Datasets:

---

ans-imran

/

github-issues

like 0

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6596). 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) | 0.004768 / 0.011353 (-0.006585) | 0.003084 / 0.011008 (-0.007924) | 0.062775 / 0.038508 (0.024267) | 0.029909 / 0.023109 (0.006800) | 0.242905 / 0.275898 (-0.032993) | 0.265609 / 0.323480 (-0.057871) | 0.003856 / 0.007986 (-0.004130) | 0.002610 / 0.004328 (-0.001718) | 0.048631 / 0.004250 (0.044381) | 0.040464 / 0.037052 (0.003412) | 0.256023 / 0.258489 (-0.002467) | 0.285914 / 0.293841 (-0.007927) | 0.027305 / 0.128546 (-0.101241) | 0.010345 / 0.075646 (-0.065301) | 0.206264 / 0.419271 (-0.213008) | 0.035290 / 0.043533 (-0.008243) | 0.247785 / 0.255139 (-0.007353) | 0.267053 / 0.283200 (-0.016147) | 0.017910 / 0.141683 (-0.123773) | 1.166096 / 1.452155 (-0.286059) | 1.210717 / 1.492716 (-0.281999) |\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) | 0.095759 / 0.018006 (0.077753) | 0.311030 / 0.000490 (0.310540) | 0.000234 / 0.000200 (0.000034) | 0.000042 / 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) | 0.017828 / 0.037411 (-0.019583) | 0.060123 / 0.014526 (0.045597) | 0.071947 / 0.176557 (-0.104610) | 0.119353 / 0.737135 (-0.617782) | 0.073529 / 0.296338 (-0.222809) |\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) | 0.282737 / 0.215209 (0.067528) | 2.761914 / 2.077655 (0.684260) | 1.480310 / 1.504120 (-0.023810) | 1.329977 / 1.541195 (-0.211218) | 1.332686 / 1.468490 (-0.135804) | 0.566309 / 4.584777 (-4.018468) | 2.361838 / 3.745712 (-1.383874) | 2.775613 / 5.269862 (-2.494249) | 1.744985 / 4.565676 (-2.820692) | 0.063038 / 0.424275 (-0.361237) | 0.004969 / 0.007607 (-0.002638) | 0.335543 / 0.226044 (0.109499) | 3.293779 / 2.268929 (1.024851) | 1.816093 / 55.444624 (-53.628532) | 1.562658 / 6.876477 (-5.313819) | 1.544888 / 2.142072 (-0.597185) | 0.641762 / 4.805227 (-4.163465) | 0.117904 / 6.500664 (-6.382760) | 0.042534 / 0.075469 (-0.032935) |\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) | 0.935577 / 1.841788 (-0.906211) | 11.565833 / 8.074308 (3.491525) | 10.314723 / 10.191392 (0.123331) | 0.138912 / 0.680424 (-0.541512) | 0.013968 / 0.534201 (-0.520233) | 0.296270 / 0.579283 (-0.283013) | 0.266106 / 0.434364 (-0.168258) | 0.334729 / 0.540337 (-0.205609) | 0.443191 / 1.386936 (-0.943745) |\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) | 0.004865 / 0.011353 (-0.006488) | 0.003523 / 0.011008 (-0.007485) | 0.049303 / 0.038508 (0.010795) | 0.029252 / 0.023109 (0.006143) | 0.271288 / 0.275898 (-0.004610) | 0.290529 / 0.323480 (-0.032951) | 0.003982 / 0.007986 (-0.004004) | 0.002740 / 0.004328 (-0.001589) | 0.048513 / 0.004250 (0.044262) | 0.044473 / 0.037052 (0.007420) | 0.282072 / 0.258489 (0.023583) | 0.311321 / 0.293841 (0.017480) | 0.028825 / 0.128546 (-0.099721) | 0.010311 / 0.075646 (-0.065335) | 0.057071 / 0.419271 (-0.362200) | 0.052629 / 0.043533 (0.009097) | 0.273134 / 0.255139 (0.017995) | 0.290989 / 0.283200 (0.007789) | 0.018074 / 0.141683 (-0.123609) | 1.171724 / 1.452155 (-0.280431) | 1.236178 / 1.492716 (-0.256538) |\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) | 0.097099 / 0.018006 (0.079093) | 0.309788 / 0.000490 (0.309298) | 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) | 0.021703 / 0.037411 (-0.015708) | 0.076104 / 0.014526 (0.061578) | 0.088202 / 0.176557 (-0.088355) | 0.127351 / 0.737135 (-0.609784) | 0.089754 / 0.296338 (-0.206585) |\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) | 0.294574 / 0.215209 (0.079365) | 2.851581 / 2.077655 (0.773926) | 1.599117 / 1.504120 (0.094997) | 1.476183 / 1.541195 (-0.065012) | 1.512309 / 1.468490 (0.043819) | 0.559785 / 4.584777 (-4.024992) | 2.453287 / 3.745712 (-1.292425) | 2.660101 / 5.269862 (-2.609760) | 1.743043 / 4.565676 (-2.822633) | 0.063450 / 0.424275 (-0.360825) | 0.005019 / 0.007607 (-0.002589) | 0.351507 / 0.226044 (0.125462) | 3.431587 / 2.268929 (1.162658) | 1.943349 / 55.444624 (-53.501275) | 1.658706 / 6.876477 (-5.217771) | 1.780042 / 2.142072 (-0.362030) | 0.641364 / 4.805227 (-4.163863) | 0.118052 / 6.500664 (-6.382612) | 0.040961 / 0.075469 (-0.034508) |\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) | 0.974219 / 1.841788 (-0.867568) | 12.257824 / 8.074308 (4.183516) | 10.821225 / 10.191392 (0.629833) | 0.139399 / 0.680424 (-0.541025) | 0.015277 / 0.534201 (-0.518924) | 0.286975 / 0.579283 (-0.292309) | 0.283419 / 0.434364 (-0.150945) | 0.324299 / 0.540337 (-0.216039) | 0.424538 / 1.386936 (-0.962398) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2ba3f30bae5ff75ac48f0e653240b924d7982d5 \"CML watermark\")\n" ]

[]

[]

[ "Hi ! I think the issue comes from the \"float16\" features that are not supported yet in Parquet\r\n\r\nFeel free to open an issue in `pyarrow` about this. In the meantime, I'd encourage you to use \"float32\" for your \"pooled_prompt_embeds\" and \"prompt_embeds\" features.\r\n\r\nYou can cast them to \"float32\" using\r\n\r\n```python\r\nfrom datasets import Value\r\n\r\nds = ds.cast_column(\"pooled_prompt_embeds\", Value(\"float32\"))\r\nds = ds.cast_column(\"prompt_embeds\", Value(\"float32\"))\r\n```", "@lhoestq hm. Thank you very much.\r\n\r\nDo you think it won't have any impact on the training? That it won't break it or the quality won't degrade because of this?\r\n\r\nI need to use it for [SDXL training](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)", "Increasing the precision should not degrade training (it only increases the precision), but make sure that it doesn't break your pytorch code (e.g. if it expects a float16 instead of a float32 somewhere)", "@lhoestq just fyi pyarrow 15.0.0 (just released) supports float16 as the underlying parquetcpp does as well now :)", "Oh that's amazing ! (and great timing ^^)\r\n\r\n@kopyl can you try to update `pyarrow` and try again ?\r\n\r\nBtw @assignUser there seems to be some casting implementations missing with float16 in 15.0.0, e.g.\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\n```\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float\r\n```", "Ah you are right casting is not implemented yet, it's even mentioned in the docs. This pr references the relevant issues if you'd like to track them\nhttps://github.com/apache/arrow/pull/38494", "Cool thank you :)", "@lhoestq i just recently found out that it's supported in 15.0.0, but wanted to try it first before telling you...\r\n\r\nTrying this right now and it seemingly works (although i need to wait till the end to make sure there is nothing wrong). Will update you when it's finished.\r\n\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/4821e215-e782-4736-8c76-d06187078175\">\r\n\r\nA couple of questions though:\r\n\r\n1. What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n2. Do you know how to `push_to_hub` with multiple processes?", "@lhoestq also it's strange that there was no error for a dataset with the same features, same data type, but smaller (much smaller).\r\n\r\nAltho i'm not sure about this, but chances are the dataset was loaded directly, not `load_from_disk`.... Maybe because of this.", "> What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n\r\nNothing for you, just that casting to float16 using `.cast_column(\"my_column_name\", Value(\"float16\"))` raises an error\r\n\r\n> Do you know how to push_to_hub with multiple processes?\r\n\r\nIt's not possible (yet ?). Mostly because we haven't implemented yet how to do parallel uploads to the Hub from `datasets`.\r\nThough if you want faster uploads you can already enable `hf_transfer` \r\n\r\n```\r\npip install hf_transfer\r\n```\r\n\r\nand setting `HF_HUB_ENABLE_HF_TRANSFER=1` as an environment variable\r\n\r\nsee https://huggingface.co/docs/huggingface_hub/guides/upload#tips-and-tricks-for-large-uploads", "@lhoestq thank you very much.\r\n\r\nThat would be amazing, I need to create a feature request for this :)\r\n\r\nBy the way, in short, how does hf_transfer improves the upload speed under the hood?", "@lhoestq i was just able to successfully upload without the dataset with the new pyarrow update and without increasing the precision :)", "Awesome !\r\n\r\nRegarding hf_transfer: it's been optimized in rust ;)", "@lhoestq wow, cool :)" ]

[]

[]

[]

[]

[]

[ "Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress." ]

[]

[]

[ "Hi! Indeed, Dropbox is not a reliable host. I've just merged https://huggingface.co/datasets/PolyAI/minds14/discussions/24 to fix this by hosting the data files inside the repo." ]

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

[]

[]

[]

[]

[ "We'll do a new release of `datasets` in the coming days with a fix !", "@lhoestq Thank you very much!" ]

[]

[]

[]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "friendly bump", "<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) | 0.005403 / 0.011353 (-0.005950) | 0.003807 / 0.011008 (-0.007201) | 0.063850 / 0.038508 (0.025342) | 0.028242 / 0.023109 (0.005132) | 0.242866 / 0.275898 (-0.033032) | 0.266015 / 0.323480 (-0.057464) | 0.004111 / 0.007986 (-0.003875) | 0.002816 / 0.004328 (-0.001513) | 0.048862 / 0.004250 (0.044611) | 0.043036 / 0.037052 (0.005984) | 0.255149 / 0.258489 (-0.003340) | 0.280105 / 0.293841 (-0.013736) | 0.028182 / 0.128546 (-0.100365) | 0.010997 / 0.075646 (-0.064649) | 0.208131 / 0.419271 (-0.211141) | 0.036030 / 0.043533 (-0.007502) | 0.241551 / 0.255139 (-0.013588) | 0.260741 / 0.283200 (-0.022459) | 0.018045 / 0.141683 (-0.123638) | 1.175308 / 1.452155 (-0.276847) | 1.192160 / 1.492716 (-0.300556) |\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) | 0.094579 / 0.018006 (0.076573) | 0.309850 / 0.000490 (0.309360) | 0.000232 / 0.000200 (0.000032) | 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) | 0.019519 / 0.037411 (-0.017892) | 0.062201 / 0.014526 (0.047675) | 0.074017 / 0.176557 (-0.102539) | 0.121987 / 0.737135 (-0.615148) | 0.078958 / 0.296338 (-0.217380) |\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) | 0.286306 / 0.215209 (0.071097) | 2.777004 / 2.077655 (0.699350) | 1.481445 / 1.504120 (-0.022675) | 1.348643 / 1.541195 (-0.192552) | 1.382257 / 1.468490 (-0.086234) | 0.571436 / 4.584777 (-4.013341) | 2.373279 / 3.745712 (-1.372433) | 2.749366 / 5.269862 (-2.520496) | 1.724937 / 4.565676 (-2.840739) | 0.062233 / 0.424275 (-0.362042) | 0.005013 / 0.007607 (-0.002594) | 0.339623 / 0.226044 (0.113579) | 3.385770 / 2.268929 (1.116842) | 1.832023 / 55.444624 (-53.612601) | 1.556172 / 6.876477 (-5.320305) | 1.573301 / 2.142072 (-0.568772) | 0.648866 / 4.805227 (-4.156361) | 0.121228 / 6.500664 (-6.379436) | 0.041684 / 0.075469 (-0.033786) |\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) | 0.974595 / 1.841788 (-0.867192) | 11.519692 / 8.074308 (3.445383) | 9.773075 / 10.191392 (-0.418317) | 0.138149 / 0.680424 (-0.542274) | 0.014068 / 0.534201 (-0.520133) | 0.288161 / 0.579283 (-0.291122) | 0.272832 / 0.434364 (-0.161532) | 0.324476 / 0.540337 (-0.215862) | 0.419962 / 1.386936 (-0.966974) |\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) | 0.005668 / 0.011353 (-0.005685) | 0.003637 / 0.011008 (-0.007371) | 0.049582 / 0.038508 (0.011074) | 0.030982 / 0.023109 (0.007872) | 0.273036 / 0.275898 (-0.002862) | 0.297562 / 0.323480 (-0.025918) | 0.004382 / 0.007986 (-0.003603) | 0.002763 / 0.004328 (-0.001566) | 0.050807 / 0.004250 (0.046556) | 0.046914 / 0.037052 (0.009862) | 0.287443 / 0.258489 (0.028954) | 0.319694 / 0.293841 (0.025853) | 0.051110 / 0.128546 (-0.077436) | 0.010650 / 0.075646 (-0.064997) | 0.058254 / 0.419271 (-0.361018) | 0.033419 / 0.043533 (-0.010114) | 0.275634 / 0.255139 (0.020495) | 0.288618 / 0.283200 (0.005419) | 0.018004 / 0.141683 (-0.123678) | 1.134166 / 1.452155 (-0.317989) | 1.192533 / 1.492716 (-0.300183) |\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) | 0.098573 / 0.018006 (0.080566) | 0.308152 / 0.000490 (0.307662) | 0.000249 / 0.000200 (0.000049) | 0.000045 / 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) | 0.022443 / 0.037411 (-0.014968) | 0.075628 / 0.014526 (0.061103) | 0.088807 / 0.176557 (-0.087750) | 0.127519 / 0.737135 (-0.609617) | 0.090156 / 0.296338 (-0.206182) |\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) | 0.294493 / 0.215209 (0.079284) | 2.862084 / 2.077655 (0.784429) | 1.585962 / 1.504120 (0.081842) | 1.466366 / 1.541195 (-0.074829) | 1.503306 / 1.468490 (0.034816) | 0.581524 / 4.584777 (-4.003253) | 2.475593 / 3.745712 (-1.270120) | 2.852014 / 5.269862 (-2.417847) | 1.834047 / 4.565676 (-2.731630) | 0.064009 / 0.424275 (-0.360266) | 0.005094 / 0.007607 (-0.002514) | 0.355960 / 0.226044 (0.129916) | 3.428849 / 2.268929 (1.159920) | 1.958501 / 55.444624 (-53.486124) | 1.675448 / 6.876477 (-5.201029) | 1.719960 / 2.142072 (-0.422113) | 0.659609 / 4.805227 (-4.145618) | 0.119036 / 6.500664 (-6.381628) | 0.041800 / 0.075469 (-0.033669) |\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) | 1.025955 / 1.841788 (-0.815833) | 12.432417 / 8.074308 (4.358108) | 10.444854 / 10.191392 (0.253462) | 0.130106 / 0.680424 (-0.550318) | 0.015655 / 0.534201 (-0.518546) | 0.288184 / 0.579283 (-0.291099) | 0.285023 / 0.434364 (-0.149340) | 0.329244 / 0.540337 (-0.211093) | 0.415484 / 1.386936 (-0.971452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262b060525efd973cac3f2073ba3944f3ddd7e3 \"CML watermark\")\n" ]

[]

[]

[ "@JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`.\r\nThere is short discussion about it [here](https://github.com/huggingface/blog/issues/1623).\r\nWould be great if you can check if your PR would also fix that!", "> @JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`. There is short discussion about it [here](https://github.com/huggingface/blog/issues/1623). Would be great if you can check if your PR would also fix that!\r\n\r\nHi @thiagobarbosa, it might be related but I didn't worked with `push_to_hub` yet. I don't see a minimal example reproducing the specific error in your link. However, if you have a running version producing the error locally you can test it by pulling this PR and run your specific example locally. ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6586). 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) | 0.004729 / 0.011353 (-0.006624) | 0.002983 / 0.011008 (-0.008025) | 0.062482 / 0.038508 (0.023974) | 0.028406 / 0.023109 (0.005297) | 0.255896 / 0.275898 (-0.020002) | 0.276423 / 0.323480 (-0.047057) | 0.003828 / 0.007986 (-0.004157) | 0.002601 / 0.004328 (-0.001728) | 0.048954 / 0.004250 (0.044704) | 0.040661 / 0.037052 (0.003609) | 0.277710 / 0.258489 (0.019221) | 0.290360 / 0.293841 (-0.003481) | 0.027105 / 0.128546 (-0.101441) | 0.010168 / 0.075646 (-0.065478) | 0.206835 / 0.419271 (-0.212436) | 0.035226 / 0.043533 (-0.008306) | 0.262567 / 0.255139 (0.007428) | 0.273979 / 0.283200 (-0.009221) | 0.017576 / 0.141683 (-0.124106) | 1.125588 / 1.452155 (-0.326566) | 1.185018 / 1.492716 (-0.307698) |\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) | 0.092192 / 0.018006 (0.074186) | 0.298350 / 0.000490 (0.297861) | 0.000217 / 0.000200 (0.000017) | 0.000051 / 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) | 0.017925 / 0.037411 (-0.019486) | 0.060285 / 0.014526 (0.045759) | 0.076579 / 0.176557 (-0.099978) | 0.118830 / 0.737135 (-0.618305) | 0.073017 / 0.296338 (-0.223322) |\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) | 0.288149 / 0.215209 (0.072940) | 2.840004 / 2.077655 (0.762349) | 1.495758 / 1.504120 (-0.008361) | 1.362338 / 1.541195 (-0.178857) | 1.389746 / 1.468490 (-0.078744) | 0.576891 / 4.584777 (-4.007886) | 2.375724 / 3.745712 (-1.369988) | 2.707405 / 5.269862 (-2.562457) | 1.719850 / 4.565676 (-2.845826) | 0.067055 / 0.424275 (-0.357220) | 0.005039 / 0.007607 (-0.002568) | 0.346626 / 0.226044 (0.120581) | 3.468346 / 2.268929 (1.199418) | 1.860686 / 55.444624 (-53.583938) | 1.582929 / 6.876477 (-5.293548) | 1.613131 / 2.142072 (-0.528941) | 0.659022 / 4.805227 (-4.146206) | 0.118477 / 6.500664 (-6.382187) | 0.041614 / 0.075469 (-0.033855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005062 / 1.841788 (-0.836726) | 11.203210 / 8.074308 (3.128902) | 10.320764 / 10.191392 (0.129372) | 0.128541 / 0.680424 (-0.551883) | 0.014646 / 0.534201 (-0.519555) | 0.285280 / 0.579283 (-0.294003) | 0.263613 / 0.434364 (-0.170751) | 0.321161 / 0.540337 (-0.219177) | 0.420565 / 1.386936 (-0.966371) |\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) | 0.005288 / 0.011353 (-0.006065) | 0.003048 / 0.011008 (-0.007960) | 0.049196 / 0.038508 (0.010688) | 0.032104 / 0.023109 (0.008994) | 0.279345 / 0.275898 (0.003447) | 0.300194 / 0.323480 (-0.023286) | 0.004045 / 0.007986 (-0.003941) | 0.002594 / 0.004328 (-0.001735) | 0.047680 / 0.004250 (0.043430) | 0.044294 / 0.037052 (0.007241) | 0.292330 / 0.258489 (0.033841) | 0.318610 / 0.293841 (0.024769) | 0.050417 / 0.128546 (-0.078129) | 0.010326 / 0.075646 (-0.065320) | 0.057372 / 0.419271 (-0.361899) | 0.032985 / 0.043533 (-0.010548) | 0.277717 / 0.255139 (0.022579) | 0.295692 / 0.283200 (0.012493) | 0.017756 / 0.141683 (-0.123927) | 1.166277 / 1.452155 (-0.285877) | 1.213337 / 1.492716 (-0.279380) |\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) | 0.091365 / 0.018006 (0.073359) | 0.296261 / 0.000490 (0.295772) | 0.000225 / 0.000200 (0.000025) | 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) | 0.021973 / 0.037411 (-0.015438) | 0.074631 / 0.014526 (0.060106) | 0.085645 / 0.176557 (-0.090911) | 0.125181 / 0.737135 (-0.611955) | 0.086893 / 0.296338 (-0.209445) |\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) | 0.294110 / 0.215209 (0.078901) | 2.855531 / 2.077655 (0.777876) | 1.583204 / 1.504120 (0.079084) | 1.453911 / 1.541195 (-0.087284) | 1.467031 / 1.468490 (-0.001460) | 0.581214 / 4.584777 (-4.003562) | 2.423626 / 3.745712 (-1.322086) | 2.736665 / 5.269862 (-2.533197) | 1.707000 / 4.565676 (-2.858676) | 0.061171 / 0.424275 (-0.363104) | 0.004789 / 0.007607 (-0.002818) | 0.344546 / 0.226044 (0.118502) | 3.530955 / 2.268929 (1.262027) | 1.962532 / 55.444624 (-53.482092) | 1.670207 / 6.876477 (-5.206270) | 1.669041 / 2.142072 (-0.473031) | 0.642298 / 4.805227 (-4.162929) | 0.115503 / 6.500664 (-6.385161) | 0.040729 / 0.075469 (-0.034740) |\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) | 0.973101 / 1.841788 (-0.868687) | 11.823894 / 8.074308 (3.749586) | 10.664592 / 10.191392 (0.473200) | 0.139848 / 0.680424 (-0.540576) | 0.015728 / 0.534201 (-0.518473) | 0.289135 / 0.579283 (-0.290148) | 0.271325 / 0.434364 (-0.163039) | 0.332253 / 0.540337 (-0.208085) | 0.416982 / 1.386936 (-0.969954) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca76ca1152fce82bfeaab9f9a33849d4d7f9dd63 \"CML watermark\")\n" ]

[]

[ { "avatar_url": "https://avatars.githubusercontent.com/u/135010976?v=4", "events_url": "https://api.github.com/users/JochenSiegWork/events{/privacy}", "followers_url": "https://api.github.com/users/JochenSiegWork/followers", "following_url": "https://api.github.com/users/JochenSiegWork/following{/other_user}", "gists_url": "https://api.github.com/users/JochenSiegWork/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/JochenSiegWork", "id": 135010976, "login": "JochenSiegWork", "node_id": "U_kgDOCAwaoA", "organizations_url": "https://api.github.com/users/JochenSiegWork/orgs", "received_events_url": "https://api.github.com/users/JochenSiegWork/received_events", "repos_url": "https://api.github.com/users/JochenSiegWork/repos", "site_admin": false, "starred_url": "https://api.github.com/users/JochenSiegWork/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JochenSiegWork/subscriptions", "type": "User", "url": "https://api.github.com/users/JochenSiegWork" } ]

[ "Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270", "#self-assign" ]

[]

[]

[ "@lhoestq\r\nCan you provide me with some ideas？", "Hi ! What's the error ?", "@lhoestq \r\n```\r\nTraceback (most recent call last):\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/example/test.py\", line 83, in <module>\r\n data = xnumpy_fromfile(current_dir, download_config=config,dtype=numpy.float32,)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/src/datasets/download/streaming_download_manager.py\", line 765, in xnumpy_fromfile\r\n return np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config).read(), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: embedded null byte\r\n```", " not add read() \r\nthe error is \r\n\r\nreturn np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config), *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nio.UnsupportedOperation: fileno", "xopen return obj do not have fileno function\r\nI don't know why?", "I used this method to read point cloud data in the script\r\n\r\n\r\n```python\r\nwith open(velodyne_filepath,\"rb\") as obj:\r\n velodyne_data = numpy.frombuffer(obj.read(), dtype=numpy.float32).reshape([-1, 4])\r\n```" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6583). 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) | 0.005024 / 0.011353 (-0.006329) | 0.003172 / 0.011008 (-0.007836) | 0.062934 / 0.038508 (0.024426) | 0.031737 / 0.023109 (0.008628) | 0.249251 / 0.275898 (-0.026647) | 0.273084 / 0.323480 (-0.050396) | 0.002958 / 0.007986 (-0.005027) | 0.002726 / 0.004328 (-0.001603) | 0.048519 / 0.004250 (0.044269) | 0.043608 / 0.037052 (0.006556) | 0.253648 / 0.258489 (-0.004841) | 0.280095 / 0.293841 (-0.013746) | 0.027500 / 0.128546 (-0.101046) | 0.010545 / 0.075646 (-0.065101) | 0.206781 / 0.419271 (-0.212490) | 0.035515 / 0.043533 (-0.008018) | 0.259449 / 0.255139 (0.004310) | 0.271488 / 0.283200 (-0.011712) | 0.019352 / 0.141683 (-0.122331) | 1.152002 / 1.452155 (-0.300153) | 1.190325 / 1.492716 (-0.302391) |\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) | 0.093253 / 0.018006 (0.075247) | 0.302182 / 0.000490 (0.301692) | 0.000216 / 0.000200 (0.000016) | 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) | 0.017889 / 0.037411 (-0.019523) | 0.060292 / 0.014526 (0.045766) | 0.072640 / 0.176557 (-0.103917) | 0.121320 / 0.737135 (-0.615815) | 0.073866 / 0.296338 (-0.222472) |\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) | 0.282910 / 0.215209 (0.067701) | 2.779815 / 2.077655 (0.702160) | 1.537929 / 1.504120 (0.033809) | 1.405990 / 1.541195 (-0.135205) | 1.407911 / 1.468490 (-0.060579) | 0.561551 / 4.584777 (-4.023226) | 2.368053 / 3.745712 (-1.377659) | 2.732608 / 5.269862 (-2.537254) | 1.710274 / 4.565676 (-2.855402) | 0.061925 / 0.424275 (-0.362350) | 0.004975 / 0.007607 (-0.002632) | 0.338843 / 0.226044 (0.112799) | 3.328579 / 2.268929 (1.059650) | 1.865994 / 55.444624 (-53.578631) | 1.603145 / 6.876477 (-5.273332) | 1.615440 / 2.142072 (-0.526633) | 0.635646 / 4.805227 (-4.169581) | 0.116185 / 6.500664 (-6.384479) | 0.041964 / 0.075469 (-0.033505) |\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) | 0.956977 / 1.841788 (-0.884811) | 11.539802 / 8.074308 (3.465494) | 10.048855 / 10.191392 (-0.142537) | 0.128758 / 0.680424 (-0.551666) | 0.013491 / 0.534201 (-0.520710) | 0.287330 / 0.579283 (-0.291953) | 0.262416 / 0.434364 (-0.171947) | 0.327327 / 0.540337 (-0.213011) | 0.418423 / 1.386936 (-0.968513) |\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) | 0.004963 / 0.011353 (-0.006390) | 0.003335 / 0.011008 (-0.007673) | 0.052082 / 0.038508 (0.013574) | 0.029302 / 0.023109 (0.006192) | 0.284986 / 0.275898 (0.009088) | 0.304082 / 0.323480 (-0.019398) | 0.004065 / 0.007986 (-0.003921) | 0.002643 / 0.004328 (-0.001685) | 0.049504 / 0.004250 (0.045253) | 0.044514 / 0.037052 (0.007461) | 0.287064 / 0.258489 (0.028575) | 0.312921 / 0.293841 (0.019080) | 0.029195 / 0.128546 (-0.099351) | 0.010471 / 0.075646 (-0.065175) | 0.057620 / 0.419271 (-0.361651) | 0.050221 / 0.043533 (0.006689) | 0.285392 / 0.255139 (0.030253) | 0.302111 / 0.283200 (0.018912) | 0.018690 / 0.141683 (-0.122993) | 1.165637 / 1.452155 (-0.286518) | 1.203757 / 1.492716 (-0.288959) |\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) | 0.095035 / 0.018006 (0.077028) | 0.304447 / 0.000490 (0.303957) | 0.000231 / 0.000200 (0.000031) | 0.000051 / 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) | 0.022345 / 0.037411 (-0.015066) | 0.077195 / 0.014526 (0.062669) | 0.089564 / 0.176557 (-0.086992) | 0.129248 / 0.737135 (-0.607887) | 0.091974 / 0.296338 (-0.204365) |\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) | 0.300641 / 0.215209 (0.085432) | 2.936669 / 2.077655 (0.859014) | 1.649100 / 1.504120 (0.144980) | 1.510693 / 1.541195 (-0.030502) | 1.517011 / 1.468490 (0.048521) | 0.572511 / 4.584777 (-4.012266) | 2.442704 / 3.745712 (-1.303009) | 2.833089 / 5.269862 (-2.436772) | 1.762668 / 4.565676 (-2.803008) | 0.063754 / 0.424275 (-0.360521) | 0.005034 / 0.007607 (-0.002573) | 0.401631 / 0.226044 (0.175586) | 3.418986 / 2.268929 (1.150057) | 1.989639 / 55.444624 (-53.454986) | 1.695776 / 6.876477 (-5.180701) | 1.712822 / 2.142072 (-0.429250) | 0.654029 / 4.805227 (-4.151198) | 0.117624 / 6.500664 (-6.383040) | 0.041058 / 0.075469 (-0.034411) |\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) | 0.986008 / 1.841788 (-0.855779) | 12.146838 / 8.074308 (4.072530) | 11.105900 / 10.191392 (0.914508) | 0.139938 / 0.680424 (-0.540486) | 0.015117 / 0.534201 (-0.519084) | 0.286151 / 0.579283 (-0.293132) | 0.272960 / 0.434364 (-0.161404) | 0.323370 / 0.540337 (-0.216967) | 0.427379 / 1.386936 (-0.959557) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91888ea888fec1f2c96d8316a569439e64eb508e \"CML watermark\")\n" ]

[]

[]

[ "A toy example to reveal the bug.\r\n\r\n```python\r\n\"\"\"\r\nDATASETS_VERBOSITY=debug torchrun --nproc-per-node 2 main.py \r\n\"\"\"\r\nimport torch.utils.data\r\nimport torch.distributed\r\nimport datasets.distributed\r\nimport datasets\r\n\r\n# num shards = 4\r\nshards = [(0, 100), (100, 200), (200, 300), (300, 400)]\r\n\r\n\r\ndef gen(shards):\r\n for st, ed in shards:\r\n yield from range(st, ed)\r\n\r\ntorch.distributed.init_process_group()\r\n\r\n# want to create total worker = world_size * 8\r\nds = datasets.IterableDataset.from_generator(gen, gen_kwargs={'shards': shards})\r\nds = datasets.distributed.split_dataset_by_node(\r\n ds,\r\n rank=torch.distributed.get_rank(),\r\n world_size=torch.distributed.get_world_size(),\r\n)\r\ndl = torch.utils.data.DataLoader(ds, batch_size=10, num_workers=8)\r\n\r\nfor x in dl:\r\n print(f\"RANK={torch.distributed.get_rank()} {x}\")\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005401 / 0.011353 (-0.005952) | 0.004023 / 0.011008 (-0.006985) | 0.064601 / 0.038508 (0.026093) | 0.028567 / 0.023109 (0.005457) | 0.245476 / 0.275898 (-0.030422) | 0.292727 / 0.323480 (-0.030752) | 0.003080 / 0.007986 (-0.004905) | 0.002779 / 0.004328 (-0.001549) | 0.050046 / 0.004250 (0.045796) | 0.043906 / 0.037052 (0.006854) | 0.273896 / 0.258489 (0.015407) | 0.308430 / 0.293841 (0.014589) | 0.028442 / 0.128546 (-0.100104) | 0.010694 / 0.075646 (-0.064953) | 0.209048 / 0.419271 (-0.210223) | 0.036062 / 0.043533 (-0.007471) | 0.242689 / 0.255139 (-0.012450) | 0.261695 / 0.283200 (-0.021504) | 0.018519 / 0.141683 (-0.123163) | 1.122735 / 1.452155 (-0.329420) | 1.172680 / 1.492716 (-0.320036) |\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) | 0.093827 / 0.018006 (0.075820) | 0.302650 / 0.000490 (0.302161) | 0.000218 / 0.000200 (0.000018) | 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) | 0.018778 / 0.037411 (-0.018633) | 0.067516 / 0.014526 (0.052990) | 0.079693 / 0.176557 (-0.096864) | 0.125907 / 0.737135 (-0.611228) | 0.081771 / 0.296338 (-0.214568) |\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) | 0.281809 / 0.215209 (0.066600) | 2.773937 / 2.077655 (0.696283) | 1.443622 / 1.504120 (-0.060497) | 1.334359 / 1.541195 (-0.206836) | 1.364813 / 1.468490 (-0.103677) | 0.561670 / 4.584777 (-4.023107) | 2.338292 / 3.745712 (-1.407420) | 2.807595 / 5.269862 (-2.462267) | 1.734162 / 4.565676 (-2.831514) | 0.063681 / 0.424275 (-0.360594) | 0.004934 / 0.007607 (-0.002673) | 0.336781 / 0.226044 (0.110737) | 3.311744 / 2.268929 (1.042815) | 1.826802 / 55.444624 (-53.617822) | 1.579604 / 6.876477 (-5.296872) | 1.620526 / 2.142072 (-0.521546) | 0.647061 / 4.805227 (-4.158166) | 0.117729 / 6.500664 (-6.382935) | 0.042216 / 0.075469 (-0.033253) |\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) | 0.994289 / 1.841788 (-0.847499) | 12.266185 / 8.074308 (4.191877) | 9.634035 / 10.191392 (-0.557357) | 0.144521 / 0.680424 (-0.535902) | 0.013787 / 0.534201 (-0.520414) | 0.288353 / 0.579283 (-0.290930) | 0.262183 / 0.434364 (-0.172181) | 0.336960 / 0.540337 (-0.203378) | 0.441142 / 1.386936 (-0.945794) |\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) | 0.005678 / 0.011353 (-0.005675) | 0.004011 / 0.011008 (-0.006998) | 0.049319 / 0.038508 (0.010811) | 0.032543 / 0.023109 (0.009434) | 0.276389 / 0.275898 (0.000491) | 0.298495 / 0.323480 (-0.024985) | 0.004192 / 0.007986 (-0.003794) | 0.002765 / 0.004328 (-0.001563) | 0.048739 / 0.004250 (0.044489) | 0.046212 / 0.037052 (0.009160) | 0.286614 / 0.258489 (0.028125) | 0.315949 / 0.293841 (0.022108) | 0.029833 / 0.128546 (-0.098714) | 0.010762 / 0.075646 (-0.064884) | 0.058489 / 0.419271 (-0.360783) | 0.052258 / 0.043533 (0.008725) | 0.275873 / 0.255139 (0.020734) | 0.288668 / 0.283200 (0.005468) | 0.018828 / 0.141683 (-0.122855) | 1.140196 / 1.452155 (-0.311959) | 1.229500 / 1.492716 (-0.263217) |\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) | 0.094161 / 0.018006 (0.076155) | 0.303519 / 0.000490 (0.303030) | 0.000219 / 0.000200 (0.000019) | 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) | 0.022088 / 0.037411 (-0.015324) | 0.076376 / 0.014526 (0.061850) | 0.088705 / 0.176557 (-0.087851) | 0.127602 / 0.737135 (-0.609533) | 0.088689 / 0.296338 (-0.207649) |\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) | 0.292363 / 0.215209 (0.077154) | 2.859215 / 2.077655 (0.781561) | 1.566389 / 1.504120 (0.062270) | 1.439195 / 1.541195 (-0.102000) | 1.463805 / 1.468490 (-0.004685) | 0.551660 / 4.584777 (-4.033116) | 2.427462 / 3.745712 (-1.318250) | 2.712372 / 5.269862 (-2.557490) | 1.811331 / 4.565676 (-2.754346) | 0.061539 / 0.424275 (-0.362736) | 0.005062 / 0.007607 (-0.002545) | 0.341984 / 0.226044 (0.115940) | 3.352171 / 2.268929 (1.083242) | 1.917550 / 55.444624 (-53.527074) | 1.642668 / 6.876477 (-5.233809) | 1.817204 / 2.142072 (-0.324868) | 0.630849 / 4.805227 (-4.174379) | 0.115788 / 6.500664 (-6.384876) | 0.041041 / 0.075469 (-0.034428) |\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) | 1.017725 / 1.841788 (-0.824062) | 12.976994 / 8.074308 (4.902686) | 10.307414 / 10.191392 (0.116022) | 0.141090 / 0.680424 (-0.539334) | 0.015548 / 0.534201 (-0.518653) | 0.288184 / 0.579283 (-0.291099) | 0.276409 / 0.434364 (-0.157955) | 0.328289 / 0.540337 (-0.212048) | 0.429138 / 1.386936 (-0.957798) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31ae21ff806c3d1fc19a48ce41178c82d2f69368 \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6581). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "\r\nObj [\"name\"] ends with \"/\"", "@lhoestq \r\n\r\nhello,\r\nCan you help me check if there are any issues with this PR? Why hasn't anyone merged?\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004968 / 0.011353 (-0.006385) | 0.003516 / 0.011008 (-0.007492) | 0.063787 / 0.038508 (0.025279) | 0.031695 / 0.023109 (0.008586) | 0.240081 / 0.275898 (-0.035817) | 0.260984 / 0.323480 (-0.062496) | 0.003832 / 0.007986 (-0.004153) | 0.002680 / 0.004328 (-0.001648) | 0.049199 / 0.004250 (0.044948) | 0.044720 / 0.037052 (0.007668) | 0.255812 / 0.258489 (-0.002677) | 0.275923 / 0.293841 (-0.017918) | 0.026849 / 0.128546 (-0.101697) | 0.010473 / 0.075646 (-0.065174) | 0.209069 / 0.419271 (-0.210202) | 0.035731 / 0.043533 (-0.007802) | 0.246596 / 0.255139 (-0.008543) | 0.265889 / 0.283200 (-0.017311) | 0.017607 / 0.141683 (-0.124075) | 1.128648 / 1.452155 (-0.323507) | 1.174379 / 1.492716 (-0.318338) |\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) | 0.098214 / 0.018006 (0.080207) | 0.311969 / 0.000490 (0.311480) | 0.000266 / 0.000200 (0.000066) | 0.000056 / 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) | 0.018401 / 0.037411 (-0.019010) | 0.061347 / 0.014526 (0.046821) | 0.073628 / 0.176557 (-0.102928) | 0.121359 / 0.737135 (-0.615776) | 0.075148 / 0.296338 (-0.221190) |\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) | 0.274098 / 0.215209 (0.058889) | 2.707633 / 2.077655 (0.629978) | 1.453615 / 1.504120 (-0.050504) | 1.311942 / 1.541195 (-0.229253) | 1.332394 / 1.468490 (-0.136096) | 0.566947 / 4.584777 (-4.017830) | 2.383291 / 3.745712 (-1.362421) | 2.754779 / 5.269862 (-2.515083) | 1.725164 / 4.565676 (-2.840512) | 0.062124 / 0.424275 (-0.362152) | 0.005111 / 0.007607 (-0.002496) | 0.334217 / 0.226044 (0.108173) | 3.271619 / 2.268929 (1.002690) | 1.776906 / 55.444624 (-53.667718) | 1.519238 / 6.876477 (-5.357239) | 1.534722 / 2.142072 (-0.607351) | 0.646143 / 4.805227 (-4.159084) | 0.117015 / 6.500664 (-6.383649) | 0.042578 / 0.075469 (-0.032891) |\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) | 0.948488 / 1.841788 (-0.893299) | 11.598027 / 8.074308 (3.523719) | 10.269199 / 10.191392 (0.077807) | 0.144887 / 0.680424 (-0.535537) | 0.014745 / 0.534201 (-0.519456) | 0.289185 / 0.579283 (-0.290099) | 0.275243 / 0.434364 (-0.159120) | 0.328088 / 0.540337 (-0.212250) | 0.430161 / 1.386936 (-0.956775) |\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) | 0.005020 / 0.011353 (-0.006333) | 0.003246 / 0.011008 (-0.007762) | 0.049810 / 0.038508 (0.011302) | 0.032215 / 0.023109 (0.009105) | 0.271033 / 0.275898 (-0.004866) | 0.294957 / 0.323480 (-0.028523) | 0.004192 / 0.007986 (-0.003793) | 0.002652 / 0.004328 (-0.001677) | 0.049132 / 0.004250 (0.044881) | 0.047818 / 0.037052 (0.010766) | 0.292370 / 0.258489 (0.033881) | 0.316142 / 0.293841 (0.022301) | 0.049539 / 0.128546 (-0.079007) | 0.010533 / 0.075646 (-0.065113) | 0.058131 / 0.419271 (-0.361141) | 0.033807 / 0.043533 (-0.009725) | 0.277623 / 0.255139 (0.022484) | 0.292294 / 0.283200 (0.009094) | 0.021110 / 0.141683 (-0.120573) | 1.160997 / 1.452155 (-0.291157) | 1.213553 / 1.492716 (-0.279163) |\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) | 0.098220 / 0.018006 (0.080214) | 0.312342 / 0.000490 (0.311852) | 0.000231 / 0.000200 (0.000031) | 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) | 0.022893 / 0.037411 (-0.014519) | 0.075572 / 0.014526 (0.061046) | 0.088357 / 0.176557 (-0.088199) | 0.126354 / 0.737135 (-0.610782) | 0.089763 / 0.296338 (-0.206575) |\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) | 0.284368 / 0.215209 (0.069159) | 2.785497 / 2.077655 (0.707842) | 1.499364 / 1.504120 (-0.004756) | 1.376020 / 1.541195 (-0.165175) | 1.394270 / 1.468490 (-0.074220) | 0.571945 / 4.584777 (-4.012832) | 2.419148 / 3.745712 (-1.326564) | 2.796974 / 5.269862 (-2.472887) | 1.749531 / 4.565676 (-2.816145) | 0.064088 / 0.424275 (-0.360187) | 0.005294 / 0.007607 (-0.002313) | 0.336250 / 0.226044 (0.110206) | 3.315933 / 2.268929 (1.047004) | 1.877165 / 55.444624 (-53.567459) | 1.592336 / 6.876477 (-5.284140) | 1.599979 / 2.142072 (-0.542093) | 0.655617 / 4.805227 (-4.149610) | 0.117636 / 6.500664 (-6.383028) | 0.040813 / 0.075469 (-0.034656) |\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) | 0.976887 / 1.841788 (-0.864901) | 12.668753 / 8.074308 (4.594445) | 11.081253 / 10.191392 (0.889861) | 0.134494 / 0.680424 (-0.545930) | 0.016053 / 0.534201 (-0.518148) | 0.291607 / 0.579283 (-0.287676) | 0.287726 / 0.434364 (-0.146638) | 0.328108 / 0.540337 (-0.212229) | 0.425194 / 1.386936 (-0.961742) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32672349e3e5abe21505fdbda122dd3426f8920f \"CML watermark\")\n" ]

[]

[]

[]

[]

[]

[ "Hi @haok1402, I have created an issue in the Discussion tab of the corresponding dataset: https://huggingface.co/datasets/eli5/discussions/7\r\nLet's continue the discussion there!" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6578). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I added faster streaming support using streaming Requests instances in `huggingface_hub` and will be available in 0.21.\r\n\r\nThis PR can be used with https://github.com/huggingface/huggingface_hub/pull/1967 to get fast WebDataset streaming", "<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) | 0.004941 / 0.011353 (-0.006412) | 0.003431 / 0.011008 (-0.007577) | 0.062768 / 0.038508 (0.024260) | 0.029212 / 0.023109 (0.006103) | 0.253053 / 0.275898 (-0.022845) | 0.273061 / 0.323480 (-0.050419) | 0.004114 / 0.007986 (-0.003871) | 0.002713 / 0.004328 (-0.001616) | 0.048481 / 0.004250 (0.044231) | 0.040001 / 0.037052 (0.002949) | 0.268461 / 0.258489 (0.009971) | 0.287767 / 0.293841 (-0.006074) | 0.027885 / 0.128546 (-0.100661) | 0.010474 / 0.075646 (-0.065172) | 0.207989 / 0.419271 (-0.211282) | 0.035893 / 0.043533 (-0.007640) | 0.256833 / 0.255139 (0.001694) | 0.274197 / 0.283200 (-0.009003) | 0.017283 / 0.141683 (-0.124400) | 1.133597 / 1.452155 (-0.318558) | 1.206661 / 1.492716 (-0.286055) |\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) | 0.089610 / 0.018006 (0.071604) | 0.306051 / 0.000490 (0.305562) | 0.000217 / 0.000200 (0.000017) | 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) | 0.018686 / 0.037411 (-0.018725) | 0.061253 / 0.014526 (0.046727) | 0.073654 / 0.176557 (-0.102903) | 0.120499 / 0.737135 (-0.616637) | 0.074827 / 0.296338 (-0.221511) |\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) | 0.293756 / 0.215209 (0.078547) | 2.897755 / 2.077655 (0.820100) | 1.558146 / 1.504120 (0.054026) | 1.458020 / 1.541195 (-0.083174) | 1.453489 / 1.468490 (-0.015001) | 0.576666 / 4.584777 (-4.008111) | 2.423441 / 3.745712 (-1.322271) | 2.727760 / 5.269862 (-2.542102) | 1.750287 / 4.565676 (-2.815390) | 0.062094 / 0.424275 (-0.362181) | 0.004940 / 0.007607 (-0.002667) | 0.338815 / 0.226044 (0.112770) | 3.342677 / 2.268929 (1.073748) | 1.928335 / 55.444624 (-53.516290) | 1.629965 / 6.876477 (-5.246511) | 1.651836 / 2.142072 (-0.490236) | 0.644354 / 4.805227 (-4.160874) | 0.117890 / 6.500664 (-6.382774) | 0.041907 / 0.075469 (-0.033562) |\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) | 0.984399 / 1.841788 (-0.857389) | 11.516572 / 8.074308 (3.442264) | 10.326922 / 10.191392 (0.135530) | 0.130821 / 0.680424 (-0.549603) | 0.014084 / 0.534201 (-0.520117) | 0.287078 / 0.579283 (-0.292205) | 0.263466 / 0.434364 (-0.170898) | 0.326867 / 0.540337 (-0.213470) | 0.425313 / 1.386936 (-0.961623) |\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) | 0.005305 / 0.011353 (-0.006048) | 0.003646 / 0.011008 (-0.007362) | 0.049402 / 0.038508 (0.010894) | 0.031719 / 0.023109 (0.008610) | 0.272579 / 0.275898 (-0.003319) | 0.295241 / 0.323480 (-0.028239) | 0.004309 / 0.007986 (-0.003677) | 0.002781 / 0.004328 (-0.001548) | 0.048134 / 0.004250 (0.043883) | 0.044702 / 0.037052 (0.007650) | 0.288201 / 0.258489 (0.029712) | 0.320351 / 0.293841 (0.026510) | 0.051327 / 0.128546 (-0.077219) | 0.011019 / 0.075646 (-0.064628) | 0.057983 / 0.419271 (-0.361288) | 0.034211 / 0.043533 (-0.009322) | 0.272856 / 0.255139 (0.017717) | 0.290007 / 0.283200 (0.006807) | 0.018656 / 0.141683 (-0.123027) | 1.135017 / 1.452155 (-0.317138) | 1.183904 / 1.492716 (-0.308813) |\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) | 0.090854 / 0.018006 (0.072847) | 0.299654 / 0.000490 (0.299165) | 0.000224 / 0.000200 (0.000024) | 0.000063 / 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) | 0.021882 / 0.037411 (-0.015529) | 0.075297 / 0.014526 (0.060771) | 0.086620 / 0.176557 (-0.089937) | 0.127125 / 0.737135 (-0.610011) | 0.088622 / 0.296338 (-0.207717) |\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) | 0.287104 / 0.215209 (0.071895) | 2.802723 / 2.077655 (0.725068) | 1.570137 / 1.504120 (0.066017) | 1.452234 / 1.541195 (-0.088961) | 1.465457 / 1.468490 (-0.003033) | 0.564965 / 4.584777 (-4.019812) | 2.416724 / 3.745712 (-1.328988) | 2.645057 / 5.269862 (-2.624805) | 1.727599 / 4.565676 (-2.838078) | 0.063338 / 0.424275 (-0.360937) | 0.005018 / 0.007607 (-0.002589) | 0.345280 / 0.226044 (0.119235) | 3.384323 / 2.268929 (1.115395) | 1.957227 / 55.444624 (-53.487397) | 1.667620 / 6.876477 (-5.208856) | 1.795339 / 2.142072 (-0.346733) | 0.642049 / 4.805227 (-4.163178) | 0.114853 / 6.500664 (-6.385811) | 0.040459 / 0.075469 (-0.035010) |\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) | 1.023640 / 1.841788 (-0.818147) | 11.998130 / 8.074308 (3.923822) | 10.858137 / 10.191392 (0.666744) | 0.130235 / 0.680424 (-0.550189) | 0.016201 / 0.534201 (-0.518000) | 0.289743 / 0.579283 (-0.289540) | 0.275100 / 0.434364 (-0.159264) | 0.329299 / 0.540337 (-0.211039) | 0.418632 / 1.386936 (-0.968304) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98495237883c5ed5a36fac125e68cad97598916f \"CML watermark\")\n" ]

[ { "color": "fef2c0", "default": false, "description": "", "id": 3287858981, "name": "streaming", "node_id": "MDU6TGFiZWwzMjg3ODU4OTgx", "url": "https://api.github.com/repos/huggingface/datasets/labels/streaming" } ]

[]

[ "cc @mariosasko @lhoestq ", "Hi! We should be able to avoid this error by retrying to read the data when it happens. I'll open a PR in `huggingface_hub` to address this.", "Thanks for the fix @mariosasko! Just wondering whether \"500 error\" should also be excluded? I got these errors overnight:\r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256/resolve/91e6a0cd0356605b021384ded813cfcf356a221c/train/tra\r\nin-02618-of-04012.parquet (Request ID: Root=1-65b18b81-627f2c2943bbb8ab68d19ee2;129537bd-1934-4257-a4d8-1cb774f8e1f8) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! \r\n```", "Gently pining @mariosasko and @Wauplin - when trying to stream this large dataset from the HF Hub, I'm running into `500 Internal Server Errors` as described above. I'd love to be able to use the Hub exclusively to stream data when training, but this error pops up a few times a week, terminating training runs and causing me to have to rewind to the last saved checkpoint. Do we reckon there's a way we can protect Datasets' streaming against these errors? The same reproducer as the [original comment](https://github.com/huggingface/datasets/issues/6577#issue-2074790848) can be used, but it's somewhat random whether we hit a 500 error. Leaving the full traceback below: \r\n\r\n```\r\nTraceback (most recent call last): \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loo\r\np \r\n data = fetcher.fetch(index) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch \r\n data.append(next(self.dataset_iter)) \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1367, in __iter__ \r\n yield from self._iter_pytorch() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1302, in _iter_pytorch \r\n for key, example in ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 987, in __iter__ \r\n for x in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 867, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 904, in _iter \r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 679, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 741, in _iter [235/1892]\r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1119, in __iter__ \r\n for key, example in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 282, in __iter__ \r\n for key, pa_table in self.generate_tables_fn(**self.kwargs): \r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables \r\n for batch_idx, record_batch in enumerate( \r\n File \"pyarrow/_parquet.pyx\", line 1587, in iter_batches \r\n File \"pyarrow/types.pxi\", line 88, in pyarrow.lib._datatype_to_pep3118 \r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retrie\r\ns \r\n out = read(*args, **kwargs) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read \r\n out = self.cache._fetch(self.loc, self.loc + length) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch \r\n self.cache = self.fetcher(start, end) # new block replaces old \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 629, in _fetch_rang\r\ne \r\n hf_raise_for_status(r) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py\", line 362, in hf_raise_for\r\n_status \r\n raise HfHubHTTPError(str(e), response=response) from e \r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256-conditioned/resolve/3c3c0cce51df9f9d2e75968bb2a1851894f504\r\n0d/train/train-03515-of-04010.parquet (Request ID: Root=1-65c7c4c4-153fe71401558c8c2d272c8a;fec3ec68-4a0a-4bfd-95ba-b0a0\r\n5684d612) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! ", "@sanchit-gandhi thanks for the feedback. I've opened https://github.com/huggingface/huggingface_hub/pull/2026 to make the download process more robust. I believe that you've witness this problem on Saturday due to the Hub outage. Hope the PR will make your life easier though :)", "Awesome, thanks @Wauplin! Makes sense re the Hub outage" ]

[]

[]

[ "Thanks for reporting! I've opened a PR with a fix." ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6575). 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) | 0.005095 / 0.011353 (-0.006257) | 0.003531 / 0.011008 (-0.007478) | 0.063634 / 0.038508 (0.025126) | 0.031187 / 0.023109 (0.008078) | 0.246375 / 0.275898 (-0.029523) | 0.261204 / 0.323480 (-0.062276) | 0.002898 / 0.007986 (-0.005088) | 0.003280 / 0.004328 (-0.001049) | 0.050739 / 0.004250 (0.046488) | 0.042905 / 0.037052 (0.005852) | 0.244506 / 0.258489 (-0.013983) | 0.269403 / 0.293841 (-0.024438) | 0.027588 / 0.128546 (-0.100959) | 0.010860 / 0.075646 (-0.064787) | 0.208332 / 0.419271 (-0.210939) | 0.035762 / 0.043533 (-0.007771) | 0.244448 / 0.255139 (-0.010691) | 0.278464 / 0.283200 (-0.004735) | 0.019839 / 0.141683 (-0.121844) | 1.145340 / 1.452155 (-0.306815) | 1.173240 / 1.492716 (-0.319476) |\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) | 0.090472 / 0.018006 (0.072466) | 0.300883 / 0.000490 (0.300394) | 0.000202 / 0.000200 (0.000003) | 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) | 0.017884 / 0.037411 (-0.019527) | 0.060629 / 0.014526 (0.046103) | 0.073157 / 0.176557 (-0.103400) | 0.120065 / 0.737135 (-0.617070) | 0.074519 / 0.296338 (-0.221820) |\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) | 0.289586 / 0.215209 (0.074377) | 2.821042 / 2.077655 (0.743387) | 1.515515 / 1.504120 (0.011395) | 1.390569 / 1.541195 (-0.150625) | 1.433238 / 1.468490 (-0.035252) | 0.567357 / 4.584777 (-4.017420) | 2.345483 / 3.745712 (-1.400229) | 2.803964 / 5.269862 (-2.465898) | 1.775343 / 4.565676 (-2.790334) | 0.063186 / 0.424275 (-0.361089) | 0.005013 / 0.007607 (-0.002594) | 0.335607 / 0.226044 (0.109562) | 3.307071 / 2.268929 (1.038143) | 1.875228 / 55.444624 (-53.569396) | 1.618286 / 6.876477 (-5.258191) | 1.615963 / 2.142072 (-0.526109) | 0.642633 / 4.805227 (-4.162594) | 0.117222 / 6.500664 (-6.383443) | 0.042590 / 0.075469 (-0.032879) |\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) | 0.960724 / 1.841788 (-0.881064) | 11.652978 / 8.074308 (3.578670) | 10.069318 / 10.191392 (-0.122074) | 0.128161 / 0.680424 (-0.552263) | 0.014095 / 0.534201 (-0.520106) | 0.288386 / 0.579283 (-0.290897) | 0.260373 / 0.434364 (-0.173991) | 0.327443 / 0.540337 (-0.212894) | 0.419020 / 1.386936 (-0.967916) |\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) | 0.005018 / 0.011353 (-0.006335) | 0.003503 / 0.011008 (-0.007505) | 0.049718 / 0.038508 (0.011210) | 0.029311 / 0.023109 (0.006202) | 0.271097 / 0.275898 (-0.004801) | 0.297370 / 0.323480 (-0.026110) | 0.004230 / 0.007986 (-0.003755) | 0.002741 / 0.004328 (-0.001587) | 0.049686 / 0.004250 (0.045435) | 0.044171 / 0.037052 (0.007119) | 0.274851 / 0.258489 (0.016362) | 0.309554 / 0.293841 (0.015714) | 0.029488 / 0.128546 (-0.099058) | 0.010767 / 0.075646 (-0.064880) | 0.057739 / 0.419271 (-0.361532) | 0.053319 / 0.043533 (0.009786) | 0.277739 / 0.255139 (0.022600) | 0.291341 / 0.283200 (0.008142) | 0.019587 / 0.141683 (-0.122096) | 1.113823 / 1.452155 (-0.338332) | 1.169409 / 1.492716 (-0.323307) |\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) | 0.091889 / 0.018006 (0.073883) | 0.309162 / 0.000490 (0.308672) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 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) | 0.022202 / 0.037411 (-0.015209) | 0.076113 / 0.014526 (0.061587) | 0.088416 / 0.176557 (-0.088141) | 0.126822 / 0.737135 (-0.610314) | 0.089540 / 0.296338 (-0.206798) |\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) | 0.293697 / 0.215209 (0.078487) | 2.880680 / 2.077655 (0.803026) | 1.580122 / 1.504120 (0.076002) | 1.449492 / 1.541195 (-0.091703) | 1.478900 / 1.468490 (0.010410) | 0.563402 / 4.584777 (-4.021375) | 2.408692 / 3.745712 (-1.337020) | 2.794108 / 5.269862 (-2.475754) | 1.728549 / 4.565676 (-2.837128) | 0.063152 / 0.424275 (-0.361123) | 0.004985 / 0.007607 (-0.002622) | 0.343340 / 0.226044 (0.117295) | 3.426454 / 2.268929 (1.157525) | 1.932918 / 55.444624 (-53.511706) | 1.649533 / 6.876477 (-5.226944) | 1.673416 / 2.142072 (-0.468656) | 0.640000 / 4.805227 (-4.165227) | 0.115501 / 6.500664 (-6.385163) | 0.040756 / 0.075469 (-0.034713) |\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) | 0.992468 / 1.841788 (-0.849319) | 12.392072 / 8.074308 (4.317764) | 11.025362 / 10.191392 (0.833970) | 0.130788 / 0.680424 (-0.549635) | 0.015647 / 0.534201 (-0.518554) | 0.285914 / 0.579283 (-0.293369) | 0.277208 / 0.434364 (-0.157156) | 0.322917 / 0.540337 (-0.217420) | 0.427308 / 1.386936 (-0.959628) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#999790bcf52f883de1b5233c5632ae73395021cf \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6574). 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) | 0.005447 / 0.011353 (-0.005906) | 0.004030 / 0.011008 (-0.006978) | 0.063770 / 0.038508 (0.025262) | 0.032602 / 0.023109 (0.009493) | 0.247722 / 0.275898 (-0.028176) | 0.286507 / 0.323480 (-0.036973) | 0.003035 / 0.007986 (-0.004951) | 0.003638 / 0.004328 (-0.000690) | 0.048790 / 0.004250 (0.044540) | 0.045358 / 0.037052 (0.008306) | 0.256308 / 0.258489 (-0.002181) | 0.286601 / 0.293841 (-0.007239) | 0.028644 / 0.128546 (-0.099903) | 0.011149 / 0.075646 (-0.064497) | 0.209796 / 0.419271 (-0.209475) | 0.036737 / 0.043533 (-0.006796) | 0.247427 / 0.255139 (-0.007712) | 0.274564 / 0.283200 (-0.008636) | 0.019717 / 0.141683 (-0.121966) | 1.107423 / 1.452155 (-0.344732) | 1.167830 / 1.492716 (-0.324886) |\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) | 0.095695 / 0.018006 (0.077688) | 0.305675 / 0.000490 (0.305185) | 0.000211 / 0.000200 (0.000011) | 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) | 0.018969 / 0.037411 (-0.018443) | 0.063764 / 0.014526 (0.049239) | 0.075831 / 0.176557 (-0.100726) | 0.125340 / 0.737135 (-0.611795) | 0.077585 / 0.296338 (-0.218753) |\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) | 0.280876 / 0.215209 (0.065667) | 2.748107 / 2.077655 (0.670452) | 1.452201 / 1.504120 (-0.051919) | 1.328001 / 1.541195 (-0.213194) | 1.415581 / 1.468490 (-0.052909) | 0.568228 / 4.584777 (-4.016549) | 2.410486 / 3.745712 (-1.335226) | 2.975157 / 5.269862 (-2.294704) | 1.854096 / 4.565676 (-2.711581) | 0.063275 / 0.424275 (-0.361000) | 0.005121 / 0.007607 (-0.002487) | 0.340006 / 0.226044 (0.113961) | 3.362404 / 2.268929 (1.093476) | 1.803913 / 55.444624 (-53.640711) | 1.540557 / 6.876477 (-5.335919) | 1.629240 / 2.142072 (-0.512833) | 0.653595 / 4.805227 (-4.151632) | 0.119558 / 6.500664 (-6.381107) | 0.044365 / 0.075469 (-0.031104) |\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) | 0.964557 / 1.841788 (-0.877231) | 12.550303 / 8.074308 (4.475995) | 10.261302 / 10.191392 (0.069910) | 0.130834 / 0.680424 (-0.549589) | 0.014458 / 0.534201 (-0.519743) | 0.294833 / 0.579283 (-0.284450) | 0.268141 / 0.434364 (-0.166223) | 0.332492 / 0.540337 (-0.207845) | 0.427835 / 1.386936 (-0.959101) |\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) | 0.005577 / 0.011353 (-0.005776) | 0.003823 / 0.011008 (-0.007185) | 0.050815 / 0.038508 (0.012307) | 0.031197 / 0.023109 (0.008088) | 0.269869 / 0.275898 (-0.006029) | 0.294371 / 0.323480 (-0.029109) | 0.004153 / 0.007986 (-0.003833) | 0.002884 / 0.004328 (-0.001445) | 0.048985 / 0.004250 (0.044735) | 0.047824 / 0.037052 (0.010772) | 0.270062 / 0.258489 (0.011573) | 0.306354 / 0.293841 (0.012514) | 0.030614 / 0.128546 (-0.097932) | 0.011209 / 0.075646 (-0.064438) | 0.058943 / 0.419271 (-0.360329) | 0.060824 / 0.043533 (0.017291) | 0.273580 / 0.255139 (0.018441) | 0.288375 / 0.283200 (0.005175) | 0.022097 / 0.141683 (-0.119585) | 1.159109 / 1.452155 (-0.293046) | 1.201463 / 1.492716 (-0.291253) |\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) | 0.093024 / 0.018006 (0.075018) | 0.302838 / 0.000490 (0.302348) | 0.000223 / 0.000200 (0.000023) | 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) | 0.022991 / 0.037411 (-0.014420) | 0.081575 / 0.014526 (0.067050) | 0.090134 / 0.176557 (-0.086423) | 0.129506 / 0.737135 (-0.607629) | 0.091747 / 0.296338 (-0.204592) |\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) | 0.294735 / 0.215209 (0.079525) | 2.857557 / 2.077655 (0.779902) | 1.590577 / 1.504120 (0.086457) | 1.479404 / 1.541195 (-0.061790) | 1.515746 / 1.468490 (0.047256) | 0.579934 / 4.584777 (-4.004843) | 2.462790 / 3.745712 (-1.282922) | 2.944498 / 5.269862 (-2.325363) | 1.836767 / 4.565676 (-2.728909) | 0.064899 / 0.424275 (-0.359376) | 0.005232 / 0.007607 (-0.002375) | 0.349708 / 0.226044 (0.123664) | 3.424801 / 2.268929 (1.155873) | 1.945331 / 55.444624 (-53.499294) | 1.688862 / 6.876477 (-5.187615) | 1.712593 / 2.142072 (-0.429480) | 0.665894 / 4.805227 (-4.139333) | 0.121356 / 6.500664 (-6.379308) | 0.046908 / 0.075469 (-0.028561) |\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) | 0.983507 / 1.841788 (-0.858280) | 13.279790 / 8.074308 (5.205482) | 11.623531 / 10.191392 (1.432139) | 0.144567 / 0.680424 (-0.535857) | 0.016253 / 0.534201 (-0.517948) | 0.291842 / 0.579283 (-0.287441) | 0.278389 / 0.434364 (-0.155975) | 0.328971 / 0.540337 (-0.211366) | 0.443204 / 1.386936 (-0.943732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9fad0c69738434aec91b61d52c0450336f7535ed \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6573). 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) | 0.005421 / 0.011353 (-0.005932) | 0.003915 / 0.011008 (-0.007094) | 0.065109 / 0.038508 (0.026601) | 0.031274 / 0.023109 (0.008165) | 0.248702 / 0.275898 (-0.027196) | 0.275688 / 0.323480 (-0.047792) | 0.003007 / 0.007986 (-0.004978) | 0.002942 / 0.004328 (-0.001387) | 0.050928 / 0.004250 (0.046678) | 0.043751 / 0.037052 (0.006699) | 0.263860 / 0.258489 (0.005371) | 0.291499 / 0.293841 (-0.002342) | 0.028268 / 0.128546 (-0.100278) | 0.011467 / 0.075646 (-0.064180) | 0.210531 / 0.419271 (-0.208740) | 0.036302 / 0.043533 (-0.007231) | 0.251565 / 0.255139 (-0.003574) | 0.272001 / 0.283200 (-0.011199) | 0.020370 / 0.141683 (-0.121313) | 1.175493 / 1.452155 (-0.276662) | 1.229167 / 1.492716 (-0.263550) |\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) | 0.095713 / 0.018006 (0.077707) | 0.308912 / 0.000490 (0.308422) | 0.000231 / 0.000200 (0.000031) | 0.000057 / 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) | 0.019080 / 0.037411 (-0.018332) | 0.062043 / 0.014526 (0.047517) | 0.075642 / 0.176557 (-0.100915) | 0.122789 / 0.737135 (-0.614347) | 0.077507 / 0.296338 (-0.218831) |\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) | 0.279929 / 0.215209 (0.064720) | 2.773336 / 2.077655 (0.695682) | 1.481740 / 1.504120 (-0.022379) | 1.357207 / 1.541195 (-0.183987) | 1.414314 / 1.468490 (-0.054176) | 0.573776 / 4.584777 (-4.011000) | 2.399273 / 3.745712 (-1.346439) | 2.918885 / 5.269862 (-2.350977) | 1.798867 / 4.565676 (-2.766809) | 0.064352 / 0.424275 (-0.359923) | 0.005164 / 0.007607 (-0.002443) | 0.337141 / 0.226044 (0.111097) | 3.402291 / 2.268929 (1.133362) | 1.854308 / 55.444624 (-53.590317) | 1.555789 / 6.876477 (-5.320687) | 1.625873 / 2.142072 (-0.516199) | 0.658589 / 4.805227 (-4.146638) | 0.122273 / 6.500664 (-6.378391) | 0.043910 / 0.075469 (-0.031560) |\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) | 0.933127 / 1.841788 (-0.908660) | 12.436657 / 8.074308 (4.362348) | 10.891750 / 10.191392 (0.700358) | 0.143236 / 0.680424 (-0.537187) | 0.014636 / 0.534201 (-0.519565) | 0.290375 / 0.579283 (-0.288908) | 0.275473 / 0.434364 (-0.158891) | 0.327007 / 0.540337 (-0.213331) | 0.425888 / 1.386936 (-0.961048) |\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) | 0.005820 / 0.011353 (-0.005533) | 0.003684 / 0.011008 (-0.007324) | 0.050234 / 0.038508 (0.011726) | 0.030744 / 0.023109 (0.007634) | 0.279560 / 0.275898 (0.003662) | 0.305829 / 0.323480 (-0.017651) | 0.004053 / 0.007986 (-0.003933) | 0.002743 / 0.004328 (-0.001585) | 0.051087 / 0.004250 (0.046836) | 0.047601 / 0.037052 (0.010549) | 0.290441 / 0.258489 (0.031951) | 0.326719 / 0.293841 (0.032878) | 0.030245 / 0.128546 (-0.098301) | 0.011508 / 0.075646 (-0.064139) | 0.058436 / 0.419271 (-0.360835) | 0.059235 / 0.043533 (0.015702) | 0.278978 / 0.255139 (0.023839) | 0.298146 / 0.283200 (0.014946) | 0.020926 / 0.141683 (-0.120757) | 1.205608 / 1.452155 (-0.246547) | 1.224920 / 1.492716 (-0.267796) |\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) | 0.098384 / 0.018006 (0.080378) | 0.307975 / 0.000490 (0.307485) | 0.000233 / 0.000200 (0.000033) | 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) | 0.023012 / 0.037411 (-0.014399) | 0.077450 / 0.014526 (0.062924) | 0.089314 / 0.176557 (-0.087242) | 0.128610 / 0.737135 (-0.608526) | 0.091521 / 0.296338 (-0.204818) |\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) | 0.310620 / 0.215209 (0.095411) | 3.030374 / 2.077655 (0.952720) | 1.653468 / 1.504120 (0.149348) | 1.526860 / 1.541195 (-0.014334) | 1.605328 / 1.468490 (0.136838) | 0.585444 / 4.584777 (-3.999333) | 2.471700 / 3.745712 (-1.274012) | 2.791268 / 5.269862 (-2.478594) | 1.815965 / 4.565676 (-2.749712) | 0.064713 / 0.424275 (-0.359562) | 0.005095 / 0.007607 (-0.002512) | 0.364843 / 0.226044 (0.138799) | 3.601633 / 2.268929 (1.332705) | 2.022642 / 55.444624 (-53.421982) | 1.737164 / 6.876477 (-5.139312) | 1.923636 / 2.142072 (-0.218437) | 0.670673 / 4.805227 (-4.134554) | 0.121547 / 6.500664 (-6.379117) | 0.042880 / 0.075469 (-0.032589) |\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) | 0.987873 / 1.841788 (-0.853914) | 13.279323 / 8.074308 (5.205015) | 11.480806 / 10.191392 (1.289414) | 0.142118 / 0.680424 (-0.538306) | 0.016486 / 0.534201 (-0.517715) | 0.291617 / 0.579283 (-0.287667) | 0.284639 / 0.434364 (-0.149725) | 0.329596 / 0.540337 (-0.210742) | 0.430168 / 1.386936 (-0.956768) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a5b7d9562231c9fbb36e30c1cf0ac54133d1e77 \"CML watermark\")\n" ]

[]

[]

[ "On closer examination, this appears to be unnecessary. " ]

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

[]

[]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" }, { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]

[]

[ "Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 ", "Yes, I saw it. Maybe @mishig25 can give us some hint...", "fixed https://huggingface.co/docs/datasets/v2.16.0/en/index", "Still missing 2.16.1.", "> Still missing 2.16.1.\r\n\r\nre-running the doc-buld job for the missing ones should fix\r\n\r\n", "Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583", "Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index" ]

[]

[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" } ]

[]

[]

[]

[ "Seems like I just used the old code which did not have `keep_in_memory=True` argument, sorry.\r\n\r\nAlthough i encountered a different problem – at 97% my python process just hung for around 11 minutes with no logs (when running dataset.map without `keep_in_memory=True` over around 3 million of dataset samples)...", "Can you open a new issue and provide a bit more details ? What kind of map operations did you run ?", "Hey. I will try to find some free time to describe it.\r\n\r\n(can't do it now, cause i need to reproduce it myself to be sure about everything, which requires spinning a new Azuree VM, copying a huge dataset to drive from network disk for a long time etc...)", "@lhoestq loading dataset like this does not spawn 50 python processes:\r\n\r\n```\r\ndatasets.load_dataset(\"/preprocessed_2256k/train\", num_proc=50)\r\n```\r\n\r\nI have 64 vCPU so i hoped it could speed up the dataset loading...\r\n\r\nMy dataset onlly has images and metadata.csv with text column alongside image file path column", "now noticed\r\n```\r\n'Setting num_proc from 50 back to 1 for the train split to disable multiprocessing as it only contains one shard\r\n```\r\n\r\nAny way to work around this?", "@lhoestq thanks, [this helped](https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/arrow_dataset.py#L1053)\r\n\r\n" ]

[]

[]

[ "I think you are reporting an issue with the `transformers` library. Note this is the repository of the `datasets` library. I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\n\r\nEDIT: I have not the rights to transfer the issue\r\n~~I am transferring your issue to their repository.~~", "Thanks, I hope someone from transformers library addresses this issue.\r\n\r\nOn Mon, Jan 8, 2024 at 15:29 Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> I think you are reporting an issue with the transformers library. Note\r\n> this is the repository of the datasets library. I am transferring your\r\n> issue to their repository.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6567#issuecomment-1880688586>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNOYMD6WJMXFKPMH6DLYNO7PJAVCNFSM6AAAAABBQ63HWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGY4DQNJYGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n", "@andysingal, I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\nI don't have the rights to transfer this issue to their repo." ]

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

[]

[ "I also see the same error and get passed it by casting that line to float. \r\n\r\nso `for x in obj.detach().cpu().numpy()` becomes `for x in obj.detach().to(torch.float).cpu().numpy()`\r\n\r\nI got the idea from [this ](https://github.com/kohya-ss/sd-webui-additional-networks/pull/128/files) PR where someone was facing a similar issue (in a different repository). I guess numpy doesn't support bfloat16.\r\n\r\n" ]

[]

[]

[ "My current workaround this issue is to return `None` in the second element and then filter out samples which have `None` in them.\r\n\r\n```python\r\ndef merge_samples(batch):\r\n if len(batch['a']) == 1:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [None]\r\n else:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [batch['a'][1]]\r\n return batch\r\n \r\ndef filter_fn(x):\r\n return x['d'] is not None\r\n\r\n# other code...\r\nmapped = mapped.filter(filter_fn)\r\n```" ]

[]

[]

[ "Thanks for reporting! I've opened a PR with a fix", "@mariosasko thank you very much :)" ]

[]

[]

[ "@Wauplin Do you happen to know what's up?", "<del>Installing `datasets` from `main` did the trick so I guess it will be fixed in the next release.\r\n\r\nNVM https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/utils/info_utils.py#L5", "@wasertech upgrading `huggingface_hub` to a newer version should fix your issue. Latest version is 0.20.2. ", "Ha yes I had pinned `tokenizers` to an old version so it downgraded `huggingface_hub`. Note to myself keep HuggingFace modules relatively close together chronologically release wise.", "Glad to know your problem's solved! ", "@Wauplin Thanks for your insight 👍", "pip install --upgrade huggingface-hub" ]

[]

[]

[]

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

[]

[ "In particular, I would like to have an example of how to replace the following configuration (from https://huggingface.co/docs/hub/datasets-manual-configuration#splits)\r\n\r\n```\r\n---\r\nconfigs:\r\n- config_name: default\r\n data_files:\r\n - split: train\r\n path: \"data/*.csv\"\r\n - split: test\r\n path: \"holdout/*.csv\"\r\n---\r\n```\r\n\r\nwith the `data_dir` field." ]

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

[]

[]

[]

[]

[ "Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n\r\nYou can load it this way instead:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ncache_dir = 'path/to/your/cache/directory'\r\ndataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n```", "> Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n> \r\n> You can load it this way instead:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> cache_dir = 'path/to/your/cache/directory'\r\n> dataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n> ```\r\n\r\nthanks, the command run successfully in the latest version\r\n", "> Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n> \r\n> You can load it this way instead:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> cache_dir = 'path/to/your/cache/directory'\r\n> dataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n> ```\r\n\r\n@lhoestq \r\nIn this case, should we traverse through al 1024 json files to load the whole dataset?\r\nThanks!", "It will only load the first file (`data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}` only mentions one file)", "> It will only load the first file (`data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}` only mentions one file)\r\n\r\nThen what if we want to load the whole dataset?", "There is a \"en\" subset that you can load (see the list in the \"subset\" dropdown at https://huggingface.co/datasets/allenai/c4)\r\n\r\n```python\r\ndataset = load_dataset('allenai/c4', 'en', split=\"train\")\r\n```\r\n\r\nalternatively you can specify all the the files yourself using a glob pattern (or a list):\r\n\r\n```python\r\ndataset = load_dataset('allenai/c4', data_files='en/c4-train.00000-of-*.json.gz', split=\"train\")\r\n```", "> There is a \"en\" subset that you can load (see the list in the \"subset\" dropdown at https://huggingface.co/datasets/allenai/c4)\r\n> \r\n> ```python\r\n> dataset = load_dataset('allenai/c4', 'en', split=\"train\")\r\n> ```\r\n> \r\n> alternatively you can specify all the the files yourself using a glob pattern (or a list):\r\n> \r\n> ```python\r\n> dataset = load_dataset('allenai/c4', data_files='en/c4-train.00000-of-*.json.gz', split=\"train\")\r\n> ```\r\n\r\nThanks, the second solution works. The first line simply fails due to missing schema specific to this dataset.", "The latest version of `datasets` seems to have broken my dataset for my users (see this Hugging Face issue: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/discussions/3). I changed it by renaming my dataset's config to `default` instead of `train` and then updating my dataset card accordingly." ]

[]

[]

[ "You can add \r\n\r\n```python\r\nfrom PIL import ImageFile\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n```\r\n\r\nafter the imports to be able to read truncated images." ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6557). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq \r\nhello\r\nI think it should be defined in config.py\r\nDATASET_ README_ FILENAME=\"README. md\"\r\nThis can replace all \"README. md\"\r\n", "Thanks for the feedback :) merging now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004890 / 0.011353 (-0.006463) | 0.003535 / 0.011008 (-0.007473) | 0.062894 / 0.038508 (0.024386) | 0.029133 / 0.023109 (0.006024) | 0.242387 / 0.275898 (-0.033511) | 0.262720 / 0.323480 (-0.060760) | 0.002880 / 0.007986 (-0.005106) | 0.002674 / 0.004328 (-0.001655) | 0.048932 / 0.004250 (0.044682) | 0.041669 / 0.037052 (0.004617) | 0.255922 / 0.258489 (-0.002567) | 0.282106 / 0.293841 (-0.011734) | 0.028137 / 0.128546 (-0.100409) | 0.010620 / 0.075646 (-0.065026) | 0.207799 / 0.419271 (-0.211473) | 0.035499 / 0.043533 (-0.008034) | 0.246158 / 0.255139 (-0.008981) | 0.262671 / 0.283200 (-0.020528) | 0.017297 / 0.141683 (-0.124386) | 1.118681 / 1.452155 (-0.333474) | 1.156732 / 1.492716 (-0.335985) |\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) | 0.091670 / 0.018006 (0.073664) | 0.300327 / 0.000490 (0.299837) | 0.000212 / 0.000200 (0.000012) | 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) | 0.018080 / 0.037411 (-0.019332) | 0.060357 / 0.014526 (0.045831) | 0.072221 / 0.176557 (-0.104336) | 0.119281 / 0.737135 (-0.617855) | 0.073861 / 0.296338 (-0.222477) |\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) | 0.289848 / 0.215209 (0.074639) | 2.845203 / 2.077655 (0.767549) | 1.531271 / 1.504120 (0.027152) | 1.366110 / 1.541195 (-0.175085) | 1.395041 / 1.468490 (-0.073449) | 0.563353 / 4.584777 (-4.021424) | 2.389074 / 3.745712 (-1.356638) | 2.752960 / 5.269862 (-2.516901) | 1.715508 / 4.565676 (-2.850168) | 0.063063 / 0.424275 (-0.361212) | 0.004967 / 0.007607 (-0.002640) | 0.340757 / 0.226044 (0.114713) | 3.387667 / 2.268929 (1.118739) | 1.845182 / 55.444624 (-53.599442) | 1.569616 / 6.876477 (-5.306861) | 1.571393 / 2.142072 (-0.570679) | 0.643455 / 4.805227 (-4.161772) | 0.116919 / 6.500664 (-6.383745) | 0.042551 / 0.075469 (-0.032918) |\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) | 0.943761 / 1.841788 (-0.898027) | 11.481068 / 8.074308 (3.406760) | 10.422180 / 10.191392 (0.230788) | 0.132015 / 0.680424 (-0.548408) | 0.013932 / 0.534201 (-0.520268) | 0.288340 / 0.579283 (-0.290943) | 0.263695 / 0.434364 (-0.170669) | 0.324459 / 0.540337 (-0.215878) | 0.415204 / 1.386936 (-0.971732) |\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) | 0.005042 / 0.011353 (-0.006310) | 0.003465 / 0.011008 (-0.007543) | 0.050107 / 0.038508 (0.011599) | 0.029542 / 0.023109 (0.006433) | 0.273645 / 0.275898 (-0.002253) | 0.293661 / 0.323480 (-0.029818) | 0.004099 / 0.007986 (-0.003887) | 0.002667 / 0.004328 (-0.001661) | 0.048281 / 0.004250 (0.044030) | 0.044406 / 0.037052 (0.007353) | 0.284245 / 0.258489 (0.025756) | 0.312303 / 0.293841 (0.018462) | 0.030057 / 0.128546 (-0.098489) | 0.010675 / 0.075646 (-0.064971) | 0.058404 / 0.419271 (-0.360868) | 0.051874 / 0.043533 (0.008342) | 0.273308 / 0.255139 (0.018169) | 0.289356 / 0.283200 (0.006157) | 0.018628 / 0.141683 (-0.123055) | 1.148764 / 1.452155 (-0.303391) | 1.194181 / 1.492716 (-0.298535) |\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) | 0.091383 / 0.018006 (0.073376) | 0.300221 / 0.000490 (0.299731) | 0.000232 / 0.000200 (0.000032) | 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) | 0.021814 / 0.037411 (-0.015597) | 0.076420 / 0.014526 (0.061894) | 0.087404 / 0.176557 (-0.089152) | 0.126184 / 0.737135 (-0.610951) | 0.089738 / 0.296338 (-0.206600) |\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) | 0.299839 / 0.215209 (0.084630) | 2.929260 / 2.077655 (0.851605) | 1.608327 / 1.504120 (0.104207) | 1.479757 / 1.541195 (-0.061437) | 1.494768 / 1.468490 (0.026278) | 0.563873 / 4.584777 (-4.020904) | 2.434442 / 3.745712 (-1.311270) | 2.641384 / 5.269862 (-2.628478) | 1.724222 / 4.565676 (-2.841454) | 0.062125 / 0.424275 (-0.362150) | 0.004994 / 0.007607 (-0.002613) | 0.350895 / 0.226044 (0.124851) | 3.448550 / 2.268929 (1.179621) | 1.928910 / 55.444624 (-53.515714) | 1.669887 / 6.876477 (-5.206590) | 1.781304 / 2.142072 (-0.360768) | 0.649301 / 4.805227 (-4.155926) | 0.116255 / 6.500664 (-6.384409) | 0.040947 / 0.075469 (-0.034522) |\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) | 0.977537 / 1.841788 (-0.864251) | 12.119913 / 8.074308 (4.045605) | 10.874078 / 10.191392 (0.682686) | 0.130174 / 0.680424 (-0.550250) | 0.016176 / 0.534201 (-0.518025) | 0.287967 / 0.579283 (-0.291316) | 0.280591 / 0.434364 (-0.153773) | 0.324332 / 0.540337 (-0.216005) | 0.419479 / 1.386936 (-0.967457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d6d16117a30ba345b0236407975f701c5b288d4 \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6556). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Fixed in dataset viewer: https://huggingface.co/datasets/multimodalart/repro_1_image\r\n\r\n<img width=\"682\" alt=\"Capture d’écran 2024-02-12 à 22 57 08\" src=\"https://github.com/huggingface/datasets/assets/1676121/be9a8dbc-2d78-4ffc-aed4-293a7c57bc0d\">\r\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6555). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "As shared on slack, `HubDatasetModuleFactoryWithParquetExport` raises a `DatasetsServerError` already if the user tries to load another revision that the one from the parquet export. And therefore it fall backs on using `HubDatasetModuleFactoryWithScript`", "@lhoestq I would say that although current implementation finally returns `HubDatasetModuleFactoryWithScript` as expected, with this PR we avoid the useless call to `HubDatasetModuleFactoryWithParquetExport.get_module`, so this is more optimal.", "<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) | 0.005596 / 0.011353 (-0.005757) | 0.004022 / 0.011008 (-0.006986) | 0.064041 / 0.038508 (0.025533) | 0.030683 / 0.023109 (0.007574) | 0.245236 / 0.275898 (-0.030662) | 0.269657 / 0.323480 (-0.053823) | 0.003142 / 0.007986 (-0.004844) | 0.002821 / 0.004328 (-0.001507) | 0.048774 / 0.004250 (0.044523) | 0.043771 / 0.037052 (0.006719) | 0.258202 / 0.258489 (-0.000287) | 0.288381 / 0.293841 (-0.005460) | 0.028154 / 0.128546 (-0.100392) | 0.011071 / 0.075646 (-0.064576) | 0.209836 / 0.419271 (-0.209436) | 0.035923 / 0.043533 (-0.007609) | 0.248361 / 0.255139 (-0.006777) | 0.268728 / 0.283200 (-0.014472) | 0.019982 / 0.141683 (-0.121701) | 1.172330 / 1.452155 (-0.279824) | 1.192262 / 1.492716 (-0.300455) |\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) | 0.089231 / 0.018006 (0.071225) | 0.299192 / 0.000490 (0.298702) | 0.000214 / 0.000200 (0.000014) | 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) | 0.018358 / 0.037411 (-0.019053) | 0.062633 / 0.014526 (0.048107) | 0.076276 / 0.176557 (-0.100280) | 0.120862 / 0.737135 (-0.616274) | 0.075958 / 0.296338 (-0.220380) |\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) | 0.291575 / 0.215209 (0.076366) | 2.855908 / 2.077655 (0.778253) | 1.459891 / 1.504120 (-0.044229) | 1.374945 / 1.541195 (-0.166250) | 1.333759 / 1.468490 (-0.134731) | 0.575428 / 4.584777 (-4.009348) | 2.414253 / 3.745712 (-1.331459) | 2.768222 / 5.269862 (-2.501639) | 1.705005 / 4.565676 (-2.860672) | 0.063406 / 0.424275 (-0.360869) | 0.004981 / 0.007607 (-0.002626) | 0.343826 / 0.226044 (0.117781) | 3.418143 / 2.268929 (1.149215) | 1.856571 / 55.444624 (-53.588053) | 1.571318 / 6.876477 (-5.305159) | 1.609897 / 2.142072 (-0.532175) | 0.646779 / 4.805227 (-4.158448) | 0.118143 / 6.500664 (-6.382521) | 0.042408 / 0.075469 (-0.033061) |\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) | 0.965091 / 1.841788 (-0.876697) | 11.569655 / 8.074308 (3.495347) | 10.587818 / 10.191392 (0.396426) | 0.128518 / 0.680424 (-0.551905) | 0.013954 / 0.534201 (-0.520247) | 0.287244 / 0.579283 (-0.292039) | 0.263755 / 0.434364 (-0.170609) | 0.321661 / 0.540337 (-0.218676) | 0.428753 / 1.386936 (-0.958183) |\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) | 0.005568 / 0.011353 (-0.005785) | 0.003755 / 0.011008 (-0.007253) | 0.049134 / 0.038508 (0.010626) | 0.032113 / 0.023109 (0.009004) | 0.276645 / 0.275898 (0.000747) | 0.299240 / 0.323480 (-0.024240) | 0.004297 / 0.007986 (-0.003689) | 0.002727 / 0.004328 (-0.001602) | 0.048420 / 0.004250 (0.044170) | 0.045070 / 0.037052 (0.008017) | 0.288597 / 0.258489 (0.030108) | 0.320824 / 0.293841 (0.026983) | 0.053293 / 0.128546 (-0.075253) | 0.011002 / 0.075646 (-0.064644) | 0.057747 / 0.419271 (-0.361524) | 0.034389 / 0.043533 (-0.009143) | 0.277914 / 0.255139 (0.022775) | 0.292919 / 0.283200 (0.009719) | 0.018252 / 0.141683 (-0.123431) | 1.187245 / 1.452155 (-0.264910) | 1.199823 / 1.492716 (-0.292893) |\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) | 0.088338 / 0.018006 (0.070332) | 0.297498 / 0.000490 (0.297008) | 0.000206 / 0.000200 (0.000006) | 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) | 0.021445 / 0.037411 (-0.015966) | 0.075522 / 0.014526 (0.060996) | 0.086010 / 0.176557 (-0.090546) | 0.124938 / 0.737135 (-0.612197) | 0.087542 / 0.296338 (-0.208796) |\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) | 0.292460 / 0.215209 (0.077251) | 2.841290 / 2.077655 (0.763635) | 1.537941 / 1.504120 (0.033821) | 1.409903 / 1.541195 (-0.131291) | 1.435339 / 1.468490 (-0.033151) | 0.578967 / 4.584777 (-4.005810) | 2.398588 / 3.745712 (-1.347125) | 2.662342 / 5.269862 (-2.607520) | 1.743055 / 4.565676 (-2.822622) | 0.064043 / 0.424275 (-0.360232) | 0.005030 / 0.007607 (-0.002577) | 0.348542 / 0.226044 (0.122498) | 3.395854 / 2.268929 (1.126926) | 1.918935 / 55.444624 (-53.525689) | 1.639320 / 6.876477 (-5.237157) | 1.740406 / 2.142072 (-0.401666) | 0.653346 / 4.805227 (-4.151881) | 0.117298 / 6.500664 (-6.383366) | 0.040635 / 0.075469 (-0.034834) |\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) | 1.008277 / 1.841788 (-0.833510) | 12.069369 / 8.074308 (3.995061) | 10.967322 / 10.191392 (0.775930) | 0.131938 / 0.680424 (-0.548486) | 0.015418 / 0.534201 (-0.518783) | 0.297257 / 0.579283 (-0.282026) | 0.270742 / 0.434364 (-0.163622) | 0.332296 / 0.540337 (-0.208042) | 0.421606 / 1.386936 (-0.965330) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f22ec79a1ce4fbf0a1728d53f0338d5fdf664d8 \"CML watermark\")\n" ]

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

[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]

[ "I don't think this bug is a thing ? Do you have some code that leads to this issue ?" ]

[]

[]

[ "I don't know My conpany conputer cannot work. but in my computer, it work?", "Do you have a folder in your working directory called datasets?" ]

[]

[]

[ "This bug comes from the `huggingface_hub` library, see: https://github.com/huggingface/huggingface_hub/issues/1952\r\n\r\nA fix is provided at https://github.com/huggingface/huggingface_hub/pull/1953. Feel free to install `huggingface_hub` from this PR, or wait for it to be merged and the new version of `huggingface_hub` to be released", "Thanks!" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6551). 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) | 0.005002 / 0.011353 (-0.006350) | 0.003300 / 0.011008 (-0.007708) | 0.062509 / 0.038508 (0.024001) | 0.029807 / 0.023109 (0.006698) | 0.249935 / 0.275898 (-0.025963) | 0.264320 / 0.323480 (-0.059160) | 0.003790 / 0.007986 (-0.004195) | 0.002554 / 0.004328 (-0.001774) | 0.048207 / 0.004250 (0.043956) | 0.042033 / 0.037052 (0.004981) | 0.245725 / 0.258489 (-0.012764) | 0.276695 / 0.293841 (-0.017146) | 0.026502 / 0.128546 (-0.102044) | 0.010379 / 0.075646 (-0.065268) | 0.207002 / 0.419271 (-0.212269) | 0.034648 / 0.043533 (-0.008885) | 0.247957 / 0.255139 (-0.007182) | 0.263921 / 0.283200 (-0.019278) | 0.017710 / 0.141683 (-0.123973) | 1.105851 / 1.452155 (-0.346304) | 1.163315 / 1.492716 (-0.329401) |\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) | 0.089842 / 0.018006 (0.071836) | 0.352499 / 0.000490 (0.352009) | 0.000201 / 0.000200 (0.000001) | 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) | 0.018094 / 0.037411 (-0.019317) | 0.060463 / 0.014526 (0.045937) | 0.073257 / 0.176557 (-0.103300) | 0.119771 / 0.737135 (-0.617364) | 0.075210 / 0.296338 (-0.221128) |\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) | 0.288365 / 0.215209 (0.073156) | 2.825377 / 2.077655 (0.747722) | 1.532436 / 1.504120 (0.028316) | 1.393475 / 1.541195 (-0.147719) | 1.381859 / 1.468490 (-0.086632) | 0.564155 / 4.584777 (-4.020622) | 2.398177 / 3.745712 (-1.347535) | 2.730271 / 5.269862 (-2.539590) | 1.713779 / 4.565676 (-2.851898) | 0.062789 / 0.424275 (-0.361486) | 0.004991 / 0.007607 (-0.002616) | 0.340789 / 0.226044 (0.114744) | 3.323543 / 2.268929 (1.054615) | 1.861925 / 55.444624 (-53.582700) | 1.555181 / 6.876477 (-5.321296) | 1.559512 / 2.142072 (-0.582560) | 0.634565 / 4.805227 (-4.170663) | 0.116529 / 6.500664 (-6.384135) | 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) | 0.945739 / 1.841788 (-0.896049) | 11.376130 / 8.074308 (3.301822) | 10.007752 / 10.191392 (-0.183640) | 0.126815 / 0.680424 (-0.553609) | 0.013898 / 0.534201 (-0.520303) | 0.287438 / 0.579283 (-0.291845) | 0.261532 / 0.434364 (-0.172832) | 0.320197 / 0.540337 (-0.220140) | 0.414444 / 1.386936 (-0.972492) |\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) | 0.004994 / 0.011353 (-0.006359) | 0.003407 / 0.011008 (-0.007601) | 0.049281 / 0.038508 (0.010773) | 0.042815 / 0.023109 (0.019706) | 0.268291 / 0.275898 (-0.007607) | 0.285877 / 0.323480 (-0.037603) | 0.004006 / 0.007986 (-0.003980) | 0.002607 / 0.004328 (-0.001721) | 0.047682 / 0.004250 (0.043431) | 0.044281 / 0.037052 (0.007228) | 0.268287 / 0.258489 (0.009798) | 0.298649 / 0.293841 (0.004808) | 0.028607 / 0.128546 (-0.099939) | 0.010367 / 0.075646 (-0.065279) | 0.057114 / 0.419271 (-0.362158) | 0.053753 / 0.043533 (0.010220) | 0.269010 / 0.255139 (0.013871) | 0.285057 / 0.283200 (0.001858) | 0.017693 / 0.141683 (-0.123990) | 1.134718 / 1.452155 (-0.317436) | 1.186609 / 1.492716 (-0.306107) |\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) | 0.091109 / 0.018006 (0.073103) | 0.298603 / 0.000490 (0.298113) | 0.000216 / 0.000200 (0.000016) | 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) | 0.022125 / 0.037411 (-0.015286) | 0.076570 / 0.014526 (0.062044) | 0.088903 / 0.176557 (-0.087654) | 0.126427 / 0.737135 (-0.610708) | 0.091001 / 0.296338 (-0.205338) |\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) | 0.300332 / 0.215209 (0.085123) | 2.971106 / 2.077655 (0.893452) | 1.617886 / 1.504120 (0.113766) | 1.476679 / 1.541195 (-0.064516) | 1.483750 / 1.468490 (0.015260) | 0.582569 / 4.584777 (-4.002208) | 2.441804 / 3.745712 (-1.303908) | 2.753927 / 5.269862 (-2.515935) | 1.733546 / 4.565676 (-2.832130) | 0.062653 / 0.424275 (-0.361622) | 0.005019 / 0.007607 (-0.002588) | 0.355556 / 0.226044 (0.129512) | 3.497431 / 2.268929 (1.228503) | 1.951711 / 55.444624 (-53.492913) | 1.663874 / 6.876477 (-5.212602) | 1.657363 / 2.142072 (-0.484709) | 0.653488 / 4.805227 (-4.151739) | 0.117055 / 6.500664 (-6.383609) | 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) | 0.969485 / 1.841788 (-0.872303) | 12.064793 / 8.074308 (3.990485) | 10.851531 / 10.191392 (0.660139) | 0.129060 / 0.680424 (-0.551364) | 0.015339 / 0.534201 (-0.518862) | 0.287215 / 0.579283 (-0.292069) | 0.276545 / 0.434364 (-0.157819) | 0.322748 / 0.540337 (-0.217589) | 0.421363 / 1.386936 (-0.965573) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d26abadce0b884db32382b92422d8a6aa997d40a \"CML watermark\")\n", "@lhoestq \r\n<img width=\"1015\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/b19b9d92-c6f7-4e3a-8c9d-1178e56c67ea\">\r\nit's still not fixed =(", "@lhoestq i was thinking uninstalling `datasets` and then `pip install git+https://github.com/huggingface/datasets.git` has to fix it. Buuuuut. I'm not sure what's going on actually...\r\n\r\nNow instead of showing progress bars one after another it seems to be downloading the dataset way way way faster (like 4 mins instead of 58, thank you very much) but does not show any progress bars related to downloading at all.\r\n\r\n<img width=\"1170\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/21a84908-c44d-41b4-bb0d-8061cab3bc64\">\r\n\r\n<img width=\"1159\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/26684a8a-c10a-4fa2-bd84-cab4f938ffcc\">\r\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6550). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @lhoestq . This is a very important fix for code to run on multiple GPUs. Otherwise, only one GPU is working. I wish it can be merged soon. \r\nI also wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example in case it can be helpful to someone. Please feel free to use complete example in any documentation. \r\n", "Thanks a lot @forrestbao ! I reused parts of your code for the documentation, I'm sure it will be useful to many people !", "<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) | 0.005662 / 0.011353 (-0.005691) | 0.003930 / 0.011008 (-0.007078) | 0.063807 / 0.038508 (0.025299) | 0.030227 / 0.023109 (0.007118) | 0.235338 / 0.275898 (-0.040560) | 0.264433 / 0.323480 (-0.059047) | 0.004226 / 0.007986 (-0.003759) | 0.002847 / 0.004328 (-0.001481) | 0.048998 / 0.004250 (0.044747) | 0.042713 / 0.037052 (0.005660) | 0.250504 / 0.258489 (-0.007985) | 0.281101 / 0.293841 (-0.012740) | 0.029123 / 0.128546 (-0.099423) | 0.011388 / 0.075646 (-0.064258) | 0.211342 / 0.419271 (-0.207930) | 0.036437 / 0.043533 (-0.007096) | 0.238909 / 0.255139 (-0.016230) | 0.255853 / 0.283200 (-0.027347) | 0.018852 / 0.141683 (-0.122831) | 1.131870 / 1.452155 (-0.320284) | 1.209007 / 1.492716 (-0.283710) |\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) | 0.092433 / 0.018006 (0.074427) | 0.303045 / 0.000490 (0.302556) | 0.000291 / 0.000200 (0.000091) | 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) | 0.018349 / 0.037411 (-0.019062) | 0.062527 / 0.014526 (0.048002) | 0.075347 / 0.176557 (-0.101210) | 0.120587 / 0.737135 (-0.616549) | 0.075171 / 0.296338 (-0.221167) |\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) | 0.288364 / 0.215209 (0.073155) | 2.775779 / 2.077655 (0.698124) | 1.490875 / 1.504120 (-0.013245) | 1.375451 / 1.541195 (-0.165744) | 1.398923 / 1.468490 (-0.069567) | 0.588659 / 4.584777 (-3.996117) | 2.458114 / 3.745712 (-1.287598) | 2.928910 / 5.269862 (-2.340951) | 1.834221 / 4.565676 (-2.731456) | 0.064503 / 0.424275 (-0.359772) | 0.005028 / 0.007607 (-0.002580) | 0.340386 / 0.226044 (0.114341) | 3.408697 / 2.268929 (1.139769) | 1.843613 / 55.444624 (-53.601012) | 1.569300 / 6.876477 (-5.307177) | 1.636761 / 2.142072 (-0.505312) | 0.687854 / 4.805227 (-4.117374) | 0.123462 / 6.500664 (-6.377202) | 0.042877 / 0.075469 (-0.032593) |\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) | 0.984054 / 1.841788 (-0.857734) | 12.243934 / 8.074308 (4.169626) | 10.835244 / 10.191392 (0.643852) | 0.131609 / 0.680424 (-0.548815) | 0.014000 / 0.534201 (-0.520201) | 0.292070 / 0.579283 (-0.287213) | 0.271958 / 0.434364 (-0.162406) | 0.326866 / 0.540337 (-0.213471) | 0.440880 / 1.386936 (-0.946056) |\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) | 0.005954 / 0.011353 (-0.005399) | 0.004123 / 0.011008 (-0.006885) | 0.050371 / 0.038508 (0.011863) | 0.034387 / 0.023109 (0.011277) | 0.273254 / 0.275898 (-0.002644) | 0.297785 / 0.323480 (-0.025695) | 0.004619 / 0.007986 (-0.003367) | 0.002884 / 0.004328 (-0.001444) | 0.050236 / 0.004250 (0.045986) | 0.048586 / 0.037052 (0.011533) | 0.283878 / 0.258489 (0.025389) | 0.315218 / 0.293841 (0.021377) | 0.060688 / 0.128546 (-0.067859) | 0.011991 / 0.075646 (-0.063655) | 0.059518 / 0.419271 (-0.359753) | 0.036113 / 0.043533 (-0.007420) | 0.274767 / 0.255139 (0.019628) | 0.290620 / 0.283200 (0.007420) | 0.020070 / 0.141683 (-0.121613) | 1.164635 / 1.452155 (-0.287519) | 1.189482 / 1.492716 (-0.303234) |\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) | 0.095171 / 0.018006 (0.077165) | 0.307129 / 0.000490 (0.306639) | 0.000227 / 0.000200 (0.000027) | 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) | 0.022777 / 0.037411 (-0.014634) | 0.076761 / 0.014526 (0.062235) | 0.087654 / 0.176557 (-0.088902) | 0.126729 / 0.737135 (-0.610406) | 0.089491 / 0.296338 (-0.206847) |\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) | 0.292208 / 0.215209 (0.076999) | 2.890491 / 2.077655 (0.812836) | 1.625696 / 1.504120 (0.121576) | 1.463484 / 1.541195 (-0.077710) | 1.490889 / 1.468490 (0.022399) | 0.582155 / 4.584777 (-4.002622) | 2.492209 / 3.745712 (-1.253503) | 2.817020 / 5.269862 (-2.452842) | 1.806812 / 4.565676 (-2.758864) | 0.065830 / 0.424275 (-0.358445) | 0.005089 / 0.007607 (-0.002518) | 0.356067 / 0.226044 (0.130022) | 3.489652 / 2.268929 (1.220723) | 1.959276 / 55.444624 (-53.485348) | 1.678819 / 6.876477 (-5.197657) | 1.853581 / 2.142072 (-0.288491) | 0.660515 / 4.805227 (-4.144712) | 0.119884 / 6.500664 (-6.380780) | 0.041713 / 0.075469 (-0.033757) |\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) | 1.021701 / 1.841788 (-0.820087) | 12.918290 / 8.074308 (4.843982) | 11.469371 / 10.191392 (1.277979) | 0.144830 / 0.680424 (-0.535594) | 0.015858 / 0.534201 (-0.518343) | 0.290136 / 0.579283 (-0.289148) | 0.277894 / 0.434364 (-0.156470) | 0.330091 / 0.540337 (-0.210247) | 0.422697 / 1.386936 (-0.964240) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13b36ee5c6d77f7eacbb4dd545a21e785db7fd3e \"CML watermark\")\n" ]

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

[]

[ "Maybe we can add a helper message like `Maybe try again using \"hf://path/without/resolve\"` if the path contains `/resolve/` ?\r\n\r\ne.g.\r\n\r\n```\r\nFileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'\r\nIt looks like you used parts of the URL of the file from the Hugging Face website, but you should remove the \"/resolve/<revision>\" part to have a valid `hf://` path.\r\nPlease try again using this path instead:\r\n hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json\r\n```\r\n\r\nand suggest `f\"hf://datasets/HuggingFaceTB/eval_data@{revision}/eval_data_context_and_answers.json\"` if revision != \"main\"\r\n\r\nEDIT: I think this message should also be raised from the `huggingface_hub`'s `HfFileSystem` implementation" ]

[]

[]

[ "It looks like a transient DNS issue. It should work fine now if you try again.\r\n\r\nThere is no parameter in load_dataset to skip failed downloads. In your case it would have skipped every single subsequent download until the DNS issue was resolved anyway." ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6547). 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) | 0.004855 / 0.011353 (-0.006498) | 0.003552 / 0.011008 (-0.007456) | 0.062328 / 0.038508 (0.023820) | 0.031142 / 0.023109 (0.008032) | 0.247726 / 0.275898 (-0.028172) | 0.270951 / 0.323480 (-0.052528) | 0.002887 / 0.007986 (-0.005099) | 0.002663 / 0.004328 (-0.001665) | 0.047888 / 0.004250 (0.043638) | 0.042932 / 0.037052 (0.005880) | 0.253660 / 0.258489 (-0.004829) | 0.274997 / 0.293841 (-0.018844) | 0.027200 / 0.128546 (-0.101347) | 0.010851 / 0.075646 (-0.064796) | 0.206566 / 0.419271 (-0.212706) | 0.035311 / 0.043533 (-0.008222) | 0.254146 / 0.255139 (-0.000993) | 0.269074 / 0.283200 (-0.014126) | 0.019221 / 0.141683 (-0.122462) | 1.101986 / 1.452155 (-0.350169) | 1.155541 / 1.492716 (-0.337175) |\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) | 0.004749 / 0.018006 (-0.013257) | 0.301627 / 0.000490 (0.301138) | 0.000208 / 0.000200 (0.000008) | 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) | 0.018205 / 0.037411 (-0.019206) | 0.060420 / 0.014526 (0.045894) | 0.072533 / 0.176557 (-0.104023) | 0.119807 / 0.737135 (-0.617328) | 0.073249 / 0.296338 (-0.223089) |\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) | 0.284947 / 0.215209 (0.069738) | 2.796939 / 2.077655 (0.719285) | 1.486076 / 1.504120 (-0.018043) | 1.358247 / 1.541195 (-0.182948) | 1.383680 / 1.468490 (-0.084811) | 0.550253 / 4.584777 (-4.034524) | 2.364783 / 3.745712 (-1.380929) | 2.765631 / 5.269862 (-2.504230) | 1.695694 / 4.565676 (-2.869983) | 0.061519 / 0.424275 (-0.362756) | 0.004914 / 0.007607 (-0.002693) | 0.340370 / 0.226044 (0.114325) | 3.313175 / 2.268929 (1.044247) | 1.805421 / 55.444624 (-53.639203) | 1.532151 / 6.876477 (-5.344325) | 1.541195 / 2.142072 (-0.600878) | 0.625266 / 4.805227 (-4.179961) | 0.119980 / 6.500664 (-6.380684) | 0.042334 / 0.075469 (-0.033135) |\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) | 0.952893 / 1.841788 (-0.888895) | 11.322232 / 8.074308 (3.247924) | 9.982108 / 10.191392 (-0.209284) | 0.130034 / 0.680424 (-0.550389) | 0.013192 / 0.534201 (-0.521009) | 0.286041 / 0.579283 (-0.293243) | 0.269802 / 0.434364 (-0.164562) | 0.323582 / 0.540337 (-0.216755) | 0.428641 / 1.386936 (-0.958295) |\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) | 0.005071 / 0.011353 (-0.006282) | 0.003368 / 0.011008 (-0.007640) | 0.049003 / 0.038508 (0.010495) | 0.029507 / 0.023109 (0.006398) | 0.271859 / 0.275898 (-0.004039) | 0.294660 / 0.323480 (-0.028820) | 0.004218 / 0.007986 (-0.003767) | 0.002686 / 0.004328 (-0.001642) | 0.047947 / 0.004250 (0.043696) | 0.044499 / 0.037052 (0.007447) | 0.273982 / 0.258489 (0.015493) | 0.303393 / 0.293841 (0.009552) | 0.029649 / 0.128546 (-0.098898) | 0.010555 / 0.075646 (-0.065091) | 0.057553 / 0.419271 (-0.361718) | 0.051686 / 0.043533 (0.008153) | 0.274079 / 0.255139 (0.018940) | 0.292535 / 0.283200 (0.009335) | 0.019211 / 0.141683 (-0.122472) | 1.130629 / 1.452155 (-0.321526) | 1.196791 / 1.492716 (-0.295925) |\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) | 0.093617 / 0.018006 (0.075611) | 0.302698 / 0.000490 (0.302209) | 0.000222 / 0.000200 (0.000022) | 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) | 0.022830 / 0.037411 (-0.014581) | 0.077061 / 0.014526 (0.062535) | 0.089464 / 0.176557 (-0.087092) | 0.127487 / 0.737135 (-0.609649) | 0.092133 / 0.296338 (-0.204205) |\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) | 0.295362 / 0.215209 (0.080153) | 2.902251 / 2.077655 (0.824596) | 1.600508 / 1.504120 (0.096388) | 1.477763 / 1.541195 (-0.063431) | 1.492242 / 1.468490 (0.023752) | 0.569347 / 4.584777 (-4.015430) | 2.449873 / 3.745712 (-1.295839) | 2.787207 / 5.269862 (-2.482655) | 1.723852 / 4.565676 (-2.841825) | 0.063076 / 0.424275 (-0.361199) | 0.005060 / 0.007607 (-0.002547) | 0.349614 / 0.226044 (0.123569) | 3.429735 / 2.268929 (1.160806) | 1.953883 / 55.444624 (-53.490741) | 1.664232 / 6.876477 (-5.212245) | 1.648864 / 2.142072 (-0.493209) | 0.640295 / 4.805227 (-4.164932) | 0.117053 / 6.500664 (-6.383611) | 0.041314 / 0.075469 (-0.034156) |\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) | 0.970663 / 1.841788 (-0.871125) | 12.144810 / 8.074308 (4.070502) | 10.938985 / 10.191392 (0.747593) | 0.140502 / 0.680424 (-0.539922) | 0.015522 / 0.534201 (-0.518679) | 0.286629 / 0.579283 (-0.292654) | 0.283695 / 0.434364 (-0.150669) | 0.327298 / 0.540337 (-0.213039) | 0.424635 / 1.386936 (-0.962301) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e23a59ef7ba2b50d4e5588825c41212a3cfd1331 \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6546). 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) | 0.005415 / 0.011353 (-0.005938) | 0.003733 / 0.011008 (-0.007275) | 0.064178 / 0.038508 (0.025670) | 0.033162 / 0.023109 (0.010053) | 0.249799 / 0.275898 (-0.026099) | 0.274875 / 0.323480 (-0.048605) | 0.002977 / 0.007986 (-0.005009) | 0.002696 / 0.004328 (-0.001633) | 0.050042 / 0.004250 (0.045792) | 0.047127 / 0.037052 (0.010074) | 0.250865 / 0.258489 (-0.007624) | 0.289758 / 0.293841 (-0.004083) | 0.028007 / 0.128546 (-0.100539) | 0.010671 / 0.075646 (-0.064975) | 0.207123 / 0.419271 (-0.212148) | 0.036403 / 0.043533 (-0.007130) | 0.261527 / 0.255139 (0.006388) | 0.277277 / 0.283200 (-0.005922) | 0.019418 / 0.141683 (-0.122264) | 1.118019 / 1.452155 (-0.334136) | 1.180254 / 1.492716 (-0.312462) |\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) | 0.004604 / 0.018006 (-0.013402) | 0.308129 / 0.000490 (0.307639) | 0.000202 / 0.000200 (0.000002) | 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) | 0.018400 / 0.037411 (-0.019011) | 0.060777 / 0.014526 (0.046251) | 0.073059 / 0.176557 (-0.103498) | 0.119677 / 0.737135 (-0.617458) | 0.074076 / 0.296338 (-0.222263) |\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) | 0.275353 / 0.215209 (0.060144) | 2.694079 / 2.077655 (0.616424) | 1.419670 / 1.504120 (-0.084450) | 1.302079 / 1.541195 (-0.239116) | 1.342077 / 1.468490 (-0.126413) | 0.549794 / 4.584777 (-4.034983) | 2.377149 / 3.745712 (-1.368563) | 2.800362 / 5.269862 (-2.469500) | 1.728152 / 4.565676 (-2.837524) | 0.061774 / 0.424275 (-0.362501) | 0.004898 / 0.007607 (-0.002709) | 0.330996 / 0.226044 (0.104952) | 3.262010 / 2.268929 (0.993082) | 1.761106 / 55.444624 (-53.683518) | 1.489783 / 6.876477 (-5.386694) | 1.532470 / 2.142072 (-0.609602) | 0.648814 / 4.805227 (-4.156414) | 0.116893 / 6.500664 (-6.383771) | 0.042167 / 0.075469 (-0.033303) |\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) | 0.937679 / 1.841788 (-0.904109) | 11.621632 / 8.074308 (3.547324) | 10.226177 / 10.191392 (0.034785) | 0.129242 / 0.680424 (-0.551182) | 0.014884 / 0.534201 (-0.519317) | 0.287619 / 0.579283 (-0.291664) | 0.261677 / 0.434364 (-0.172687) | 0.336361 / 0.540337 (-0.203976) | 0.426461 / 1.386936 (-0.960475) |\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) | 0.005246 / 0.011353 (-0.006106) | 0.003533 / 0.011008 (-0.007475) | 0.051691 / 0.038508 (0.013182) | 0.031551 / 0.023109 (0.008442) | 0.297884 / 0.275898 (0.021986) | 0.323100 / 0.323480 (-0.000380) | 0.004101 / 0.007986 (-0.003884) | 0.002668 / 0.004328 (-0.001661) | 0.048764 / 0.004250 (0.044513) | 0.045429 / 0.037052 (0.008377) | 0.300107 / 0.258489 (0.041618) | 0.335650 / 0.293841 (0.041809) | 0.030061 / 0.128546 (-0.098485) | 0.010878 / 0.075646 (-0.064768) | 0.058561 / 0.419271 (-0.360710) | 0.052829 / 0.043533 (0.009296) | 0.302704 / 0.255139 (0.047565) | 0.320527 / 0.283200 (0.037327) | 0.018995 / 0.141683 (-0.122688) | 1.144050 / 1.452155 (-0.308105) | 1.255275 / 1.492716 (-0.237441) |\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) | 0.092708 / 0.018006 (0.074701) | 0.305204 / 0.000490 (0.304714) | 0.000224 / 0.000200 (0.000024) | 0.000055 / 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) | 0.021607 / 0.037411 (-0.015805) | 0.075938 / 0.014526 (0.061412) | 0.090864 / 0.176557 (-0.085693) | 0.128248 / 0.737135 (-0.608887) | 0.090322 / 0.296338 (-0.206017) |\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) | 0.302095 / 0.215209 (0.086886) | 2.925686 / 2.077655 (0.848032) | 1.617767 / 1.504120 (0.113648) | 1.477975 / 1.541195 (-0.063220) | 1.508576 / 1.468490 (0.040086) | 0.574376 / 4.584777 (-4.010401) | 2.467483 / 3.745712 (-1.278229) | 2.832500 / 5.269862 (-2.437362) | 1.765233 / 4.565676 (-2.800443) | 0.064105 / 0.424275 (-0.360170) | 0.005090 / 0.007607 (-0.002517) | 0.349819 / 0.226044 (0.123774) | 3.468916 / 2.268929 (1.199987) | 1.946499 / 55.444624 (-53.498126) | 1.684369 / 6.876477 (-5.192107) | 1.711036 / 2.142072 (-0.431036) | 0.650153 / 4.805227 (-4.155075) | 0.116598 / 6.500664 (-6.384066) | 0.041213 / 0.075469 (-0.034256) |\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) | 0.990842 / 1.841788 (-0.850946) | 12.348468 / 8.074308 (4.274160) | 11.174441 / 10.191392 (0.983049) | 0.140950 / 0.680424 (-0.539473) | 0.016100 / 0.534201 (-0.518101) | 0.286486 / 0.579283 (-0.292797) | 0.282054 / 0.434364 (-0.152310) | 0.324261 / 0.540337 (-0.216076) | 0.420717 / 1.386936 (-0.966219) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b2bcd76457de720454c3ac304f2ed5c6f40acaa \"CML watermark\")\n" ]

[]

[]

[]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6544). 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) | 0.005462 / 0.011353 (-0.005891) | 0.003918 / 0.011008 (-0.007090) | 0.065021 / 0.038508 (0.026513) | 0.032620 / 0.023109 (0.009511) | 0.249794 / 0.275898 (-0.026104) | 0.277330 / 0.323480 (-0.046150) | 0.002962 / 0.007986 (-0.005023) | 0.003435 / 0.004328 (-0.000894) | 0.048992 / 0.004250 (0.044742) | 0.046841 / 0.037052 (0.009788) | 0.252459 / 0.258489 (-0.006030) | 0.287889 / 0.293841 (-0.005952) | 0.028322 / 0.128546 (-0.100224) | 0.011214 / 0.075646 (-0.064432) | 0.208555 / 0.419271 (-0.210717) | 0.037004 / 0.043533 (-0.006529) | 0.262537 / 0.255139 (0.007398) | 0.307418 / 0.283200 (0.024218) | 0.021552 / 0.141683 (-0.120131) | 1.144252 / 1.452155 (-0.307903) | 1.195687 / 1.492716 (-0.297029) |\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) | 0.004766 / 0.018006 (-0.013240) | 0.301926 / 0.000490 (0.301436) | 0.000218 / 0.000200 (0.000018) | 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) | 0.017891 / 0.037411 (-0.019521) | 0.066848 / 0.014526 (0.052322) | 0.075522 / 0.176557 (-0.101035) | 0.120762 / 0.737135 (-0.616374) | 0.075980 / 0.296338 (-0.220359) |\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) | 0.284843 / 0.215209 (0.069634) | 2.816260 / 2.077655 (0.738605) | 1.484370 / 1.504120 (-0.019750) | 1.362090 / 1.541195 (-0.179104) | 1.421729 / 1.468490 (-0.046762) | 0.561673 / 4.584777 (-4.023104) | 2.370793 / 3.745712 (-1.374919) | 2.982639 / 5.269862 (-2.287223) | 1.834614 / 4.565676 (-2.731063) | 0.063158 / 0.424275 (-0.361117) | 0.005044 / 0.007607 (-0.002563) | 0.339834 / 0.226044 (0.113790) | 3.369051 / 2.268929 (1.100122) | 1.821040 / 55.444624 (-53.623584) | 1.544009 / 6.876477 (-5.332468) | 1.603902 / 2.142072 (-0.538171) | 0.638151 / 4.805227 (-4.167076) | 0.117012 / 6.500664 (-6.383652) | 0.042999 / 0.075469 (-0.032470) |\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) | 0.941809 / 1.841788 (-0.899978) | 12.279635 / 8.074308 (4.205326) | 10.212876 / 10.191392 (0.021484) | 0.129904 / 0.680424 (-0.550519) | 0.014210 / 0.534201 (-0.519991) | 0.286140 / 0.579283 (-0.293143) | 0.267453 / 0.434364 (-0.166911) | 0.324417 / 0.540337 (-0.215921) | 0.428262 / 1.386936 (-0.958674) |\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) | 0.005351 / 0.011353 (-0.006002) | 0.003591 / 0.011008 (-0.007417) | 0.048755 / 0.038508 (0.010247) | 0.030857 / 0.023109 (0.007748) | 0.270301 / 0.275898 (-0.005597) | 0.294459 / 0.323480 (-0.029021) | 0.004265 / 0.007986 (-0.003720) | 0.002712 / 0.004328 (-0.001616) | 0.047725 / 0.004250 (0.043475) | 0.048392 / 0.037052 (0.011339) | 0.274226 / 0.258489 (0.015737) | 0.304010 / 0.293841 (0.010169) | 0.029283 / 0.128546 (-0.099263) | 0.011196 / 0.075646 (-0.064450) | 0.057213 / 0.419271 (-0.362058) | 0.057504 / 0.043533 (0.013971) | 0.266091 / 0.255139 (0.010952) | 0.285991 / 0.283200 (0.002791) | 0.020030 / 0.141683 (-0.121653) | 1.121514 / 1.452155 (-0.330641) | 1.192608 / 1.492716 (-0.300108) |\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) | 0.095041 / 0.018006 (0.077035) | 0.301255 / 0.000490 (0.300765) | 0.000218 / 0.000200 (0.000018) | 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) | 0.022265 / 0.037411 (-0.015146) | 0.078416 / 0.014526 (0.063890) | 0.091097 / 0.176557 (-0.085460) | 0.129864 / 0.737135 (-0.607272) | 0.091683 / 0.296338 (-0.204655) |\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) | 0.294104 / 0.215209 (0.078895) | 2.886809 / 2.077655 (0.809154) | 1.601931 / 1.504120 (0.097811) | 1.469353 / 1.541195 (-0.071842) | 1.525132 / 1.468490 (0.056642) | 0.565164 / 4.584777 (-4.019613) | 2.432873 / 3.745712 (-1.312839) | 2.885849 / 5.269862 (-2.384013) | 1.780474 / 4.565676 (-2.785203) | 0.064358 / 0.424275 (-0.359917) | 0.005186 / 0.007607 (-0.002421) | 0.349374 / 0.226044 (0.123329) | 3.424751 / 2.268929 (1.155823) | 1.956874 / 55.444624 (-53.487750) | 1.679002 / 6.876477 (-5.197475) | 1.718821 / 2.142072 (-0.423252) | 0.656974 / 4.805227 (-4.148254) | 0.120645 / 6.500664 (-6.380019) | 0.042355 / 0.075469 (-0.033114) |\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) | 1.001923 / 1.841788 (-0.839864) | 13.208127 / 8.074308 (5.133819) | 11.164863 / 10.191392 (0.973471) | 0.131964 / 0.680424 (-0.548460) | 0.015344 / 0.534201 (-0.518857) | 0.287961 / 0.579283 (-0.291322) | 0.273986 / 0.434364 (-0.160378) | 0.327280 / 0.540337 (-0.213058) | 0.426761 / 1.386936 (-0.960175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ebb913eca80807521239efece1ff305625cb89b4 \"CML watermark\")\n", "Thanks for the fix and the patch release. This confirms that, as I suggested in the Summer, maybe we should avoid making a release right before leaving on holidays." ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6543). 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) | 0.004950 / 0.011353 (-0.006403) | 0.003502 / 0.011008 (-0.007506) | 0.062517 / 0.038508 (0.024009) | 0.030965 / 0.023109 (0.007856) | 0.250661 / 0.275898 (-0.025237) | 0.279165 / 0.323480 (-0.044314) | 0.002960 / 0.007986 (-0.005026) | 0.003382 / 0.004328 (-0.000946) | 0.048174 / 0.004250 (0.043923) | 0.042975 / 0.037052 (0.005922) | 0.248079 / 0.258489 (-0.010410) | 0.283770 / 0.293841 (-0.010070) | 0.027935 / 0.128546 (-0.100611) | 0.010634 / 0.075646 (-0.065012) | 0.207039 / 0.419271 (-0.212233) | 0.035863 / 0.043533 (-0.007670) | 0.257426 / 0.255139 (0.002287) | 0.274222 / 0.283200 (-0.008978) | 0.017590 / 0.141683 (-0.124093) | 1.126889 / 1.452155 (-0.325266) | 1.160795 / 1.492716 (-0.331921) |\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) | 0.004298 / 0.018006 (-0.013708) | 0.301366 / 0.000490 (0.300876) | 0.000202 / 0.000200 (0.000002) | 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) | 0.018159 / 0.037411 (-0.019252) | 0.060566 / 0.014526 (0.046041) | 0.072500 / 0.176557 (-0.104057) | 0.119612 / 0.737135 (-0.617523) | 0.074467 / 0.296338 (-0.221871) |\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) | 0.281859 / 0.215209 (0.066650) | 2.760157 / 2.077655 (0.682502) | 1.450632 / 1.504120 (-0.053487) | 1.326636 / 1.541195 (-0.214559) | 1.363381 / 1.468490 (-0.105109) | 0.576199 / 4.584777 (-4.008578) | 2.355776 / 3.745712 (-1.389936) | 2.807308 / 5.269862 (-2.462553) | 1.745449 / 4.565676 (-2.820228) | 0.063413 / 0.424275 (-0.360862) | 0.004978 / 0.007607 (-0.002630) | 0.332738 / 0.226044 (0.106693) | 3.267677 / 2.268929 (0.998748) | 1.766074 / 55.444624 (-53.678551) | 1.500853 / 6.876477 (-5.375624) | 1.532434 / 2.142072 (-0.609639) | 0.648238 / 4.805227 (-4.156989) | 0.116030 / 6.500664 (-6.384634) | 0.042018 / 0.075469 (-0.033451) |\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) | 0.934325 / 1.841788 (-0.907463) | 11.439765 / 8.074308 (3.365457) | 9.958624 / 10.191392 (-0.232768) | 0.130295 / 0.680424 (-0.550129) | 0.014437 / 0.534201 (-0.519764) | 0.286073 / 0.579283 (-0.293210) | 0.262430 / 0.434364 (-0.171934) | 0.323905 / 0.540337 (-0.216432) | 0.416615 / 1.386936 (-0.970321) |\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) | 0.005149 / 0.011353 (-0.006204) | 0.003517 / 0.011008 (-0.007491) | 0.048658 / 0.038508 (0.010150) | 0.029638 / 0.023109 (0.006529) | 0.271002 / 0.275898 (-0.004896) | 0.324910 / 0.323480 (0.001430) | 0.004086 / 0.007986 (-0.003900) | 0.002609 / 0.004328 (-0.001719) | 0.047806 / 0.004250 (0.043556) | 0.045422 / 0.037052 (0.008369) | 0.274317 / 0.258489 (0.015828) | 0.304544 / 0.293841 (0.010703) | 0.029318 / 0.128546 (-0.099229) | 0.010626 / 0.075646 (-0.065020) | 0.057838 / 0.419271 (-0.361434) | 0.052408 / 0.043533 (0.008875) | 0.267736 / 0.255139 (0.012597) | 0.292024 / 0.283200 (0.008824) | 0.019244 / 0.141683 (-0.122439) | 1.167728 / 1.452155 (-0.284427) | 1.226364 / 1.492716 (-0.266352) |\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) | 0.092441 / 0.018006 (0.074435) | 0.310316 / 0.000490 (0.309827) | 0.000218 / 0.000200 (0.000018) | 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) | 0.021818 / 0.037411 (-0.015594) | 0.076515 / 0.014526 (0.061989) | 0.089179 / 0.176557 (-0.087377) | 0.127034 / 0.737135 (-0.610102) | 0.089646 / 0.296338 (-0.206692) |\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) | 0.292056 / 0.215209 (0.076847) | 2.841410 / 2.077655 (0.763756) | 1.550626 / 1.504120 (0.046506) | 1.426204 / 1.541195 (-0.114990) | 1.445838 / 1.468490 (-0.022652) | 0.555777 / 4.584777 (-4.029000) | 2.441077 / 3.745712 (-1.304635) | 2.773445 / 5.269862 (-2.496416) | 1.728951 / 4.565676 (-2.836726) | 0.062579 / 0.424275 (-0.361697) | 0.005063 / 0.007607 (-0.002544) | 0.350749 / 0.226044 (0.124705) | 3.461702 / 2.268929 (1.192773) | 1.892506 / 55.444624 (-53.552118) | 1.625958 / 6.876477 (-5.250519) | 1.649175 / 2.142072 (-0.492898) | 0.636123 / 4.805227 (-4.169105) | 0.116548 / 6.500664 (-6.384116) | 0.041174 / 0.075469 (-0.034295) |\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) | 0.973468 / 1.841788 (-0.868320) | 12.104761 / 8.074308 (4.030453) | 11.131691 / 10.191392 (0.940299) | 0.132309 / 0.680424 (-0.548115) | 0.016191 / 0.534201 (-0.518010) | 0.284748 / 0.579283 (-0.294535) | 0.282661 / 0.434364 (-0.151703) | 0.323797 / 0.540337 (-0.216540) | 0.417767 / 1.386936 (-0.969169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72b440325f6a84d341ea57539d8c368a001e2e75 \"CML watermark\")\n" ]

[]

[]

[ "Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?\r\n\r\n```python\r\nwiki_dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\r\n```", "This bug has been fixed in `2.16.1` thanks to https://github.com/huggingface/datasets/pull/6544, feel free to update `datasets` and re-run your code :)\r\n\r\n```\r\npip install -U datasets\r\n```" ]

[]

[]

[ "This is a problem with your environment. You should be able to fix it by upgrading `numpy` based on [this](https://github.com/numpy/numpy/issues/23570) issue.", "Bro I already update numpy package.", "Then, this shouldn't throw an error on your machine:\r\n```python\r\nimport numpy\r\nnumpy._no_nep50_warning\r\n```\r\n\r\nIf it does, run `python -m pip install numpy` to ensure the correct `pip` is used for the package installation.", "Your suggestion to run `python -m pip install numpy` proved to be successful, and my issue has been resolved. I am grateful for your assistance, @mariosasko" ]

[]

[]

[ "Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).\r\nCan you share the snippet of code you are using to merge your datasets and save them to disk ?" ]

[]

[]

[]

[]