 0.737135 (-0.613667) | 0.077432 / 0.296338 (-0.218906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288931 / 0.215209 (0.073722) | 2.828745 / 2.077655 (0.751091) | 1.471061 / 1.504120 (-0.033059) | 1.332289 / 1.541195 (-0.208906) | 1.379797 / 1.468490 (-0.088693) | 0.708053 / 4.584777 (-3.876724) | 2.382431 / 3.745712 (-1.363281) | 2.952672 / 5.269862 (-2.317190) | 1.957517 / 4.565676 (-2.608160) | 0.078730 / 0.424275 (-0.345546) | 0.005093 / 0.007607 (-0.002514) | 0.338147 / 0.226044 (0.112102) | 3.340841 / 2.268929 (1.071912) | 1.857083 / 55.444624 (-53.587541) | 1.533659 / 6.876477 (-5.342818) | 1.750549 / 2.142072 (-0.391523) | 0.804125 / 4.805227 (-4.001103) | 0.134618 / 6.500664 (-6.366046) | 0.042517 / 0.075469 (-0.032952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968608 / 1.841788 (-0.873180) | 12.326994 / 8.074308 (4.252686) | 9.464889 / 10.191392 (-0.726503) | 0.143979 / 0.680424 (-0.536445) | 0.014577 / 0.534201 (-0.519624) | 0.303205 / 0.579283 (-0.276078) | 0.269866 / 0.434364 (-0.164498) | 0.344846 / 0.540337 (-0.195491) | 0.443794 / 1.386936 (-0.943142) |\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) | 0.006452 / 0.011353 (-0.004900) | 0.004264 / 0.011008 (-0.006745) | 0.051355 / 0.038508 (0.012847) | 0.035188 / 0.023109 (0.012079) | 0.267697 / 0.275898 (-0.008201) | 0.295853 / 0.323480 (-0.027627) | 0.004611 / 0.007986 (-0.003374) | 0.005395 / 0.004328 (0.001066) | 0.049903 / 0.004250 (0.045652) | 0.044582 / 0.037052 (0.007530) | 0.284706 / 0.258489 (0.026217) | 0.321623 / 0.293841 (0.027782) | 0.033228 / 0.128546 (-0.095318) | 0.013077 / 0.075646 (-0.062569) | 0.061867 / 0.419271 (-0.357405) | 0.034625 / 0.043533 (-0.008908) | 0.269088 / 0.255139 (0.013949) | 0.284899 / 0.283200 (0.001699) | 0.019972 / 0.141683 (-0.121710) | 1.157976 / 1.452155 (-0.294178) | 1.181658 / 1.492716 (-0.311058) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.111072 / 0.018006 (0.093066) | 0.333310 / 0.000490 (0.332820) | 0.000251 / 0.000200 (0.000051) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013652) | 0.080746 / 0.014526 (0.066221) | 0.090231 / 0.176557 (-0.086326) | 0.132200 / 0.737135 (-0.604936) | 0.095679 / 0.296338 (-0.200660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297404 / 0.215209 (0.082195) | 2.919779 / 2.077655 (0.842124) | 1.577470 / 1.504120 (0.073350) | 1.452924 / 1.541195 (-0.088271) | 1.523683 / 1.468490 (0.055193) | 0.743801 / 4.584777 (-3.840976) | 1.006944 / 3.745712 (-2.738768) | 3.218161 / 5.269862 (-2.051701) | 2.069762 / 4.565676 (-2.495914) | 0.082900 / 0.424275 (-0.341375) | 0.005239 / 0.007607 (-0.002368) | 0.360124 / 0.226044 (0.134080) | 3.505349 / 2.268929 (1.236420) | 1.959324 / 55.444624 (-53.485300) | 1.663782 / 6.876477 (-5.212694) | 1.725745 / 2.142072 (-0.416327) | 0.825268 / 4.805227 (-3.979959) | 0.138577 / 6.500664 (-6.362087) | 0.042716 / 0.075469 (-0.032753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.021138 / 1.841788 (-0.820650) | 13.907954 / 8.074308 (5.833646) | 11.023796 / 10.191392 (0.832404) | 0.135224 / 0.680424 (-0.545200) | 0.016232 / 0.534201 (-0.517969) | 0.330389 / 0.579283 (-0.248894) | 0.131702 / 0.434364 (-0.302662) | 0.372499 / 0.540337 (-0.167838) | 0.472702 / 1.386936 (-0.914234) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f4c2088854ff33e817e724e75179e9975c1b02 \"CML watermark\")\n" ]
[ "In addition, when I use `set_format ` and index the ds, the following error occurs:\r\nthe code\r\n```python\r\nds.set_format(type=\"np\", colums=\"pixel_values\")\r\n```\r\nerror\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/user-attachments/assets/b28bbff2-20ea-4d28-ab62-b4ed2d944996\">\r\n", "> Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n\r\nUnder the hood the data is saved in Arrow format using the same precision as your numpy arrays?\r\nBy default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision", "(you can fix your second issue by fixing the typo `colums` -> `columns`)", "> (you can fix your second issue by fixing the typo `colums` -> `columns`)\r\n\r\nYou are right, I was careless. Thank you.", "> > Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n> \r\n> Under the hood the data is saved in Arrow format using the same precision as your numpy arrays? By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision\r\n\r\nYes, after testing I found that there was no loss of precision. Thanks again for your answer." ]
[ "Could you please check your `numpy` version?", "I got this issue while using numpy version 2.0. \r\n\r\nI solved it by switching back to numpy 1.26.0 :) ", "We recently added support for numpy 2.0, but it is not released yet.", "Ok I see, thanks! I think we can close this issue for now as switching back to version 1.26.0 solves the problem :) " ]
[ "To anyone else who finds themselves in this predicament, it's possible to read the parquet file in the same way that datasets writes it, and then manually break it into pieces. Although, you need a couple of magic options (`thrift_*`) to deal with the huge metadata, otherwise pyarrow immediately crashes.\r\n```python\r\nimport pyarrow.parquet as pq\r\nimport pyarrow as pa\r\n\r\nr = pq.ParquetReader()\r\n\r\nr.open(\"./outrageous-file.parquet\",thrift_string_size_limit=2**31-1, thrift_container_size_limit=2**31-1)\r\n\r\nfrom more_itertools import chunked\r\nimport tqdm\r\n\r\nfor i,chunk in tqdm.tqdm(enumerate(chunked(range(r.num_row_groups),10000))):\r\n w = pq.ParquetWriter(f\"./chunks.parquet/chunk{i}.parquet\",schema=r.schema_arrow)\r\n for idx in chunk:\r\n w.write_table(r.read_row_group(idx))\r\n w.close()\r\n```", "You can also use `.shard()` and call `to_parquet()` on each shard in the meantime:\r\n\r\n```python\r\nnum_shards = 128\r\noutput_path_template = \"output_dir/{index:05d}.parquet\"\r\nfor index in range(num_shards):\r\n shard = ds.shard(index=index, num_shards=num_shards, contiguous=True)\r\n shard.to_parquet(output_path_template.format(index=index))\r\n```" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7046). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005897 / 0.011353 (-0.005456) | 0.003958 / 0.011008 (-0.007050) | 0.063684 / 0.038508 (0.025176) | 0.031743 / 0.023109 (0.008634) | 0.246725 / 0.275898 (-0.029173) | 0.275519 / 0.323480 (-0.047961) | 0.003347 / 0.007986 (-0.004639) | 0.004089 / 0.004328 (-0.000240) | 0.049591 / 0.004250 (0.045341) | 0.049386 / 0.037052 (0.012333) | 0.264929 / 0.258489 (0.006440) | 0.317157 / 0.293841 (0.023316) | 0.029929 / 0.128546 (-0.098617) | 0.012264 / 0.075646 (-0.063382) | 0.209208 / 0.419271 (-0.210064) | 0.037073 / 0.043533 (-0.006460) | 0.247999 / 0.255139 (-0.007140) | 0.273457 / 0.283200 (-0.009742) | 0.020354 / 0.141683 (-0.121328) | 1.109874 / 1.452155 (-0.342281) | 1.180085 / 1.492716 (-0.312631) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099935 / 0.018006 (0.081929) | 0.305607 / 0.000490 (0.305118) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020019 / 0.037411 (-0.017392) | 0.066608 / 0.014526 (0.052083) | 0.079354 / 0.176557 (-0.097202) | 0.123416 / 0.737135 (-0.613719) | 0.078171 / 0.296338 (-0.218167) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281627 / 0.215209 (0.066418) | 2.809807 / 2.077655 (0.732152) | 1.467007 / 1.504120 (-0.037112) | 1.351367 / 1.541195 (-0.189828) | 1.396782 / 1.468490 (-0.071708) | 0.735605 / 4.584777 (-3.849172) | 2.378455 / 3.745712 (-1.367257) | 2.971739 / 5.269862 (-2.298122) | 2.004970 / 4.565676 (-2.560707) | 0.078156 / 0.424275 (-0.346119) | 0.005276 / 0.007607 (-0.002331) | 0.340370 / 0.226044 (0.114325) | 3.347552 / 2.268929 (1.078624) | 1.851098 / 55.444624 (-53.593527) | 1.518079 / 6.876477 (-5.358398) | 1.703145 / 2.142072 (-0.438927) | 0.799574 / 4.805227 (-4.005654) | 0.133591 / 6.500664 (-6.367074) | 0.043329 / 0.075469 (-0.032141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977268 / 1.841788 (-0.864520) | 12.720209 / 8.074308 (4.645901) | 9.798126 / 10.191392 (-0.393266) | 0.132106 / 0.680424 (-0.548318) | 0.014456 / 0.534201 (-0.519745) | 0.312965 / 0.579283 (-0.266318) | 0.271348 / 0.434364 (-0.163016) | 0.343951 / 0.540337 (-0.196386) | 0.449814 / 1.386936 (-0.937122) |\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) | 0.005944 / 0.011353 (-0.005409) | 0.004054 / 0.011008 (-0.006954) | 0.050573 / 0.038508 (0.012065) | 0.034580 / 0.023109 (0.011470) | 0.261439 / 0.275898 (-0.014459) | 0.286057 / 0.323480 (-0.037423) | 0.004463 / 0.007986 (-0.003523) | 0.002891 / 0.004328 (-0.001437) | 0.049169 / 0.004250 (0.044919) | 0.041622 / 0.037052 (0.004570) | 0.275216 / 0.258489 (0.016727) | 0.305847 / 0.293841 (0.012006) | 0.032615 / 0.128546 (-0.095932) | 0.012304 / 0.075646 (-0.063343) | 0.062890 / 0.419271 (-0.356382) | 0.033846 / 0.043533 (-0.009687) | 0.262758 / 0.255139 (0.007619) | 0.279451 / 0.283200 (-0.003748) | 0.018953 / 0.141683 (-0.122730) | 1.149158 / 1.452155 (-0.302997) | 1.173981 / 1.492716 (-0.318735) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100462 / 0.018006 (0.082456) | 0.308390 / 0.000490 (0.307900) | 0.000207 / 0.000200 (0.000007) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023089 / 0.037411 (-0.014322) | 0.078610 / 0.014526 (0.064084) | 0.090348 / 0.176557 (-0.086208) | 0.130784 / 0.737135 (-0.606351) | 0.092538 / 0.296338 (-0.203801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296255 / 0.215209 (0.081046) | 2.899159 / 2.077655 (0.821504) | 1.603524 / 1.504120 (0.099404) | 1.418002 / 1.541195 (-0.123192) | 1.470221 / 1.468490 (0.001731) | 0.722129 / 4.584777 (-3.862648) | 0.956146 / 3.745712 (-2.789566) | 3.011640 / 5.269862 (-2.258222) | 1.910966 / 4.565676 (-2.654711) | 0.078771 / 0.424275 (-0.345504) | 0.005154 / 0.007607 (-0.002453) | 0.354001 / 0.226044 (0.127956) | 3.484224 / 2.268929 (1.215296) | 1.913612 / 55.444624 (-53.531012) | 1.634492 / 6.876477 (-5.241985) | 1.693292 / 2.142072 (-0.448780) | 0.816837 / 4.805227 (-3.988390) | 0.136631 / 6.500664 (-6.364033) | 0.042291 / 0.075469 (-0.033178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.994887 / 1.841788 (-0.846901) | 13.144865 / 8.074308 (5.070557) | 10.820098 / 10.191392 (0.628706) | 0.132557 / 0.680424 (-0.547867) | 0.015467 / 0.534201 (-0.518734) | 0.302026 / 0.579283 (-0.277257) | 0.128763 / 0.434364 (-0.305601) | 0.347908 / 0.540337 (-0.192430) | 0.444829 / 1.386936 (-0.942107) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf6f41e94d9b2f1c620cf937a2e85e5754a8b960 \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7045). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005426 / 0.011353 (-0.005927) | 0.003896 / 0.011008 (-0.007112) | 0.063492 / 0.038508 (0.024984) | 0.030199 / 0.023109 (0.007090) | 0.249892 / 0.275898 (-0.026006) | 0.291311 / 0.323480 (-0.032168) | 0.004389 / 0.007986 (-0.003597) | 0.002829 / 0.004328 (-0.001500) | 0.049685 / 0.004250 (0.045435) | 0.043351 / 0.037052 (0.006299) | 0.264265 / 0.258489 (0.005776) | 0.290463 / 0.293841 (-0.003378) | 0.030007 / 0.128546 (-0.098539) | 0.012146 / 0.075646 (-0.063500) | 0.203841 / 0.419271 (-0.215430) | 0.037159 / 0.043533 (-0.006373) | 0.253377 / 0.255139 (-0.001762) | 0.275990 / 0.283200 (-0.007209) | 0.018334 / 0.141683 (-0.123349) | 1.112616 / 1.452155 (-0.339539) | 1.157507 / 1.492716 (-0.335209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097781 / 0.018006 (0.079775) | 0.314381 / 0.000490 (0.313891) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018704 / 0.037411 (-0.018708) | 0.062293 / 0.014526 (0.047767) | 0.073997 / 0.176557 (-0.102559) | 0.120309 / 0.737135 (-0.616826) | 0.075592 / 0.296338 (-0.220747) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283178 / 0.215209 (0.067969) | 2.798027 / 2.077655 (0.720372) | 1.431320 / 1.504120 (-0.072800) | 1.316135 / 1.541195 (-0.225060) | 1.345528 / 1.468490 (-0.122962) | 0.717300 / 4.584777 (-3.867477) | 2.401019 / 3.745712 (-1.344693) | 2.866411 / 5.269862 (-2.403451) | 1.933198 / 4.565676 (-2.632479) | 0.079505 / 0.424275 (-0.344771) | 0.005089 / 0.007607 (-0.002519) | 0.333614 / 0.226044 (0.107569) | 3.315449 / 2.268929 (1.046520) | 1.807667 / 55.444624 (-53.636957) | 1.490537 / 6.876477 (-5.385939) | 1.633305 / 2.142072 (-0.508767) | 0.807732 / 4.805227 (-3.997495) | 0.133825 / 6.500664 (-6.366839) | 0.041696 / 0.075469 (-0.033774) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969063 / 1.841788 (-0.872724) | 11.825985 / 8.074308 (3.751677) | 9.808041 / 10.191392 (-0.383351) | 0.143338 / 0.680424 (-0.537085) | 0.014714 / 0.534201 (-0.519487) | 0.304360 / 0.579283 (-0.274923) | 0.266863 / 0.434364 (-0.167501) | 0.342374 / 0.540337 (-0.197963) | 0.442120 / 1.386936 (-0.944816) |\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) | 0.005574 / 0.011353 (-0.005778) | 0.003735 / 0.011008 (-0.007273) | 0.051021 / 0.038508 (0.012513) | 0.032825 / 0.023109 (0.009716) | 0.267775 / 0.275898 (-0.008123) | 0.286015 / 0.323480 (-0.037464) | 0.004332 / 0.007986 (-0.003653) | 0.002796 / 0.004328 (-0.001532) | 0.050183 / 0.004250 (0.045933) | 0.040191 / 0.037052 (0.003138) | 0.279777 / 0.258489 (0.021288) | 0.312161 / 0.293841 (0.018320) | 0.031993 / 0.128546 (-0.096553) | 0.012168 / 0.075646 (-0.063478) | 0.061622 / 0.419271 (-0.357650) | 0.033577 / 0.043533 (-0.009956) | 0.267300 / 0.255139 (0.012161) | 0.284595 / 0.283200 (0.001396) | 0.018476 / 0.141683 (-0.123207) | 1.135917 / 1.452155 (-0.316237) | 1.164516 / 1.492716 (-0.328200) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108194 / 0.018006 (0.090188) | 0.309514 / 0.000490 (0.309025) | 0.000211 / 0.000200 (0.000011) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022998 / 0.037411 (-0.014413) | 0.077126 / 0.014526 (0.062600) | 0.088779 / 0.176557 (-0.087778) | 0.128646 / 0.737135 (-0.608489) | 0.089895 / 0.296338 (-0.206443) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295131 / 0.215209 (0.079922) | 2.887380 / 2.077655 (0.809726) | 1.586450 / 1.504120 (0.082330) | 1.449831 / 1.541195 (-0.091363) | 1.468805 / 1.468490 (0.000315) | 0.721578 / 4.584777 (-3.863199) | 0.970499 / 3.745712 (-2.775214) | 2.975604 / 5.269862 (-2.294258) | 1.935809 / 4.565676 (-2.629867) | 0.078504 / 0.424275 (-0.345771) | 0.005219 / 0.007607 (-0.002388) | 0.347168 / 0.226044 (0.121124) | 3.417040 / 2.268929 (1.148111) | 1.928707 / 55.444624 (-53.515917) | 1.629398 / 6.876477 (-5.247078) | 1.653014 / 2.142072 (-0.489058) | 0.796097 / 4.805227 (-4.009130) | 0.133956 / 6.500664 (-6.366708) | 0.041567 / 0.075469 (-0.033902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.995511 / 1.841788 (-0.846277) | 12.577211 / 8.074308 (4.502903) | 10.562561 / 10.191392 (0.371169) | 0.144288 / 0.680424 (-0.536136) | 0.016345 / 0.534201 (-0.517856) | 0.304364 / 0.579283 (-0.274920) | 0.134630 / 0.434364 (-0.299734) | 0.341494 / 0.540337 (-0.198843) | 0.436238 / 1.386936 (-0.950698) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b708bb6611a88c3f00f58ec3c63fe0da2c2b1e1 \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7044). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005797 / 0.011353 (-0.005556) | 0.004017 / 0.011008 (-0.006991) | 0.063829 / 0.038508 (0.025321) | 0.031329 / 0.023109 (0.008220) | 0.249388 / 0.275898 (-0.026510) | 0.273129 / 0.323480 (-0.050351) | 0.004250 / 0.007986 (-0.003736) | 0.002821 / 0.004328 (-0.001507) | 0.049250 / 0.004250 (0.044999) | 0.046175 / 0.037052 (0.009123) | 0.252040 / 0.258489 (-0.006449) | 0.296537 / 0.293841 (0.002696) | 0.030579 / 0.128546 (-0.097967) | 0.012436 / 0.075646 (-0.063210) | 0.205829 / 0.419271 (-0.213443) | 0.036979 / 0.043533 (-0.006554) | 0.251354 / 0.255139 (-0.003785) | 0.272262 / 0.283200 (-0.010938) | 0.019047 / 0.141683 (-0.122636) | 1.112410 / 1.452155 (-0.339745) | 1.137445 / 1.492716 (-0.355271) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097270 / 0.018006 (0.079264) | 0.309329 / 0.000490 (0.308839) | 0.000221 / 0.000200 (0.000021) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019021 / 0.037411 (-0.018390) | 0.066801 / 0.014526 (0.052276) | 0.075280 / 0.176557 (-0.101276) | 0.122499 / 0.737135 (-0.614637) | 0.077424 / 0.296338 (-0.218914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279469 / 0.215209 (0.064259) | 2.787511 / 2.077655 (0.709856) | 1.411389 / 1.504120 (-0.092731) | 1.285796 / 1.541195 (-0.255399) | 1.354252 / 1.468490 (-0.114238) | 0.735341 / 4.584777 (-3.849436) | 2.418557 / 3.745712 (-1.327155) | 2.983406 / 5.269862 (-2.286455) | 2.005853 / 4.565676 (-2.559823) | 0.080440 / 0.424275 (-0.343835) | 0.005242 / 0.007607 (-0.002365) | 0.343557 / 0.226044 (0.117513) | 3.358984 / 2.268929 (1.090055) | 1.816709 / 55.444624 (-53.627915) | 1.500225 / 6.876477 (-5.376252) | 1.715405 / 2.142072 (-0.426667) | 0.829054 / 4.805227 (-3.976174) | 0.138352 / 6.500664 (-6.362312) | 0.043709 / 0.075469 (-0.031760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969135 / 1.841788 (-0.872652) | 12.510750 / 8.074308 (4.436442) | 10.140368 / 10.191392 (-0.051024) | 0.133117 / 0.680424 (-0.547307) | 0.015775 / 0.534201 (-0.518426) | 0.302203 / 0.579283 (-0.277080) | 0.268214 / 0.434364 (-0.166150) | 0.347041 / 0.540337 (-0.193296) | 0.456095 / 1.386936 (-0.930841) |\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) | 0.006255 / 0.011353 (-0.005098) | 0.004453 / 0.011008 (-0.006555) | 0.052298 / 0.038508 (0.013790) | 0.034808 / 0.023109 (0.011699) | 0.274723 / 0.275898 (-0.001175) | 0.297199 / 0.323480 (-0.026281) | 0.004499 / 0.007986 (-0.003486) | 0.003086 / 0.004328 (-0.001242) | 0.051315 / 0.004250 (0.047065) | 0.042764 / 0.037052 (0.005712) | 0.285636 / 0.258489 (0.027147) | 0.321819 / 0.293841 (0.027978) | 0.033350 / 0.128546 (-0.095196) | 0.013457 / 0.075646 (-0.062189) | 0.063930 / 0.419271 (-0.355342) | 0.034537 / 0.043533 (-0.008996) | 0.272630 / 0.255139 (0.017491) | 0.289245 / 0.283200 (0.006045) | 0.018910 / 0.141683 (-0.122773) | 1.153064 / 1.452155 (-0.299091) | 1.207065 / 1.492716 (-0.285651) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093008 / 0.018006 (0.075002) | 0.301313 / 0.000490 (0.300823) | 0.000214 / 0.000200 (0.000014) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023168 / 0.037411 (-0.014244) | 0.080837 / 0.014526 (0.066312) | 0.089667 / 0.176557 (-0.086889) | 0.135849 / 0.737135 (-0.601286) | 0.092082 / 0.296338 (-0.204257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298933 / 0.215209 (0.083723) | 2.847736 / 2.077655 (0.770082) | 1.550268 / 1.504120 (0.046148) | 1.425675 / 1.541195 (-0.115520) | 1.469251 / 1.468490 (0.000761) | 0.720446 / 4.584777 (-3.864331) | 0.976149 / 3.745712 (-2.769563) | 3.081804 / 5.269862 (-2.188057) | 1.982797 / 4.565676 (-2.582880) | 0.078598 / 0.424275 (-0.345677) | 0.005229 / 0.007607 (-0.002379) | 0.345475 / 0.226044 (0.119430) | 3.421312 / 2.268929 (1.152384) | 1.929034 / 55.444624 (-53.515590) | 1.631523 / 6.876477 (-5.244953) | 1.671996 / 2.142072 (-0.470077) | 0.776916 / 4.805227 (-4.028311) | 0.133966 / 6.500664 (-6.366699) | 0.042183 / 0.075469 (-0.033286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993023 / 1.841788 (-0.848764) | 12.981642 / 8.074308 (4.907334) | 10.610457 / 10.191392 (0.419065) | 0.146748 / 0.680424 (-0.533676) | 0.016556 / 0.534201 (-0.517645) | 0.303613 / 0.579283 (-0.275670) | 0.132671 / 0.434364 (-0.301693) | 0.344786 / 0.540337 (-0.195552) | 0.443049 / 1.386936 (-0.943887) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8419c40a085d67eb5832cecebf3ef8213112857d \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7043). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005147 / 0.011353 (-0.006205) | 0.003403 / 0.011008 (-0.007605) | 0.061367 / 0.038508 (0.022859) | 0.030295 / 0.023109 (0.007186) | 0.233503 / 0.275898 (-0.042395) | 0.252644 / 0.323480 (-0.070836) | 0.004072 / 0.007986 (-0.003913) | 0.002678 / 0.004328 (-0.001650) | 0.049099 / 0.004250 (0.044848) | 0.043032 / 0.037052 (0.005979) | 0.248823 / 0.258489 (-0.009666) | 0.274895 / 0.293841 (-0.018946) | 0.029307 / 0.128546 (-0.099239) | 0.011186 / 0.075646 (-0.064460) | 0.197142 / 0.419271 (-0.222129) | 0.035924 / 0.043533 (-0.007609) | 0.234728 / 0.255139 (-0.020411) | 0.252990 / 0.283200 (-0.030209) | 0.017589 / 0.141683 (-0.124094) | 1.108252 / 1.452155 (-0.343903) | 1.135949 / 1.492716 (-0.356767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093096 / 0.018006 (0.075090) | 0.289284 / 0.000490 (0.288794) | 0.000208 / 0.000200 (0.000008) | 0.000038 / 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) | 0.017633 / 0.037411 (-0.019778) | 0.060621 / 0.014526 (0.046095) | 0.073194 / 0.176557 (-0.103363) | 0.120176 / 0.737135 (-0.616959) | 0.073575 / 0.296338 (-0.222764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277168 / 0.215209 (0.061959) | 2.689714 / 2.077655 (0.612060) | 1.427558 / 1.504120 (-0.076562) | 1.331350 / 1.541195 (-0.209844) | 1.353069 / 1.468490 (-0.115421) | 0.716657 / 4.584777 (-3.868120) | 2.321145 / 3.745712 (-1.424567) | 2.757986 / 5.269862 (-2.511876) | 1.851604 / 4.565676 (-2.714072) | 0.089530 / 0.424275 (-0.334745) | 0.004884 / 0.007607 (-0.002723) | 0.327859 / 0.226044 (0.101814) | 3.290749 / 2.268929 (1.021821) | 1.831090 / 55.444624 (-53.613535) | 1.509247 / 6.876477 (-5.367229) | 1.616545 / 2.142072 (-0.525527) | 0.775228 / 4.805227 (-4.029999) | 0.133794 / 6.500664 (-6.366870) | 0.040644 / 0.075469 (-0.034825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950816 / 1.841788 (-0.890972) | 11.109938 / 8.074308 (3.035630) | 9.560673 / 10.191392 (-0.630719) | 0.130685 / 0.680424 (-0.549738) | 0.014096 / 0.534201 (-0.520105) | 0.297222 / 0.579283 (-0.282061) | 0.262777 / 0.434364 (-0.171587) | 0.340983 / 0.540337 (-0.199355) | 0.426107 / 1.386936 (-0.960829) |\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) | 0.005547 / 0.011353 (-0.005806) | 0.003425 / 0.011008 (-0.007584) | 0.049791 / 0.038508 (0.011283) | 0.032660 / 0.023109 (0.009550) | 0.257640 / 0.275898 (-0.018258) | 0.283483 / 0.323480 (-0.039997) | 0.004330 / 0.007986 (-0.003655) | 0.002297 / 0.004328 (-0.002032) | 0.047999 / 0.004250 (0.043748) | 0.039875 / 0.037052 (0.002822) | 0.273300 / 0.258489 (0.014811) | 0.303384 / 0.293841 (0.009543) | 0.031696 / 0.128546 (-0.096851) | 0.011913 / 0.075646 (-0.063733) | 0.060330 / 0.419271 (-0.358942) | 0.033253 / 0.043533 (-0.010280) | 0.255378 / 0.255139 (0.000240) | 0.271647 / 0.283200 (-0.011553) | 0.018772 / 0.141683 (-0.122910) | 1.116079 / 1.452155 (-0.336075) | 1.165133 / 1.492716 (-0.327583) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094325 / 0.018006 (0.076319) | 0.297523 / 0.000490 (0.297034) | 0.000210 / 0.000200 (0.000011) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022485 / 0.037411 (-0.014926) | 0.073731 / 0.014526 (0.059205) | 0.089039 / 0.176557 (-0.087518) | 0.124035 / 0.737135 (-0.613101) | 0.088053 / 0.296338 (-0.208286) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286676 / 0.215209 (0.071467) | 2.794678 / 2.077655 (0.717024) | 1.541401 / 1.504120 (0.037281) | 1.432928 / 1.541195 (-0.108267) | 1.454940 / 1.468490 (-0.013550) | 0.721779 / 4.584777 (-3.862998) | 0.956514 / 3.745712 (-2.789198) | 2.889533 / 5.269862 (-2.380329) | 1.863980 / 4.565676 (-2.701696) | 0.078366 / 0.424275 (-0.345909) | 0.005137 / 0.007607 (-0.002470) | 0.338835 / 0.226044 (0.112791) | 3.320921 / 2.268929 (1.051993) | 1.903654 / 55.444624 (-53.540970) | 1.615294 / 6.876477 (-5.261182) | 1.624777 / 2.142072 (-0.517295) | 0.792417 / 4.805227 (-4.012810) | 0.133321 / 6.500664 (-6.367343) | 0.040127 / 0.075469 (-0.035342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982357 / 1.841788 (-0.859430) | 11.585106 / 8.074308 (3.510798) | 9.991577 / 10.191392 (-0.199815) | 0.149292 / 0.680424 (-0.531131) | 0.015693 / 0.534201 (-0.518508) | 0.297416 / 0.579283 (-0.281867) | 0.118565 / 0.434364 (-0.315799) | 0.335640 / 0.540337 (-0.204697) | 0.429484 / 1.386936 (-0.957452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3091d7608f20e182f21bb7d0b68be66c0798509a \"CML watermark\")\n" ]
[]
[ "`filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping." ]
[ "When you pass `streaming=True`, the cache is ignored. The remote data URL is used instead and the data is streamed from the remote server.", "Thanks for your reply! So is there any solution to get my expected behavior besides clone the whole repo ? Or could I adjust my script to load the downloaded arrow files and generate the dataset streamingly?" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7039). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "The test before confirms the bug.\r\n\r\nThere are different possible solutions to this issue:\r\n- the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed\r\n- alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient=\"records\"`\r\n- others?" ]
[ "This is the `datasets` repository, and the issue should be opened in the `transformers` repo instead." ]
[ "Thanks for reporting, @LinglingGreat.\r\n\r\nI confirm this is a bug." ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7036). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005582 / 0.011353 (-0.005771) | 0.003968 / 0.011008 (-0.007041) | 0.063672 / 0.038508 (0.025164) | 0.032360 / 0.023109 (0.009251) | 0.241351 / 0.275898 (-0.034547) | 0.264926 / 0.323480 (-0.058554) | 0.003186 / 0.007986 (-0.004800) | 0.003423 / 0.004328 (-0.000906) | 0.049600 / 0.004250 (0.045350) | 0.045558 / 0.037052 (0.008506) | 0.253326 / 0.258489 (-0.005163) | 0.289474 / 0.293841 (-0.004367) | 0.030285 / 0.128546 (-0.098261) | 0.012424 / 0.075646 (-0.063222) | 0.203914 / 0.419271 (-0.215358) | 0.036569 / 0.043533 (-0.006964) | 0.245252 / 0.255139 (-0.009887) | 0.261971 / 0.283200 (-0.021228) | 0.018276 / 0.141683 (-0.123406) | 1.120386 / 1.452155 (-0.331769) | 1.181736 / 1.492716 (-0.310980) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095427 / 0.018006 (0.077421) | 0.300666 / 0.000490 (0.300176) | 0.000205 / 0.000200 (0.000005) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019255 / 0.037411 (-0.018156) | 0.062645 / 0.014526 (0.048119) | 0.074822 / 0.176557 (-0.101734) | 0.121222 / 0.737135 (-0.615913) | 0.076136 / 0.296338 (-0.220202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279756 / 0.215209 (0.064547) | 2.769680 / 2.077655 (0.692025) | 1.466156 / 1.504120 (-0.037964) | 1.348337 / 1.541195 (-0.192857) | 1.348311 / 1.468490 (-0.120179) | 0.710414 / 4.584777 (-3.874363) | 2.379192 / 3.745712 (-1.366520) | 2.990227 / 5.269862 (-2.279635) | 1.909749 / 4.565676 (-2.655928) | 0.079677 / 0.424275 (-0.344598) | 0.005116 / 0.007607 (-0.002491) | 0.335442 / 0.226044 (0.109398) | 3.308757 / 2.268929 (1.039828) | 1.831681 / 55.444624 (-53.612944) | 1.528642 / 6.876477 (-5.347835) | 1.554577 / 2.142072 (-0.587496) | 0.777722 / 4.805227 (-4.027505) | 0.132164 / 6.500664 (-6.368501) | 0.042277 / 0.075469 (-0.033193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964461 / 1.841788 (-0.877327) | 11.436569 / 8.074308 (3.362261) | 9.801367 / 10.191392 (-0.390025) | 0.130214 / 0.680424 (-0.550210) | 0.015288 / 0.534201 (-0.518913) | 0.303992 / 0.579283 (-0.275292) | 0.258128 / 0.434364 (-0.176236) | 0.347259 / 0.540337 (-0.193078) | 0.438156 / 1.386936 (-0.948780) |\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) | 0.006019 / 0.011353 (-0.005334) | 0.003872 / 0.011008 (-0.007136) | 0.050763 / 0.038508 (0.012255) | 0.033993 / 0.023109 (0.010884) | 0.271789 / 0.275898 (-0.004109) | 0.298849 / 0.323480 (-0.024631) | 0.004486 / 0.007986 (-0.003500) | 0.002789 / 0.004328 (-0.001540) | 0.049926 / 0.004250 (0.045676) | 0.040470 / 0.037052 (0.003418) | 0.287533 / 0.258489 (0.029044) | 0.320066 / 0.293841 (0.026225) | 0.033039 / 0.128546 (-0.095508) | 0.011842 / 0.075646 (-0.063804) | 0.061016 / 0.419271 (-0.358256) | 0.034807 / 0.043533 (-0.008726) | 0.272079 / 0.255139 (0.016940) | 0.291603 / 0.283200 (0.008403) | 0.018676 / 0.141683 (-0.123007) | 1.171214 / 1.452155 (-0.280940) | 1.210691 / 1.492716 (-0.282025) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093045 / 0.018006 (0.075038) | 0.301045 / 0.000490 (0.300556) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022616 / 0.037411 (-0.014795) | 0.077271 / 0.014526 (0.062746) | 0.088959 / 0.176557 (-0.087598) | 0.129961 / 0.737135 (-0.607174) | 0.090495 / 0.296338 (-0.205843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301864 / 0.215209 (0.086655) | 2.947486 / 2.077655 (0.869831) | 1.587123 / 1.504120 (0.083003) | 1.453799 / 1.541195 (-0.087396) | 1.474296 / 1.468490 (0.005806) | 0.718609 / 4.584777 (-3.866168) | 0.948426 / 3.745712 (-2.797286) | 2.877275 / 5.269862 (-2.392586) | 1.930940 / 4.565676 (-2.634736) | 0.079207 / 0.424275 (-0.345068) | 0.005379 / 0.007607 (-0.002228) | 0.357969 / 0.226044 (0.131925) | 3.576455 / 2.268929 (1.307527) | 1.985058 / 55.444624 (-53.459566) | 1.663730 / 6.876477 (-5.212747) | 1.812752 / 2.142072 (-0.329320) | 0.800200 / 4.805227 (-4.005027) | 0.135124 / 6.500664 (-6.365540) | 0.041211 / 0.075469 (-0.034258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032394 / 1.841788 (-0.809394) | 12.082436 / 8.074308 (4.008128) | 10.198703 / 10.191392 (0.007311) | 0.143578 / 0.680424 (-0.536846) | 0.015576 / 0.534201 (-0.518625) | 0.301450 / 0.579283 (-0.277833) | 0.126596 / 0.434364 (-0.307768) | 0.339437 / 0.540337 (-0.200900) | 0.445454 / 1.386936 (-0.941482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#347f1664a31c1c0fcb6a1a0914ebfb99c134e116 \"CML watermark\")\n" ]
[]
[]
[ "Thanks for reporting, @pminervini.\r\n\r\nI agree we should give the option to define the split name.\r\n\r\nIndeed, there is a PR that addresses precisely this issue:\r\n- #7015\r\n\r\nI am reviewing it.", "Booom! thank you guys :)" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7032). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@albertvillanova hm I don't know tbh, it's just that \"mlfoundations/dclm-baseline-1.0\" dataset contains [files](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/tree/main/global-shard_01_of_10/local-shard_0_of_10) with this extension and these files seem to be valid ", "not sure why CI is failing but seems to be unrelated to this pr? can I merge @lhoestq @albertvillanova ?", "yes you can merge, the CI failure is unrelated (surely an issue with hub-ci)", "ah why not, you could try opening a PR\r\n\r\nbtw there is a channel with them at (internal) https://app.slack.com/client/T1RCG4490/C079AKTV11P if you want to let them know", "@lhoestq, your previous comment was addressed to me or Polina?\r\n\r\n@polinaeterna let me know if it is OK for you.", "I opened https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/discussions/7", "Should we close this PR then?" ]
[]
[ "You can disable progress bars for all of `datasets` with `disable_progress_bars`. [Link](https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars)\r\n\r\nSo you could do something like:\r\n\r\n```python\r\nfrom datasets import load_from_disk, enable_progress_bars, disable_progress_bars\r\n\r\ndisable_progress_bars()\r\n# Your code\r\nload_from_disk(....)\r\n\r\nenable_progress_bars()\r\n```\r\n", "Thank you! Closing the issue." ]
[ "hi ! can you share the full stack trace ? this should help locate what files is not written in the cache_dir" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7028). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005748 / 0.011353 (-0.005605) | 0.004109 / 0.011008 (-0.006899) | 0.067017 / 0.038508 (0.028509) | 0.031950 / 0.023109 (0.008841) | 0.239939 / 0.275898 (-0.035959) | 0.266339 / 0.323480 (-0.057141) | 0.003176 / 0.007986 (-0.004809) | 0.003556 / 0.004328 (-0.000773) | 0.050725 / 0.004250 (0.046475) | 0.047711 / 0.037052 (0.010658) | 0.251048 / 0.258489 (-0.007441) | 0.287049 / 0.293841 (-0.006792) | 0.029919 / 0.128546 (-0.098627) | 0.012562 / 0.075646 (-0.063085) | 0.212903 / 0.419271 (-0.206369) | 0.036570 / 0.043533 (-0.006963) | 0.240975 / 0.255139 (-0.014164) | 0.266473 / 0.283200 (-0.016726) | 0.019959 / 0.141683 (-0.121724) | 1.152224 / 1.452155 (-0.299931) | 1.186046 / 1.492716 (-0.306671) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095836 / 0.018006 (0.077829) | 0.303402 / 0.000490 (0.302913) | 0.000210 / 0.000200 (0.000010) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020552 / 0.037411 (-0.016859) | 0.063619 / 0.014526 (0.049093) | 0.076969 / 0.176557 (-0.099588) | 0.123368 / 0.737135 (-0.613767) | 0.077005 / 0.296338 (-0.219334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282005 / 0.215209 (0.066796) | 2.794144 / 2.077655 (0.716489) | 1.463569 / 1.504120 (-0.040551) | 1.334295 / 1.541195 (-0.206899) | 1.387198 / 1.468490 (-0.081292) | 0.707654 / 4.584777 (-3.877123) | 2.341698 / 3.745712 (-1.404014) | 2.865131 / 5.269862 (-2.404731) | 1.945168 / 4.565676 (-2.620509) | 0.077926 / 0.424275 (-0.346349) | 0.005470 / 0.007607 (-0.002137) | 0.336498 / 0.226044 (0.110454) | 3.330262 / 2.268929 (1.061334) | 1.865574 / 55.444624 (-53.579050) | 1.536932 / 6.876477 (-5.339545) | 1.720960 / 2.142072 (-0.421113) | 0.794753 / 4.805227 (-4.010475) | 0.133491 / 6.500664 (-6.367173) | 0.042437 / 0.075469 (-0.033032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976788 / 1.841788 (-0.865000) | 11.895137 / 8.074308 (3.820829) | 9.211969 / 10.191392 (-0.979423) | 0.141798 / 0.680424 (-0.538626) | 0.014354 / 0.534201 (-0.519847) | 0.306044 / 0.579283 (-0.273239) | 0.265016 / 0.434364 (-0.169348) | 0.340877 / 0.540337 (-0.199460) | 0.470449 / 1.386936 (-0.916487) |\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) | 0.006134 / 0.011353 (-0.005219) | 0.004023 / 0.011008 (-0.006985) | 0.050419 / 0.038508 (0.011911) | 0.033853 / 0.023109 (0.010744) | 0.266799 / 0.275898 (-0.009099) | 0.291248 / 0.323480 (-0.032232) | 0.004474 / 0.007986 (-0.003511) | 0.002847 / 0.004328 (-0.001481) | 0.049895 / 0.004250 (0.045645) | 0.041160 / 0.037052 (0.004108) | 0.278818 / 0.258489 (0.020329) | 0.314027 / 0.293841 (0.020186) | 0.032303 / 0.128546 (-0.096243) | 0.012367 / 0.075646 (-0.063279) | 0.061495 / 0.419271 (-0.357776) | 0.033512 / 0.043533 (-0.010021) | 0.266168 / 0.255139 (0.011029) | 0.283129 / 0.283200 (-0.000071) | 0.018674 / 0.141683 (-0.123009) | 1.124453 / 1.452155 (-0.327701) | 1.164527 / 1.492716 (-0.328189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098522 / 0.018006 (0.080516) | 0.315069 / 0.000490 (0.314579) | 0.000202 / 0.000200 (0.000002) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022809 / 0.037411 (-0.014602) | 0.078409 / 0.014526 (0.063883) | 0.088558 / 0.176557 (-0.087998) | 0.130004 / 0.737135 (-0.607131) | 0.090507 / 0.296338 (-0.205832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291323 / 0.215209 (0.076114) | 2.836363 / 2.077655 (0.758708) | 1.548889 / 1.504120 (0.044769) | 1.423857 / 1.541195 (-0.117337) | 1.461667 / 1.468490 (-0.006823) | 0.714956 / 4.584777 (-3.869821) | 0.948170 / 3.745712 (-2.797542) | 3.036151 / 5.269862 (-2.233711) | 1.923824 / 4.565676 (-2.641853) | 0.078002 / 0.424275 (-0.346273) | 0.005198 / 0.007607 (-0.002409) | 0.337007 / 0.226044 (0.110963) | 3.310255 / 2.268929 (1.041327) | 1.910371 / 55.444624 (-53.534253) | 1.619855 / 6.876477 (-5.256622) | 1.682093 / 2.142072 (-0.459979) | 0.789903 / 4.805227 (-4.015324) | 0.132117 / 6.500664 (-6.368547) | 0.041312 / 0.075469 (-0.034157) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.997658 / 1.841788 (-0.844130) | 12.447878 / 8.074308 (4.373570) | 10.277662 / 10.191392 (0.086270) | 0.143580 / 0.680424 (-0.536844) | 0.016472 / 0.534201 (-0.517729) | 0.307235 / 0.579283 (-0.272048) | 0.125469 / 0.434364 (-0.308895) | 0.339525 / 0.540337 (-0.200813) | 0.427371 / 1.386936 (-0.959566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#689447f8c86f777829a4db9ccc5d8133c12ec84c \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7027). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005612 / 0.011353 (-0.005741) | 0.004023 / 0.011008 (-0.006985) | 0.065578 / 0.038508 (0.027070) | 0.030476 / 0.023109 (0.007367) | 0.237131 / 0.275898 (-0.038767) | 0.269388 / 0.323480 (-0.054092) | 0.003364 / 0.007986 (-0.004622) | 0.002938 / 0.004328 (-0.001390) | 0.050867 / 0.004250 (0.046617) | 0.049456 / 0.037052 (0.012403) | 0.249587 / 0.258489 (-0.008902) | 0.291132 / 0.293841 (-0.002709) | 0.029373 / 0.128546 (-0.099174) | 0.012266 / 0.075646 (-0.063380) | 0.206239 / 0.419271 (-0.213033) | 0.037192 / 0.043533 (-0.006340) | 0.244902 / 0.255139 (-0.010237) | 0.269779 / 0.283200 (-0.013421) | 0.019870 / 0.141683 (-0.121813) | 1.123697 / 1.452155 (-0.328458) | 1.181256 / 1.492716 (-0.311460) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108535 / 0.018006 (0.090529) | 0.317838 / 0.000490 (0.317348) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019097 / 0.037411 (-0.018315) | 0.063836 / 0.014526 (0.049310) | 0.075446 / 0.176557 (-0.101111) | 0.124503 / 0.737135 (-0.612632) | 0.077730 / 0.296338 (-0.218608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284688 / 0.215209 (0.069479) | 2.817832 / 2.077655 (0.740178) | 1.487342 / 1.504120 (-0.016778) | 1.354037 / 1.541195 (-0.187158) | 1.426904 / 1.468490 (-0.041586) | 0.728754 / 4.584777 (-3.856022) | 2.361140 / 3.745712 (-1.384573) | 2.926215 / 5.269862 (-2.343647) | 1.981767 / 4.565676 (-2.583909) | 0.079278 / 0.424275 (-0.344997) | 0.005567 / 0.007607 (-0.002040) | 0.336590 / 0.226044 (0.110546) | 3.371062 / 2.268929 (1.102134) | 1.845343 / 55.444624 (-53.599282) | 1.537699 / 6.876477 (-5.338777) | 1.731407 / 2.142072 (-0.410665) | 0.796148 / 4.805227 (-4.009079) | 0.133830 / 6.500664 (-6.366835) | 0.043117 / 0.075469 (-0.032352) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980786 / 1.841788 (-0.861001) | 12.653553 / 8.074308 (4.579245) | 9.402636 / 10.191392 (-0.788756) | 0.143756 / 0.680424 (-0.536667) | 0.014896 / 0.534201 (-0.519304) | 0.328796 / 0.579283 (-0.250487) | 0.275108 / 0.434364 (-0.159255) | 0.343397 / 0.540337 (-0.196940) | 0.472301 / 1.386936 (-0.914635) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005882 / 0.011353 (-0.005471) | 0.003982 / 0.011008 (-0.007026) | 0.050484 / 0.038508 (0.011976) | 0.035217 / 0.023109 (0.012108) | 0.271683 / 0.275898 (-0.004215) | 0.291498 / 0.323480 (-0.031982) | 0.004429 / 0.007986 (-0.003557) | 0.002928 / 0.004328 (-0.001401) | 0.049386 / 0.004250 (0.045136) | 0.040868 / 0.037052 (0.003815) | 0.280968 / 0.258489 (0.022479) | 0.314880 / 0.293841 (0.021039) | 0.032590 / 0.128546 (-0.095956) | 0.012319 / 0.075646 (-0.063327) | 0.060354 / 0.419271 (-0.358917) | 0.034138 / 0.043533 (-0.009394) | 0.267491 / 0.255139 (0.012352) | 0.283077 / 0.283200 (-0.000123) | 0.017784 / 0.141683 (-0.123899) | 1.154835 / 1.452155 (-0.297320) | 1.179271 / 1.492716 (-0.313446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100519 / 0.018006 (0.082513) | 0.309043 / 0.000490 (0.308553) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024056 / 0.037411 (-0.013356) | 0.077810 / 0.014526 (0.063284) | 0.092682 / 0.176557 (-0.083875) | 0.132101 / 0.737135 (-0.605034) | 0.091986 / 0.296338 (-0.204352) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298186 / 0.215209 (0.082977) | 2.905134 / 2.077655 (0.827479) | 1.552364 / 1.504120 (0.048245) | 1.424644 / 1.541195 (-0.116551) | 1.457667 / 1.468490 (-0.010823) | 0.717606 / 4.584777 (-3.867171) | 0.944470 / 3.745712 (-2.801242) | 3.056236 / 5.269862 (-2.213626) | 1.946453 / 4.565676 (-2.619223) | 0.080525 / 0.424275 (-0.343750) | 0.005235 / 0.007607 (-0.002372) | 0.348561 / 0.226044 (0.122516) | 3.449350 / 2.268929 (1.180421) | 1.930165 / 55.444624 (-53.514459) | 1.620883 / 6.876477 (-5.255593) | 1.671963 / 2.142072 (-0.470109) | 0.801978 / 4.805227 (-4.003249) | 0.134494 / 6.500664 (-6.366170) | 0.041888 / 0.075469 (-0.033581) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005961 / 1.841788 (-0.835826) | 12.687638 / 8.074308 (4.613330) | 10.398730 / 10.191392 (0.207338) | 0.134503 / 0.680424 (-0.545920) | 0.015839 / 0.534201 (-0.518362) | 0.307465 / 0.579283 (-0.271819) | 0.130805 / 0.434364 (-0.303559) | 0.349079 / 0.540337 (-0.191259) | 0.437609 / 1.386936 (-0.949327) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc6ac9e5f70811a450198203ddc077c0c7bff206 \"CML watermark\")\n" ]
[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7026). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005637 / 0.011353 (-0.005716) | 0.003967 / 0.011008 (-0.007041) | 0.064187 / 0.038508 (0.025679) | 0.031356 / 0.023109 (0.008246) | 0.239203 / 0.275898 (-0.036695) | 0.261033 / 0.323480 (-0.062447) | 0.003256 / 0.007986 (-0.004730) | 0.003416 / 0.004328 (-0.000913) | 0.049673 / 0.004250 (0.045423) | 0.047021 / 0.037052 (0.009969) | 0.252146 / 0.258489 (-0.006343) | 0.283663 / 0.293841 (-0.010178) | 0.030223 / 0.128546 (-0.098324) | 0.012342 / 0.075646 (-0.063304) | 0.213061 / 0.419271 (-0.206211) | 0.036867 / 0.043533 (-0.006665) | 0.242589 / 0.255139 (-0.012550) | 0.265584 / 0.283200 (-0.017616) | 0.019149 / 0.141683 (-0.122533) | 1.108909 / 1.452155 (-0.343246) | 1.148484 / 1.492716 (-0.344232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096815 / 0.018006 (0.078809) | 0.299633 / 0.000490 (0.299143) | 0.000212 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018947 / 0.037411 (-0.018464) | 0.061640 / 0.014526 (0.047114) | 0.074621 / 0.176557 (-0.101935) | 0.120830 / 0.737135 (-0.616305) | 0.075472 / 0.296338 (-0.220866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284626 / 0.215209 (0.069417) | 2.805299 / 2.077655 (0.727644) | 1.469879 / 1.504120 (-0.034241) | 1.355524 / 1.541195 (-0.185671) | 1.388246 / 1.468490 (-0.080244) | 0.726740 / 4.584777 (-3.858037) | 2.387461 / 3.745712 (-1.358251) | 2.834137 / 5.269862 (-2.435724) | 1.915750 / 4.565676 (-2.649927) | 0.079223 / 0.424275 (-0.345052) | 0.005489 / 0.007607 (-0.002118) | 0.335517 / 0.226044 (0.109473) | 3.299332 / 2.268929 (1.030403) | 1.817726 / 55.444624 (-53.626898) | 1.520834 / 6.876477 (-5.355642) | 1.696285 / 2.142072 (-0.445788) | 0.815147 / 4.805227 (-3.990080) | 0.136566 / 6.500664 (-6.364098) | 0.043482 / 0.075469 (-0.031987) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981382 / 1.841788 (-0.860406) | 11.472890 / 8.074308 (3.398582) | 9.274181 / 10.191392 (-0.917211) | 0.133051 / 0.680424 (-0.547373) | 0.015417 / 0.534201 (-0.518784) | 0.306098 / 0.579283 (-0.273185) | 0.261424 / 0.434364 (-0.172940) | 0.338946 / 0.540337 (-0.201391) | 0.460776 / 1.386936 (-0.926160) |\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) | 0.005806 / 0.011353 (-0.005547) | 0.004274 / 0.011008 (-0.006734) | 0.050831 / 0.038508 (0.012323) | 0.033717 / 0.023109 (0.010607) | 0.280561 / 0.275898 (0.004663) | 0.302437 / 0.323480 (-0.021043) | 0.004543 / 0.007986 (-0.003442) | 0.002905 / 0.004328 (-0.001424) | 0.048897 / 0.004250 (0.044646) | 0.041089 / 0.037052 (0.004037) | 0.291439 / 0.258489 (0.032950) | 0.319762 / 0.293841 (0.025921) | 0.033178 / 0.128546 (-0.095368) | 0.012336 / 0.075646 (-0.063311) | 0.061033 / 0.419271 (-0.358238) | 0.034018 / 0.043533 (-0.009515) | 0.278514 / 0.255139 (0.023375) | 0.295648 / 0.283200 (0.012448) | 0.018621 / 0.141683 (-0.123062) | 1.160250 / 1.452155 (-0.291905) | 1.183867 / 1.492716 (-0.308850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096354 / 0.018006 (0.078348) | 0.301907 / 0.000490 (0.301417) | 0.000205 / 0.000200 (0.000006) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022357 / 0.037411 (-0.015054) | 0.076218 / 0.014526 (0.061692) | 0.088172 / 0.176557 (-0.088385) | 0.128621 / 0.737135 (-0.608515) | 0.089250 / 0.296338 (-0.207089) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292633 / 0.215209 (0.077424) | 2.862456 / 2.077655 (0.784801) | 1.581967 / 1.504120 (0.077847) | 1.459822 / 1.541195 (-0.081373) | 1.475896 / 1.468490 (0.007406) | 0.728550 / 4.584777 (-3.856226) | 0.958819 / 3.745712 (-2.786893) | 3.011074 / 5.269862 (-2.258788) | 1.934393 / 4.565676 (-2.631283) | 0.079831 / 0.424275 (-0.344444) | 0.005249 / 0.007607 (-0.002358) | 0.346334 / 0.226044 (0.120290) | 3.438979 / 2.268929 (1.170051) | 1.935567 / 55.444624 (-53.509057) | 1.648723 / 6.876477 (-5.227754) | 1.685489 / 2.142072 (-0.456583) | 0.800992 / 4.805227 (-4.004236) | 0.139388 / 6.500664 (-6.361276) | 0.042518 / 0.075469 (-0.032951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031715 / 1.841788 (-0.810072) | 12.486711 / 8.074308 (4.412403) | 10.430191 / 10.191392 (0.238799) | 0.146884 / 0.680424 (-0.533540) | 0.015735 / 0.534201 (-0.518466) | 0.303938 / 0.579283 (-0.275346) | 0.140374 / 0.434364 (-0.293989) | 0.338508 / 0.540337 (-0.201830) | 0.429551 / 1.386936 (-0.957385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e32336195f3ea69988148df5f129f9f59d3ab595 \"CML watermark\")\n" ]
[ "requesting review - @albertvillanova @lhoestq ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7025). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq rebased the PR, It would be really helpful to have this feature into datasets, please let me know if there is anything pending on this PR, thanks. ", "@lhoestq \r\n\r\nHave added the unit test to generate tables for both the arrow formats - file and streaming.\r\n\r\nLet me know if we have any docs changes as well. Thanks\r\n\r\n<img width=\"568\" alt=\"Screenshot 2024-07-25 at 7 04 26 PM\" src=\"https://github.com/user-attachments/assets/69fd0906-bda9-45fa-8f7e-8092e351ac29\">\r\n" ]
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