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[ "I don't reproduce, I was able to create a new repo: https://huggingface.co/datasets/severo/reproduce-datasets-issues-7108. Can you confirm it's still broken?", "I have just tried again.\r\n\r\nFirefox: The `Create dataset` doesn't work. It has worked in the past. It's my preferred browser.\r\n\r\nChrome: The `Create dataset` works.\r\n\r\nIt seems to be a Firefox specific issue.", "I have updated Firefox 129.0 (64 bit), and now the `Create dataset` is working again in Firefox.\r\n\r\nUX: It would be nice with better error messages on HuggingFace.", "maybe an issue with the cookie. cc @Wauplin @coyotte508 " ]

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[ "There seems to be a PR related to the load_dataset path that went into 2.21.0 -- https://github.com/huggingface/datasets/pull/6862/files\r\n\r\nTaking a look at it now", "+1\r\n\r\nDowngrading to 2.20.0 fixed my issue, hopefully helpful for others.", "I tried adding a simple test to `test_load.py` with the alpaca eval dataset but the test didn't fail :(. \r\n\r\nSo looks like this might have something to do with the environment? ", "There was an issue with the script of the \"tatsu-lab/alpaca_eval\" dataset.\r\n\r\nI was fixed with this PR: \r\n- [Fix FileNotFoundError](https://huggingface.co/datasets/tatsu-lab/alpaca_eval/discussions/2)\r\n\r\nIt should work now if you retry to load the dataset." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7106). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7105). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Nice\r\n\r\n<img width=\"141\" alt=\"Capture d’écran 2024-08-19 à 15 25 00\" src=\"https://github.com/user-attachments/assets/18c7b3ec-a57e-45d7-9b19-0b12df9feccd\">\r\n", "fyi the CI failure on test_py310_numpy2 is unrelated to this PR (it's a dependency install failure)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7104). 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) | 0.005343 / 0.011353 (-0.006010) | 0.003562 / 0.011008 (-0.007447) | 0.062785 / 0.038508 (0.024277) | 0.031459 / 0.023109 (0.008349) | 0.246497 / 0.275898 (-0.029401) | 0.268258 / 0.323480 (-0.055222) | 0.003201 / 0.007986 (-0.004785) | 0.004153 / 0.004328 (-0.000175) | 0.049003 / 0.004250 (0.044753) | 0.042780 / 0.037052 (0.005728) | 0.263857 / 0.258489 (0.005368) | 0.278578 / 0.293841 (-0.015263) | 0.030357 / 0.128546 (-0.098190) | 0.012341 / 0.075646 (-0.063305) | 0.206010 / 0.419271 (-0.213262) | 0.036244 / 0.043533 (-0.007289) | 0.245799 / 0.255139 (-0.009340) | 0.265467 / 0.283200 (-0.017733) | 0.019473 / 0.141683 (-0.122210) | 1.147913 / 1.452155 (-0.304242) | 1.209968 / 1.492716 (-0.282749) |\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) | 0.099393 / 0.018006 (0.081387) | 0.300898 / 0.000490 (0.300408) | 0.000258 / 0.000200 (0.000058) | 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) | 0.018888 / 0.037411 (-0.018523) | 0.062452 / 0.014526 (0.047926) | 0.073799 / 0.176557 (-0.102757) | 0.121297 / 0.737135 (-0.615839) | 0.074855 / 0.296338 (-0.221484) |\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) | 0.283969 / 0.215209 (0.068760) | 2.808820 / 2.077655 (0.731165) | 1.446106 / 1.504120 (-0.058014) | 1.321622 / 1.541195 (-0.219573) | 1.348317 / 1.468490 (-0.120173) | 0.738369 / 4.584777 (-3.846408) | 2.349825 / 3.745712 (-1.395887) | 2.913964 / 5.269862 (-2.355897) | 1.870585 / 4.565676 (-2.695092) | 0.080141 / 0.424275 (-0.344134) | 0.005174 / 0.007607 (-0.002433) | 0.335977 / 0.226044 (0.109933) | 3.356267 / 2.268929 (1.087338) | 1.811149 / 55.444624 (-53.633475) | 1.510685 / 6.876477 (-5.365792) | 1.524960 / 2.142072 (-0.617112) | 0.803900 / 4.805227 (-4.001328) | 0.138294 / 6.500664 (-6.362370) | 0.042241 / 0.075469 (-0.033229) |\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) | 0.975597 / 1.841788 (-0.866191) | 11.395109 / 8.074308 (3.320801) | 9.837724 / 10.191392 (-0.353668) | 0.141474 / 0.680424 (-0.538950) | 0.015075 / 0.534201 (-0.519126) | 0.304285 / 0.579283 (-0.274998) | 0.267845 / 0.434364 (-0.166519) | 0.342808 / 0.540337 (-0.197529) | 0.434299 / 1.386936 (-0.952637) |\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) | 0.005612 / 0.011353 (-0.005741) | 0.003808 / 0.011008 (-0.007201) | 0.050533 / 0.038508 (0.012024) | 0.032635 / 0.023109 (0.009526) | 0.265522 / 0.275898 (-0.010376) | 0.289763 / 0.323480 (-0.033716) | 0.004395 / 0.007986 (-0.003590) | 0.002868 / 0.004328 (-0.001460) | 0.048443 / 0.004250 (0.044193) | 0.040047 / 0.037052 (0.002995) | 0.279013 / 0.258489 (0.020524) | 0.314499 / 0.293841 (0.020658) | 0.032321 / 0.128546 (-0.096225) | 0.011902 / 0.075646 (-0.063744) | 0.059827 / 0.419271 (-0.359445) | 0.034388 / 0.043533 (-0.009145) | 0.270660 / 0.255139 (0.015521) | 0.290776 / 0.283200 (0.007576) | 0.017875 / 0.141683 (-0.123808) | 1.188085 / 1.452155 (-0.264070) | 1.221384 / 1.492716 (-0.271332) |\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) | 0.095619 / 0.018006 (0.077613) | 0.305331 / 0.000490 (0.304841) | 0.000217 / 0.000200 (0.000018) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022481 / 0.037411 (-0.014930) | 0.076957 / 0.014526 (0.062431) | 0.087830 / 0.176557 (-0.088726) | 0.128290 / 0.737135 (-0.608845) | 0.090565 / 0.296338 (-0.205774) |\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) | 0.291861 / 0.215209 (0.076652) | 2.869776 / 2.077655 (0.792121) | 1.575114 / 1.504120 (0.070994) | 1.449873 / 1.541195 (-0.091322) | 1.450333 / 1.468490 (-0.018158) | 0.723319 / 4.584777 (-3.861458) | 0.972603 / 3.745712 (-2.773109) | 2.940909 / 5.269862 (-2.328953) | 1.889664 / 4.565676 (-2.676012) | 0.078654 / 0.424275 (-0.345621) | 0.005197 / 0.007607 (-0.002410) | 0.344380 / 0.226044 (0.118336) | 3.387509 / 2.268929 (1.118580) | 1.981590 / 55.444624 (-53.463034) | 1.643214 / 6.876477 (-5.233263) | 1.640435 / 2.142072 (-0.501638) | 0.802037 / 4.805227 (-4.003191) | 0.133016 / 6.500664 (-6.367648) | 0.040861 / 0.075469 (-0.034608) |\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) | 1.026372 / 1.841788 (-0.815416) | 11.959931 / 8.074308 (3.885623) | 10.122523 / 10.191392 (-0.068869) | 0.144443 / 0.680424 (-0.535981) | 0.015629 / 0.534201 (-0.518572) | 0.304802 / 0.579283 (-0.274481) | 0.120538 / 0.434364 (-0.313826) | 0.343394 / 0.540337 (-0.196943) | 0.437544 / 1.386936 (-0.949392) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84832c07f614e5f51a762166b2fa9ac27e988173 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7103). 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) | 0.005255 / 0.011353 (-0.006098) | 0.003344 / 0.011008 (-0.007664) | 0.062062 / 0.038508 (0.023554) | 0.030154 / 0.023109 (0.007045) | 0.233728 / 0.275898 (-0.042170) | 0.258799 / 0.323480 (-0.064681) | 0.004105 / 0.007986 (-0.003880) | 0.002708 / 0.004328 (-0.001621) | 0.048689 / 0.004250 (0.044439) | 0.041864 / 0.037052 (0.004812) | 0.247221 / 0.258489 (-0.011268) | 0.274067 / 0.293841 (-0.019774) | 0.029108 / 0.128546 (-0.099439) | 0.011867 / 0.075646 (-0.063779) | 0.203181 / 0.419271 (-0.216090) | 0.035162 / 0.043533 (-0.008371) | 0.239723 / 0.255139 (-0.015416) | 0.256679 / 0.283200 (-0.026521) | 0.018362 / 0.141683 (-0.123321) | 1.139974 / 1.452155 (-0.312181) | 1.193946 / 1.492716 (-0.298770) |\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) | 0.135477 / 0.018006 (0.117471) | 0.298500 / 0.000490 (0.298011) | 0.000225 / 0.000200 (0.000025) | 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) | 0.018743 / 0.037411 (-0.018668) | 0.062999 / 0.014526 (0.048474) | 0.073466 / 0.176557 (-0.103090) | 0.119227 / 0.737135 (-0.617908) | 0.074338 / 0.296338 (-0.222000) |\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) | 0.280747 / 0.215209 (0.065538) | 2.750660 / 2.077655 (0.673006) | 1.461004 / 1.504120 (-0.043116) | 1.348439 / 1.541195 (-0.192756) | 1.365209 / 1.468490 (-0.103281) | 0.718416 / 4.584777 (-3.866361) | 2.333568 / 3.745712 (-1.412144) | 2.854639 / 5.269862 (-2.415223) | 1.821144 / 4.565676 (-2.744532) | 0.077234 / 0.424275 (-0.347041) | 0.005111 / 0.007607 (-0.002497) | 0.330749 / 0.226044 (0.104705) | 3.277189 / 2.268929 (1.008260) | 1.825886 / 55.444624 (-53.618739) | 1.515078 / 6.876477 (-5.361399) | 1.527288 / 2.142072 (-0.614785) | 0.786922 / 4.805227 (-4.018305) | 0.131539 / 6.500664 (-6.369125) | 0.042365 / 0.075469 (-0.033104) |\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) | 0.961809 / 1.841788 (-0.879979) | 11.184540 / 8.074308 (3.110232) | 9.473338 / 10.191392 (-0.718054) | 0.138460 / 0.680424 (-0.541964) | 0.014588 / 0.534201 (-0.519613) | 0.301503 / 0.579283 (-0.277780) | 0.261092 / 0.434364 (-0.173271) | 0.336480 / 0.540337 (-0.203857) | 0.427665 / 1.386936 (-0.959271) |\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) | 0.005517 / 0.011353 (-0.005836) | 0.003417 / 0.011008 (-0.007591) | 0.049338 / 0.038508 (0.010830) | 0.033411 / 0.023109 (0.010302) | 0.264328 / 0.275898 (-0.011570) | 0.286750 / 0.323480 (-0.036730) | 0.004299 / 0.007986 (-0.003686) | 0.002506 / 0.004328 (-0.001823) | 0.049511 / 0.004250 (0.045260) | 0.041471 / 0.037052 (0.004418) | 0.276732 / 0.258489 (0.018243) | 0.311908 / 0.293841 (0.018067) | 0.031683 / 0.128546 (-0.096863) | 0.011700 / 0.075646 (-0.063946) | 0.060084 / 0.419271 (-0.359188) | 0.037757 / 0.043533 (-0.005776) | 0.265342 / 0.255139 (0.010203) | 0.287782 / 0.283200 (0.004583) | 0.018692 / 0.141683 (-0.122990) | 1.163462 / 1.452155 (-0.288692) | 1.219236 / 1.492716 (-0.273481) |\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) | 0.094102 / 0.018006 (0.076096) | 0.303976 / 0.000490 (0.303487) | 0.000208 / 0.000200 (0.000008) | 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) | 0.023252 / 0.037411 (-0.014160) | 0.076986 / 0.014526 (0.062461) | 0.088831 / 0.176557 (-0.087726) | 0.128661 / 0.737135 (-0.608475) | 0.089082 / 0.296338 (-0.207256) |\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) | 0.297428 / 0.215209 (0.082218) | 2.951568 / 2.077655 (0.873913) | 1.597627 / 1.504120 (0.093508) | 1.466556 / 1.541195 (-0.074639) | 1.455522 / 1.468490 (-0.012968) | 0.723576 / 4.584777 (-3.861201) | 0.951113 / 3.745712 (-2.794599) | 2.889671 / 5.269862 (-2.380190) | 1.877330 / 4.565676 (-2.688347) | 0.079124 / 0.424275 (-0.345151) | 0.005146 / 0.007607 (-0.002461) | 0.344063 / 0.226044 (0.118018) | 3.432190 / 2.268929 (1.163261) | 1.927049 / 55.444624 (-53.517576) | 1.638552 / 6.876477 (-5.237924) | 1.647791 / 2.142072 (-0.494282) | 0.800526 / 4.805227 (-4.004701) | 0.131858 / 6.500664 (-6.368806) | 0.040852 / 0.075469 (-0.034618) |\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) | 1.025536 / 1.841788 (-0.816252) | 11.798302 / 8.074308 (3.723994) | 10.012051 / 10.191392 (-0.179341) | 0.137701 / 0.680424 (-0.542723) | 0.015151 / 0.534201 (-0.519050) | 0.298972 / 0.579283 (-0.280311) | 0.123816 / 0.434364 (-0.310548) | 0.337292 / 0.540337 (-0.203046) | 0.432729 / 1.386936 (-0.954207) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bececdac927160b5c7e883736d7cc79d5699ad0a \"CML watermark\")\n" ]

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[ "Hi @lajd , I was skeptical about how we are saving the shards each as their own dataset (arrow file) in the script above, and so I updated the script to try out saving the shards in a few different file formats. From the experiments I ran, I saw binary format show significantly the best performance, with arrow and parquet about the same. However, I was unable to reproduce a drastically slower iteration speed after shuffling in any case when using the revised script -- pasting below:\r\n\r\n```python\r\nimport time\r\nfrom datasets import load_dataset, Dataset, IterableDataset\r\nfrom pathlib import Path\r\nimport torch\r\nimport pandas as pd\r\nimport pickle\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\n\r\ndef generate_random_example():\r\n return {\r\n 'inputs': torch.randn(128).tolist(),\r\n 'indices': torch.randint(0, 10000, (2, 20000)).tolist(),\r\n 'values': torch.randn(20000).tolist(),\r\n }\r\n\r\n\r\ndef generate_shard_data(examples_per_shard: int = 512):\r\n return [generate_random_example() for _ in range(examples_per_shard)]\r\n\r\n\r\ndef save_shard_as_arrow(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Convert data to a Hugging Face Dataset\r\n dataset = Dataset.from_dict({\r\n 'inputs': [example['inputs'] for example in shard_data],\r\n 'indices': [example['indices'] for example in shard_data],\r\n 'values': [example['values'] for example in shard_data],\r\n })\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}\"\r\n\r\n # Save the dataset to disk using the Arrow format\r\n dataset.save_to_disk(str(shard_write_path))\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef save_shard_as_parquet(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Convert data to a pandas DataFrame for easy conversion to Parquet\r\n df = pd.DataFrame(shard_data)\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}.parquet\"\r\n\r\n # Convert DataFrame to PyArrow Table for Parquet saving\r\n table = pa.Table.from_pandas(df)\r\n\r\n # Save the table as a Parquet file\r\n pq.write_table(table, shard_write_path)\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef save_shard_as_binary(shard_idx, save_dir, examples_per_shard):\r\n # Generate shard data\r\n shard_data = generate_shard_data(examples_per_shard)\r\n\r\n # Define the shard save path\r\n shard_write_path = Path(save_dir) / f\"shard_{shard_idx}.bin\"\r\n\r\n # Save each example as a serialized binary object using pickle\r\n with open(shard_write_path, 'wb') as f:\r\n for example in shard_data:\r\n f.write(pickle.dumps(example))\r\n\r\n return str(shard_write_path)\r\n\r\n\r\ndef generate_split_shards(save_dir, filetype=\"parquet\", num_shards: int = 16, examples_per_shard: int = 512):\r\n shard_filepaths = []\r\n for shard_idx in range(num_shards):\r\n if filetype == \"parquet\":\r\n shard_filepaths.append(save_shard_as_parquet(shard_idx, save_dir, examples_per_shard))\r\n elif filetype == \"binary\":\r\n shard_filepaths.append(save_shard_as_binary(shard_idx, save_dir, examples_per_shard))\r\n elif filetype == \"arrow\":\r\n shard_filepaths.append(save_shard_as_arrow(shard_idx, save_dir, examples_per_shard))\r\n else:\r\n raise ValueError(f\"Unsupported filetype: {filetype}. Choose either 'parquet' or 'binary'.\")\r\n return shard_filepaths\r\n\r\n\r\ndef _binary_dataset_generator(files):\r\n for filepath in files:\r\n with open(filepath, 'rb') as f:\r\n while True:\r\n try:\r\n example = pickle.load(f)\r\n yield example\r\n except EOFError:\r\n break\r\n\r\n\r\ndef load_binary_dataset(shard_filepaths):\r\n return IterableDataset.from_generator(\r\n _binary_dataset_generator, gen_kwargs={\"files\": shard_filepaths},\r\n )\r\n\r\n\r\ndef load_parquet_dataset(shard_filepaths):\r\n # Load the dataset as an IterableDataset\r\n return load_dataset(\r\n \"parquet\",\r\n data_files={split: shard_filepaths},\r\n streaming=True,\r\n split=split,\r\n )\r\n\r\n\r\ndef load_arrow_dataset(shard_filepaths):\r\n # Load the dataset as an IterableDataset\r\n shard_filepaths = [f + \"/data-00000-of-00001.arrow\" for f in shard_filepaths]\r\n return load_dataset(\r\n \"arrow\",\r\n data_files={split: shard_filepaths},\r\n streaming=True,\r\n split=split,\r\n )\r\n\r\n\r\ndef load_dataset_wrapper(filetype: str, shard_filepaths: list[str]):\r\n if filetype == \"parquet\":\r\n return load_parquet_dataset(shard_filepaths)\r\n if filetype == \"binary\":\r\n return load_binary_dataset(shard_filepaths)\r\n if filetype == \"arrow\":\r\n return load_arrow_dataset(shard_filepaths)\r\n else:\r\n raise ValueError(\"Unsupported filetype\")\r\n\r\n\r\n# Example usage:\r\nsplit = \"train\"\r\nsplit_save_dir = \"/tmp/random_split\"\r\n\r\nfiletype = \"binary\" # or \"parquet\", or \"arrow\"\r\nnum_shards = 16\r\n\r\nshard_filepaths = generate_split_shards(split_save_dir, filetype=filetype, num_shards=num_shards)\r\ndataset = load_dataset_wrapper(filetype=filetype, shard_filepaths=shard_filepaths)\r\n\r\ndataset = dataset.shuffle(buffer_size=100, seed=42)\r\n\r\nstart_time = time.time()\r\nfor count, item in enumerate(dataset):\r\n if count > 0 and count % 100 == 0:\r\n elapsed_time = time.time() - start_time\r\n iterations_per_second = count / elapsed_time\r\n print(f\"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second\")\r\n```", "update: I was able to reproduce the issue you described -- but ONLY if I do \r\n\r\n```\r\nrandom_dataset = random_dataset.with_format(\"numpy\")\r\n```\r\n\r\nIf I do this, I see similar numbers as what you reported. If I do not use numpy format, parquet and arrow are about 17 iterations per second regardless of whether or not we shuffle. Using binary, (again no numpy format tried with this yet), still shows the fastest speeds on average (shuffle and no shuffle) of about 850 it/sec.\r\n\r\nI suspect some issues with arrow and numpy being optimized for sequential reads, and shuffling cuases issuses... hmm" ]

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[ "Having looked into this further it seems the core of the issue is with two different formats in the same repo.\r\n\r\nWhen the `parquet` config is first, the `WebDataset`s are loaded as `parquet`, if the `WebDataset` configs are first, the `parquet` is loaded as `WebDataset`.\r\n\r\nA workaround in my case would be to just turn the `parquet` into a `WebDataset`, although I'd still need the Dataset Viewer config limit increasing. In other cases using the same format may not be possible.\r\n\r\nRelevant code: \r\n- [HubDatasetModuleFactoryWithoutScript](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/load.py#L964)\r\n- [get_data_patterns](https://github.com/huggingface/datasets/blob/5f42139a2c5583a55d34a2f60d537f5fba285c28/src/datasets/data_files.py#L415)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7099). 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) | 0.005649 / 0.011353 (-0.005704) | 0.003918 / 0.011008 (-0.007091) | 0.064333 / 0.038508 (0.025825) | 0.031909 / 0.023109 (0.008800) | 0.249020 / 0.275898 (-0.026878) | 0.273563 / 0.323480 (-0.049917) | 0.004184 / 0.007986 (-0.003802) | 0.002809 / 0.004328 (-0.001519) | 0.049066 / 0.004250 (0.044816) | 0.043324 / 0.037052 (0.006272) | 0.257889 / 0.258489 (-0.000600) | 0.285410 / 0.293841 (-0.008431) | 0.030681 / 0.128546 (-0.097865) | 0.012389 / 0.075646 (-0.063258) | 0.206172 / 0.419271 (-0.213100) | 0.036500 / 0.043533 (-0.007032) | 0.253674 / 0.255139 (-0.001465) | 0.272086 / 0.283200 (-0.011114) | 0.019558 / 0.141683 (-0.122125) | 1.149501 / 1.452155 (-0.302653) | 1.198036 / 1.492716 (-0.294680) |\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) | 0.139977 / 0.018006 (0.121971) | 0.301149 / 0.000490 (0.300659) | 0.000253 / 0.000200 (0.000053) | 0.000049 / 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) | 0.019137 / 0.037411 (-0.018274) | 0.062616 / 0.014526 (0.048090) | 0.075965 / 0.176557 (-0.100591) | 0.120976 / 0.737135 (-0.616159) | 0.076384 / 0.296338 (-0.219954) |\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) | 0.283801 / 0.215209 (0.068592) | 2.794074 / 2.077655 (0.716419) | 1.475633 / 1.504120 (-0.028487) | 1.336270 / 1.541195 (-0.204925) | 1.376159 / 1.468490 (-0.092331) | 0.718768 / 4.584777 (-3.866009) | 2.375970 / 3.745712 (-1.369742) | 2.969121 / 5.269862 (-2.300741) | 1.900236 / 4.565676 (-2.665440) | 0.082463 / 0.424275 (-0.341812) | 0.005159 / 0.007607 (-0.002448) | 0.329057 / 0.226044 (0.103012) | 3.250535 / 2.268929 (0.981607) | 1.846415 / 55.444624 (-53.598210) | 1.496622 / 6.876477 (-5.379855) | 1.538125 / 2.142072 (-0.603947) | 0.806127 / 4.805227 (-3.999101) | 0.135272 / 6.500664 (-6.365392) | 0.042668 / 0.075469 (-0.032801) |\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) | 0.983035 / 1.841788 (-0.858753) | 11.725835 / 8.074308 (3.651527) | 9.962818 / 10.191392 (-0.228574) | 0.131928 / 0.680424 (-0.548496) | 0.015784 / 0.534201 (-0.518417) | 0.301640 / 0.579283 (-0.277643) | 0.266251 / 0.434364 (-0.168113) | 0.339723 / 0.540337 (-0.200614) | 0.443384 / 1.386936 (-0.943552) |\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) | 0.006301 / 0.011353 (-0.005052) | 0.004346 / 0.011008 (-0.006662) | 0.051406 / 0.038508 (0.012898) | 0.032263 / 0.023109 (0.009154) | 0.273715 / 0.275898 (-0.002183) | 0.300982 / 0.323480 (-0.022498) | 0.004533 / 0.007986 (-0.003452) | 0.002911 / 0.004328 (-0.001418) | 0.050464 / 0.004250 (0.046214) | 0.041131 / 0.037052 (0.004078) | 0.289958 / 0.258489 (0.031469) | 0.328632 / 0.293841 (0.034791) | 0.033545 / 0.128546 (-0.095001) | 0.013145 / 0.075646 (-0.062501) | 0.062241 / 0.419271 (-0.357031) | 0.035095 / 0.043533 (-0.008438) | 0.273303 / 0.255139 (0.018164) | 0.293652 / 0.283200 (0.010452) | 0.019980 / 0.141683 (-0.121703) | 1.155432 / 1.452155 (-0.296722) | 1.211409 / 1.492716 (-0.281307) |\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) | 0.094885 / 0.018006 (0.076879) | 0.307423 / 0.000490 (0.306933) | 0.000254 / 0.000200 (0.000054) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023462 / 0.037411 (-0.013949) | 0.081980 / 0.014526 (0.067454) | 0.089890 / 0.176557 (-0.086666) | 0.131058 / 0.737135 (-0.606078) | 0.091873 / 0.296338 (-0.204465) |\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) | 0.298522 / 0.215209 (0.083313) | 2.981771 / 2.077655 (0.904116) | 1.632515 / 1.504120 (0.128395) | 1.502885 / 1.541195 (-0.038310) | 1.496868 / 1.468490 (0.028377) | 0.750145 / 4.584777 (-3.834632) | 0.988853 / 3.745712 (-2.756859) | 3.029162 / 5.269862 (-2.240700) | 1.952304 / 4.565676 (-2.613373) | 0.082418 / 0.424275 (-0.341857) | 0.005724 / 0.007607 (-0.001883) | 0.356914 / 0.226044 (0.130870) | 3.523804 / 2.268929 (1.254875) | 1.983254 / 55.444624 (-53.461370) | 1.673135 / 6.876477 (-5.203342) | 1.716639 / 2.142072 (-0.425433) | 0.821568 / 4.805227 (-3.983659) | 0.136113 / 6.500664 (-6.364551) | 0.041593 / 0.075469 (-0.033876) |\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) | 1.044670 / 1.841788 (-0.797118) | 12.739375 / 8.074308 (4.665066) | 10.263619 / 10.191392 (0.072227) | 0.132811 / 0.680424 (-0.547613) | 0.015491 / 0.534201 (-0.518710) | 0.305545 / 0.579283 (-0.273738) | 0.129226 / 0.434364 (-0.305138) | 0.345532 / 0.540337 (-0.194805) | 0.460406 / 1.386936 (-0.926530) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ebec2691fb1e40145429f63375cef3f46d3011ab \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7098). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]

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[ "Hi @albertvillanova, is this PR looking okay to you? Anything else you'd like to see?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7096). 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) | 0.005278 / 0.011353 (-0.006075) | 0.003536 / 0.011008 (-0.007472) | 0.062604 / 0.038508 (0.024096) | 0.030704 / 0.023109 (0.007595) | 0.242178 / 0.275898 (-0.033720) | 0.264335 / 0.323480 (-0.059145) | 0.004118 / 0.007986 (-0.003868) | 0.002789 / 0.004328 (-0.001539) | 0.048813 / 0.004250 (0.044563) | 0.041787 / 0.037052 (0.004735) | 0.252369 / 0.258489 (-0.006120) | 0.280981 / 0.293841 (-0.012859) | 0.029646 / 0.128546 (-0.098900) | 0.012093 / 0.075646 (-0.063553) | 0.203036 / 0.419271 (-0.216235) | 0.035814 / 0.043533 (-0.007719) | 0.248929 / 0.255139 (-0.006210) | 0.266568 / 0.283200 (-0.016632) | 0.018761 / 0.141683 (-0.122922) | 1.188443 / 1.452155 (-0.263712) | 1.219324 / 1.492716 (-0.273392) |\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) | 0.095256 / 0.018006 (0.077250) | 0.301069 / 0.000490 (0.300579) | 0.000219 / 0.000200 (0.000019) | 0.000054 / 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) | 0.018541 / 0.037411 (-0.018870) | 0.067333 / 0.014526 (0.052807) | 0.075483 / 0.176557 (-0.101073) | 0.121301 / 0.737135 (-0.615834) | 0.076924 / 0.296338 (-0.219414) |\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) | 0.284722 / 0.215209 (0.069513) | 2.817656 / 2.077655 (0.740001) | 1.483827 / 1.504120 (-0.020293) | 1.363072 / 1.541195 (-0.178123) | 1.380472 / 1.468490 (-0.088018) | 0.739543 / 4.584777 (-3.845234) | 2.390699 / 3.745712 (-1.355013) | 2.980347 / 5.269862 (-2.289515) | 1.897881 / 4.565676 (-2.667795) | 0.078827 / 0.424275 (-0.345448) | 0.005193 / 0.007607 (-0.002414) | 0.342739 / 0.226044 (0.116695) | 3.370871 / 2.268929 (1.101942) | 1.846475 / 55.444624 (-53.598150) | 1.577860 / 6.876477 (-5.298617) | 1.628606 / 2.142072 (-0.513466) | 0.815686 / 4.805227 (-3.989541) | 0.134985 / 6.500664 (-6.365679) | 0.042330 / 0.075469 (-0.033139) |\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) | 0.962530 / 1.841788 (-0.879258) | 11.271449 / 8.074308 (3.197141) | 9.615452 / 10.191392 (-0.575940) | 0.140322 / 0.680424 (-0.540101) | 0.014057 / 0.534201 (-0.520144) | 0.306212 / 0.579283 (-0.273071) | 0.266758 / 0.434364 (-0.167606) | 0.341229 / 0.540337 (-0.199108) | 0.428974 / 1.386936 (-0.957962) |\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) | 0.005980 / 0.011353 (-0.005373) | 0.003831 / 0.011008 (-0.007177) | 0.049837 / 0.038508 (0.011329) | 0.030602 / 0.023109 (0.007493) | 0.274107 / 0.275898 (-0.001791) | 0.298175 / 0.323480 (-0.025305) | 0.004492 / 0.007986 (-0.003494) | 0.002840 / 0.004328 (-0.001489) | 0.048984 / 0.004250 (0.044733) | 0.040001 / 0.037052 (0.002949) | 0.286130 / 0.258489 (0.027641) | 0.321546 / 0.293841 (0.027705) | 0.032675 / 0.128546 (-0.095871) | 0.012222 / 0.075646 (-0.063424) | 0.060321 / 0.419271 (-0.358950) | 0.034456 / 0.043533 (-0.009077) | 0.272408 / 0.255139 (0.017269) | 0.294714 / 0.283200 (0.011515) | 0.018568 / 0.141683 (-0.123115) | 1.169826 / 1.452155 (-0.282329) | 1.223906 / 1.492716 (-0.268810) |\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) | 0.093734 / 0.018006 (0.075727) | 0.305915 / 0.000490 (0.305425) | 0.000210 / 0.000200 (0.000010) | 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) | 0.022389 / 0.037411 (-0.015022) | 0.076640 / 0.014526 (0.062114) | 0.088660 / 0.176557 (-0.087897) | 0.128998 / 0.737135 (-0.608137) | 0.090346 / 0.296338 (-0.205992) |\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) | 0.291642 / 0.215209 (0.076433) | 2.897270 / 2.077655 (0.819615) | 1.571564 / 1.504120 (0.067444) | 1.449533 / 1.541195 (-0.091662) | 1.458744 / 1.468490 (-0.009746) | 0.725465 / 4.584777 (-3.859312) | 0.962597 / 3.745712 (-2.783115) | 3.035056 / 5.269862 (-2.234806) | 1.902542 / 4.565676 (-2.663135) | 0.079869 / 0.424275 (-0.344407) | 0.005172 / 0.007607 (-0.002435) | 0.352099 / 0.226044 (0.126055) | 3.469058 / 2.268929 (1.200129) | 1.953402 / 55.444624 (-53.491222) | 1.647182 / 6.876477 (-5.229294) | 1.686473 / 2.142072 (-0.455599) | 0.797218 / 4.805227 (-4.008009) | 0.134161 / 6.500664 (-6.366503) | 0.041563 / 0.075469 (-0.033906) |\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) | 1.045855 / 1.841788 (-0.795933) | 12.271390 / 8.074308 (4.197082) | 10.186889 / 10.191392 (-0.004503) | 0.141141 / 0.680424 (-0.539283) | 0.015482 / 0.534201 (-0.518719) | 0.305699 / 0.579283 (-0.273584) | 0.128539 / 0.434364 (-0.305825) | 0.348492 / 0.540337 (-0.191845) | 0.444867 / 1.386936 (-0.942069) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#93dc73501298ccb1d31d854ba20fcf2c3b2fea8b \"CML watermark\")\n" ]

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[ "I’ll take a look", "Possible definitions of done for this issue:\r\n\r\n1. A fix so you can load your dataset specifically\r\n2. A general fix for datasets similar to this in the `datasets` library\r\n\r\nOption 1 is trivial. I think option 2 requires significant changes to the library.\r\n\r\nSince you outlined something akin to option 2 in `Expected behavior` I'm assuming that's what you'd like to see done. Is that right?\r\n\r\nIn the meantime, here's a solution for option 1:\r\n\r\n```python\r\nimport datasets\r\n\r\ndata_dir = './data/annotated/api'\r\n\r\nfeatures = datasets.Features({'id': datasets.Value(dtype='string'),\r\n 'name': datasets.Value(dtype='string'),\r\n 'author': datasets.Value(dtype='string'),\r\n 'description': datasets.Value(dtype='string'),\r\n 'tags': datasets.Sequence(feature=datasets.Value(dtype='string'), length=-1),\r\n 'likes': datasets.Value(dtype='int64'),\r\n 'viewed': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'date': datasets.Value(dtype='string'),\r\n 'time_retrieved': datasets.Value(dtype='string'),\r\n 'image_code': datasets.Value(dtype='string'),\r\n 'image_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'common_code': datasets.Value(dtype='string'),\r\n 'sound_code': datasets.Value(dtype='string'),\r\n 'sound_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'buffer_a_code': datasets.Value(dtype='string'),\r\n 'buffer_a_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'buffer_b_code': datasets.Value(dtype='string'),\r\n 'buffer_b_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'buffer_c_code': datasets.Value(dtype='string'),\r\n 'buffer_c_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'buffer_d_code': datasets.Value(dtype='string'),\r\n 'buffer_d_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'cube_a_code': datasets.Value(dtype='string'),\r\n 'cube_a_inputs': [{'channel': datasets.Value(dtype='int64'),\r\n 'ctype': datasets.Value(dtype='string'),\r\n 'id': datasets.Value(dtype='int64'),\r\n 'published': datasets.Value(dtype='int64'),\r\n 'sampler': {'filter': datasets.Value(dtype='string'),\r\n 'internal': datasets.Value(dtype='string'),\r\n 'srgb': datasets.Value(dtype='string'),\r\n 'vflip': datasets.Value(dtype='string'),\r\n 'wrap': datasets.Value(dtype='string')},\r\n 'src': datasets.Value(dtype='string')}],\r\n 'thumbnail': datasets.Value(dtype='string'),\r\n 'access': datasets.Value(dtype='string'),\r\n 'license': datasets.Value(dtype='string'),\r\n 'functions': datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value(dtype='int64'), length=-1), length=-1),\r\n 'test': datasets.Value(dtype='string')})\r\n\r\ndatasets.load_dataset('json', data_dir=data_dir, features=features)\r\n```", "As pointed out by @hvaara, you can define explicit features so that you avoid the `datasets` library having to infer them (from the first few samples).\r\n\r\nNote that the feature inference is done from the first few samples of JSON-Lines on purpose, so that the entire data does not need to be parsed twice (it would be inefficient for very large datasets).", "I understand this. But can there be a solution that doesn't require the end user to write this shema by hand(in my case there is some fields that contain a nested structure)? \r\n\r\nMaybe offer an option to infer the shema automatically before loading the dataset. Or perhaps - trigger such a method when this error arises? \r\n\r\nIs this \"first few files\" heuristics accessible via kwargs perhaps. Maybe an error that says \r\n`Cloud not cast some structure into feature shema, consider increasing shema_files to a large number or all\".\r\n\r\nThere might be efficient implementations to solve this problem for larger datasets. ", "@Vipitis raised a good point on the HF Discord regarding the use of a [dataset script](https://huggingface.co/docs/datasets/en/dataset_script) to provide the schema during initialization. Using this approach requires setting `trust_remote_code=True`, which is not allowed in certain evaluation frameworks.\r\n\r\nFor cases where using a dataset script is acceptable, would it be helpful to add functionality to the library (not necessarily in `load_dataset`) that can automatically discover the feature definitions and output them, so you don't have to manually define them?\r\n\r\nAlternatively, for situations where features need to be known at load-time without using a dataset script, another option could be loading the dataset schema from a file format that doesn't require `trust_remote_code=True`." ]

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[ "Thanks for reporting.\r\n\r\nIt is weird, because the language entry is in the list. See: https://github.com/huggingface/huggingface.js/blob/98e32f0ed4ee057a596f66a1dec738e5db9643d5/packages/languages/src/languages_iso_639_3.ts#L15186-L15189\r\n\r\nI have reported the issue:\r\n- https://github.com/huggingface/huggingface.js/issues/834\r\n\r\n", "As explained in the reported issue above, the problem only appears in the autocomplete field: you can still enter the `lut` language directly in the markdown editor window." ]

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[ "@lhoestq I detected this regression over on [DataDreamer](https://github.com/datadreamer-dev/DataDreamer)'s test suite. I put in these [monkey patches](https://github.com/datadreamer-dev/DataDreamer/blob/4cbaf9f39cf7bedde72bbaa68346e169788fbecb/src/_patches/datasets_reset_state_hack.py) in case that fixed it our tests failing in case it helps you figure out where this is coming from. I found it hard to reason through the resumable IterableDataset code though, so hopefully you have more intuition to implement a proper fix.", "I believe these lines in `TypedExamplesIterable` are responsible for stopping the re-iteration of `IterableDataset`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ebec2691fb1e40145429f63375cef3f46d3011ab/src/datasets/iterable_dataset.py#L1616-L1619\r\n\r\nIn contrast to other `Iterable`s, there is no check on whether `self._state_dict` is None or not. This particular case stands out and seems less straightforward to comprehend why. @lhoestq could you please assist us with this? Your help is much appreciated." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7082). 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) | 0.005280 / 0.011353 (-0.006073) | 0.003726 / 0.011008 (-0.007282) | 0.067028 / 0.038508 (0.028520) | 0.030833 / 0.023109 (0.007724) | 0.256888 / 0.275898 (-0.019010) | 0.271252 / 0.323480 (-0.052228) | 0.003149 / 0.007986 (-0.004836) | 0.004031 / 0.004328 (-0.000298) | 0.051178 / 0.004250 (0.046927) | 0.042751 / 0.037052 (0.005699) | 0.268385 / 0.258489 (0.009896) | 0.295547 / 0.293841 (0.001706) | 0.030218 / 0.128546 (-0.098328) | 0.012033 / 0.075646 (-0.063613) | 0.206389 / 0.419271 (-0.212882) | 0.036227 / 0.043533 (-0.007306) | 0.258778 / 0.255139 (0.003639) | 0.276027 / 0.283200 (-0.007172) | 0.020309 / 0.141683 (-0.121374) | 1.109689 / 1.452155 (-0.342466) | 1.139979 / 1.492716 (-0.352738) |\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) | 0.093615 / 0.018006 (0.075609) | 0.301279 / 0.000490 (0.300789) | 0.000207 / 0.000200 (0.000007) | 0.000048 / 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) | 0.018697 / 0.037411 (-0.018715) | 0.062627 / 0.014526 (0.048101) | 0.075119 / 0.176557 (-0.101438) | 0.119960 / 0.737135 (-0.617175) | 0.074606 / 0.296338 (-0.221732) |\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) | 0.281042 / 0.215209 (0.065833) | 2.746232 / 2.077655 (0.668578) | 1.422351 / 1.504120 (-0.081769) | 1.290087 / 1.541195 (-0.251108) | 1.321067 / 1.468490 (-0.147423) | 0.727514 / 4.584777 (-3.857263) | 2.407086 / 3.745712 (-1.338626) | 2.914191 / 5.269862 (-2.355670) | 1.872206 / 4.565676 (-2.693471) | 0.079538 / 0.424275 (-0.344738) | 0.005250 / 0.007607 (-0.002357) | 0.335536 / 0.226044 (0.109491) | 3.324922 / 2.268929 (1.055994) | 1.790688 / 55.444624 (-53.653936) | 1.475738 / 6.876477 (-5.400739) | 1.492465 / 2.142072 (-0.649607) | 0.812342 / 4.805227 (-3.992885) | 0.135036 / 6.500664 (-6.365628) | 0.041484 / 0.075469 (-0.033985) |\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) | 0.948425 / 1.841788 (-0.893363) | 11.321564 / 8.074308 (3.247256) | 9.635661 / 10.191392 (-0.555731) | 0.142793 / 0.680424 (-0.537631) | 0.014988 / 0.534201 (-0.519213) | 0.300209 / 0.579283 (-0.279074) | 0.262303 / 0.434364 (-0.172061) | 0.337927 / 0.540337 (-0.202411) | 0.427962 / 1.386936 (-0.958975) |\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) | 0.005664 / 0.011353 (-0.005689) | 0.003946 / 0.011008 (-0.007062) | 0.050034 / 0.038508 (0.011526) | 0.031652 / 0.023109 (0.008543) | 0.281139 / 0.275898 (0.005241) | 0.299203 / 0.323480 (-0.024277) | 0.004332 / 0.007986 (-0.003653) | 0.002769 / 0.004328 (-0.001560) | 0.048336 / 0.004250 (0.044086) | 0.039744 / 0.037052 (0.002692) | 0.289344 / 0.258489 (0.030855) | 0.320470 / 0.293841 (0.026629) | 0.032372 / 0.128546 (-0.096174) | 0.012090 / 0.075646 (-0.063557) | 0.060838 / 0.419271 (-0.358433) | 0.034227 / 0.043533 (-0.009306) | 0.275007 / 0.255139 (0.019868) | 0.293455 / 0.283200 (0.010256) | 0.017203 / 0.141683 (-0.124480) | 1.141577 / 1.452155 (-0.310578) | 1.176761 / 1.492716 (-0.315955) |\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) | 0.093562 / 0.018006 (0.075556) | 0.302695 / 0.000490 (0.302205) | 0.000215 / 0.000200 (0.000015) | 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) | 0.022638 / 0.037411 (-0.014774) | 0.078788 / 0.014526 (0.064262) | 0.088474 / 0.176557 (-0.088082) | 0.128421 / 0.737135 (-0.608714) | 0.089297 / 0.296338 (-0.207041) |\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) | 0.302669 / 0.215209 (0.087459) | 2.963855 / 2.077655 (0.886200) | 1.600053 / 1.504120 (0.095933) | 1.461456 / 1.541195 (-0.079739) | 1.469877 / 1.468490 (0.001387) | 0.725752 / 4.584777 (-3.859025) | 0.968970 / 3.745712 (-2.776742) | 2.910502 / 5.269862 (-2.359359) | 1.902762 / 4.565676 (-2.662914) | 0.079977 / 0.424275 (-0.344298) | 0.005582 / 0.007607 (-0.002025) | 0.351626 / 0.226044 (0.125581) | 3.520593 / 2.268929 (1.251664) | 1.968950 / 55.444624 (-53.475675) | 1.662190 / 6.876477 (-5.214286) | 1.677909 / 2.142072 (-0.464163) | 0.791541 / 4.805227 (-4.013687) | 0.134647 / 6.500664 (-6.366017) | 0.040687 / 0.075469 (-0.034782) |\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) | 1.028885 / 1.841788 (-0.812903) | 11.928358 / 8.074308 (3.854050) | 10.199165 / 10.191392 (0.007773) | 0.142930 / 0.680424 (-0.537493) | 0.016479 / 0.534201 (-0.517722) | 0.302993 / 0.579283 (-0.276290) | 0.128878 / 0.434364 (-0.305486) | 0.342591 / 0.540337 (-0.197747) | 0.456735 / 1.386936 (-0.930201) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d298f5549893228c03e9e3a42727327cb83f3dff \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7081). 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) | 0.005665 / 0.011353 (-0.005688) | 0.004130 / 0.011008 (-0.006878) | 0.064231 / 0.038508 (0.025723) | 0.030738 / 0.023109 (0.007628) | 0.251896 / 0.275898 (-0.024002) | 0.275182 / 0.323480 (-0.048298) | 0.003364 / 0.007986 (-0.004621) | 0.003569 / 0.004328 (-0.000759) | 0.049407 / 0.004250 (0.045157) | 0.048177 / 0.037052 (0.011124) | 0.253739 / 0.258489 (-0.004751) | 0.304087 / 0.293841 (0.010246) | 0.030457 / 0.128546 (-0.098089) | 0.012762 / 0.075646 (-0.062885) | 0.214312 / 0.419271 (-0.204959) | 0.036673 / 0.043533 (-0.006860) | 0.251838 / 0.255139 (-0.003301) | 0.274049 / 0.283200 (-0.009151) | 0.021133 / 0.141683 (-0.120550) | 1.143743 / 1.452155 (-0.308412) | 1.203681 / 1.492716 (-0.289036) |\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) | 0.094668 / 0.018006 (0.076662) | 0.300323 / 0.000490 (0.299833) | 0.000222 / 0.000200 (0.000022) | 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) | 0.018565 / 0.037411 (-0.018846) | 0.066096 / 0.014526 (0.051570) | 0.075700 / 0.176557 (-0.100857) | 0.122185 / 0.737135 (-0.614950) | 0.077688 / 0.296338 (-0.218651) |\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) | 0.288804 / 0.215209 (0.073595) | 2.838336 / 2.077655 (0.760681) | 1.530575 / 1.504120 (0.026455) | 1.406716 / 1.541195 (-0.134478) | 1.438885 / 1.468490 (-0.029605) | 0.744809 / 4.584777 (-3.839968) | 2.447992 / 3.745712 (-1.297721) | 3.126261 / 5.269862 (-2.143601) | 1.999687 / 4.565676 (-2.565990) | 0.081536 / 0.424275 (-0.342739) | 0.005827 / 0.007607 (-0.001780) | 0.346367 / 0.226044 (0.120323) | 3.373268 / 2.268929 (1.104339) | 1.890293 / 55.444624 (-53.554332) | 1.590384 / 6.876477 (-5.286093) | 1.652101 / 2.142072 (-0.489971) | 0.805888 / 4.805227 (-3.999339) | 0.137687 / 6.500664 (-6.362977) | 0.044536 / 0.075469 (-0.030933) |\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) | 0.998393 / 1.841788 (-0.843395) | 12.392241 / 8.074308 (4.317933) | 10.055638 / 10.191392 (-0.135754) | 0.132347 / 0.680424 (-0.548077) | 0.014635 / 0.534201 (-0.519566) | 0.301939 / 0.579283 (-0.277344) | 0.266756 / 0.434364 (-0.167608) | 0.342730 / 0.540337 (-0.197608) | 0.435463 / 1.386936 (-0.951473) |\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) | 0.006421 / 0.011353 (-0.004932) | 0.004494 / 0.011008 (-0.006514) | 0.051315 / 0.038508 (0.012806) | 0.035570 / 0.023109 (0.012460) | 0.271635 / 0.275898 (-0.004263) | 0.297082 / 0.323480 (-0.026398) | 0.004572 / 0.007986 (-0.003414) | 0.002886 / 0.004328 (-0.001443) | 0.049152 / 0.004250 (0.044902) | 0.043000 / 0.037052 (0.005948) | 0.281921 / 0.258489 (0.023432) | 0.321097 / 0.293841 (0.027256) | 0.033488 / 0.128546 (-0.095058) | 0.012835 / 0.075646 (-0.062811) | 0.061831 / 0.419271 (-0.357441) | 0.034674 / 0.043533 (-0.008858) | 0.272885 / 0.255139 (0.017746) | 0.292726 / 0.283200 (0.009527) | 0.019906 / 0.141683 (-0.121777) | 1.132234 / 1.452155 (-0.319920) | 1.155359 / 1.492716 (-0.337357) |\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) | 0.096943 / 0.018006 (0.078937) | 0.308980 / 0.000490 (0.308490) | 0.000225 / 0.000200 (0.000025) | 0.000047 / 0.000054 (-0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023551 / 0.037411 (-0.013861) | 0.081682 / 0.014526 (0.067156) | 0.090987 / 0.176557 (-0.085569) | 0.132542 / 0.737135 (-0.604593) | 0.092844 / 0.296338 (-0.203494) |\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) | 0.304190 / 0.215209 (0.088981) | 2.958591 / 2.077655 (0.880936) | 1.610211 / 1.504120 (0.106091) | 1.488216 / 1.541195 (-0.052978) | 1.525429 / 1.468490 (0.056939) | 0.752811 / 4.584777 (-3.831966) | 0.967887 / 3.745712 (-2.777825) | 2.982760 / 5.269862 (-2.287102) | 1.996623 / 4.565676 (-2.569053) | 0.080783 / 0.424275 (-0.343492) | 0.005337 / 0.007607 (-0.002270) | 0.354996 / 0.226044 (0.128951) | 3.540788 / 2.268929 (1.271860) | 1.997445 / 55.444624 (-53.447179) | 1.682232 / 6.876477 (-5.194245) | 1.883198 / 2.142072 (-0.258875) | 0.814444 / 4.805227 (-3.990783) | 0.135798 / 6.500664 (-6.364867) | 0.041750 / 0.075469 (-0.033719) |\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) | 1.048688 / 1.841788 (-0.793099) | 13.122809 / 8.074308 (5.048501) | 10.893354 / 10.191392 (0.701962) | 0.133710 / 0.680424 (-0.546713) | 0.016357 / 0.534201 (-0.517844) | 0.304364 / 0.579283 (-0.274919) | 0.126457 / 0.434364 (-0.307907) | 0.345747 / 0.540337 (-0.194591) | 0.441620 / 1.386936 (-0.945316) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#27ea8e8ead3e76bb07aa645f882945495d238ef3 \"CML watermark\")\n" ]

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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! " ]

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