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[ "Note:\r\nI listed the datasets and grepped around to find what appears to be an alternative source for this:\r\n\r\nraw_datasets = load_dataset(\"espejelomar/code_search_net_python_10000_examples\", \"python\")", "Thanks for reporting, @jason-brian-anderson.\r\n\r\nYes, this is a known issue: the [CodeSearchNet](https://github.com/github/CodeSearchNet) repo has been archived (Apr 11, 2023) and their source data files are no longer accessible in their S3: e.g. https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/python.zip gives 403 Forbidden error. See:\r\n- https://huggingface.co/datasets/code_search_net/discussions/3\r\n\r\nWe have contacted one of the authors of the dataset to find a solution. I'll keep you informed.\r\n\r\nCC: @hamelsmu", "cc: @julianeagu", "This issue is fixed because we are hosting the CodeSearchNet data files in the Hugging Face Hub. See: https://huggingface.co/datasets/code_search_net/discussions/7", "I learned that @mallamanis has uploaded the dataset [here as well](https://zenodo.org/record/7908468) ", "Thanks @hamelsmu for the Zenodo link. I am adding it to the dataset card on the Hugging Face Hub, so that the community knows about this \"official\" source data. I guess this link is not well known, because some community members already hosted an \"unofficial\" version of the data on Zenodo as well: https://zenodo.org/record/7857872\r\n\r\n" ]

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[ "We just fixed this on `main` and will do a new release soon :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010336 / 0.011353 (-0.001017) | 0.007085 / 0.011008 (-0.003924) | 0.135577 / 0.038508 (0.097069) | 0.038301 / 0.023109 (0.015192) | 0.427919 / 0.275898 (0.152021) | 0.461451 / 0.323480 (0.137971) | 0.008929 / 0.007986 (0.000944) | 0.005260 / 0.004328 (0.000931) | 0.103481 / 0.004250 (0.099231) | 0.054885 / 0.037052 (0.017833) | 0.434956 / 0.258489 (0.176467) | 0.466915 / 0.293841 (0.173074) | 0.052403 / 0.128546 (-0.076144) | 0.021128 / 0.075646 (-0.054518) | 0.466847 / 0.419271 (0.047576) | 0.085096 / 0.043533 (0.041563) | 0.439935 / 0.255139 (0.184796) | 0.453613 / 0.283200 (0.170413) | 0.123913 / 0.141683 (-0.017769) | 1.930114 / 1.452155 (0.477959) | 2.052083 / 1.492716 (0.559366) |\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) | 0.280612 / 0.018006 (0.262606) | 0.583937 / 0.000490 (0.583447) | 0.004542 / 0.000200 (0.004342) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035901 / 0.037411 (-0.001510) | 0.160357 / 0.014526 (0.145831) | 0.141661 / 0.176557 (-0.034896) | 0.234915 / 0.737135 (-0.502220) | 0.164110 / 0.296338 (-0.132228) |\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) | 0.659901 / 0.215209 (0.444692) | 6.529102 / 2.077655 (4.451447) | 2.635324 / 1.504120 (1.131204) | 2.275777 / 1.541195 (0.734583) | 2.343205 / 1.468490 (0.874715) | 1.241310 / 4.584777 (-3.343467) | 5.683784 / 3.745712 (1.938072) | 3.377162 / 5.269862 (-1.892700) | 2.176404 / 4.565676 (-2.389273) | 0.144303 / 0.424275 (-0.279972) | 0.016352 / 0.007607 (0.008745) | 0.817383 / 0.226044 (0.591339) | 8.148356 / 2.268929 (5.879428) | 3.489277 / 55.444624 (-51.955347) | 2.848086 / 6.876477 (-4.028391) | 2.973304 / 2.142072 (0.831232) | 1.517821 / 4.805227 (-3.287407) | 0.278794 / 6.500664 (-6.221870) | 0.096385 / 0.075469 (0.020916) |\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) | 1.631693 / 1.841788 (-0.210095) | 19.564716 / 8.074308 (11.490408) | 23.583081 / 10.191392 (13.391689) | 0.252363 / 0.680424 (-0.428061) | 0.027644 / 0.534201 (-0.506557) | 0.579634 / 0.579283 (0.000351) | 0.645702 / 0.434364 (0.211338) | 0.667302 / 0.540337 (0.126965) | 0.766425 / 1.386936 (-0.620511) |\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) | 0.011186 / 0.011353 (-0.000167) | 0.007327 / 0.011008 (-0.003681) | 0.105441 / 0.038508 (0.066933) | 0.040293 / 0.023109 (0.017184) | 0.480557 / 0.275898 (0.204659) | 0.522049 / 0.323480 (0.198569) | 0.007779 / 0.007986 (-0.000207) | 0.007338 / 0.004328 (0.003009) | 0.104744 / 0.004250 (0.100494) | 0.059463 / 0.037052 (0.022411) | 0.494055 / 0.258489 (0.235566) | 0.534340 / 0.293841 (0.240499) | 0.062800 / 0.128546 (-0.065746) | 0.020687 / 0.075646 (-0.054959) | 0.135833 / 0.419271 (-0.283439) | 0.087472 / 0.043533 (0.043939) | 0.465019 / 0.255139 (0.209880) | 0.526713 / 0.283200 (0.243513) | 0.131424 / 0.141683 (-0.010259) | 1.884759 / 1.452155 (0.432605) | 2.015817 / 1.492716 (0.523101) |\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) | 0.237032 / 0.018006 (0.219026) | 0.605209 / 0.000490 (0.604719) | 0.006653 / 0.000200 (0.006453) | 0.000264 / 0.000054 (0.000210) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034982 / 0.037411 (-0.002430) | 0.141409 / 0.014526 (0.126883) | 0.151635 / 0.176557 (-0.024922) | 0.217298 / 0.737135 (-0.519837) | 0.171945 / 0.296338 (-0.124393) |\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) | 0.678596 / 0.215209 (0.463387) | 6.802432 / 2.077655 (4.724777) | 3.021617 / 1.504120 (1.517497) | 2.722508 / 1.541195 (1.181313) | 2.728194 / 1.468490 (1.259704) | 1.245863 / 4.584777 (-3.338914) | 5.762676 / 3.745712 (2.016963) | 5.497855 / 5.269862 (0.227994) | 2.855764 / 4.565676 (-1.709912) | 0.157359 / 0.424275 (-0.266916) | 0.015562 / 0.007607 (0.007955) | 0.865559 / 0.226044 (0.639515) | 8.553052 / 2.268929 (6.284123) | 3.905544 / 55.444624 (-51.539081) | 3.272528 / 6.876477 (-3.603949) | 3.399481 / 2.142072 (1.257408) | 1.540155 / 4.805227 (-3.265072) | 0.275871 / 6.500664 (-6.224793) | 0.092346 / 0.075469 (0.016877) |\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) | 1.753646 / 1.841788 (-0.088142) | 20.074050 / 8.074308 (11.999742) | 23.920391 / 10.191392 (13.728999) | 0.257161 / 0.680424 (-0.423263) | 0.027805 / 0.534201 (-0.506396) | 0.565605 / 0.579283 (-0.013678) | 0.643277 / 0.434364 (0.208914) | 0.633504 / 0.540337 (0.093167) | 0.754317 / 1.386936 (-0.632619) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d34c7968ea1a3fe7d4fa7cdf23673e0354f69ac \"CML watermark\")\n" ]

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[ "Thanks for reporting, @v-yunbin.\r\n\r\nCould you please give more details, the steps to reproduce the bug, the complete error back trace and the environment information (`datasets-cli env`)?", "this is a detail error info from transformers：\r\n```\r\nTraceback (most recent call last):\r\n File \"finetune.py\", line 177, in <module>\r\n whisper_finetune(traindir,devdir,outdir)\r\n File \"finetune.py\", line 161, in whisper_finetune\r\n trainer = Seq2SeqTrainer(\r\n File \"/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer_seq2seq.py\", line 56, in __init__\r\n super().__init__(\r\n File \"/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer.py\", line 567, in __init__\r\n raise ValueError(\r\nValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.\r\n```\r\n", "How did you create `train_dataset`? The `datasets` library does not appear in your stack trace.\r\n\r\nWe need more information in order to reproduce the issue...", "```\r\ndef asr_dataset(traindir,devdir):\r\n we_voice = IterableDatasetDict()\r\n #we_voice[\"train\"] = load_from_disk(traindir,streaming=True)\r\n #we_voice[\"test\"]= load_from_disk(devdir,streaming=True)\r\n we_voice[\"train\"] = load_dataset(\"csv\",data_files=os.path.join(traindir,\"train.csv\"),split=\"train\",streaming=True)\r\n #print(load_dataset(\"csv\",data_files=os.path.join(traindir,\"train.csv\"),split=\"train\"))\r\n we_voice[\"test\"] = load_dataset(\"csv\",data_files=os.path.join(devdir,\"dev.csv\"), split=\"train\",streaming=True)\r\n we_voice = we_voice.remove_columns([\"id\"])\r\n we_voice = we_voice.cast_column(\"audio\", Audio(sampling_rate=16000))\r\n return we_voice\r\n\r\n```", "As you are using iterable datasets (`streaming=True`), their length is not defined.\r\n\r\nYou should:\r\n- Either use non-iterable datasets, which have a defined length: use `DatasetDict` and not passing `streaming=True`\r\n- Or pass `args.max_steps` to the `Trainer`", "I don't know how to give a reasonable args.max_steps...........................", "Then you should not use streaming.", "@albertvillanova I think @v-yunbin, myself, and others might be slightly confused about max_steps and how it differs from num_train_epochs.", "@lkurlandski A **step** is referring to optimizer's update after back propagation, and it's associated with a batch of data. For example, if a dataset contains 64 examples and you have an overall batch size of 4, then an epoch will have 64/4=16 batches. Therefore, in one epoch, you will have 16 optimizer **steps**." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009262 / 0.011353 (-0.002091) | 0.006157 / 0.011008 (-0.004851) | 0.125960 / 0.038508 (0.087451) | 0.036213 / 0.023109 (0.013104) | 0.399331 / 0.275898 (0.123433) | 0.453597 / 0.323480 (0.130117) | 0.006990 / 0.007986 (-0.000995) | 0.007320 / 0.004328 (0.002991) | 0.100321 / 0.004250 (0.096070) | 0.048870 / 0.037052 (0.011818) | 0.396284 / 0.258489 (0.137795) | 0.475619 / 0.293841 (0.181778) | 0.052329 / 0.128546 (-0.076217) | 0.019564 / 0.075646 (-0.056083) | 0.430942 / 0.419271 (0.011670) | 0.063224 / 0.043533 (0.019692) | 0.391717 / 0.255139 (0.136578) | 0.448342 / 0.283200 (0.165142) | 0.114055 / 0.141683 (-0.027628) | 1.793204 / 1.452155 (0.341049) | 1.895151 / 1.492716 (0.402435) |\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) | 0.283699 / 0.018006 (0.265693) | 0.597194 / 0.000490 (0.596704) | 0.007143 / 0.000200 (0.006944) | 0.000602 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034761 / 0.037411 (-0.002651) | 0.124555 / 0.014526 (0.110030) | 0.149126 / 0.176557 (-0.027430) | 0.220335 / 0.737135 (-0.516801) | 0.153109 / 0.296338 (-0.143229) |\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) | 0.620210 / 0.215209 (0.405001) | 6.229937 / 2.077655 (4.152282) | 2.615203 / 1.504120 (1.111083) | 2.239337 / 1.541195 (0.698143) | 2.262138 / 1.468490 (0.793648) | 1.196498 / 4.584777 (-3.388279) | 5.609932 / 3.745712 (1.864220) | 3.031347 / 5.269862 (-2.238515) | 2.025530 / 4.565676 (-2.540146) | 0.139828 / 0.424275 (-0.284447) | 0.015476 / 0.007607 (0.007869) | 0.768964 / 0.226044 (0.542920) | 7.728677 / 2.268929 (5.459748) | 3.336407 / 55.444624 (-52.108217) | 2.700055 / 6.876477 (-4.176422) | 2.765223 / 2.142072 (0.623151) | 1.409073 / 4.805227 (-3.396155) | 0.246849 / 6.500664 (-6.253815) | 0.081231 / 0.075469 (0.005762) |\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) | 1.593836 / 1.841788 (-0.247952) | 18.020525 / 8.074308 (9.946216) | 21.766822 / 10.191392 (11.575430) | 0.258615 / 0.680424 (-0.421809) | 0.026895 / 0.534201 (-0.507306) | 0.529823 / 0.579283 (-0.049460) | 0.623470 / 0.434364 (0.189106) | 0.628171 / 0.540337 (0.087833) | 0.745249 / 1.386936 (-0.641687) |\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) | 0.008624 / 0.011353 (-0.002729) | 0.006317 / 0.011008 (-0.004691) | 0.097315 / 0.038508 (0.058807) | 0.035217 / 0.023109 (0.012108) | 0.440197 / 0.275898 (0.164299) | 0.473863 / 0.323480 (0.150383) | 0.006722 / 0.007986 (-0.001264) | 0.006444 / 0.004328 (0.002116) | 0.102056 / 0.004250 (0.097806) | 0.047142 / 0.037052 (0.010089) | 0.452476 / 0.258489 (0.193986) | 0.487619 / 0.293841 (0.193778) | 0.052456 / 0.128546 (-0.076090) | 0.018735 / 0.075646 (-0.056911) | 0.114656 / 0.419271 (-0.304616) | 0.062577 / 0.043533 (0.019044) | 0.444471 / 0.255139 (0.189332) | 0.494264 / 0.283200 (0.211065) | 0.117112 / 0.141683 (-0.024571) | 1.848965 / 1.452155 (0.396810) | 1.984008 / 1.492716 (0.491292) |\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) | 0.290494 / 0.018006 (0.272488) | 0.588415 / 0.000490 (0.587925) | 0.000459 / 0.000200 (0.000259) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032873 / 0.037411 (-0.004538) | 0.131139 / 0.014526 (0.116614) | 0.140268 / 0.176557 (-0.036289) | 0.204561 / 0.737135 (-0.532574) | 0.147443 / 0.296338 (-0.148895) |\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) | 0.636899 / 0.215209 (0.421690) | 6.236139 / 2.077655 (4.158484) | 2.801468 / 1.504120 (1.297348) | 2.398808 / 1.541195 (0.857613) | 2.493150 / 1.468490 (1.024659) | 1.228845 / 4.584777 (-3.355932) | 5.675874 / 3.745712 (1.930162) | 3.084939 / 5.269862 (-2.184922) | 2.061310 / 4.565676 (-2.504367) | 0.142285 / 0.424275 (-0.281990) | 0.014972 / 0.007607 (0.007365) | 0.786599 / 0.226044 (0.560555) | 7.876036 / 2.268929 (5.607107) | 3.476136 / 55.444624 (-51.968489) | 2.847922 / 6.876477 (-4.028555) | 3.040326 / 2.142072 (0.898253) | 1.448538 / 4.805227 (-3.356690) | 0.257230 / 6.500664 (-6.243434) | 0.085137 / 0.075469 (0.009668) |\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) | 1.668173 / 1.841788 (-0.173615) | 18.668520 / 8.074308 (10.594212) | 20.535542 / 10.191392 (10.344150) | 0.244580 / 0.680424 (-0.435844) | 0.026364 / 0.534201 (-0.507837) | 0.531753 / 0.579283 (-0.047530) | 0.616578 / 0.434364 (0.182214) | 0.618906 / 0.540337 (0.078569) | 0.738785 / 1.386936 (-0.648151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7265cafa3103d77d6d52aa897088faefcd96659 \"CML watermark\")\n" ]

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[ "A duplicate of https://github.com/huggingface/datasets/issues/5281" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi again @lhoestq this is ready for review! Not sure I have permission to add people to the reviewers list...", "Cool ! I think you can define `IterableDataset.from_spark` instead of adding `streaming=` in `Dataset.from_spark`, it can be more intuitive IMO :)", "Thanks for reviewing! I moved the streaming behavior to IterableDataset.from_spark", "Thanks Quentin! I'll flesh out the docs in a follow-up PR", "Friendly ping @lhoestq ", "Thanks @lhoestq ! I fixed the partition order thing and added more unit tests.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006165 / 0.011353 (-0.005188) | 0.004497 / 0.011008 (-0.006511) | 0.099142 / 0.038508 (0.060634) | 0.027479 / 0.023109 (0.004369) | 0.352491 / 0.275898 (0.076593) | 0.402993 / 0.323480 (0.079513) | 0.004885 / 0.007986 (-0.003100) | 0.003315 / 0.004328 (-0.001013) | 0.075787 / 0.004250 (0.071537) | 0.035320 / 0.037052 (-0.001732) | 0.368401 / 0.258489 (0.109912) | 0.409090 / 0.293841 (0.115249) | 0.030125 / 0.128546 (-0.098421) | 0.011670 / 0.075646 (-0.063976) | 0.324381 / 0.419271 (-0.094890) | 0.050815 / 0.043533 (0.007283) | 0.352598 / 0.255139 (0.097460) | 0.389189 / 0.283200 (0.105989) | 0.092873 / 0.141683 (-0.048810) | 1.485140 / 1.452155 (0.032986) | 1.545586 / 1.492716 (0.052869) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199522 / 0.018006 (0.181516) | 0.404576 / 0.000490 (0.404087) | 0.003322 / 0.000200 (0.003122) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022945 / 0.037411 (-0.014466) | 0.095512 / 0.014526 (0.080987) | 0.103077 / 0.176557 (-0.073480) | 0.163918 / 0.737135 (-0.573217) | 0.105560 / 0.296338 (-0.190779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417360 / 0.215209 (0.202151) | 4.161693 / 2.077655 (2.084039) | 1.851941 / 1.504120 (0.347821) | 1.649872 / 1.541195 (0.108677) | 1.682099 / 1.468490 (0.213609) | 0.693187 / 4.584777 (-3.891590) | 3.462528 / 3.745712 (-0.283184) | 1.839893 / 5.269862 (-3.429968) | 1.155945 / 4.565676 (-3.409731) | 0.082611 / 0.424275 (-0.341664) | 0.012076 / 0.007607 (0.004469) | 0.514325 / 0.226044 (0.288280) | 5.155052 / 2.268929 (2.886123) | 2.307280 / 55.444624 (-53.137345) | 1.966483 / 6.876477 (-4.909994) | 2.018892 / 2.142072 (-0.123181) | 0.803068 / 4.805227 (-4.002159) | 0.152213 / 6.500664 (-6.348451) | 0.066320 / 0.075469 (-0.009149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218578 / 1.841788 (-0.623209) | 13.563869 / 8.074308 (5.489561) | 13.954596 / 10.191392 (3.763204) | 0.151527 / 0.680424 (-0.528897) | 0.016655 / 0.534201 (-0.517546) | 0.380637 / 0.579283 (-0.198646) | 0.395854 / 0.434364 (-0.038509) | 0.459111 / 0.540337 (-0.081226) | 0.560219 / 1.386936 (-0.826717) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006427 / 0.011353 (-0.004926) | 0.004728 / 0.011008 (-0.006280) | 0.080525 / 0.038508 (0.042017) | 0.027294 / 0.023109 (0.004185) | 0.414688 / 0.275898 (0.138790) | 0.449882 / 0.323480 (0.126402) | 0.004771 / 0.007986 (-0.003214) | 0.003402 / 0.004328 (-0.000926) | 0.078748 / 0.004250 (0.074497) | 0.037046 / 0.037052 (-0.000007) | 0.417398 / 0.258489 (0.158909) | 0.462921 / 0.293841 (0.169080) | 0.030364 / 0.128546 (-0.098182) | 0.011810 / 0.075646 (-0.063837) | 0.089787 / 0.419271 (-0.329485) | 0.039806 / 0.043533 (-0.003727) | 0.403401 / 0.255139 (0.148262) | 0.439477 / 0.283200 (0.156278) | 0.088431 / 0.141683 (-0.053252) | 1.534373 / 1.452155 (0.082219) | 1.592316 / 1.492716 (0.099600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217701 / 0.018006 (0.199695) | 0.384770 / 0.000490 (0.384280) | 0.000437 / 0.000200 (0.000237) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024952 / 0.037411 (-0.012459) | 0.098728 / 0.014526 (0.084202) | 0.106324 / 0.176557 (-0.070233) | 0.155484 / 0.737135 (-0.581651) | 0.109503 / 0.296338 (-0.186836) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450639 / 0.215209 (0.235430) | 4.523110 / 2.077655 (2.445455) | 2.224810 / 1.504120 (0.720690) | 2.119516 / 1.541195 (0.578321) | 2.225192 / 1.468490 (0.756702) | 0.695397 / 4.584777 (-3.889380) | 3.433559 / 3.745712 (-0.312153) | 2.633127 / 5.269862 (-2.636735) | 1.448471 / 4.565676 (-3.117206) | 0.082262 / 0.424275 (-0.342013) | 0.012246 / 0.007607 (0.004639) | 0.561243 / 0.226044 (0.335199) | 5.652711 / 2.268929 (3.383782) | 2.689771 / 55.444624 (-52.754853) | 2.359512 / 6.876477 (-4.516965) | 2.471098 / 2.142072 (0.329026) | 0.802955 / 4.805227 (-4.002272) | 0.151142 / 6.500664 (-6.349522) | 0.067494 / 0.075469 (-0.007975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306879 / 1.841788 (-0.534909) | 14.030775 / 8.074308 (5.956467) | 12.917790 / 10.191392 (2.726398) | 0.141269 / 0.680424 (-0.539155) | 0.016264 / 0.534201 (-0.517937) | 0.411957 / 0.579283 (-0.167326) | 0.393235 / 0.434364 (-0.041129) | 0.505144 / 0.540337 (-0.035193) | 0.590660 / 1.386936 (-0.796276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7790ebd7072eafff755fb575b392f3efa74069e4 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @markovalexander.\r\n\r\nUnfortunately, I'm not able to reproduce the issue: the `tiktoken` tokenizer can be used within `Dataset.map`, both in my local machine and in a Colab notebook: https://colab.research.google.com/drive/1DhJroZgk0sNFJ2Mrz-jYgrmh9jblXaCG?usp=sharing\r\n\r\nAre you sure you are using `datasets` version 2.11.0?" ]

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[ "Thanks for reporting, @yaseen157.\r\n\r\nCould you please give the complete error stack trace?", "I am not able to reproduce your issue: the dataset loads perfectly on my local machine and on a Colab notebook: https://colab.research.google.com/drive/1Fbdoa1JdNz8DOdX6gmIsOK1nCT8Abj4O?usp=sharing\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"squad\")\r\nDownloading builder script: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.27k/5.27k [00:00<00:00, 3.22MB/s]\r\nDownloading metadata: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.36k/2.36k [00:00<00:00, 1.60MB/s]\r\nDownloading readme: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7.67k/7.67k [00:00<00:00, 4.58MB/s]\r\nDownloading and preparing dataset squad/plain_text to ...t/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\nDownloading data: 30.3MB [00:00, 91.8MB/s] | 0/2 [00:00<?, ?it/s]\r\nDownloading data: 4.85MB [00:00, 75.3MB/s] \r\nDownloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.31it/s]\r\nExtracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2157.01it/s]\r\nDataset squad downloaded and prepared to .../.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 463.95it/s]\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 10570\r\n })\r\n})\r\n```", "I am at a complete loss for what's happening here. A quick summary, I have 3 machines to try this with:\r\n1) My windows 10 laptop\r\n2) Linux machine1, super computer login node\r\n3) Linux machine2, super computer compute node\r\n\r\nLet's define the following as a test script for the machines:\r\n\r\n```\r\nimport traceback\r\nimport datasets\r\nprint(f\"{datasets.__version__=}\")\r\ntry:\r\n ds = datasets.load_dataset(\"squad\")\r\nexcept:\r\n traceback.print_exc()\r\nelse:\r\n print(\"Success!\")\r\n```\r\n\r\nThe Windows laptop enters some sort of traceback recursion loop:\r\n\r\n> datasets.__version__='2.7.1'\r\n> Downloading and preparing dataset squad/plain_text to C:/Users/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%|██████████| 2/2 [00:00<?, ?it/s]\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 236, in prepare\r\n> _fixup_main_from_path(data['init_main_from_path'])\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 287, in _fixup_main_from_path\r\n> main_content = runpy.run_path(main_path,\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 267, in run_path\r\n> code, fname = _get_code_from_file(run_name, path_name)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 237, in _get_code_from_file\r\n> with io.open_code(decoded_path) as f:\r\n> OSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\yr3g17\\\\OneDrive - University of Southampton\\\\Documents\\\\PhD-repository\\\\<input>'\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\n**this error traceback is endlessly recursive**\r\n\r\nThis is a brand new issue that started today and I didn't even realise was a thing, as I had been using my windows machine to follow tracebacks for the other machines...\r\n\r\nI suspect this issue had something to do with my filepath naming, but I couldn't confirm this when I spent time trying to debug this myself weeks ago, something to do with files being locked and never released. I'm not too concerned about my laptop not working here because I've had so many issues with Microsoft OneDrive and my filesystem.\r\n\r\nLinux machines 1 and 2 were working fine for months, but have all of a sudden stopped working. Trying to run linux machine 1 (login node), I get:\r\n\r\n> datasets.__version__='2.10.1'\r\n> Downloading and preparing dataset json/squad to /home/yr3g17/.cache/hugg\r\ningface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2\r\nb650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n> Downloading data files: 100%|███████████████████████████████████████████\r\n█████████████████████████████████████████████| 2/2 [00:00<00:00, 4042.70\r\nit/s]\r\n>Extracting data files: 100%|███████████████████████████████████████\r\n███████████████████████████████████████████████████| 2/2 [00:00<00:00, 1\r\n11.15it/s]\r\n> Generating train split: 0 examples [00:00, ? examples/s]\r\n\r\n and hangs here. This has not happened to me before on the Linux machine. If I forcefully keyboard interrupt, I get:\r\n \r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 2, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/load.py\", line 1782, in load_dataset\r\n> builder_instance.download_and_prepare(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/builder.py\", line 793, in download_and_prepare\r\n> with FileLock(lock_path) if is_local else contextlib.nullcontext():\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 320, in __enter__\r\n> self.acquire()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 282, in acquire\r\n> time.sleep(poll_intervall)\r\n\r\nWhich also appears to be file lock related! I resolved this by navigating to my ~/.cache/huggingface/datasets directory and wiping out anything to do with the squad dataset in *.lock files. Now I get:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_load(\"squad\")\r\n\r\n```\r\n> Downloading and preparing dataset squad/plain_text to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb\r\n> 2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 44.75it/s]\r\n> Extracting data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 8.54it/s]\r\n> Dataset squad downloaded and prepared to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150\r\n> cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n> 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 19.77it/s]\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 87599\r\n> })\r\n> validation: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 10570\r\n> })\r\n> })\r\n> \r\n\r\nWhich all seems fine right, it's doing what it should be. But now, without ever leaving the IDE, I \"make a subsequent call\" to reuse the data by repeating the command. I encounter the following traceback\r\n\r\n`load_dataset(\"squad\")`\r\n\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1496, in load_dataset_builder\r\n> dataset_module = dataset_module_factory(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1151, in dataset_module_factory\r\n> ).get_module()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 631, in get_module\r\n> data_files = DataFilesDict.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 796, in from_local_or_remote\r\n> DataFilesList.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n> data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 369, in resolve_patterns_locally_or_by_urls\r\n> raise FileNotFoundError(error_msg)\r\n> FileNotFoundError: Unable to resolve any data file that matches '['train[-._ 0-9/]**', '**[-._ 0-9/]train[-._ 0-9/]**', 'training[-._ 0-9/]**', '**[-\r\n> ._ 0-9/]training[-._ 0-9/]**']' at /mainfs/home/yr3g17/.cache/huggingface/datasets/squad with any supported extension ['csv', 'tsv', 'json', 'jsonl',\r\n> 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'gr\r\n> ib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', '\r\n> mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', '\r\n> emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'G\r\n> RIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG',\r\n> 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF',\r\n> 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ir\r\n> cam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'O\r\n> GG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']\r\n\r\nIt doesn't even appear like I can reliably repeat this process. I'll nuke squad files in my dataset cache and run the Python code again (which downloads a new copy of the dataset to cache). It will either fail (as it just did in the quote above), or it will successfully recall the dataset.\r\n\r\nI repeated this nuking process a few times until calling load_dataset was reliably giving me the correct result (no filelocking issues or tracebacks). I then sent the test script as a job to the supercomputer compute nodes (which do not have internet access and therefore depend on cached data from Linux machine 1 login nodes)\r\n\r\n> Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810\r\n> ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n> Traceback (most recent call last):\r\n> File \"/mainfs/scratch/yr3g17/squad_qanswering/3054408/0/../../main.py\", line 5, in <module>\r\n> dataset = load_dataset(\"squad\")\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nand I have absolutely no idea why the second and third machines are producing different tracebacks. I have previously run these exact scripts successfully on the login and compute nodes of the supercomputer, this issue I'm raising has appeared fairly recently for me. This, is where I encounter the TypeError that I opened this issue with, which I was able to traceback (using my laptop before it too started not working) to whatever was dynamically importing \"builder_cls\". That bit of code wasn't doing importing builder_cls correctly and would effectively make the assignment \"builder_cls=None\" resulting in the TypeError. Does any of this help?", "I'm back on linux machine 1 (login node) now. After submitting that as a job to machine 2 and it failing with TypeError, linux machine 1 now produces identical traceback to machine 2:\r\n\r\n> (arkroyal) [yr3g17@cyan52 squad_qanswering]$ python\r\n> Python 3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0] on linux\r\n> Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>\r\n> from datasets import load_dataset\r\n> load_dataset(\"squad\")\r\n>\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nI thought it might be useful to provide you with my cache file structure:\r\n\r\n>_home_yr3g17_.cache_huggingface_datasets_casino_default_1.1.0_302c3b1ac78c48091deabe83a11f4003c7b472a4e11a8eb92799653785bd5da1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_imdb_plain_text_1.0.0_2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_squad_plain_text_1.0.0_d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_yelp_review_full_yelp_review_full_1.0.0_e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf.lock\r\n> casino\r\n> downloads\r\n> imdb\r\n> json\r\n> squad\r\n> squad_v2\r\n> yelp_review_full\r\n\r\nThe inside of squad/plain_text/1.0.0/ looks like\r\n\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.incomplete_info.lock\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453_builder.lock\r\n", "I see this is quite a complex use case...\r\n\r\nLet's try multiple things:\r\n- First, update `datasets` and make sure you use the same version in all machines, so that we can easily compare different behaviors.\r\n ```\r\n pip install -U datasets\r\n ```\r\n- Second, wherever you run the `load_dataset(\"squad\")` command, make sure there is not a local directory named \"squad\". The datasets library gives priority to any local file/directory over the datasets on the Hugging Face Hub\r\n - I tell you this, because in one of your trace backs, it seems it refers to a local directory:\r\n ```\r\n Downloading and preparing dataset json/squad to /home/yr3g17/.cache/huggingface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n ```\r\n- Third, to use the \"squad\" dataset from the Hub, you need to have internet connection, so that you can download the \"squad\" Python loading script from the Hub. Do all your machines have internet connection?\r\n - I ask this because of this error message:\r\n ```\r\n Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n ```\r\n- Fourth, to be sure that we avoid any issues with the cache, it is a good idea to remove it and regenerate it. Remove `.cache/huggingface/datasets` and also `.cache/huggingface/modules`\r\n- Fifth, as an additional debugging tool, let's be sure we use the latest \"squad\" Python loading script by passing the revision parameter:\r\n ```\r\n ds = load_dataset(\"squad\", revision=\"5fe18c4c680f9922d794e3f4dd673a751c74ee37\")\r\n ```", "Additionally, we just had an infrastructure issue on the Hugging Face Hub at around 11:30 today. That might have contributed to the connectivity issue... It is fixed now.\r\n\r\nhttps://status.huggingface.co/", "Hi again, thanks for your help and insight Albert Villanova.\r\n\r\nIt's all working now, so thank you for that. For the benefit of anyone else who ends up in this thread, I solved the problem by addressing Albert's advice:\r\n\r\n(1) Both Windows and Linux machine 1 (have internet access) and can now access the SQuAD dataset. The supercomputer login node can only access version 2.7.1, but my Windows laptop is running on datasets 2.11.0 just fine. I suspect it was just a perfect storm alongside the aforementioned \"infrastructure issue\".\r\n\r\n(2) I did have a local directory called squad, because I was using a local copy of evaluate's \"SQuAD\" metric. The supercomputer compute nodes do not have internet access and treat `metric = evaluate.load('<x>')` as a way of loading a metric at the local path `./<x>/<x>.py`, which could've been a related issue as I was storing the metric under `squad/squad.py`. Don't be lazy like me and store the evaluation code under a path with a name that can be misinterpreted.\r\n\r\n(3) I can't give internet access to the supercomputer compute nodes, so local files do just fine here.\r\n\r\n(4) The windows and Linux machine 1 can both access the internet and were getting fresh copies of the dataset from the huggingface hub. Linux machine 2 was working after I cleared the contents of ~/.cache/huggingface/....\r\n\r\nI feel silly now, knowing it was all so simple! Sorry about that Albert, and thanks again for the help. I've not raised a Github issue like this before, so I'm not sure if I should be close my own issues or if this is something you guys do?", "Thanks for your detailed feedback which for sure will be useful to other community members." ]

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[ "Closing this one in favor of the same issue opened in the `transformers` repo." ]

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[ "Hi ! Interesting :) What kind of new types would you like to use ?\r\n\r\nNote that you can already implement your own decoding by using `set_transform` that can decode data on-the-fly when rows are accessed", "An interesting proposal indeed. \r\n\r\nPandas and Polars have the \"extension API\", so doing something similar on our side could be useful, too. However, this requires defining a common interface for the existing feature types before discussing the API/workflow for defining/sharing custom feature types, and this could take some time.\r\n\r\nIt would also be nice if the datasets viewer could render these custom types.", "Thank you for your replies! @lhoestq I have a use case involving whole-slide images in digital pathology. These are very large images (potentially gigapixel scale), so standard image tools are not suitable. Essentially, encoding/decoding can be done from/to [`OpenSlide`](https://openslide.org/api/python/) objects. Though there may be interest in this use case from the digital pathology community, it may not be sufficiently useful to suggest adding the feature type, but there will likely be many other use cases for a generic custom feature type.\r\n\r\nThank you for pointing out `set_transform`! I will make sure to keep this in mind in the future.\r\n\r\n@mariosasko An \"extension API\" sounds like a good idea, though I understand that this needs to be properly defined, and that you will need to discuss it internally. Support from the viewer would be awesome, too, though the generalization to arbitrary types sounds challenging.\r\n\r\nFor now, happy to know that you're considering the feature. Feel free to let me know if I can do anything to support the process." ]

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[ "You need to remove the `text` and `text_en` columns before passing the dataset to the `DataLoader` to avoid this error:\r\n```python\r\ntokenized_datasets = tokenized_datasets.remove_columns([\"text\", \"text_en\"])\r\n```\r\n", "Thanks @mariosasko. Now I am getting this error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"client_2.py\", line 138, in <module>\r\n main()\r\n File \"client_2.py\", line 134, in main\r\n fl.client.start_numpy_client(server_address=\"localhost:8080\", client=IMDBClient())\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 208, in start_numpy_client\r\n start_client(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 142, in start_client\r\n client_message, sleep_duration, keep_going = handle(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py\", line 68, in handle\r\n return _fit(client, server_msg.fit_ins), 0, True\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py\", line 157, in _fit\r\n fit_res = client.fit(fit_ins)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 252, in _fit\r\n results = self.numpy_client.fit(parameters, ins.config) # type: ignore\r\n File \"client_2.py\", line 124, in fit\r\n train(net, trainloader, epochs=1)\r\n File \"client_2.py\", line 78, in train\r\n for batch in trainloader:\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py\", line 652, in __next__\r\n data = self._next_data()\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py\", line 692, in _next_data\r\n data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1525, in __getitem__\r\n return self._getitem(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1517, in _getitem\r\n pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 373, in query_table\r\n pa_subtable = _query_table_with_indices_mapping(table, key, indices=indices)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 55, in _query_table_with_indices_mapping\r\n return _query_table(table, key)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 79, in _query_table\r\n return table.fast_slice(key % table.num_rows, 1)\r\nZeroDivisionError: integer division or modulo by zero\r\n```\r\n\r\nThis is my code:\r\n\r\n```\r\nfrom collections import OrderedDict\r\nimport warnings\r\n\r\nimport flwr as fl\r\nimport torch\r\nimport numpy as np\r\n\r\nimport random\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, load_metric\r\n\r\nfrom transformers import AutoTokenizer, DataCollatorWithPadding\r\nfrom transformers import AutoModelForSequenceClassification\r\nfrom transformers import AdamW\r\n#from transformers import tokenized_datasets\r\n\r\n\r\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\r\n# DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\nDEVICE = \"cpu\"\r\n\r\nCHECKPOINT = \"distilbert-base-uncased\" # transformer model checkpoint\r\n\r\n\r\ndef load_data():\r\n \"\"\"Load IMDB data (training and eval)\"\"\"\r\n raw_datasets = load_dataset(\"yhavinga/imdb_dutch\")\r\n raw_datasets = raw_datasets.shuffle(seed=42)\r\n\r\n # remove unnecessary data split\r\n del raw_datasets[\"unsupervised\"]\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples[\"text\"], truncation=True)\r\n\r\n # random 100 samples\r\n population = random.sample(range(len(raw_datasets[\"train\"])), 100)\r\n\r\n tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n tokenized_datasets[\"train\"] = tokenized_datasets[\"train\"].select(population)\r\n tokenized_datasets[\"test\"] = tokenized_datasets[\"test\"].select(population)\r\n\r\n # tokenized_datasets = tokenized_datasets.remove_columns(\"text\")\r\n # tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\r\n\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"attention_mask\")\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"input_ids\")\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"label\")\r\n # tokenized_datasets = tokenized_datasets.remove_columns(\"text_en\")\r\n\r\n # tokenized_datasets = tokenized_datasets.remove_columns(raw_datasets[\"train\"].column_names)\r\n \r\n tokenized_datasets = tokenized_datasets.remove_columns([\"text\", \"text_en\"])\r\n \r\n data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\r\n trainloader = DataLoader(\r\n tokenized_datasets[\"train\"],\r\n shuffle=True,\r\n batch_size=32,\r\n collate_fn=data_collator,\r\n )\r\n\r\n testloader = DataLoader(\r\n tokenized_datasets[\"test\"], batch_size=32, collate_fn=data_collator\r\n )\r\n\r\n return trainloader, testloader\r\n\r\n\r\ndef train(net, trainloader, epochs):\r\n optimizer = AdamW(net.parameters(), lr=5e-4)\r\n net.train()\r\n for _ in range(epochs):\r\n for batch in trainloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n outputs = net(**batch)\r\n loss = outputs.loss\r\n loss.backward()\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n\r\n\r\ndef test(net, testloader):\r\n metric = load_metric(\"accuracy\")\r\n loss = 0\r\n net.eval()\r\n for batch in testloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n with torch.no_grad():\r\n outputs = net(**batch)\r\n logits = outputs.logits\r\n loss += outputs.loss.item()\r\n predictions = torch.argmax(logits, dim=-1)\r\n metric.add_batch(predictions=predictions, references=batch[\"labels\"])\r\n loss /= len(testloader.dataset)\r\n accuracy = metric.compute()[\"accuracy\"]\r\n return loss, accuracy\r\n\r\n\r\ndef main():\r\n net = AutoModelForSequenceClassification.from_pretrained(\r\n CHECKPOINT, num_labels=2\r\n ).to(DEVICE)\r\n\r\n trainloader, testloader = load_data()\r\n\r\n # Flower client\r\n class IMDBClient(fl.client.NumPyClient):\r\n def get_parameters(self, config):\r\n return [val.cpu().numpy() for _, val in net.state_dict().items()]\r\n\r\n def set_parameters(self, parameters):\r\n params_dict = zip(net.state_dict().keys(), parameters)\r\n state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\r\n net.load_state_dict(state_dict, strict=True)\r\n\r\n def fit(self, parameters, config):\r\n self.set_parameters(parameters)\r\n print(\"Training Started...\")\r\n train(net, trainloader, epochs=1)\r\n print(\"Training Finished.\")\r\n return self.get_parameters(config={}), len(trainloader), {}\r\n\r\n def evaluate(self, parameters, config):\r\n self.set_parameters(parameters)\r\n loss, accuracy = test(net, testloader)\r\n return float(loss), len(testloader), {\"accuracy\": float(accuracy)}\r\n\r\n # Start client\r\n fl.client.start_numpy_client(server_address=\"localhost:8080\", client=IMDBClient())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```", "Please also remove/comment these lines:\r\n```python\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"attention_mask\")\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"input_ids\")\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"label\")\r\n```", "Thanks @mariosasko .\r\n\r\nNow, I am trying out this [tutorial](https://flower.dev/docs/quickstart-huggingface.html) which basically trains distil-BERT with IMDB dataset (very similar to this [tutorial](https://huggingface.co/docs/transformers/main/tasks/sequence_classification)). But I don't know why my accuracy isn't increasing even after training for a significant amount of time and also by using the entire dataset. Below I have attached `client.py` file:\r\n\r\n`client.py`:\r\n\r\n```\r\nfrom collections import OrderedDict\r\nimport warnings\r\n\r\nimport flwr as fl\r\nimport torch\r\nimport numpy as np\r\n\r\nimport random\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, load_metric\r\n\r\nfrom transformers import AutoTokenizer, DataCollatorWithPadding\r\nfrom transformers import AutoModelForSequenceClassification\r\nfrom transformers import AdamW\r\n\r\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\r\n\r\nDEVICE = \"cuda:1\"\r\n\r\nCHECKPOINT = \"distilbert-base-uncased\" # transformer model checkpoint\r\n\r\n\r\ndef load_data():\r\n \"\"\"Load IMDB data (training and eval)\"\"\"\r\n raw_datasets = load_dataset(\"imdb\")\r\n raw_datasets = raw_datasets.shuffle(seed=42)\r\n\r\n # remove unnecessary data split\r\n del raw_datasets[\"unsupervised\"]\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples[\"text\"], truncation=True)\r\n\r\n tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"text\")\r\n tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\r\n\r\n data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\r\n trainloader = DataLoader(\r\n tokenized_datasets[\"train\"],\r\n shuffle=True,\r\n batch_size=32,\r\n collate_fn=data_collator,\r\n )\r\n\r\n testloader = DataLoader(\r\n tokenized_datasets[\"test\"], batch_size=32, collate_fn=data_collator\r\n )\r\n\r\n return trainloader, testloader\r\n\r\n\r\ndef train(net, trainloader, epochs):\r\n optimizer = AdamW(net.parameters(), lr=5e-5)\r\n net.train()\r\n for i in range(epochs):\r\n print(\"Epoch: \", i+1)\r\n j = 1\r\n print(\"####################### The length of the trainloader is: \", len(trainloader)) \r\n for batch in trainloader:\r\n print(\"####################### The batch number is: \", j)\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n outputs = net(**batch)\r\n loss = outputs.loss\r\n loss.backward()\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n j += 1\r\n\r\n\r\ndef test(net, testloader):\r\n metric = load_metric(\"accuracy\")\r\n loss = 0\r\n net.eval()\r\n for batch in testloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n with torch.no_grad():\r\n outputs = net(**batch)\r\n logits = outputs.logits\r\n loss += outputs.loss.item()\r\n predictions = torch.argmax(logits, dim=-1)\r\n metric.add_batch(predictions=predictions, references=batch[\"labels\"])\r\n loss /= len(testloader.dataset)\r\n accuracy = metric.compute()[\"accuracy\"]\r\n return loss, accuracy\r\n\r\n\r\ndef main():\r\n net = AutoModelForSequenceClassification.from_pretrained(\r\n CHECKPOINT, num_labels=2\r\n ).to(DEVICE)\r\n\r\n trainloader, testloader = load_data()\r\n\r\n # Flower client\r\n class IMDBClient(fl.client.NumPyClient):\r\n def get_parameters(self, config):\r\n return [val.cpu().numpy() for _, val in net.state_dict().items()]\r\n\r\n def set_parameters(self, parameters):\r\n params_dict = zip(net.state_dict().keys(), parameters)\r\n state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\r\n net.load_state_dict(state_dict, strict=True)\r\n\r\n def fit(self, parameters, config):\r\n self.set_parameters(parameters)\r\n print(\"Training Started...\")\r\n train(net, trainloader, epochs=1)\r\n print(\"Training Finished.\")\r\n return self.get_parameters(config={}), len(trainloader), {}\r\n\r\n def evaluate(self, parameters, config):\r\n self.set_parameters(parameters)\r\n loss, accuracy = test(net, testloader)\r\n print({\"loss\": float(loss), \"accuracy\": float(accuracy)})\r\n return float(loss), len(testloader), {\"loss\": float(loss), \"accuracy\": float(accuracy)}\r\n\r\n # Start client\r\n fl.client.start_numpy_client(server_address=\"localhost:5040\", client=IMDBClient())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nCan I get any help, please?" ]

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[ "Thanks for reporting, @sauravtii.\r\n\r\nUnfortunately, I'm not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"josianem/imdb\")\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25799\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25000\r\n })\r\n unsupervised: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 50000\r\n })\r\n})\r\n```\r\n\r\nCould you please retry to load the dataset? Maybe there was a temporary connection issue to Dropbox.", "Thanks @albertvillanova. I am facing another issue now\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 738, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nThis is my code\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\")\r\n```", "Your connection didn't work and you got an empty dataset (`num_bytes=0, num_examples=0`):\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: \r\n[\r\n {\r\n 'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }\r\n]\r\n```\r\n\r\nCould you please try the link in your browser and see if it works? https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n- If it does not work, you should contact the author of the dataset in their Community tab (https://huggingface.co/datasets/josianem/imdb/discussions) and inform them, so that they can host their data elsewhere, for example on the Hugging Face Hub itself\r\n\r\nIf the link works, you should try to load the dataset but forcing the re-download of the data files (so that the cache is refreshed with the actual data file), by passing `download_mode=\"force_redownload\"`:\r\n```python\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "After pasting the link in the browser, it did start the download so it seems that the link is working. But even after including the `download_mode` in my code I am facing the same issue:\r\n\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 704, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/saurav/.cache/huggingface/modules/datasets_modules/datasets/imdb/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f/imdb.py\", line 79, in _split_generators\r\n archive = dl_manager.download(_DOWNLOAD_URL)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 197, in map_nested\r\n return function(data_struct)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 289, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 606, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n```\r\n\r\nMy code:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "I have tried again to reproduce your issue without success: the dataset loads perfectly, both in my local machine and in a Colab notebook.\r\n- See: https://colab.research.google.com/drive/1dky3T0XGFuldggy22NNQQN-UqOFqvnuY?usp=sharing\r\n\r\nI think the cause maight be that you are using a very old version of `datasets`. Please, could you update it and retry?\r\n```\r\npip install -U datasets\r\n```", "That worked!! Thanks @albertvillanova : )\r\n\r\n```\r\nDownloading builder script: 100%|███████| 4.20k/4.20k [00:00<00:00, 6.69MB/s]\r\nDownloading metadata: 100%|█████████████| 2.60k/2.60k [00:00<00:00, 3.41MB/s]\r\nDownloading readme: 100%|███████████████| 7.52k/7.52k [00:00<00:00, 12.6MB/s]\r\nDownloading and preparing dataset imdb/plain_text to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f...\r\nDownloading data: 100%|███████████████████| 301M/301M [01:32<00:00, 3.25MB/s]\r\nDataset imdb downloaded and prepared to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f. Subsequent calls will reuse this data.\r\n100%|█████████████████████████████████████████| 3/3 [00:00<00:00, 794.83it/s]\r\n```\r\n\r\nThe code I used:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n\r\n```\r\n\r\nBut when I remove `download_mode=\"force_redownload\"` I get the same error. Any guess on that?", "That is because the cache got the \"empty\" download file the first time you tried and got the connection error.\r\n\r\nThen, once you no longer get the connection error, you need to refresh the cache by passing `download_mode=\"force_redownload\"`." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006804 / 0.011353 (-0.004549) | 0.004984 / 0.011008 (-0.006024) | 0.096781 / 0.038508 (0.058273) | 0.033049 / 0.023109 (0.009939) | 0.297681 / 0.275898 (0.021783) | 0.329553 / 0.323480 (0.006073) | 0.005697 / 0.007986 (-0.002289) | 0.004019 / 0.004328 (-0.000310) | 0.072691 / 0.004250 (0.068441) | 0.046921 / 0.037052 (0.009868) | 0.311467 / 0.258489 (0.052978) | 0.337616 / 0.293841 (0.043775) | 0.042400 / 0.128546 (-0.086146) | 0.011919 / 0.075646 (-0.063727) | 0.331390 / 0.419271 (-0.087881) | 0.051004 / 0.043533 (0.007471) | 0.295317 / 0.255139 (0.040178) | 0.316570 / 0.283200 (0.033371) | 0.099283 / 0.141683 (-0.042400) | 1.430583 / 1.452155 (-0.021572) | 1.493550 / 1.492716 (0.000834) |\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) | 0.213634 / 0.018006 (0.195628) | 0.432557 / 0.000490 (0.432067) | 0.001586 / 0.000200 (0.001386) | 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) | 0.025249 / 0.037411 (-0.012162) | 0.105433 / 0.014526 (0.090908) | 0.113474 / 0.176557 (-0.063082) | 0.168799 / 0.737135 (-0.568336) | 0.119363 / 0.296338 (-0.176975) |\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) | 0.412450 / 0.215209 (0.197241) | 4.117432 / 2.077655 (2.039777) | 1.935176 / 1.504120 (0.431056) | 1.745674 / 1.541195 (0.204479) | 1.853872 / 1.468490 (0.385382) | 0.703429 / 4.584777 (-3.881348) | 3.756981 / 3.745712 (0.011269) | 3.730607 / 5.269862 (-1.539255) | 1.839052 / 4.565676 (-2.726624) | 0.087574 / 0.424275 (-0.336701) | 0.012293 / 0.007607 (0.004686) | 0.517234 / 0.226044 (0.291190) | 5.189759 / 2.268929 (2.920831) | 2.418739 / 55.444624 (-53.025885) | 2.081424 / 6.876477 (-4.795053) | 2.204464 / 2.142072 (0.062392) | 0.842768 / 4.805227 (-3.962459) | 0.169014 / 6.500664 (-6.331650) | 0.063711 / 0.075469 (-0.011758) |\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) | 1.180636 / 1.841788 (-0.661152) | 14.816088 / 8.074308 (6.741779) | 14.290085 / 10.191392 (4.098693) | 0.165267 / 0.680424 (-0.515156) | 0.017290 / 0.534201 (-0.516911) | 0.419678 / 0.579283 (-0.159605) | 0.418164 / 0.434364 (-0.016200) | 0.492210 / 0.540337 (-0.048127) | 0.588528 / 1.386936 (-0.798408) |\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) | 0.007144 / 0.011353 (-0.004209) | 0.005223 / 0.011008 (-0.005785) | 0.073583 / 0.038508 (0.035075) | 0.033534 / 0.023109 (0.010425) | 0.339020 / 0.275898 (0.063122) | 0.366546 / 0.323480 (0.043066) | 0.006245 / 0.007986 (-0.001741) | 0.004081 / 0.004328 (-0.000247) | 0.073089 / 0.004250 (0.068839) | 0.047024 / 0.037052 (0.009971) | 0.342540 / 0.258489 (0.084051) | 0.379743 / 0.293841 (0.085902) | 0.037551 / 0.128546 (-0.090995) | 0.012246 / 0.075646 (-0.063400) | 0.084796 / 0.419271 (-0.334476) | 0.052256 / 0.043533 (0.008723) | 0.342675 / 0.255139 (0.087536) | 0.367157 / 0.283200 (0.083957) | 0.102939 / 0.141683 (-0.038744) | 1.409039 / 1.452155 (-0.043115) | 1.526137 / 1.492716 (0.033420) |\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) | 0.208143 / 0.018006 (0.190136) | 0.437940 / 0.000490 (0.437450) | 0.000424 / 0.000200 (0.000224) | 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) | 0.028321 / 0.037411 (-0.009091) | 0.110417 / 0.014526 (0.095891) | 0.119449 / 0.176557 (-0.057107) | 0.168081 / 0.737135 (-0.569054) | 0.126658 / 0.296338 (-0.169681) |\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) | 0.429302 / 0.215209 (0.214093) | 4.270547 / 2.077655 (2.192892) | 2.061323 / 1.504120 (0.557203) | 1.857877 / 1.541195 (0.316682) | 1.873317 / 1.468490 (0.404827) | 0.688750 / 4.584777 (-3.896027) | 3.767951 / 3.745712 (0.022239) | 2.011436 / 5.269862 (-3.258426) | 1.299965 / 4.565676 (-3.265712) | 0.084799 / 0.424275 (-0.339476) | 0.012082 / 0.007607 (0.004475) | 0.521981 / 0.226044 (0.295937) | 5.265333 / 2.268929 (2.996405) | 2.494326 / 55.444624 (-52.950298) | 2.144672 / 6.876477 (-4.731804) | 2.365624 / 2.142072 (0.223551) | 0.839868 / 4.805227 (-3.965359) | 0.166614 / 6.500664 (-6.334050) | 0.063804 / 0.075469 (-0.011665) |\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) | 1.264623 / 1.841788 (-0.577164) | 14.946515 / 8.074308 (6.872207) | 14.450115 / 10.191392 (4.258723) | 0.163878 / 0.680424 (-0.516546) | 0.017501 / 0.534201 (-0.516700) | 0.420992 / 0.579283 (-0.158291) | 0.423005 / 0.434364 (-0.011359) | 0.489505 / 0.540337 (-0.050832) | 0.594631 / 1.386936 (-0.792305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd893098627230cc734f6009ad04cf885c979ac4 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @surya-narayanan.\r\n\r\nI see you already started a discussion about this on the Community tab of the corresponding dataset: https://huggingface.co/datasets/EleutherAI/the_pile/discussions/10\r\nLet's continue the discussion there!" ]

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[ "Also, when generated from a zip archive, the dataset contains only a few images. In my case, 20 versus 2000+ contained in the archive. The generation from folders works as expected.", "Thanks for reporting, @blghtr.\r\n\r\nYou should include the `metadata.jsonl` in your ZIP archives, at the root level directory.\r\n\r\nI agree that our documentation is not clear enough. Maybe we could improve it.", "You can find a dummy dataset example here: https://huggingface.co/datasets/albertvillanova/tmp-imagefolder-metadata\r\n\r\n```\r\ntmp-imagefolder-metadata/\r\n└── data/\r\n ├── train.zip\r\n └── valid.zip\r\n```\r\nwhere, the directory structure within the `train.zip` archive is:\r\n```\r\nmetadata.jsonl\r\ntrain/\r\n ├── bharatanatyam/\r\n └── bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\r\n └── kathak/\r\n └── kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\r\n```\r\nand the metadata file contains:\r\n```\r\n{\"file_name\": \"train/bharatanatyam/bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\", \"text\": \"first\"}\r\n{\"file_name\": \"train/kathak/kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\", \"text\": \"second\"}\r\n```" ]

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[ "Supporting this could be useful (I remember a use-case for this on the Hub). Do you agree @polinaeterna? \r\n\r\nImplementing this should be possible if we iterate over metadata files and build image/audio file paths instead of iterating over image/audio files and looking for the corresponding entries in metadata files.", "I've build a similar feature from scratch and would be interested to combine it with the datasets package.\r\n\r\nMy solution works something like this:\r\nInterpret the first element of each column as a file path. If the path exists and is a file, (try to) load the files for the entire column. Thereby, one isn't restricted to a particular column name, with comes in handy when dealing with multiple file columns.\r\n\r\nI've looked into the code to try to implement this, but didn't find the right places. I'm also open to contribute, but will need some guidance." ]

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[ "Thanks for reporting, @LZY-the-boys.\r\n\r\nCould you please give more details about what is your intended dataset structure? What are the names of the columns and the value of each row?\r\n\r\nCurrently, the JSON-Lines format is supported:\r\n- Each line correspond to one row of the dataset\r\n- Each line is composed of one JSON object, where the names are the names of the columns, and the values are the values for the row-column pair." ]

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[ "The CI can be fixed by merging `main` into your branch. Can you do that before we merge ?", "_The documentation is not available anymore as the PR was closed or merged._", "Done.\n\nOn Thu, Apr 20, 2023 at 6:01 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> The CI can be fixed by merging main into your branch. Can you do that\n> before we merge ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/5758#issuecomment-1516488124>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73QPLA735AMN4PFDYRTXCFFTJANCNFSM6AAAAAAXACBUQU>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n", "Nice thanks !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007161 / 0.011353 (-0.004192) | 0.005099 / 0.011008 (-0.005909) | 0.099301 / 0.038508 (0.060793) | 0.034144 / 0.023109 (0.011034) | 0.298273 / 0.275898 (0.022375) | 0.329009 / 0.323480 (0.005529) | 0.005486 / 0.007986 (-0.002500) | 0.003887 / 0.004328 (-0.000441) | 0.074769 / 0.004250 (0.070518) | 0.047505 / 0.037052 (0.010453) | 0.306550 / 0.258489 (0.048061) | 0.335380 / 0.293841 (0.041540) | 0.034796 / 0.128546 (-0.093750) | 0.012152 / 0.075646 (-0.063495) | 0.332194 / 0.419271 (-0.087077) | 0.049661 / 0.043533 (0.006128) | 0.296832 / 0.255139 (0.041693) | 0.316417 / 0.283200 (0.033218) | 0.098234 / 0.141683 (-0.043449) | 1.494114 / 1.452155 (0.041959) | 1.566468 / 1.492716 (0.073751) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221309 / 0.018006 (0.203303) | 0.440855 / 0.000490 (0.440365) | 0.003025 / 0.000200 (0.002825) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026594 / 0.037411 (-0.010817) | 0.110406 / 0.014526 (0.095880) | 0.116117 / 0.176557 (-0.060439) | 0.173502 / 0.737135 (-0.563633) | 0.121988 / 0.296338 (-0.174351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403307 / 0.215209 (0.188098) | 4.034146 / 2.077655 (1.956492) | 1.852162 / 1.504120 (0.348042) | 1.675643 / 1.541195 (0.134448) | 1.748851 / 1.468490 (0.280360) | 0.703458 / 4.584777 (-3.881319) | 3.809055 / 3.745712 (0.063343) | 2.118060 / 5.269862 (-3.151801) | 1.338394 / 4.565676 (-3.227282) | 0.086319 / 0.424275 (-0.337956) | 0.012195 / 0.007607 (0.004588) | 0.520814 / 0.226044 (0.294769) | 5.201074 / 2.268929 (2.932145) | 2.418384 / 55.444624 (-53.026240) | 2.085496 / 6.876477 (-4.790980) | 2.245638 / 2.142072 (0.103565) | 0.849042 / 4.805227 (-3.956185) | 0.171912 / 6.500664 (-6.328752) | 0.065691 / 0.075469 (-0.009778) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.159985 / 1.841788 (-0.681803) | 14.910867 / 8.074308 (6.836559) | 14.473926 / 10.191392 (4.282534) | 0.181532 / 0.680424 (-0.498891) | 0.017203 / 0.534201 (-0.516998) | 0.420805 / 0.579283 (-0.158479) | 0.426455 / 0.434364 (-0.007909) | 0.497086 / 0.540337 (-0.043251) | 0.593909 / 1.386936 (-0.793027) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007688 / 0.011353 (-0.003665) | 0.005353 / 0.011008 (-0.005656) | 0.076869 / 0.038508 (0.038361) | 0.035030 / 0.023109 (0.011921) | 0.344649 / 0.275898 (0.068751) | 0.387669 / 0.323480 (0.064190) | 0.005913 / 0.007986 (-0.002072) | 0.004107 / 0.004328 (-0.000221) | 0.074111 / 0.004250 (0.069860) | 0.049351 / 0.037052 (0.012299) | 0.346061 / 0.258489 (0.087572) | 0.395499 / 0.293841 (0.101658) | 0.035549 / 0.128546 (-0.092997) | 0.012340 / 0.075646 (-0.063307) | 0.087031 / 0.419271 (-0.332241) | 0.049088 / 0.043533 (0.005556) | 0.342774 / 0.255139 (0.087635) | 0.362037 / 0.283200 (0.078837) | 0.100329 / 0.141683 (-0.041354) | 1.442349 / 1.452155 (-0.009806) | 1.551079 / 1.492716 (0.058363) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228458 / 0.018006 (0.210452) | 0.446190 / 0.000490 (0.445701) | 0.000413 / 0.000200 (0.000213) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029884 / 0.037411 (-0.007527) | 0.117527 / 0.014526 (0.103002) | 0.123221 / 0.176557 (-0.053335) | 0.172290 / 0.737135 (-0.564845) | 0.128682 / 0.296338 (-0.167657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420905 / 0.215209 (0.205696) | 4.199342 / 2.077655 (2.121687) | 2.007327 / 1.504120 (0.503207) | 1.814732 / 1.541195 (0.273537) | 1.893999 / 1.468490 (0.425509) | 0.712259 / 4.584777 (-3.872518) | 3.843402 / 3.745712 (0.097690) | 3.198514 / 5.269862 (-2.071348) | 1.678732 / 4.565676 (-2.886945) | 0.086435 / 0.424275 (-0.337840) | 0.012233 / 0.007607 (0.004626) | 0.526121 / 0.226044 (0.300077) | 5.190578 / 2.268929 (2.921650) | 2.473259 / 55.444624 (-52.971366) | 2.142795 / 6.876477 (-4.733682) | 2.277594 / 2.142072 (0.135521) | 0.846117 / 4.805227 (-3.959110) | 0.169458 / 6.500664 (-6.331206) | 0.065017 / 0.075469 (-0.010452) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272479 / 1.841788 (-0.569309) | 15.086473 / 8.074308 (7.012165) | 14.659728 / 10.191392 (4.468336) | 0.163915 / 0.680424 (-0.516509) | 0.017561 / 0.534201 (-0.516640) | 0.422074 / 0.579283 (-0.157209) | 0.421963 / 0.434364 (-0.012401) | 0.490321 / 0.540337 (-0.050016) | 0.586854 / 1.386936 (-0.800083) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7ce0ac60c7efc10886471932854903a7c19f172 \"CML watermark\")\n" ]

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[ "Hi! I've merged a PR on the Hub with a fix: https://huggingface.co/datasets/fashion_mnist/discussions/3", "Thanks, this appears to have fixed the issue.\r\n\r\nI've created a PR for the same change in the mnist dataset: https://huggingface.co/datasets/mnist/discussions/3/files" ]

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[ "update the version. fix" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006479 / 0.011353 (-0.004874) | 0.004592 / 0.011008 (-0.006416) | 0.097239 / 0.038508 (0.058731) | 0.028609 / 0.023109 (0.005499) | 0.309225 / 0.275898 (0.033327) | 0.340015 / 0.323480 (0.016535) | 0.004857 / 0.007986 (-0.003129) | 0.004649 / 0.004328 (0.000320) | 0.074770 / 0.004250 (0.070520) | 0.038351 / 0.037052 (0.001299) | 0.313360 / 0.258489 (0.054871) | 0.350256 / 0.293841 (0.056416) | 0.030770 / 0.128546 (-0.097776) | 0.011591 / 0.075646 (-0.064055) | 0.322444 / 0.419271 (-0.096828) | 0.043704 / 0.043533 (0.000171) | 0.311790 / 0.255139 (0.056651) | 0.339183 / 0.283200 (0.055984) | 0.088041 / 0.141683 (-0.053642) | 1.490649 / 1.452155 (0.038494) | 1.561789 / 1.492716 (0.069072) |\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) | 0.208984 / 0.018006 (0.190978) | 0.406105 / 0.000490 (0.405616) | 0.003152 / 0.000200 (0.002952) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022622 / 0.037411 (-0.014790) | 0.095819 / 0.014526 (0.081294) | 0.105132 / 0.176557 (-0.071424) | 0.165684 / 0.737135 (-0.571451) | 0.106706 / 0.296338 (-0.189632) |\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) | 0.426126 / 0.215209 (0.210917) | 4.233864 / 2.077655 (2.156209) | 1.918727 / 1.504120 (0.414607) | 1.729905 / 1.541195 (0.188710) | 1.760342 / 1.468490 (0.291852) | 0.695449 / 4.584777 (-3.889328) | 3.413531 / 3.745712 (-0.332181) | 1.904557 / 5.269862 (-3.365305) | 1.270604 / 4.565676 (-3.295072) | 0.083018 / 0.424275 (-0.341257) | 0.012760 / 0.007607 (0.005152) | 0.523991 / 0.226044 (0.297947) | 5.236132 / 2.268929 (2.967204) | 2.360959 / 55.444624 (-53.083665) | 1.996533 / 6.876477 (-4.879943) | 2.072934 / 2.142072 (-0.069138) | 0.804133 / 4.805227 (-4.001094) | 0.150976 / 6.500664 (-6.349688) | 0.065503 / 0.075469 (-0.009966) |\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) | 1.211828 / 1.841788 (-0.629960) | 13.657743 / 8.074308 (5.583435) | 13.887148 / 10.191392 (3.695756) | 0.145996 / 0.680424 (-0.534428) | 0.016562 / 0.534201 (-0.517639) | 0.380359 / 0.579283 (-0.198924) | 0.388698 / 0.434364 (-0.045666) | 0.440373 / 0.540337 (-0.099965) | 0.531753 / 1.386936 (-0.855183) |\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) | 0.006444 / 0.011353 (-0.004909) | 0.004569 / 0.011008 (-0.006439) | 0.076239 / 0.038508 (0.037731) | 0.028462 / 0.023109 (0.005352) | 0.365540 / 0.275898 (0.089642) | 0.398242 / 0.323480 (0.074762) | 0.005785 / 0.007986 (-0.002200) | 0.003346 / 0.004328 (-0.000982) | 0.076296 / 0.004250 (0.072046) | 0.039853 / 0.037052 (0.002800) | 0.367684 / 0.258489 (0.109195) | 0.409570 / 0.293841 (0.115730) | 0.030536 / 0.128546 (-0.098010) | 0.011534 / 0.075646 (-0.064112) | 0.084962 / 0.419271 (-0.334309) | 0.042708 / 0.043533 (-0.000825) | 0.344058 / 0.255139 (0.088919) | 0.389096 / 0.283200 (0.105897) | 0.090559 / 0.141683 (-0.051124) | 1.507101 / 1.452155 (0.054946) | 1.563977 / 1.492716 (0.071260) |\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) | 0.228740 / 0.018006 (0.210734) | 0.396890 / 0.000490 (0.396400) | 0.000392 / 0.000200 (0.000192) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025052 / 0.037411 (-0.012360) | 0.099951 / 0.014526 (0.085426) | 0.106847 / 0.176557 (-0.069710) | 0.156666 / 0.737135 (-0.580469) | 0.110344 / 0.296338 (-0.185994) |\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) | 0.442363 / 0.215209 (0.227154) | 4.429571 / 2.077655 (2.351917) | 2.076501 / 1.504120 (0.572381) | 1.875226 / 1.541195 (0.334031) | 1.909093 / 1.468490 (0.440603) | 0.703047 / 4.584777 (-3.881730) | 3.457036 / 3.745712 (-0.288676) | 2.866648 / 5.269862 (-2.403214) | 1.524430 / 4.565676 (-3.041246) | 0.083687 / 0.424275 (-0.340588) | 0.012251 / 0.007607 (0.004643) | 0.543945 / 0.226044 (0.317901) | 5.440559 / 2.268929 (3.171630) | 2.522924 / 55.444624 (-52.921700) | 2.188770 / 6.876477 (-4.687707) | 2.249632 / 2.142072 (0.107559) | 0.813499 / 4.805227 (-3.991728) | 0.152861 / 6.500664 (-6.347803) | 0.067189 / 0.075469 (-0.008280) |\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) | 1.284255 / 1.841788 (-0.557533) | 14.207864 / 8.074308 (6.133556) | 14.279691 / 10.191392 (4.088299) | 0.167027 / 0.680424 (-0.513396) | 0.016455 / 0.534201 (-0.517746) | 0.380798 / 0.579283 (-0.198485) | 0.390013 / 0.434364 (-0.044351) | 0.445493 / 0.540337 (-0.094845) | 0.526278 / 1.386936 (-0.860658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3fdb46c526b9d070df0eb2d56b0ecacdace7cb9a \"CML watermark\")\n" ]

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[ "Problem with the code snippet! Using global vars and functions was not a good idea with iterable datasets!\r\n\r\nIf we update to:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# now add a new column to our streaming dataset using our hack\r\nname = \"new_column\"\r\ncolumn_1 = [f\"new dataset 1, row {i}\" for i in range(50)]\r\n\r\nnew_features = original_dataset.features.copy()\r\nnew_features[name] = new_features[\"file\"] # I know that \"file\" has the right column type to match our new feature\r\n\r\ndef add_column_fn_1(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column_1[idx]}\r\n\r\nmodified_dataset_1 = original_dataset.map(add_column_fn_1, with_indices=True, features=new_features)\r\n\r\n# now create a second modified dataset using the same trick\r\ncolumn_2 = [f\"new dataset 2, row {i}\" for i in range(50)]\r\n\r\ndef add_column_fn_2(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column_2[idx]}\r\n\r\nmodified_dataset_2 = original_dataset.map(add_column_fn_2, with_indices=True, features=new_features)\r\n\r\ninterleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])\r\n\r\nfor i, sample in enumerate(interleaved_dataset):\r\n print(sample[\"new_column\"])\r\n if i == 10:\r\n break\r\n```\r\nwe get the correct outputs:\r\n```python\r\nnew dataset 1, row 0\r\nnew dataset 2, row 0\r\nnew dataset 1, row 1\r\nnew dataset 2, row 1\r\nnew dataset 1, row 2\r\nnew dataset 2, row 2\r\nnew dataset 1, row 3\r\nnew dataset 2, row 3\r\nnew dataset 1, row 4\r\nnew dataset 2, row 4\r\nnew dataset 1, row 5\r\n```\r\n" ]

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[ "I believe the issue resides in this line:\r\nhttps://github.com/huggingface/datasets/blob/7c3a9b057c476c40d157bd7a5d57f49066239df0/src/datasets/iterable_dataset.py#L1415\r\n\r\nIf we pass the **new** features of the dataset to the `.map` method we can return the features after adding a column, e.g.:\r\n```python\r\nfrom datasets import load_dataset, Value\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\nprint(original_dataset.features.keys())\r\n\r\n# now add a new column to our streaming dataset using our hack\r\nname = \"new_column\"\r\ncolumn = [\"some random text\" for _ in range(50)]\r\n\r\nnew_features = original_dataset.features.copy()\r\nnew_features[name] = Value(dtype=\"string\", id=None) # I know the correct column type for this feature\r\n\r\ndef add_column_fn(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column[idx]}\r\n\r\nmodified_dataset = original_dataset.map(add_column_fn, with_indices=True, features=new_features)\r\n\r\nprint(modified_dataset.features.keys())\r\n```\r\n**Print Output:**\r\n```\r\ndict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\ndict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id', 'new_column'])\r\n```\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010459 / 0.011353 (-0.000894) | 0.007009 / 0.011008 (-0.003999) | 0.153885 / 0.038508 (0.115377) | 0.037308 / 0.023109 (0.014199) | 0.431931 / 0.275898 (0.156033) | 0.452940 / 0.323480 (0.129461) | 0.008572 / 0.007986 (0.000586) | 0.007479 / 0.004328 (0.003150) | 0.093835 / 0.004250 (0.089584) | 0.050172 / 0.037052 (0.013120) | 0.428855 / 0.258489 (0.170366) | 0.517814 / 0.293841 (0.223974) | 0.058558 / 0.128546 (-0.069988) | 0.019550 / 0.075646 (-0.056096) | 0.449837 / 0.419271 (0.030566) | 0.069710 / 0.043533 (0.026177) | 0.444163 / 0.255139 (0.189024) | 0.469003 / 0.283200 (0.185803) | 0.114665 / 0.141683 (-0.027018) | 1.822415 / 1.452155 (0.370261) | 1.956360 / 1.492716 (0.463644) |\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) | 0.237489 / 0.018006 (0.219483) | 0.556947 / 0.000490 (0.556457) | 0.006988 / 0.000200 (0.006789) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037047 / 0.037411 (-0.000364) | 0.133973 / 0.014526 (0.119447) | 0.137072 / 0.176557 (-0.039485) | 0.201520 / 0.737135 (-0.535615) | 0.144177 / 0.296338 (-0.152161) |\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) | 0.694853 / 0.215209 (0.479644) | 6.805746 / 2.077655 (4.728091) | 2.717864 / 1.504120 (1.213744) | 2.360529 / 1.541195 (0.819335) | 2.384403 / 1.468490 (0.915913) | 1.337512 / 4.584777 (-3.247265) | 5.734090 / 3.745712 (1.988378) | 5.344909 / 5.269862 (0.075047) | 2.906218 / 4.565676 (-1.659458) | 0.160148 / 0.424275 (-0.264127) | 0.015159 / 0.007607 (0.007551) | 0.871356 / 0.226044 (0.645312) | 8.550965 / 2.268929 (6.282037) | 3.613522 / 55.444624 (-51.831103) | 2.868508 / 6.876477 (-4.007969) | 2.912263 / 2.142072 (0.770190) | 1.652548 / 4.805227 (-3.152680) | 0.274117 / 6.500664 (-6.226547) | 0.085911 / 0.075469 (0.010442) |\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) | 1.624798 / 1.841788 (-0.216989) | 18.413303 / 8.074308 (10.338995) | 21.742854 / 10.191392 (11.551462) | 0.255937 / 0.680424 (-0.424487) | 0.029492 / 0.534201 (-0.504709) | 0.541932 / 0.579283 (-0.037351) | 0.638594 / 0.434364 (0.204230) | 0.607427 / 0.540337 (0.067090) | 0.763046 / 1.386936 (-0.623890) |\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) | 0.020543 / 0.011353 (0.009190) | 0.006079 / 0.011008 (-0.004929) | 0.100558 / 0.038508 (0.062050) | 0.039474 / 0.023109 (0.016365) | 0.468889 / 0.275898 (0.192991) | 0.477731 / 0.323480 (0.154251) | 0.006999 / 0.007986 (-0.000987) | 0.005845 / 0.004328 (0.001516) | 0.110022 / 0.004250 (0.105772) | 0.056885 / 0.037052 (0.019833) | 0.447296 / 0.258489 (0.188807) | 0.489007 / 0.293841 (0.195166) | 0.055086 / 0.128546 (-0.073460) | 0.020623 / 0.075646 (-0.055024) | 0.129599 / 0.419271 (-0.289672) | 0.064316 / 0.043533 (0.020784) | 0.446681 / 0.255139 (0.191542) | 0.488897 / 0.283200 (0.205698) | 0.119121 / 0.141683 (-0.022562) | 1.836248 / 1.452155 (0.384093) | 2.002456 / 1.492716 (0.509740) |\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) | 0.249344 / 0.018006 (0.231338) | 0.544320 / 0.000490 (0.543830) | 0.000459 / 0.000200 (0.000259) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038771 / 0.037411 (0.001359) | 0.129527 / 0.014526 (0.115002) | 0.144681 / 0.176557 (-0.031876) | 0.208237 / 0.737135 (-0.528898) | 0.149502 / 0.296338 (-0.146836) |\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) | 0.668457 / 0.215209 (0.453248) | 6.729550 / 2.077655 (4.651895) | 2.741076 / 1.504120 (1.236956) | 2.394737 / 1.541195 (0.853542) | 2.415242 / 1.468490 (0.946752) | 1.322334 / 4.584777 (-3.262442) | 5.787454 / 3.745712 (2.041742) | 3.309847 / 5.269862 (-1.960015) | 2.199181 / 4.565676 (-2.366495) | 0.170740 / 0.424275 (-0.253535) | 0.015095 / 0.007607 (0.007487) | 0.864157 / 0.226044 (0.638112) | 8.701858 / 2.268929 (6.432929) | 3.617966 / 55.444624 (-51.826658) | 2.847144 / 6.876477 (-4.029332) | 3.011391 / 2.142072 (0.869319) | 1.595466 / 4.805227 (-3.209762) | 0.284010 / 6.500664 (-6.216654) | 0.091054 / 0.075469 (0.015585) |\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) | 1.702404 / 1.841788 (-0.139384) | 19.427130 / 8.074308 (11.352822) | 21.900446 / 10.191392 (11.709053) | 0.244088 / 0.680424 (-0.436336) | 0.027428 / 0.534201 (-0.506773) | 0.552226 / 0.579283 (-0.027057) | 0.653102 / 0.434364 (0.218738) | 0.635379 / 0.540337 (0.095042) | 0.771842 / 1.386936 (-0.615094) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#efde2a0b9ad937defc83e0ac3f14bbb90fb5f345 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006547 / 0.011353 (-0.004806) | 0.004569 / 0.011008 (-0.006439) | 0.097782 / 0.038508 (0.059274) | 0.028157 / 0.023109 (0.005048) | 0.319017 / 0.275898 (0.043119) | 0.340758 / 0.323480 (0.017278) | 0.005078 / 0.007986 (-0.002907) | 0.003343 / 0.004328 (-0.000985) | 0.074194 / 0.004250 (0.069944) | 0.037918 / 0.037052 (0.000866) | 0.310298 / 0.258489 (0.051809) | 0.349441 / 0.293841 (0.055600) | 0.030375 / 0.128546 (-0.098171) | 0.011527 / 0.075646 (-0.064119) | 0.320499 / 0.419271 (-0.098773) | 0.042639 / 0.043533 (-0.000894) | 0.312182 / 0.255139 (0.057043) | 0.329058 / 0.283200 (0.045858) | 0.085517 / 0.141683 (-0.056165) | 1.532603 / 1.452155 (0.080448) | 1.583996 / 1.492716 (0.091279) |\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) | 0.208286 / 0.018006 (0.190280) | 0.418696 / 0.000490 (0.418206) | 0.007051 / 0.000200 (0.006851) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024055 / 0.037411 (-0.013356) | 0.098420 / 0.014526 (0.083894) | 0.104785 / 0.176557 (-0.071771) | 0.163618 / 0.737135 (-0.573517) | 0.110006 / 0.296338 (-0.186332) |\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) | 0.418756 / 0.215209 (0.203547) | 4.179557 / 2.077655 (2.101902) | 1.881708 / 1.504120 (0.377588) | 1.683393 / 1.541195 (0.142198) | 1.731909 / 1.468490 (0.263419) | 0.696674 / 4.584777 (-3.888103) | 3.384167 / 3.745712 (-0.361545) | 3.173479 / 5.269862 (-2.096382) | 1.620019 / 4.565676 (-2.945658) | 0.082850 / 0.424275 (-0.341426) | 0.012396 / 0.007607 (0.004789) | 0.519743 / 0.226044 (0.293699) | 5.208480 / 2.268929 (2.939552) | 2.312917 / 55.444624 (-53.131708) | 1.963486 / 6.876477 (-4.912991) | 2.084553 / 2.142072 (-0.057519) | 0.805486 / 4.805227 (-3.999742) | 0.153429 / 6.500664 (-6.347235) | 0.069451 / 0.075469 (-0.006018) |\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) | 1.197185 / 1.841788 (-0.644603) | 14.341005 / 8.074308 (6.266696) | 14.476162 / 10.191392 (4.284770) | 0.157372 / 0.680424 (-0.523052) | 0.016444 / 0.534201 (-0.517757) | 0.383721 / 0.579283 (-0.195562) | 0.380800 / 0.434364 (-0.053564) | 0.441137 / 0.540337 (-0.099200) | 0.524778 / 1.386936 (-0.862158) |\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) | 0.006728 / 0.011353 (-0.004625) | 0.004536 / 0.011008 (-0.006472) | 0.076266 / 0.038508 (0.037757) | 0.028133 / 0.023109 (0.005024) | 0.351072 / 0.275898 (0.075174) | 0.375823 / 0.323480 (0.052344) | 0.005166 / 0.007986 (-0.002819) | 0.004717 / 0.004328 (0.000388) | 0.076130 / 0.004250 (0.071880) | 0.041354 / 0.037052 (0.004301) | 0.345904 / 0.258489 (0.087415) | 0.384119 / 0.293841 (0.090278) | 0.030759 / 0.128546 (-0.097787) | 0.011659 / 0.075646 (-0.063988) | 0.085269 / 0.419271 (-0.334002) | 0.042161 / 0.043533 (-0.001372) | 0.340806 / 0.255139 (0.085667) | 0.366832 / 0.283200 (0.083632) | 0.092187 / 0.141683 (-0.049495) | 1.520035 / 1.452155 (0.067880) | 1.603856 / 1.492716 (0.111140) |\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) | 0.237763 / 0.018006 (0.219757) | 0.413406 / 0.000490 (0.412916) | 0.000415 / 0.000200 (0.000215) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026095 / 0.037411 (-0.011317) | 0.105775 / 0.014526 (0.091249) | 0.108452 / 0.176557 (-0.068105) | 0.160014 / 0.737135 (-0.577122) | 0.112385 / 0.296338 (-0.183953) |\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) | 0.437327 / 0.215209 (0.222118) | 4.374949 / 2.077655 (2.297294) | 2.090292 / 1.504120 (0.586172) | 1.885946 / 1.541195 (0.344752) | 1.946768 / 1.468490 (0.478278) | 0.704124 / 4.584777 (-3.880653) | 3.394994 / 3.745712 (-0.350718) | 1.905189 / 5.269862 (-3.364673) | 1.182300 / 4.565676 (-3.383376) | 0.082920 / 0.424275 (-0.341355) | 0.012781 / 0.007607 (0.005174) | 0.535467 / 0.226044 (0.309423) | 5.362799 / 2.268929 (3.093870) | 2.504825 / 55.444624 (-52.939799) | 2.180458 / 6.876477 (-4.696019) | 2.317750 / 2.142072 (0.175677) | 0.811182 / 4.805227 (-3.994045) | 0.151654 / 6.500664 (-6.349010) | 0.067925 / 0.075469 (-0.007544) |\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) | 1.290746 / 1.841788 (-0.551042) | 14.799309 / 8.074308 (6.725001) | 14.439722 / 10.191392 (4.248330) | 0.144358 / 0.680424 (-0.536066) | 0.016688 / 0.534201 (-0.517513) | 0.392907 / 0.579283 (-0.186376) | 0.383109 / 0.434364 (-0.051255) | 0.450069 / 0.540337 (-0.090269) | 0.532534 / 1.386936 (-0.854402) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87c061032972509a2a1b4103763e62fb74912128 \"CML watermark\")\n", "I turned it into a draft to fix the failing tests, but CI is now green, so there is no good reason for it :)" ]

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[ "`from_generator` expects a generator function, not a generator object, so this should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(rows)\r\n\r\nfor r in ds:\r\n print(r)\r\n```", "@mariosasko your code was incomplete, so I tried to fix it:\r\n\r\n```py\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen():\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nThe error is also present in this case:\r\n\r\n```\r\n_pickle.PicklingError: Pickling client objects is explicitly not supported.\r\nClients have non-trivial state that is local and unpickleable.\r\n```\r\n\r\nI think it doesn't matter if the generator is an object or a function. The problem is that the generator is referencing an object that is not pickable (the client in this case). ", "It does matter: this function expects a generator function, as stated in the docs.\r\n\r\nThis should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\ndef gen():\r\n client = bigquery.Client()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nWe could allow passing non-picklable objects and use a random hash for the generated arrow file. In that case, the caching mechanism would not work, meaning repeated calls with the same set of arguments would generate new datasets instead of reusing the cached version, but this behavior is still better than raising an error.", "Thank you @mariosasko . Your last code is working indeed. Curiously, the important detail here was to wrap the client instantiation within the generator itself. If the line `client = bigquery.Client()` is moved outside, then the error is back.\r\n\r\nI see now also your point in regard to the generator being a generator function. We can close the issue if you want." ]

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[ "I got the same error, and the official website for visual genome is down. Did you solve this problem? ", "I am in the same situation now :( ", "Thanks for reporting, @gulnaz-zh.\r\n\r\nI am investigating it.", "The host server is down: https://visualgenome.org/\r\n\r\nWe are contacting the dataset authors.", "Apart form data host server being down, there is an additional issue with the `datasets` library introduced by this PR:\r\n- #5238\r\n\r\nI am working to fix it.", "PR that fixes the AttributeError: https://huggingface.co/datasets/visual_genome/discussions/2", "For the issue with their data host server being down, I have opened a discussion in the \"Community\" tab of the Hub dataset: https://huggingface.co/datasets/visual_genome/discussions/3\r\nLet's continue the discussion there.", "The authors just replied to us with their new URL: https://homes.cs.washington.edu/~ranjay/visualgenome/\r\n\r\nWe have fixed the datasets loading script, which is operative again." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006461 / 0.011353 (-0.004892) | 0.004671 / 0.011008 (-0.006337) | 0.097329 / 0.038508 (0.058821) | 0.028380 / 0.023109 (0.005270) | 0.369892 / 0.275898 (0.093994) | 0.398244 / 0.323480 (0.074764) | 0.004795 / 0.007986 (-0.003190) | 0.004866 / 0.004328 (0.000538) | 0.075060 / 0.004250 (0.070809) | 0.035678 / 0.037052 (-0.001374) | 0.372197 / 0.258489 (0.113708) | 0.407509 / 0.293841 (0.113668) | 0.031557 / 0.128546 (-0.096989) | 0.011608 / 0.075646 (-0.064038) | 0.325467 / 0.419271 (-0.093805) | 0.042590 / 0.043533 (-0.000943) | 0.373738 / 0.255139 (0.118599) | 0.395793 / 0.283200 (0.112593) | 0.082335 / 0.141683 (-0.059348) | 1.471582 / 1.452155 (0.019427) | 1.535834 / 1.492716 (0.043117) |\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) | 0.192432 / 0.018006 (0.174426) | 0.404423 / 0.000490 (0.403933) | 0.003252 / 0.000200 (0.003052) | 0.000073 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025312 / 0.037411 (-0.012099) | 0.099964 / 0.014526 (0.085438) | 0.108779 / 0.176557 (-0.067777) | 0.170438 / 0.737135 (-0.566697) | 0.110116 / 0.296338 (-0.186223) |\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) | 0.420402 / 0.215209 (0.205193) | 4.179142 / 2.077655 (2.101487) | 1.858114 / 1.504120 (0.353994) | 1.674452 / 1.541195 (0.133257) | 1.697839 / 1.468490 (0.229349) | 0.694707 / 4.584777 (-3.890070) | 3.394321 / 3.745712 (-0.351391) | 1.918437 / 5.269862 (-3.351425) | 1.277954 / 4.565676 (-3.287723) | 0.082357 / 0.424275 (-0.341918) | 0.012206 / 0.007607 (0.004598) | 0.522093 / 0.226044 (0.296049) | 5.239604 / 2.268929 (2.970675) | 2.347764 / 55.444624 (-53.096860) | 1.996864 / 6.876477 (-4.879613) | 2.050820 / 2.142072 (-0.091253) | 0.806110 / 4.805227 (-3.999118) | 0.151061 / 6.500664 (-6.349603) | 0.066438 / 0.075469 (-0.009031) |\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) | 1.211233 / 1.841788 (-0.630554) | 14.054422 / 8.074308 (5.980114) | 14.110141 / 10.191392 (3.918749) | 0.129962 / 0.680424 (-0.550462) | 0.017271 / 0.534201 (-0.516930) | 0.386410 / 0.579283 (-0.192873) | 0.392648 / 0.434364 (-0.041716) | 0.444940 / 0.540337 (-0.095398) | 0.533535 / 1.386936 (-0.853401) |\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) | 0.006865 / 0.011353 (-0.004488) | 0.004662 / 0.011008 (-0.006346) | 0.077837 / 0.038508 (0.039329) | 0.028258 / 0.023109 (0.005149) | 0.346136 / 0.275898 (0.070238) | 0.380414 / 0.323480 (0.056934) | 0.005039 / 0.007986 (-0.002947) | 0.004967 / 0.004328 (0.000638) | 0.077774 / 0.004250 (0.073523) | 0.037504 / 0.037052 (0.000452) | 0.341550 / 0.258489 (0.083061) | 0.382494 / 0.293841 (0.088653) | 0.031881 / 0.128546 (-0.096665) | 0.011746 / 0.075646 (-0.063901) | 0.087087 / 0.419271 (-0.332185) | 0.043108 / 0.043533 (-0.000425) | 0.344103 / 0.255139 (0.088964) | 0.366613 / 0.283200 (0.083413) | 0.090399 / 0.141683 (-0.051284) | 1.492675 / 1.452155 (0.040520) | 1.588666 / 1.492716 (0.095950) |\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) | 0.191859 / 0.018006 (0.173853) | 0.412514 / 0.000490 (0.412025) | 0.001953 / 0.000200 (0.001753) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025159 / 0.037411 (-0.012252) | 0.100125 / 0.014526 (0.085599) | 0.106000 / 0.176557 (-0.070556) | 0.160710 / 0.737135 (-0.576425) | 0.110449 / 0.296338 (-0.185889) |\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) | 0.436636 / 0.215209 (0.221427) | 4.364597 / 2.077655 (2.286942) | 2.077492 / 1.504120 (0.573372) | 1.868248 / 1.541195 (0.327053) | 1.911218 / 1.468490 (0.442728) | 0.700306 / 4.584777 (-3.884471) | 3.385428 / 3.745712 (-0.360284) | 2.965384 / 5.269862 (-2.304478) | 1.522093 / 4.565676 (-3.043583) | 0.082805 / 0.424275 (-0.341470) | 0.012432 / 0.007607 (0.004825) | 0.538478 / 0.226044 (0.312433) | 5.383207 / 2.268929 (3.114278) | 2.525177 / 55.444624 (-52.919447) | 2.179632 / 6.876477 (-4.696845) | 2.280768 / 2.142072 (0.138695) | 0.805869 / 4.805227 (-3.999358) | 0.152716 / 6.500664 (-6.347948) | 0.067848 / 0.075469 (-0.007621) |\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) | 1.318899 / 1.841788 (-0.522889) | 14.416310 / 8.074308 (6.342002) | 14.172804 / 10.191392 (3.981412) | 0.141729 / 0.680424 (-0.538695) | 0.016785 / 0.534201 (-0.517416) | 0.378626 / 0.579283 (-0.200657) | 0.387153 / 0.434364 (-0.047211) | 0.439950 / 0.540337 (-0.100388) | 0.523958 / 1.386936 (-0.862978) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c3a9b057c476c40d157bd7a5d57f49066239df0 \"CML watermark\")\n" ]

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[ "Have met the same problem with datasets==2.8.0, pandas==2.0.0. It could be solved by installing the latest version of datasets or using datasets==2.8.0, pandas==1.5.3.", "Pandas 2.0.0 has removed support to passing `mangle_dupe_cols`.\r\n\r\nHowever, our `datasets` library does not use this parameter: it only passes it to pandas if the user passes it to `load_dataset`.\r\n\r\nYou should better:\r\n- Either \"take steps to stop the use of 'mangle_dupe_cols'\" (as it was suggested in the deprecation warning in pandas-1.5.3)\r\n- Or pin pandas (< 2.0.0) in your local requirements file\r\n\r\nPlease note that from `datasets` library, we don't want to force users to use a specific pandas version. We would like to support users as well:\r\n- that use pandas < 1.5.3\r\n- that use pandas >= 2.0.0 and that do not pass the 'mangle_dupe_cols' parameter", "`datasets` 2.11 doesn't pass `mangle_dupe_cols` unless the user specifies it indeed, so I think we're fine" ]

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[ "Thanks for reporting, @keyboardAnt.\r\n\r\nWe haven't noticed any crash in our CI tests. Could you please indicate specifically the `load_dataset` command that crashes in your side, so that we can reproduce it?", "This has been fixed in `datasets` 2.11" ]

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[ "We no longer depend on `dataclasses` (for almost a year), so I don't think our package is the problematic one. \r\n\r\nI think it makes more sense to raise this issue in the `dataclasses` repo: https://github.com/ericvsmith/dataclasses." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006693 / 0.011353 (-0.004660) | 0.004586 / 0.011008 (-0.006422) | 0.097238 / 0.038508 (0.058730) | 0.027912 / 0.023109 (0.004802) | 0.347339 / 0.275898 (0.071441) | 0.393847 / 0.323480 (0.070368) | 0.005105 / 0.007986 (-0.002880) | 0.004750 / 0.004328 (0.000422) | 0.074671 / 0.004250 (0.070421) | 0.037912 / 0.037052 (0.000860) | 0.368973 / 0.258489 (0.110483) | 0.403983 / 0.293841 (0.110142) | 0.030817 / 0.128546 (-0.097730) | 0.011813 / 0.075646 (-0.063833) | 0.324470 / 0.419271 (-0.094802) | 0.044232 / 0.043533 (0.000699) | 0.347623 / 0.255139 (0.092484) | 0.382458 / 0.283200 (0.099259) | 0.086603 / 0.141683 (-0.055080) | 1.485778 / 1.452155 (0.033623) | 1.549776 / 1.492716 (0.057059) |\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) | 0.200154 / 0.018006 (0.182147) | 0.440645 / 0.000490 (0.440155) | 0.003664 / 0.000200 (0.003464) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023635 / 0.037411 (-0.013776) | 0.094969 / 0.014526 (0.080443) | 0.103630 / 0.176557 (-0.072927) | 0.168655 / 0.737135 (-0.568480) | 0.105850 / 0.296338 (-0.190488) |\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) | 0.425224 / 0.215209 (0.210015) | 4.236618 / 2.077655 (2.158963) | 1.917091 / 1.504120 (0.412971) | 1.746984 / 1.541195 (0.205789) | 1.817766 / 1.468490 (0.349276) | 0.700989 / 4.584777 (-3.883788) | 3.412577 / 3.745712 (-0.333135) | 3.049311 / 5.269862 (-2.220551) | 1.607692 / 4.565676 (-2.957984) | 0.083410 / 0.424275 (-0.340865) | 0.012601 / 0.007607 (0.004994) | 0.528244 / 0.226044 (0.302200) | 5.284134 / 2.268929 (3.015206) | 2.391885 / 55.444624 (-53.052740) | 2.020018 / 6.876477 (-4.856459) | 2.105908 / 2.142072 (-0.036164) | 0.801262 / 4.805227 (-4.003965) | 0.151467 / 6.500664 (-6.349197) | 0.066529 / 0.075469 (-0.008940) |\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) | 1.203894 / 1.841788 (-0.637894) | 13.827561 / 8.074308 (5.753253) | 14.136730 / 10.191392 (3.945338) | 0.143829 / 0.680424 (-0.536595) | 0.016410 / 0.534201 (-0.517791) | 0.378194 / 0.579283 (-0.201089) | 0.391235 / 0.434364 (-0.043129) | 0.439261 / 0.540337 (-0.101076) | 0.527181 / 1.386936 (-0.859755) |\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) | 0.006639 / 0.011353 (-0.004714) | 0.004469 / 0.011008 (-0.006540) | 0.076495 / 0.038508 (0.037987) | 0.027880 / 0.023109 (0.004771) | 0.342807 / 0.275898 (0.066909) | 0.374258 / 0.323480 (0.050778) | 0.005543 / 0.007986 (-0.002443) | 0.003362 / 0.004328 (-0.000966) | 0.075064 / 0.004250 (0.070813) | 0.039209 / 0.037052 (0.002156) | 0.342490 / 0.258489 (0.084001) | 0.382135 / 0.293841 (0.088294) | 0.030356 / 0.128546 (-0.098191) | 0.011762 / 0.075646 (-0.063884) | 0.086031 / 0.419271 (-0.333241) | 0.041991 / 0.043533 (-0.001542) | 0.340323 / 0.255139 (0.085184) | 0.364160 / 0.283200 (0.080961) | 0.088483 / 0.141683 (-0.053200) | 1.502836 / 1.452155 (0.050681) | 1.570438 / 1.492716 (0.077722) |\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) | 0.218486 / 0.018006 (0.200480) | 0.405251 / 0.000490 (0.404761) | 0.000398 / 0.000200 (0.000198) | 0.000062 / 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) | 0.025738 / 0.037411 (-0.011673) | 0.100390 / 0.014526 (0.085864) | 0.109913 / 0.176557 (-0.066644) | 0.161310 / 0.737135 (-0.575826) | 0.113269 / 0.296338 (-0.183069) |\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) | 0.438083 / 0.215209 (0.222874) | 4.377742 / 2.077655 (2.300087) | 2.069949 / 1.504120 (0.565829) | 1.857807 / 1.541195 (0.316613) | 1.881315 / 1.468490 (0.412825) | 0.695373 / 4.584777 (-3.889404) | 3.440287 / 3.745712 (-0.305425) | 1.842888 / 5.269862 (-3.426973) | 1.146655 / 4.565676 (-3.419022) | 0.083386 / 0.424275 (-0.340889) | 0.012290 / 0.007607 (0.004683) | 0.545672 / 0.226044 (0.319628) | 5.469568 / 2.268929 (3.200639) | 2.511886 / 55.444624 (-52.932739) | 2.184210 / 6.876477 (-4.692267) | 2.329822 / 2.142072 (0.187749) | 0.804114 / 4.805227 (-4.001114) | 0.151651 / 6.500664 (-6.349013) | 0.067269 / 0.075469 (-0.008200) |\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) | 1.272564 / 1.841788 (-0.569223) | 14.180708 / 8.074308 (6.106400) | 14.181657 / 10.191392 (3.990265) | 0.131443 / 0.680424 (-0.548981) | 0.016513 / 0.534201 (-0.517688) | 0.383786 / 0.579283 (-0.195497) | 0.397678 / 0.434364 (-0.036686) | 0.447003 / 0.540337 (-0.093334) | 0.539453 / 1.386936 (-0.847483) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#649d5a3315f9e7666713b6affe318ee00c7163a0 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007448 / 0.011353 (-0.003905) | 0.005182 / 0.011008 (-0.005826) | 0.098718 / 0.038508 (0.060210) | 0.034594 / 0.023109 (0.011485) | 0.317301 / 0.275898 (0.041403) | 0.357800 / 0.323480 (0.034320) | 0.005860 / 0.007986 (-0.002126) | 0.004267 / 0.004328 (-0.000061) | 0.074876 / 0.004250 (0.070626) | 0.048002 / 0.037052 (0.010950) | 0.333360 / 0.258489 (0.074871) | 0.362080 / 0.293841 (0.068239) | 0.035957 / 0.128546 (-0.092589) | 0.012245 / 0.075646 (-0.063401) | 0.332970 / 0.419271 (-0.086301) | 0.050825 / 0.043533 (0.007293) | 0.313936 / 0.255139 (0.058797) | 0.340684 / 0.283200 (0.057485) | 0.106630 / 0.141683 (-0.035053) | 1.427898 / 1.452155 (-0.024257) | 1.547518 / 1.492716 (0.054801) |\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) | 0.296952 / 0.018006 (0.278945) | 0.515708 / 0.000490 (0.515218) | 0.004225 / 0.000200 (0.004025) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029365 / 0.037411 (-0.008046) | 0.111142 / 0.014526 (0.096616) | 0.124414 / 0.176557 (-0.052142) | 0.185227 / 0.737135 (-0.551908) | 0.129545 / 0.296338 (-0.166793) |\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) | 0.403303 / 0.215209 (0.188094) | 4.044138 / 2.077655 (1.966483) | 1.803622 / 1.504120 (0.299502) | 1.615436 / 1.541195 (0.074242) | 1.703576 / 1.468490 (0.235086) | 0.706398 / 4.584777 (-3.878379) | 3.912995 / 3.745712 (0.167283) | 4.004575 / 5.269862 (-1.265287) | 2.101592 / 4.565676 (-2.464085) | 0.087280 / 0.424275 (-0.336995) | 0.012564 / 0.007607 (0.004957) | 0.508484 / 0.226044 (0.282440) | 5.089351 / 2.268929 (2.820422) | 2.269022 / 55.444624 (-53.175602) | 1.933375 / 6.876477 (-4.943102) | 2.136783 / 2.142072 (-0.005289) | 0.862624 / 4.805227 (-3.942603) | 0.172107 / 6.500664 (-6.328557) | 0.066694 / 0.075469 (-0.008775) |\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) | 1.172513 / 1.841788 (-0.669275) | 15.877519 / 8.074308 (7.803211) | 14.687476 / 10.191392 (4.496084) | 0.189392 / 0.680424 (-0.491032) | 0.017334 / 0.534201 (-0.516866) | 0.420201 / 0.579283 (-0.159082) | 0.418502 / 0.434364 (-0.015862) | 0.489130 / 0.540337 (-0.051207) | 0.580678 / 1.386936 (-0.806258) |\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) | 0.007942 / 0.011353 (-0.003411) | 0.005312 / 0.011008 (-0.005696) | 0.074684 / 0.038508 (0.036176) | 0.035952 / 0.023109 (0.012843) | 0.349672 / 0.275898 (0.073774) | 0.377157 / 0.323480 (0.053678) | 0.006399 / 0.007986 (-0.001586) | 0.005769 / 0.004328 (0.001441) | 0.074283 / 0.004250 (0.070032) | 0.053217 / 0.037052 (0.016165) | 0.342545 / 0.258489 (0.084056) | 0.383663 / 0.293841 (0.089822) | 0.037234 / 0.128546 (-0.091312) | 0.012349 / 0.075646 (-0.063298) | 0.086522 / 0.419271 (-0.332749) | 0.049888 / 0.043533 (0.006355) | 0.337686 / 0.255139 (0.082547) | 0.361564 / 0.283200 (0.078365) | 0.104902 / 0.141683 (-0.036781) | 1.478259 / 1.452155 (0.026104) | 1.576376 / 1.492716 (0.083660) |\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) | 0.339760 / 0.018006 (0.321753) | 0.530946 / 0.000490 (0.530456) | 0.000474 / 0.000200 (0.000274) | 0.000063 / 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) | 0.029685 / 0.037411 (-0.007726) | 0.109409 / 0.014526 (0.094883) | 0.125579 / 0.176557 (-0.050978) | 0.175378 / 0.737135 (-0.561757) | 0.130672 / 0.296338 (-0.165667) |\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) | 0.428456 / 0.215209 (0.213247) | 4.238731 / 2.077655 (2.161077) | 2.046703 / 1.504120 (0.542583) | 1.850701 / 1.541195 (0.309506) | 1.909290 / 1.468490 (0.440800) | 0.714314 / 4.584777 (-3.870463) | 3.816056 / 3.745712 (0.070344) | 2.118567 / 5.269862 (-3.151295) | 1.348017 / 4.565676 (-3.217659) | 0.087140 / 0.424275 (-0.337135) | 0.012546 / 0.007607 (0.004938) | 0.538041 / 0.226044 (0.311997) | 5.381822 / 2.268929 (3.112893) | 2.525685 / 55.444624 (-52.918939) | 2.178659 / 6.876477 (-4.697817) | 2.381054 / 2.142072 (0.238981) | 0.844404 / 4.805227 (-3.960823) | 0.171802 / 6.500664 (-6.328862) | 0.065630 / 0.075469 (-0.009839) |\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) | 1.262187 / 1.841788 (-0.579600) | 16.197668 / 8.074308 (8.123360) | 15.148636 / 10.191392 (4.957244) | 0.152601 / 0.680424 (-0.527823) | 0.020238 / 0.534201 (-0.513963) | 0.420141 / 0.579283 (-0.159142) | 0.416295 / 0.434364 (-0.018068) | 0.487051 / 0.540337 (-0.053286) | 0.581942 / 1.386936 (-0.804994) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9615e5af75b190c4e7b66792f9ba444f352765a0 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007003 / 0.011353 (-0.004350) | 0.004854 / 0.011008 (-0.006154) | 0.096982 / 0.038508 (0.058474) | 0.033218 / 0.023109 (0.010109) | 0.314088 / 0.275898 (0.038190) | 0.351315 / 0.323480 (0.027835) | 0.005679 / 0.007986 (-0.002307) | 0.005404 / 0.004328 (0.001075) | 0.071773 / 0.004250 (0.067522) | 0.044593 / 0.037052 (0.007540) | 0.323643 / 0.258489 (0.065154) | 0.357172 / 0.293841 (0.063331) | 0.036782 / 0.128546 (-0.091764) | 0.012146 / 0.075646 (-0.063501) | 0.334874 / 0.419271 (-0.084397) | 0.051475 / 0.043533 (0.007942) | 0.305949 / 0.255139 (0.050810) | 0.339326 / 0.283200 (0.056126) | 0.101509 / 0.141683 (-0.040174) | 1.458254 / 1.452155 (0.006099) | 1.535252 / 1.492716 (0.042535) |\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) | 0.264837 / 0.018006 (0.246831) | 0.441444 / 0.000490 (0.440955) | 0.003331 / 0.000200 (0.003131) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026529 / 0.037411 (-0.010882) | 0.105924 / 0.014526 (0.091398) | 0.117191 / 0.176557 (-0.059365) | 0.176606 / 0.737135 (-0.560529) | 0.123452 / 0.296338 (-0.172887) |\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) | 0.412351 / 0.215209 (0.197142) | 4.135468 / 2.077655 (2.057813) | 1.912820 / 1.504120 (0.408700) | 1.738993 / 1.541195 (0.197798) | 1.754228 / 1.468490 (0.285738) | 0.692239 / 4.584777 (-3.892538) | 3.765672 / 3.745712 (0.019959) | 2.081141 / 5.269862 (-3.188720) | 1.425153 / 4.565676 (-3.140523) | 0.085055 / 0.424275 (-0.339220) | 0.011918 / 0.007607 (0.004311) | 0.517573 / 0.226044 (0.291529) | 5.179809 / 2.268929 (2.910881) | 2.471620 / 55.444624 (-52.973005) | 2.140634 / 6.876477 (-4.735843) | 2.200150 / 2.142072 (0.058077) | 0.831662 / 4.805227 (-3.973566) | 0.168828 / 6.500664 (-6.331836) | 0.062755 / 0.075469 (-0.012714) |\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) | 1.196890 / 1.841788 (-0.644898) | 14.826423 / 8.074308 (6.752114) | 14.020782 / 10.191392 (3.829390) | 0.161275 / 0.680424 (-0.519149) | 0.017467 / 0.534201 (-0.516734) | 0.422278 / 0.579283 (-0.157005) | 0.424053 / 0.434364 (-0.010311) | 0.490768 / 0.540337 (-0.049570) | 0.584490 / 1.386936 (-0.802446) |\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) | 0.007102 / 0.011353 (-0.004250) | 0.005145 / 0.011008 (-0.005863) | 0.073823 / 0.038508 (0.035315) | 0.032947 / 0.023109 (0.009838) | 0.336978 / 0.275898 (0.061080) | 0.368961 / 0.323480 (0.045481) | 0.006052 / 0.007986 (-0.001934) | 0.003970 / 0.004328 (-0.000358) | 0.072925 / 0.004250 (0.068674) | 0.044502 / 0.037052 (0.007450) | 0.340849 / 0.258489 (0.082360) | 0.381487 / 0.293841 (0.087646) | 0.037207 / 0.128546 (-0.091339) | 0.012095 / 0.075646 (-0.063551) | 0.085206 / 0.419271 (-0.334065) | 0.056236 / 0.043533 (0.012703) | 0.334048 / 0.255139 (0.078909) | 0.360442 / 0.283200 (0.077242) | 0.104402 / 0.141683 (-0.037281) | 1.446907 / 1.452155 (-0.005248) | 1.542430 / 1.492716 (0.049713) |\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) | 0.238720 / 0.018006 (0.220714) | 0.445857 / 0.000490 (0.445367) | 0.009280 / 0.000200 (0.009080) | 0.000150 / 0.000054 (0.000095) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028414 / 0.037411 (-0.008998) | 0.110506 / 0.014526 (0.095981) | 0.124593 / 0.176557 (-0.051964) | 0.170951 / 0.737135 (-0.566184) | 0.128033 / 0.296338 (-0.168305) |\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) | 0.426206 / 0.215209 (0.210997) | 4.267289 / 2.077655 (2.189634) | 2.026880 / 1.504120 (0.522760) | 1.844052 / 1.541195 (0.302858) | 1.897697 / 1.468490 (0.429207) | 0.713545 / 4.584777 (-3.871232) | 3.815052 / 3.745712 (0.069339) | 3.217091 / 5.269862 (-2.052770) | 1.790546 / 4.565676 (-2.775130) | 0.087501 / 0.424275 (-0.336774) | 0.012136 / 0.007607 (0.004529) | 0.534495 / 0.226044 (0.308451) | 5.325913 / 2.268929 (3.056984) | 2.484309 / 55.444624 (-52.960315) | 2.149721 / 6.876477 (-4.726756) | 2.158764 / 2.142072 (0.016692) | 0.855273 / 4.805227 (-3.949954) | 0.170374 / 6.500664 (-6.330290) | 0.064053 / 0.075469 (-0.011416) |\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) | 1.253171 / 1.841788 (-0.588617) | 15.254562 / 8.074308 (7.180254) | 14.242119 / 10.191392 (4.050727) | 0.159298 / 0.680424 (-0.521126) | 0.017504 / 0.534201 (-0.516696) | 0.419710 / 0.579283 (-0.159574) | 0.417879 / 0.434364 (-0.016485) | 0.486328 / 0.540337 (-0.054009) | 0.578933 / 1.386936 (-0.808003) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc38663c8e2c2b0b246791c3ed8bddbff163dd64 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008476 / 0.011353 (-0.002877) | 0.005745 / 0.011008 (-0.005263) | 0.115307 / 0.038508 (0.076799) | 0.039356 / 0.023109 (0.016247) | 0.367155 / 0.275898 (0.091257) | 0.422147 / 0.323480 (0.098667) | 0.006817 / 0.007986 (-0.001168) | 0.004652 / 0.004328 (0.000323) | 0.084045 / 0.004250 (0.079795) | 0.055483 / 0.037052 (0.018431) | 0.364249 / 0.258489 (0.105760) | 0.415975 / 0.293841 (0.122134) | 0.041322 / 0.128546 (-0.087224) | 0.014178 / 0.075646 (-0.061469) | 0.392658 / 0.419271 (-0.026614) | 0.060156 / 0.043533 (0.016623) | 0.373938 / 0.255139 (0.118799) | 0.397494 / 0.283200 (0.114294) | 0.113811 / 0.141683 (-0.027872) | 1.688581 / 1.452155 (0.236427) | 1.790374 / 1.492716 (0.297658) |\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) | 0.222203 / 0.018006 (0.204196) | 0.471109 / 0.000490 (0.470619) | 0.007071 / 0.000200 (0.006871) | 0.000156 / 0.000054 (0.000102) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032112 / 0.037411 (-0.005299) | 0.118726 / 0.014526 (0.104200) | 0.134918 / 0.176557 (-0.041639) | 0.207766 / 0.737135 (-0.529369) | 0.139756 / 0.296338 (-0.156582) |\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) | 0.479858 / 0.215209 (0.264649) | 4.798428 / 2.077655 (2.720773) | 2.221573 / 1.504120 (0.717453) | 1.964956 / 1.541195 (0.423761) | 2.021763 / 1.468490 (0.553273) | 0.820401 / 4.584777 (-3.764376) | 4.533887 / 3.745712 (0.788175) | 4.121332 / 5.269862 (-1.148529) | 2.195807 / 4.565676 (-2.369869) | 0.103133 / 0.424275 (-0.321142) | 0.014620 / 0.007607 (0.007013) | 0.605012 / 0.226044 (0.378967) | 5.966623 / 2.268929 (3.697694) | 2.844118 / 55.444624 (-52.600506) | 2.463569 / 6.876477 (-4.412907) | 2.597177 / 2.142072 (0.455105) | 0.983201 / 4.805227 (-3.822026) | 0.199500 / 6.500664 (-6.301164) | 0.078387 / 0.075469 (0.002918) |\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) | 1.401083 / 1.841788 (-0.440705) | 17.258725 / 8.074308 (9.184417) | 16.825992 / 10.191392 (6.634600) | 0.216762 / 0.680424 (-0.463662) | 0.021135 / 0.534201 (-0.513066) | 0.513688 / 0.579283 (-0.065595) | 0.488892 / 0.434364 (0.054529) | 0.566745 / 0.540337 (0.026408) | 0.688958 / 1.386936 (-0.697978) |\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) | 0.007948 / 0.011353 (-0.003405) | 0.005981 / 0.011008 (-0.005027) | 0.084474 / 0.038508 (0.045966) | 0.037952 / 0.023109 (0.014843) | 0.383359 / 0.275898 (0.107461) | 0.409324 / 0.323480 (0.085844) | 0.006641 / 0.007986 (-0.001344) | 0.004785 / 0.004328 (0.000456) | 0.083214 / 0.004250 (0.078964) | 0.053177 / 0.037052 (0.016125) | 0.393147 / 0.258489 (0.134658) | 0.438496 / 0.293841 (0.144655) | 0.042090 / 0.128546 (-0.086456) | 0.013373 / 0.075646 (-0.062273) | 0.097585 / 0.419271 (-0.321686) | 0.056359 / 0.043533 (0.012826) | 0.378113 / 0.255139 (0.122974) | 0.403874 / 0.283200 (0.120674) | 0.123503 / 0.141683 (-0.018180) | 1.639557 / 1.452155 (0.187403) | 1.759787 / 1.492716 (0.267071) |\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) | 0.242534 / 0.018006 (0.224528) | 0.459040 / 0.000490 (0.458550) | 0.000454 / 0.000200 (0.000254) | 0.000066 / 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) | 0.031747 / 0.037411 (-0.005664) | 0.125823 / 0.014526 (0.111297) | 0.138985 / 0.176557 (-0.037571) | 0.194371 / 0.737135 (-0.542764) | 0.148905 / 0.296338 (-0.147433) |\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) | 0.508201 / 0.215209 (0.292992) | 5.007519 / 2.077655 (2.929865) | 2.412956 / 1.504120 (0.908836) | 2.143378 / 1.541195 (0.602183) | 2.192966 / 1.468490 (0.724476) | 0.828497 / 4.584777 (-3.756280) | 4.496457 / 3.745712 (0.750745) | 2.397546 / 5.269862 (-2.872315) | 1.522889 / 4.565676 (-3.042787) | 0.099904 / 0.424275 (-0.324371) | 0.014561 / 0.007607 (0.006954) | 0.627417 / 0.226044 (0.401373) | 6.296441 / 2.268929 (4.027512) | 2.962858 / 55.444624 (-52.481767) | 2.543083 / 6.876477 (-4.333394) | 2.711884 / 2.142072 (0.569811) | 0.997969 / 4.805227 (-3.807259) | 0.200283 / 6.500664 (-6.300382) | 0.075934 / 0.075469 (0.000465) |\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) | 1.541707 / 1.841788 (-0.300081) | 17.791559 / 8.074308 (9.717251) | 16.782877 / 10.191392 (6.591485) | 0.171954 / 0.680424 (-0.508470) | 0.020506 / 0.534201 (-0.513695) | 0.504189 / 0.579283 (-0.075094) | 0.501655 / 0.434364 (0.067291) | 0.583120 / 0.540337 (0.042782) | 0.694931 / 1.386936 (-0.692005) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53355f308f4ffb9b4071f5d420b5c6767799ef1c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007613 / 0.011353 (-0.003740) | 0.005057 / 0.011008 (-0.005951) | 0.099147 / 0.038508 (0.060639) | 0.035358 / 0.023109 (0.012249) | 0.303442 / 0.275898 (0.027544) | 0.336898 / 0.323480 (0.013418) | 0.006216 / 0.007986 (-0.001770) | 0.004085 / 0.004328 (-0.000244) | 0.074567 / 0.004250 (0.070317) | 0.050917 / 0.037052 (0.013865) | 0.301786 / 0.258489 (0.043297) | 0.341362 / 0.293841 (0.047521) | 0.037019 / 0.128546 (-0.091528) | 0.011977 / 0.075646 (-0.063669) | 0.334688 / 0.419271 (-0.084583) | 0.051326 / 0.043533 (0.007793) | 0.299878 / 0.255139 (0.044739) | 0.325571 / 0.283200 (0.042371) | 0.110744 / 0.141683 (-0.030939) | 1.480898 / 1.452155 (0.028743) | 1.566917 / 1.492716 (0.074201) |\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) | 0.253249 / 0.018006 (0.235242) | 0.558576 / 0.000490 (0.558086) | 0.003838 / 0.000200 (0.003638) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028731 / 0.037411 (-0.008681) | 0.110643 / 0.014526 (0.096117) | 0.119560 / 0.176557 (-0.056996) | 0.178010 / 0.737135 (-0.559126) | 0.130286 / 0.296338 (-0.166053) |\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) | 0.400190 / 0.215209 (0.184981) | 3.999326 / 2.077655 (1.921672) | 1.797332 / 1.504120 (0.293212) | 1.610808 / 1.541195 (0.069613) | 1.679949 / 1.468490 (0.211459) | 0.696539 / 4.584777 (-3.888238) | 3.784766 / 3.745712 (0.039054) | 2.205008 / 5.269862 (-3.064854) | 1.501697 / 4.565676 (-3.063979) | 0.085553 / 0.424275 (-0.338723) | 0.012223 / 0.007607 (0.004616) | 0.494858 / 0.226044 (0.268813) | 4.968535 / 2.268929 (2.699606) | 2.258759 / 55.444624 (-53.185865) | 1.926236 / 6.876477 (-4.950241) | 2.072155 / 2.142072 (-0.069917) | 0.838354 / 4.805227 (-3.966873) | 0.168810 / 6.500664 (-6.331854) | 0.064347 / 0.075469 (-0.011122) |\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) | 1.166696 / 1.841788 (-0.675091) | 14.721287 / 8.074308 (6.646979) | 14.319272 / 10.191392 (4.127880) | 0.144534 / 0.680424 (-0.535890) | 0.017502 / 0.534201 (-0.516699) | 0.422682 / 0.579283 (-0.156601) | 0.424426 / 0.434364 (-0.009938) | 0.493561 / 0.540337 (-0.046777) | 0.586765 / 1.386936 (-0.800171) |\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) | 0.007764 / 0.011353 (-0.003589) | 0.005516 / 0.011008 (-0.005492) | 0.074745 / 0.038508 (0.036237) | 0.034364 / 0.023109 (0.011255) | 0.344318 / 0.275898 (0.068420) | 0.374779 / 0.323480 (0.051299) | 0.005904 / 0.007986 (-0.002082) | 0.004323 / 0.004328 (-0.000005) | 0.073191 / 0.004250 (0.068941) | 0.051549 / 0.037052 (0.014496) | 0.341792 / 0.258489 (0.083303) | 0.387576 / 0.293841 (0.093735) | 0.037483 / 0.128546 (-0.091063) | 0.012410 / 0.075646 (-0.063237) | 0.086480 / 0.419271 (-0.332791) | 0.050035 / 0.043533 (0.006502) | 0.335475 / 0.255139 (0.080336) | 0.361436 / 0.283200 (0.078236) | 0.106890 / 0.141683 (-0.034792) | 1.464032 / 1.452155 (0.011877) | 1.563490 / 1.492716 (0.070774) |\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) | 0.268765 / 0.018006 (0.250758) | 0.563811 / 0.000490 (0.563321) | 0.004904 / 0.000200 (0.004704) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029885 / 0.037411 (-0.007526) | 0.113885 / 0.014526 (0.099359) | 0.124283 / 0.176557 (-0.052274) | 0.173619 / 0.737135 (-0.563517) | 0.131781 / 0.296338 (-0.164557) |\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) | 0.420296 / 0.215209 (0.205087) | 4.167656 / 2.077655 (2.090001) | 1.982356 / 1.504120 (0.478237) | 1.792181 / 1.541195 (0.250986) | 1.871459 / 1.468490 (0.402969) | 0.707066 / 4.584777 (-3.877711) | 3.835922 / 3.745712 (0.090210) | 3.506796 / 5.269862 (-1.763066) | 1.857172 / 4.565676 (-2.708505) | 0.086219 / 0.424275 (-0.338056) | 0.012404 / 0.007607 (0.004796) | 0.512393 / 0.226044 (0.286348) | 5.111623 / 2.268929 (2.842695) | 2.493523 / 55.444624 (-52.951101) | 2.188220 / 6.876477 (-4.688257) | 2.319096 / 2.142072 (0.177024) | 0.844084 / 4.805227 (-3.961144) | 0.171130 / 6.500664 (-6.329534) | 0.065913 / 0.075469 (-0.009556) |\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) | 1.284768 / 1.841788 (-0.557020) | 15.334610 / 8.074308 (7.260301) | 14.724436 / 10.191392 (4.533044) | 0.188425 / 0.680424 (-0.491999) | 0.017984 / 0.534201 (-0.516217) | 0.428150 / 0.579283 (-0.151133) | 0.429013 / 0.434364 (-0.005351) | 0.500818 / 0.540337 (-0.039519) | 0.592879 / 1.386936 (-0.794057) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ee68da958c2fab3a26d9f0efb1e207ecbcf7ce15 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006870 / 0.011353 (-0.004483) | 0.004702 / 0.011008 (-0.006306) | 0.099258 / 0.038508 (0.060750) | 0.029008 / 0.023109 (0.005899) | 0.330599 / 0.275898 (0.054701) | 0.361163 / 0.323480 (0.037683) | 0.005020 / 0.007986 (-0.002965) | 0.003474 / 0.004328 (-0.000855) | 0.075902 / 0.004250 (0.071651) | 0.037462 / 0.037052 (0.000410) | 0.336213 / 0.258489 (0.077724) | 0.370645 / 0.293841 (0.076804) | 0.032435 / 0.128546 (-0.096111) | 0.011686 / 0.075646 (-0.063960) | 0.326040 / 0.419271 (-0.093232) | 0.043750 / 0.043533 (0.000217) | 0.332629 / 0.255139 (0.077490) | 0.353302 / 0.283200 (0.070102) | 0.090421 / 0.141683 (-0.051262) | 1.470097 / 1.452155 (0.017942) | 1.544908 / 1.492716 (0.052191) |\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) | 0.213418 / 0.018006 (0.195411) | 0.434808 / 0.000490 (0.434319) | 0.005949 / 0.000200 (0.005749) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023085 / 0.037411 (-0.014327) | 0.098222 / 0.014526 (0.083696) | 0.104543 / 0.176557 (-0.072013) | 0.165423 / 0.737135 (-0.571713) | 0.108732 / 0.296338 (-0.187606) |\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) | 0.433933 / 0.215209 (0.218724) | 4.334358 / 2.077655 (2.256704) | 2.013984 / 1.504120 (0.509864) | 1.862981 / 1.541195 (0.321787) | 1.873936 / 1.468490 (0.405446) | 0.699857 / 4.584777 (-3.884920) | 3.417815 / 3.745712 (-0.327897) | 1.946403 / 5.269862 (-3.323459) | 1.308683 / 4.565676 (-3.256994) | 0.083297 / 0.424275 (-0.340978) | 0.012610 / 0.007607 (0.005003) | 0.540877 / 0.226044 (0.314832) | 5.408293 / 2.268929 (3.139365) | 2.529574 / 55.444624 (-52.915050) | 2.201047 / 6.876477 (-4.675429) | 2.392966 / 2.142072 (0.250894) | 0.812719 / 4.805227 (-3.992509) | 0.154013 / 6.500664 (-6.346651) | 0.067614 / 0.075469 (-0.007855) |\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) | 1.228150 / 1.841788 (-0.613638) | 14.037090 / 8.074308 (5.962782) | 14.259416 / 10.191392 (4.068024) | 0.155554 / 0.680424 (-0.524870) | 0.016521 / 0.534201 (-0.517680) | 0.379615 / 0.579283 (-0.199668) | 0.421352 / 0.434364 (-0.013012) | 0.446512 / 0.540337 (-0.093825) | 0.531802 / 1.386936 (-0.855134) |\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) | 0.006629 / 0.011353 (-0.004724) | 0.004432 / 0.011008 (-0.006577) | 0.076662 / 0.038508 (0.038154) | 0.027674 / 0.023109 (0.004565) | 0.341667 / 0.275898 (0.065769) | 0.376493 / 0.323480 (0.053014) | 0.005076 / 0.007986 (-0.002910) | 0.004655 / 0.004328 (0.000326) | 0.075698 / 0.004250 (0.071448) | 0.036905 / 0.037052 (-0.000147) | 0.342394 / 0.258489 (0.083905) | 0.383330 / 0.293841 (0.089489) | 0.031729 / 0.128546 (-0.096817) | 0.011582 / 0.075646 (-0.064064) | 0.085721 / 0.419271 (-0.333551) | 0.042012 / 0.043533 (-0.001521) | 0.342063 / 0.255139 (0.086924) | 0.367335 / 0.283200 (0.084136) | 0.089641 / 0.141683 (-0.052042) | 1.520353 / 1.452155 (0.068198) | 1.643653 / 1.492716 (0.150937) |\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) | 0.178995 / 0.018006 (0.160989) | 0.436544 / 0.000490 (0.436055) | 0.002311 / 0.000200 (0.002111) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025386 / 0.037411 (-0.012026) | 0.099717 / 0.014526 (0.085192) | 0.110809 / 0.176557 (-0.065747) | 0.162931 / 0.737135 (-0.574204) | 0.110430 / 0.296338 (-0.185909) |\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) | 0.438592 / 0.215209 (0.223382) | 4.372560 / 2.077655 (2.294905) | 2.069686 / 1.504120 (0.565567) | 1.860576 / 1.541195 (0.319382) | 1.898161 / 1.468490 (0.429671) | 0.698353 / 4.584777 (-3.886424) | 3.462440 / 3.745712 (-0.283272) | 1.868602 / 5.269862 (-3.401260) | 1.160498 / 4.565676 (-3.405179) | 0.082869 / 0.424275 (-0.341406) | 0.012690 / 0.007607 (0.005083) | 0.533278 / 0.226044 (0.307233) | 5.386214 / 2.268929 (3.117285) | 2.519243 / 55.444624 (-52.925382) | 2.171109 / 6.876477 (-4.705368) | 2.272617 / 2.142072 (0.130544) | 0.805843 / 4.805227 (-3.999384) | 0.152275 / 6.500664 (-6.348389) | 0.068038 / 0.075469 (-0.007431) |\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) | 1.291967 / 1.841788 (-0.549821) | 14.386474 / 8.074308 (6.312166) | 14.180693 / 10.191392 (3.989301) | 0.131714 / 0.680424 (-0.548710) | 0.016596 / 0.534201 (-0.517605) | 0.384293 / 0.579283 (-0.194990) | 0.404051 / 0.434364 (-0.030313) | 0.452167 / 0.540337 (-0.088170) | 0.542718 / 1.386936 (-0.844218) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9c770bb1a43fa7fe390286d7535266d3964d067 \"CML watermark\")\n" ]

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[ "Same problem.", "hi! \r\nI think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. \r\n@ericxsun Do you want to open a PR to fix the regex? As you already found the solution :) ", "> hi! I think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. @ericxsun Do you want to open a PR to fix the regex? As you already found the solution :)\r\n\r\nSure, please see https://github.com/huggingface/datasets/pull/5748 @polinaeterna ", "I think `string_to_dict` is ok, and that the issue is that it gets `'/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'` as input instead of `'data/test-00000-of-00001-9c49eeff30aacaa8.parquet'`. The path should be relative to the directory being loaded by `load_dataset`" ]

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[ "You need to provide a text file as `data_files`, not as a configuration:\r\n\r\n```python\r\nmy_dataset = load_dataset(\"text\", data_files=\"TextFile.txt\")\r\n```\r\n\r\nOtherwise, since `data_files` is `None`, it picks up Colab's sample datasets from the `content` dir." ]

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[ "Hi, you can use the `cast_column` function to change the feature type from a `Value(int64)` to `ClassLabel`:\r\n\r\n```py\r\ndataset = dataset.cast_column(\"label\", ClassLabel(names=[\"label_1\", \"label_2\", \"label_3\"]))\r\nprint(dataset.features)\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(names=['label_1', 'label_2', 'label_3'], id=None)}\r\n```", "thank you @stevhliu, its worked. " ]

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[ "Hi ! I couldn't reproduce your issue :/\r\n\r\nIt seems that `shutil.rmtree` failed. It is supposed to work even if the directory is not empty, but you still end up with `OSError: [Errno 39] Directory not empty:`. Can you make sure another process is not using this directory at the same time ?" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi ! What if one of the sub-iterables only has one shard ? In that case I don't think we'd end up with a correctly interleaved dataset, since only rank 0 would yield examples from this sub-iterable", "Hi ! \r\nI just tested this out with the code below and it seems to be ok. Both datasets are alternating and we get all the examples with no duplicates.\r\n\r\nOn thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).\r\n\r\n ```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=1)\r\n\r\n ds_merged = interleave_datasets([ds1, ds2], stopping_strategy=\"all_exhausted\")\r\n\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v'}]\r\n1 [{'input': 'test: Works with RTL and N'}]\r\n2 [{'input': \"train: Great It's not fully\"}]\r\n3 [{'input': 'test: Works with RTL SDR W'}]\r\n4 [{'input': 'train: Works on a Nexus 6p '}]\r\n5 [{'input': 'test: Awsome App! Easy to '}]\r\n6 [{'input': 'train: The bandwidth seemed'}]\r\n7 [{'input': \"test: I'll forgo the refun\"}]\r\n8 [{'input': 'train: Works well with my H'}]\r\n9 [{'input': 'test: looks like a great p'}]\r\n```", "<s> Could you try with `num_workers>1` ? </s>\r\n\r\nedit: Oh I see\r\n\r\n> On thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).", "Great ! It's ok to have the max amount of workers is equal to the lowest amount of shard :)\r\n\r\nSo in the case of `num_workers>min(n_shards_per_dataset)` maybe some workers should turn off, and a warning can probably be shown. This is already the case if you use a single dataset with a single shard and `num_workers>1`.\r\n\r\n\r\nRight now it seems to raise an error:\r\n\r\n```python\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 979, in __iter__\r\n yield from self._iter_pytorch(ex_iterable)\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 912, in _iter_pytorch\r\n for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in shard_data_sources\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in <listcomp>\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 125, in shard_data_sources\r\n requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/utils/sharding.py\", line 76, in _merge_gen_kwargs\r\n for key in gen_kwargs_list[0]\r\nIndexError: list index out of range\r\n```", "Good point. I have fixed the n_shards property of merged iterable datasets so that this warning is raised properly", "Hey @lhoestq, what do you think of the last modifications ? ", "Hello! No problem :)\r\n\r\n- About HorizontallyConcatenatedMultiSourcesExamplesIterable, I've haven't been able to create a bug with sharding. So either I missed something or it's working somehow:\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets, concatenate_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].rename_columns({\"input\": \"input2\"})\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=3)\r\n\r\n ds_merged = concatenate_datasets([ds1, ds2], axis=1)\r\n\r\n #n_shards is always 1 for HorizontallyConcatenatedMultiSourcesExamplesIterable\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v', 'input2': 'test: Works with RTL and N'}]\r\n1 [{'input': \"train: Great It's not fully\", 'input2': 'test: Works with RTL SDR W'}]\r\n2 [{'input': 'train: Works on a Nexus 6p ', 'input2': 'test: Awsome App! Easy to '}]\r\n3 [{'input': 'train: The bandwidth seemed', 'input2': \"test: I'll forgo the refun\"}]\r\n4 [{'input': 'train: Works well with my H', 'input2': 'test: looks like a great p'}]\r\n```\r\n\r\n- I've added a test but I'm not completely happy with it. My issue is that multiprocessing makes interleaving not completely deterministic as samples are yielded whenever ready by each process, if I'm correct.\r\nAs a result I opted to check for the amount of samples yielded and make that they are all unique, which should be equivalent.\r\nBut now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nWhat are your thoughts about this ?", "Ah indeed it works because it's set to be only 1 shard - my bad :)", "> But now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nThis looks reasonable, maybe this can be documented in the `interleave_datasets` docstring ?\r\n```\r\nNote for iterable datasets:\r\n\r\nIn a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.\r\nTherefore the \"first_exhausted\" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).\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) | 0.006441 / 0.011353 (-0.004912) | 0.004551 / 0.011008 (-0.006457) | 0.099144 / 0.038508 (0.060636) | 0.028163 / 0.023109 (0.005054) | 0.386342 / 0.275898 (0.110444) | 0.398347 / 0.323480 (0.074867) | 0.004836 / 0.007986 (-0.003150) | 0.004724 / 0.004328 (0.000395) | 0.076277 / 0.004250 (0.072027) | 0.036305 / 0.037052 (-0.000747) | 0.377179 / 0.258489 (0.118690) | 0.410694 / 0.293841 (0.116853) | 0.030196 / 0.128546 (-0.098351) | 0.011436 / 0.075646 (-0.064211) | 0.325911 / 0.419271 (-0.093360) | 0.043709 / 0.043533 (0.000177) | 0.375801 / 0.255139 (0.120662) | 0.396511 / 0.283200 (0.113311) | 0.088346 / 0.141683 (-0.053337) | 1.483427 / 1.452155 (0.031272) | 1.553708 / 1.492716 (0.060992) |\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) | 0.190974 / 0.018006 (0.172968) | 0.451309 / 0.000490 (0.450819) | 0.004045 / 0.000200 (0.003845) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023814 / 0.037411 (-0.013597) | 0.096922 / 0.014526 (0.082396) | 0.101506 / 0.176557 (-0.075050) | 0.164694 / 0.737135 (-0.572441) | 0.106899 / 0.296338 (-0.189439) |\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) | 0.432164 / 0.215209 (0.216954) | 4.308076 / 2.077655 (2.230421) | 2.092434 / 1.504120 (0.588314) | 1.937405 / 1.541195 (0.396210) | 1.988030 / 1.468490 (0.519540) | 0.695476 / 4.584777 (-3.889301) | 3.436413 / 3.745712 (-0.309299) | 2.892954 / 5.269862 (-2.376908) | 1.519906 / 4.565676 (-3.045771) | 0.082579 / 0.424275 (-0.341696) | 0.012233 / 0.007607 (0.004626) | 0.531329 / 0.226044 (0.305284) | 5.365272 / 2.268929 (3.096344) | 2.391452 / 55.444624 (-53.053172) | 2.051116 / 6.876477 (-4.825361) | 2.140663 / 2.142072 (-0.001410) | 0.807262 / 4.805227 (-3.997966) | 0.151290 / 6.500664 (-6.349374) | 0.066137 / 0.075469 (-0.009333) |\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) | 1.193106 / 1.841788 (-0.648682) | 13.577240 / 8.074308 (5.502932) | 14.280126 / 10.191392 (4.088734) | 0.142538 / 0.680424 (-0.537886) | 0.016641 / 0.534201 (-0.517560) | 0.386318 / 0.579283 (-0.192965) | 0.385991 / 0.434364 (-0.048373) | 0.440712 / 0.540337 (-0.099625) | 0.524189 / 1.386936 (-0.862747) |\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) | 0.006628 / 0.011353 (-0.004725) | 0.004664 / 0.011008 (-0.006344) | 0.077254 / 0.038508 (0.038746) | 0.028369 / 0.023109 (0.005259) | 0.343076 / 0.275898 (0.067178) | 0.376491 / 0.323480 (0.053011) | 0.005298 / 0.007986 (-0.002687) | 0.004853 / 0.004328 (0.000524) | 0.075927 / 0.004250 (0.071677) | 0.039951 / 0.037052 (0.002899) | 0.346225 / 0.258489 (0.087736) | 0.382367 / 0.293841 (0.088526) | 0.031133 / 0.128546 (-0.097413) | 0.011666 / 0.075646 (-0.063981) | 0.086383 / 0.419271 (-0.332889) | 0.042885 / 0.043533 (-0.000647) | 0.343885 / 0.255139 (0.088746) | 0.366840 / 0.283200 (0.083640) | 0.095942 / 0.141683 (-0.045741) | 1.528972 / 1.452155 (0.076817) | 1.586392 / 1.492716 (0.093676) |\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) | 0.223952 / 0.018006 (0.205946) | 0.410767 / 0.000490 (0.410277) | 0.001014 / 0.000200 (0.000814) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024210 / 0.037411 (-0.013201) | 0.100308 / 0.014526 (0.085782) | 0.106899 / 0.176557 (-0.069658) | 0.156514 / 0.737135 (-0.580621) | 0.109548 / 0.296338 (-0.186790) |\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) | 0.434763 / 0.215209 (0.219554) | 4.348485 / 2.077655 (2.270831) | 2.064255 / 1.504120 (0.560135) | 1.864394 / 1.541195 (0.323199) | 1.899732 / 1.468490 (0.431242) | 0.694147 / 4.584777 (-3.890630) | 3.357898 / 3.745712 (-0.387815) | 2.909155 / 5.269862 (-2.360707) | 1.424790 / 4.565676 (-3.140886) | 0.082597 / 0.424275 (-0.341678) | 0.012442 / 0.007607 (0.004835) | 0.538758 / 0.226044 (0.312713) | 5.390288 / 2.268929 (3.121359) | 2.532016 / 55.444624 (-52.912609) | 2.185724 / 6.876477 (-4.690753) | 2.274176 / 2.142072 (0.132104) | 0.804785 / 4.805227 (-4.000442) | 0.152649 / 6.500664 (-6.348015) | 0.067707 / 0.075469 (-0.007762) |\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) | 1.285219 / 1.841788 (-0.556568) | 13.958098 / 8.074308 (5.883790) | 14.043653 / 10.191392 (3.852261) | 0.144526 / 0.680424 (-0.535898) | 0.016813 / 0.534201 (-0.517388) | 0.390286 / 0.579283 (-0.188997) | 0.389184 / 0.434364 (-0.045180) | 0.470810 / 0.540337 (-0.069527) | 0.562391 / 1.386936 (-0.824545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb172c9772858c188f85ffc9a51f8cb1da292a0 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006240 / 0.011353 (-0.005113) | 0.004392 / 0.011008 (-0.006616) | 0.097276 / 0.038508 (0.058768) | 0.027262 / 0.023109 (0.004153) | 0.303203 / 0.275898 (0.027305) | 0.331878 / 0.323480 (0.008398) | 0.004706 / 0.007986 (-0.003279) | 0.004428 / 0.004328 (0.000100) | 0.074666 / 0.004250 (0.070416) | 0.036154 / 0.037052 (-0.000899) | 0.302997 / 0.258489 (0.044508) | 0.340350 / 0.293841 (0.046509) | 0.031011 / 0.128546 (-0.097535) | 0.011616 / 0.075646 (-0.064031) | 0.323671 / 0.419271 (-0.095601) | 0.042062 / 0.043533 (-0.001471) | 0.311381 / 0.255139 (0.056242) | 0.324697 / 0.283200 (0.041498) | 0.084248 / 0.141683 (-0.057435) | 1.471651 / 1.452155 (0.019496) | 1.533414 / 1.492716 (0.040697) |\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) | 0.193555 / 0.018006 (0.175549) | 0.393452 / 0.000490 (0.392962) | 0.002348 / 0.000200 (0.002148) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022523 / 0.037411 (-0.014889) | 0.096552 / 0.014526 (0.082026) | 0.101746 / 0.176557 (-0.074810) | 0.163145 / 0.737135 (-0.573990) | 0.106417 / 0.296338 (-0.189921) |\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) | 0.448589 / 0.215209 (0.233380) | 4.467803 / 2.077655 (2.390148) | 2.178745 / 1.504120 (0.674625) | 1.983339 / 1.541195 (0.442145) | 2.056554 / 1.468490 (0.588064) | 0.697571 / 4.584777 (-3.887206) | 3.363967 / 3.745712 (-0.381745) | 1.872526 / 5.269862 (-3.397336) | 1.258245 / 4.565676 (-3.307432) | 0.082954 / 0.424275 (-0.341321) | 0.012306 / 0.007607 (0.004699) | 0.545096 / 0.226044 (0.319052) | 5.468706 / 2.268929 (3.199777) | 2.645333 / 55.444624 (-52.799292) | 2.287659 / 6.876477 (-4.588818) | 2.346768 / 2.142072 (0.204696) | 0.803730 / 4.805227 (-4.001497) | 0.151037 / 6.500664 (-6.349627) | 0.066404 / 0.075469 (-0.009065) |\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) | 1.192982 / 1.841788 (-0.648806) | 13.631225 / 8.074308 (5.556917) | 13.830053 / 10.191392 (3.638661) | 0.141901 / 0.680424 (-0.538523) | 0.016500 / 0.534201 (-0.517701) | 0.373268 / 0.579283 (-0.206015) | 0.380123 / 0.434364 (-0.054241) | 0.430786 / 0.540337 (-0.109551) | 0.512669 / 1.386936 (-0.874267) |\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) | 0.006161 / 0.011353 (-0.005192) | 0.004399 / 0.011008 (-0.006609) | 0.076210 / 0.038508 (0.037702) | 0.026791 / 0.023109 (0.003681) | 0.341523 / 0.275898 (0.065625) | 0.370400 / 0.323480 (0.046920) | 0.004495 / 0.007986 (-0.003491) | 0.003204 / 0.004328 (-0.001125) | 0.075444 / 0.004250 (0.071194) | 0.035914 / 0.037052 (-0.001138) | 0.343806 / 0.258489 (0.085317) | 0.384320 / 0.293841 (0.090479) | 0.031438 / 0.128546 (-0.097109) | 0.011253 / 0.075646 (-0.064393) | 0.085364 / 0.419271 (-0.333908) | 0.041407 / 0.043533 (-0.002126) | 0.338831 / 0.255139 (0.083692) | 0.364357 / 0.283200 (0.081158) | 0.087417 / 0.141683 (-0.054266) | 1.520624 / 1.452155 (0.068470) | 1.572432 / 1.492716 (0.079716) |\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) | 0.232403 / 0.018006 (0.214396) | 0.388187 / 0.000490 (0.387698) | 0.001158 / 0.000200 (0.000958) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024596 / 0.037411 (-0.012816) | 0.101203 / 0.014526 (0.086677) | 0.105243 / 0.176557 (-0.071314) | 0.158215 / 0.737135 (-0.578920) | 0.110277 / 0.296338 (-0.186061) |\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) | 0.435661 / 0.215209 (0.220452) | 4.350151 / 2.077655 (2.272496) | 2.072372 / 1.504120 (0.568252) | 1.870675 / 1.541195 (0.329480) | 1.910883 / 1.468490 (0.442393) | 0.697384 / 4.584777 (-3.887393) | 3.399377 / 3.745712 (-0.346335) | 2.685008 / 5.269862 (-2.584854) | 1.476843 / 4.565676 (-3.088834) | 0.083177 / 0.424275 (-0.341098) | 0.012413 / 0.007607 (0.004806) | 0.542543 / 0.226044 (0.316498) | 5.431422 / 2.268929 (3.162494) | 2.506419 / 55.444624 (-52.938206) | 2.166342 / 6.876477 (-4.710135) | 2.164421 / 2.142072 (0.022348) | 0.800609 / 4.805227 (-4.004618) | 0.150527 / 6.500664 (-6.350137) | 0.065780 / 0.075469 (-0.009689) |\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) | 1.293409 / 1.841788 (-0.548379) | 13.814898 / 8.074308 (5.740590) | 13.940416 / 10.191392 (3.749024) | 0.149377 / 0.680424 (-0.531047) | 0.016462 / 0.534201 (-0.517739) | 0.393748 / 0.579283 (-0.185535) | 0.384327 / 0.434364 (-0.050037) | 0.489900 / 0.540337 (-0.050437) | 0.574608 / 1.386936 (-0.812328) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2607935c4e45c70c44fcb698db0363ca7ba83d4 \"CML watermark\")\n" ]

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[ "#self-assign", "The Enwik8 pipeline is not present in this codebase, and is hosted elsewhere. I have opened a PR [there](https://huggingface.co/datasets/enwik8/discussions/4) instead. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009735 / 0.011353 (-0.001618) | 0.010410 / 0.011008 (-0.000598) | 0.134986 / 0.038508 (0.096478) | 0.038392 / 0.023109 (0.015283) | 0.414451 / 0.275898 (0.138553) | 0.447775 / 0.323480 (0.124295) | 0.007223 / 0.007986 (-0.000763) | 0.006373 / 0.004328 (0.002045) | 0.102631 / 0.004250 (0.098381) | 0.048516 / 0.037052 (0.011464) | 0.410179 / 0.258489 (0.151690) | 0.467773 / 0.293841 (0.173932) | 0.053163 / 0.128546 (-0.075384) | 0.019801 / 0.075646 (-0.055845) | 0.452708 / 0.419271 (0.033436) | 0.068691 / 0.043533 (0.025159) | 0.405482 / 0.255139 (0.150343) | 0.457669 / 0.283200 (0.174470) | 0.113464 / 0.141683 (-0.028219) | 1.918143 / 1.452155 (0.465988) | 2.033123 / 1.492716 (0.540407) |\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) | 0.274564 / 0.018006 (0.256557) | 0.608855 / 0.000490 (0.608366) | 0.006266 / 0.000200 (0.006066) | 0.000105 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033704 / 0.037411 (-0.003708) | 0.130982 / 0.014526 (0.116456) | 0.143862 / 0.176557 (-0.032694) | 0.212622 / 0.737135 (-0.524513) | 0.148899 / 0.296338 (-0.147439) |\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) | 0.670968 / 0.215209 (0.455759) | 6.602911 / 2.077655 (4.525256) | 2.644290 / 1.504120 (1.140171) | 2.268593 / 1.541195 (0.727399) | 2.325393 / 1.468490 (0.856903) | 1.388156 / 4.584777 (-3.196621) | 5.958569 / 3.745712 (2.212857) | 3.310756 / 5.269862 (-1.959106) | 2.390953 / 4.565676 (-2.174724) | 0.147416 / 0.424275 (-0.276859) | 0.015201 / 0.007607 (0.007594) | 0.794109 / 0.226044 (0.568064) | 7.984855 / 2.268929 (5.715926) | 3.382275 / 55.444624 (-52.062349) | 2.676102 / 6.876477 (-4.200375) | 2.846743 / 2.142072 (0.704671) | 1.467523 / 4.805227 (-3.337704) | 0.283184 / 6.500664 (-6.217480) | 0.088655 / 0.075469 (0.013186) |\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) | 1.632765 / 1.841788 (-0.209022) | 19.102473 / 8.074308 (11.028165) | 25.632535 / 10.191392 (15.441143) | 0.255628 / 0.680424 (-0.424795) | 0.034655 / 0.534201 (-0.499546) | 0.564593 / 0.579283 (-0.014690) | 0.668339 / 0.434364 (0.233975) | 0.648414 / 0.540337 (0.108076) | 0.766735 / 1.386936 (-0.620201) |\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) | 0.009658 / 0.011353 (-0.001695) | 0.006690 / 0.011008 (-0.004318) | 0.099151 / 0.038508 (0.060643) | 0.037092 / 0.023109 (0.013983) | 0.470354 / 0.275898 (0.194456) | 0.525863 / 0.323480 (0.202383) | 0.007593 / 0.007986 (-0.000393) | 0.006637 / 0.004328 (0.002308) | 0.098782 / 0.004250 (0.094532) | 0.058524 / 0.037052 (0.021471) | 0.502569 / 0.258489 (0.244080) | 0.526410 / 0.293841 (0.232569) | 0.059486 / 0.128546 (-0.069060) | 0.019742 / 0.075646 (-0.055904) | 0.119715 / 0.419271 (-0.299556) | 0.065269 / 0.043533 (0.021736) | 0.483327 / 0.255139 (0.228188) | 0.506148 / 0.283200 (0.222948) | 0.123178 / 0.141683 (-0.018505) | 1.916624 / 1.452155 (0.464470) | 2.051410 / 1.492716 (0.558694) |\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) | 0.286481 / 0.018006 (0.268475) | 0.597300 / 0.000490 (0.596810) | 0.008906 / 0.000200 (0.008706) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031406 / 0.037411 (-0.006005) | 0.146748 / 0.014526 (0.132222) | 0.152898 / 0.176557 (-0.023658) | 0.212535 / 0.737135 (-0.524600) | 0.155577 / 0.296338 (-0.140761) |\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) | 0.660989 / 0.215209 (0.445780) | 6.688530 / 2.077655 (4.610875) | 3.039278 / 1.504120 (1.535159) | 2.660357 / 1.541195 (1.119162) | 2.696912 / 1.468490 (1.228422) | 1.259760 / 4.584777 (-3.325017) | 5.922452 / 3.745712 (2.176740) | 5.304200 / 5.269862 (0.034338) | 2.823928 / 4.565676 (-1.741748) | 0.148118 / 0.424275 (-0.276157) | 0.015575 / 0.007607 (0.007968) | 0.794404 / 0.226044 (0.568360) | 8.233651 / 2.268929 (5.964722) | 3.777482 / 55.444624 (-51.667142) | 3.064924 / 6.876477 (-3.811552) | 3.117803 / 2.142072 (0.975731) | 1.479559 / 4.805227 (-3.325668) | 0.254070 / 6.500664 (-6.246594) | 0.086806 / 0.075469 (0.011337) |\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) | 1.735515 / 1.841788 (-0.106273) | 18.934157 / 8.074308 (10.859848) | 22.645248 / 10.191392 (12.453856) | 0.227073 / 0.680424 (-0.453351) | 0.030650 / 0.534201 (-0.503551) | 0.594619 / 0.579283 (0.015336) | 0.653304 / 0.434364 (0.218940) | 0.707484 / 0.540337 (0.167147) | 0.823327 / 1.386936 (-0.563610) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273392966e434286f4f5ba2ad596730bff11056d \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The error in the CI was unrelated to this PR. I have merged main branch once that has been fixed.", "<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) | 0.006954 / 0.011353 (-0.004399) | 0.004947 / 0.011008 (-0.006061) | 0.086564 / 0.038508 (0.048056) | 0.031167 / 0.023109 (0.008058) | 0.262285 / 0.275898 (-0.013613) | 0.295753 / 0.323480 (-0.027727) | 0.005389 / 0.007986 (-0.002596) | 0.004130 / 0.004328 (-0.000198) | 0.065127 / 0.004250 (0.060877) | 0.042511 / 0.037052 (0.005458) | 0.263497 / 0.258489 (0.005008) | 0.307456 / 0.293841 (0.013615) | 0.031338 / 0.128546 (-0.097209) | 0.011023 / 0.075646 (-0.064623) | 0.295625 / 0.419271 (-0.123647) | 0.045813 / 0.043533 (0.002280) | 0.259369 / 0.255139 (0.004230) | 0.279325 / 0.283200 (-0.003875) | 0.099748 / 0.141683 (-0.041934) | 1.252572 / 1.452155 (-0.199583) | 1.347069 / 1.492716 (-0.145647) |\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) | 0.249726 / 0.018006 (0.231720) | 0.556882 / 0.000490 (0.556392) | 0.008237 / 0.000200 (0.008037) | 0.000294 / 0.000054 (0.000239) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026879 / 0.037411 (-0.010533) | 0.105141 / 0.014526 (0.090615) | 0.115473 / 0.176557 (-0.061084) | 0.172989 / 0.737135 (-0.564147) | 0.120433 / 0.296338 (-0.175906) |\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) | 0.400022 / 0.215209 (0.184812) | 3.965402 / 2.077655 (1.887747) | 1.805257 / 1.504120 (0.301138) | 1.610136 / 1.541195 (0.068941) | 1.661162 / 1.468490 (0.192672) | 0.695311 / 4.584777 (-3.889466) | 3.753757 / 3.745712 (0.008045) | 2.060609 / 5.269862 (-3.209253) | 1.333251 / 4.565676 (-3.232426) | 0.085790 / 0.424275 (-0.338485) | 0.012256 / 0.007607 (0.004649) | 0.502133 / 0.226044 (0.276088) | 5.040979 / 2.268929 (2.772051) | 2.310919 / 55.444624 (-53.133705) | 2.010534 / 6.876477 (-4.865943) | 2.132961 / 2.142072 (-0.009111) | 0.837636 / 4.805227 (-3.967592) | 0.169838 / 6.500664 (-6.330826) | 0.065003 / 0.075469 (-0.010466) |\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) | 1.218674 / 1.841788 (-0.623114) | 14.696076 / 8.074308 (6.621768) | 14.559492 / 10.191392 (4.368100) | 0.167761 / 0.680424 (-0.512663) | 0.017747 / 0.534201 (-0.516454) | 0.421624 / 0.579283 (-0.157659) | 0.414086 / 0.434364 (-0.020278) | 0.501398 / 0.540337 (-0.038940) | 0.596099 / 1.386936 (-0.790837) |\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) | 0.007230 / 0.011353 (-0.004123) | 0.005345 / 0.011008 (-0.005664) | 0.073739 / 0.038508 (0.035231) | 0.033440 / 0.023109 (0.010330) | 0.339790 / 0.275898 (0.063892) | 0.367857 / 0.323480 (0.044377) | 0.005927 / 0.007986 (-0.002058) | 0.004279 / 0.004328 (-0.000049) | 0.074247 / 0.004250 (0.069996) | 0.048971 / 0.037052 (0.011918) | 0.340235 / 0.258489 (0.081746) | 0.380521 / 0.293841 (0.086680) | 0.035322 / 0.128546 (-0.093225) | 0.012416 / 0.075646 (-0.063230) | 0.086060 / 0.419271 (-0.333212) | 0.049331 / 0.043533 (0.005799) | 0.342871 / 0.255139 (0.087732) | 0.355673 / 0.283200 (0.072473) | 0.111976 / 0.141683 (-0.029707) | 1.462530 / 1.452155 (0.010375) | 1.550336 / 1.492716 (0.057620) |\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) | 0.266560 / 0.018006 (0.248554) | 0.550886 / 0.000490 (0.550396) | 0.001069 / 0.000200 (0.000869) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028701 / 0.037411 (-0.008711) | 0.110535 / 0.014526 (0.096010) | 0.122846 / 0.176557 (-0.053711) | 0.176395 / 0.737135 (-0.560740) | 0.128653 / 0.296338 (-0.167685) |\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) | 0.431693 / 0.215209 (0.216484) | 4.283691 / 2.077655 (2.206036) | 2.013967 / 1.504120 (0.509847) | 1.823914 / 1.541195 (0.282719) | 1.872055 / 1.468490 (0.403565) | 0.703318 / 4.584777 (-3.881459) | 3.783412 / 3.745712 (0.037699) | 2.950147 / 5.269862 (-2.319715) | 1.826159 / 4.565676 (-2.739518) | 0.086897 / 0.424275 (-0.337379) | 0.012512 / 0.007607 (0.004905) | 0.526730 / 0.226044 (0.300685) | 5.263871 / 2.268929 (2.994943) | 2.552163 / 55.444624 (-52.892462) | 2.276216 / 6.876477 (-4.600261) | 2.419934 / 2.142072 (0.277862) | 0.848235 / 4.805227 (-3.956993) | 0.170405 / 6.500664 (-6.330259) | 0.064979 / 0.075469 (-0.010491) |\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) | 1.276780 / 1.841788 (-0.565008) | 15.100829 / 8.074308 (7.026521) | 15.117531 / 10.191392 (4.926139) | 0.147129 / 0.680424 (-0.533295) | 0.017806 / 0.534201 (-0.516395) | 0.422975 / 0.579283 (-0.156308) | 0.430286 / 0.434364 (-0.004078) | 0.501405 / 0.540337 (-0.038932) | 0.596810 / 1.386936 (-0.790126) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6ee2e6603fe81638256d37a6aa7ad0400e31a83 \"CML watermark\")\n" ]

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[ "Hi! Can you please paste the entire error stack trace, not only the last few lines?", "`----> 1 dataset = datasets.load_dataset(\"glue\", \"ax\")\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1767, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1762 verification_mode = VerificationMode(\r\n 1763 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS\r\n 1764 )\r\n 1766 # Create a dataset builder\r\n-> 1767 builder_instance = load_dataset_builder(\r\n 1768 path=path,\r\n 1769 name=name,\r\n 1770 data_dir=data_dir,\r\n 1771 data_files=data_files,\r\n 1772 cache_dir=cache_dir,\r\n 1773 features=features,\r\n 1774 download_config=download_config,\r\n 1775 download_mode=download_mode,\r\n 1776 revision=revision,\r\n 1777 use_auth_token=use_auth_token,\r\n 1778 storage_options=storage_options,\r\n 1779 **config_kwargs,\r\n 1780 )\r\n 1782 # Return iterable dataset in case of streaming\r\n 1783 if streaming:\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1498, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, storage_options, **config_kwargs)\r\n 1496 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1497 download_config.use_auth_token = use_auth_token\r\n-> 1498 dataset_module = dataset_module_factory(\r\n 1499 path,\r\n 1500 revision=revision,\r\n 1501 download_config=download_config,\r\n 1502 download_mode=download_mode,\r\n 1503 data_dir=data_dir,\r\n 1504 data_files=data_files,\r\n 1505 )\r\n 1507 # Get dataset builder class from the processing script\r\n 1508 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1211, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n 1209 raise e1 from None\r\n 1210 if isinstance(e1, FileNotFoundError):\r\n-> 1211 raise FileNotFoundError(\r\n 1212 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1213 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1214 ) from None\r\n 1215 raise e1 from None\r\n 1216 else:`", "Okay, this is the issue:\r\n```\r\nFileNotFoundError: [WinError 3] The system cannot find the path specified: \r\n'C:\\\\Users\\\\...\\\\.cache\\\\huggingface'\r\n``` \r\n\r\nI don't remember seeing this error before.\r\n\r\nI guess it could happen in a multi-process environment if one of the processes deletes the `datasets` cache as the other one is loading a dataset (with `load_dataset`), so make sure that's not the case. Also, you can disable the Windows max path length limit (if enabled), but this is most likely not the problem.", "Closing due to inactivity." ]

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[ "Thanks for reporting, @myluki2000.\r\n\r\nI am working on a fix." ]

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[ "Hi! You can use the `nrows` parameter for this:\r\n```python\r\ndata = load_dataset(\"json\", data_files=data_path, nrows=10)\r\n```", "@mariosasko I get:\r\n\r\n`TypeError: __init__() got an unexpected keyword argument 'nrows'`", "I misread the format in which the dataset is stored - the `nrows` parameter works for CSV, but not JSON.\r\n\r\nThis means the only option is first to create a DataFrame and then convert it to a Dataset object:\r\n```python\r\nimport pandas as pd\r\nfrom datasets import Dataset\r\n\r\ndf = pd.read_json(data_path, lines=True, nrows=10)\r\nds = Dataset.from_pandas(df)\r\n```" ]

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[ "Moving `\"en\"` to the end of the path instead of passing it as a config name should fix the error:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('/path/to/your/data/dir/en', streaming=True, split='train')\r\ndataset = dataset.shuffle(buffer_size=10_000, seed=42)\r\nnext(iter(dataset))\r\n```\r\n\r\nPS: https://github.com/huggingface/datasets/pull/5331, once merged, will allow us to define C4's configs in its README, making downloading it much more user-friendly." ]

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[ "Hmm the error doesn't seem related to data loading.\r\n\r\nRegarding `split_dataset_by_node`: it's generally used to split an iterable dataset (e.g. when streaming) in pytorch DDP. It's not needed if you use a regular dataset since the pytorch DataLoader already assigns a subset of the dataset indices to each node." ]

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[ "Edit: This behavior is true even without `.take/.set`", "I'm experiencing the same problem that @jlehrer1. I was able to reproduce it with a very small example:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\n\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n# Saving the dataset as a parquet file\r\ndataset = Dataset.from_generator(my_gen)\r\ntrain_path = \"/tmp/test.parquet\"\r\ndataset.to_parquet(train_path)\r\n\r\n# Creating a local dataset from the parquet file\r\ndata_files = {\"train\": [str(train_path)]}\r\nbuilder = load_dataset_builder(\"parquet\", data_files=data_files)\r\nbuilder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n# Loading the dataset from the local directory as streaming\r\ndataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\ndataset.with_format(\"torch\")\r\n\r\ndl = DataLoader(dataset, batch_size=2, num_workers=1)\r\nfor row in dl:\r\n print(row)\r\n```\r\n\r\nMy env info:\r\n```\r\ndatasets 2.11.0\r\ntorch 2.0.0\r\ntorchvision 0.15.1\r\nPython 3.9.16\r\n```\r\n\r\nNote that the example above doesn't fail if the number of workers used is `0`", "I cannot reproduce this error, not even with your MRE @ivanprado (your env appears to be the same as Colab's, and your code runs there without issues). ", "@mariosasko you are right, it works on Colab. I digged deeper and found that the problem arises when the multiprocessing method is set to be `spawn`. This code reproduces the problem in Colab:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\nimport multiprocessing as mp\r\n\r\nmp.set_start_method('spawn')\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n\r\ndef main():\r\n # Saving the dataset as a parquet file\r\n dataset = Dataset.from_generator(my_gen)\r\n train_path = \"/tmp/test.parquet\"\r\n dataset.to_parquet(train_path)\r\n\r\n # Creating a local dataset from the parquet file\r\n data_files = {\"train\": [str(train_path)]}\r\n builder = load_dataset_builder(\"parquet\", data_files=data_files)\r\n builder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n # Loading the dataset from the local directory as streaming\r\n dataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\n dataset.with_format(\"torch\")\r\n\r\n dl = DataLoader(dataset, batch_size=2, num_workers=1)\r\n for row in dl:\r\n print(row)\r\n\r\nmain()\r\n```", "So is there a way to fix this by changing the `mp` method? This is blocking any usage of the `datasets` library for me", "@jlehrer1 can you try adding `mp.set_start_method('fork')` at the beginning of your code? Maybe this helps you. Keep us posted. ", "I have a similar issue: \r\n> mp.set_start_method('fork')\r\n\r\n\r\nDidnt work" ]

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[ "Hi! \r\n\r\nYou need to set the format to `np` before indexing the dataset to get NumPy arrays:\r\n```python\r\nfeatures = Features(dict(seq=Array2D((2,2), 'float32'))) \r\nds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)\r\nds.set_format(\"np\")\r\na = ds[0]['seq']\r\n```\r\n\r\n> I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?\r\n\r\nThe same dataset can have examples in different types (Numpy arrays, Torch tensors, Pandas series, etc.), so recovering them all would be slow and impractical. Instead, the design of our formatting API is similar to Arrow's (the lib we use internally to store data on disk/ in RAM), which allows converting a batch of data to Python/Numpy/Pandas in a single call (and uses C++ to do so to make it faster).\r\n\r\n> Also if I change the first dimension of the Array2D shape to None, it's returning array correctly.\r\n\r\nSetting the first dimension to `None` makes it variable-length (allows passing arrays with the first dimensions of differing lengths).\r\n", "Current behavior when indexing the dataset:\r\n- Using `Array((2,2))` returns a list of lists.\r\n- Using `Array((None,2))` returns a numpy array.\r\n\r\nDon't you think this is kind of unexpected behavior from end-user perspective? \r\nAs a user, I expect that when I use `Array2D`, the behavior needs to be consistent even if I specify None or not. It should either return a list or an array. It needs to choose one. Let's say if it always return a list, then I will call `ds.set_format('np')` no problem.\r\n\r\nThe consistency can be in any of these aspects:\r\n1. preserves the type of the input data (in this case, a numpy array)\r\n2. ensure the output type is always the same (it can be either list or array, but it needs to be one of them)\r\n\r\nRight now the API doesn't conform to any of these aspects. But I think it needs to conform to one.", "I thought we made this consistent by returning lists in both scenarios...", "Fixed in #5751 " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "After this CI error: https://github.com/huggingface/datasets/actions/runs/4639528358/jobs/8210492953?pr=5718\r\n```\r\nFAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']\r\n At index 0 diff: 'random' != 'train'\r\n Full diff:\r\n - ['train', 'random']\r\n + ['random', 'train']\r\n```\r\nI have checked locally and found out that the data split order is nondeterministic. I am addressing this in a separate issue.\r\n\r\nWe should first address:\r\n- #5728 \r\n- #5729", "<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) | 0.007728 / 0.011353 (-0.003624) | 0.005275 / 0.011008 (-0.005734) | 0.097708 / 0.038508 (0.059199) | 0.039851 / 0.023109 (0.016741) | 0.333360 / 0.275898 (0.057462) | 0.376135 / 0.323480 (0.052655) | 0.006355 / 0.007986 (-0.001630) | 0.004193 / 0.004328 (-0.000135) | 0.072882 / 0.004250 (0.068631) | 0.052668 / 0.037052 (0.015615) | 0.347359 / 0.258489 (0.088870) | 0.382280 / 0.293841 (0.088440) | 0.035996 / 0.128546 (-0.092550) | 0.012517 / 0.075646 (-0.063129) | 0.334520 / 0.419271 (-0.084751) | 0.051969 / 0.043533 (0.008436) | 0.335735 / 0.255139 (0.080596) | 0.359921 / 0.283200 (0.076722) | 0.113971 / 0.141683 (-0.027712) | 1.465636 / 1.452155 (0.013481) | 1.559824 / 1.492716 (0.067108) |\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) | 0.223997 / 0.018006 (0.205991) | 0.499041 / 0.000490 (0.498551) | 0.009697 / 0.000200 (0.009497) | 0.000245 / 0.000054 (0.000190) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027031 / 0.037411 (-0.010381) | 0.110271 / 0.014526 (0.095745) | 0.115848 / 0.176557 (-0.060709) | 0.174253 / 0.737135 (-0.562883) | 0.122616 / 0.296338 (-0.173723) |\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) | 0.417275 / 0.215209 (0.202066) | 4.158678 / 2.077655 (2.081023) | 1.917585 / 1.504120 (0.413465) | 1.722219 / 1.541195 (0.181025) | 1.813284 / 1.468490 (0.344793) | 0.707193 / 4.584777 (-3.877584) | 3.853545 / 3.745712 (0.107833) | 3.369240 / 5.269862 (-1.900621) | 1.820264 / 4.565676 (-2.745412) | 0.087340 / 0.424275 (-0.336936) | 0.012305 / 0.007607 (0.004698) | 0.520326 / 0.226044 (0.294281) | 5.107383 / 2.268929 (2.838455) | 2.413977 / 55.444624 (-53.030647) | 2.074356 / 6.876477 (-4.802121) | 2.255959 / 2.142072 (0.113887) | 0.849850 / 4.805227 (-3.955377) | 0.170116 / 6.500664 (-6.330548) | 0.067203 / 0.075469 (-0.008267) |\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) | 1.168158 / 1.841788 (-0.673629) | 15.046312 / 8.074308 (6.972004) | 15.113924 / 10.191392 (4.922532) | 0.145288 / 0.680424 (-0.535136) | 0.017959 / 0.534201 (-0.516242) | 0.424666 / 0.579283 (-0.154617) | 0.422560 / 0.434364 (-0.011804) | 0.526386 / 0.540337 (-0.013952) | 0.623755 / 1.386936 (-0.763181) |\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) | 0.007676 / 0.011353 (-0.003677) | 0.005240 / 0.011008 (-0.005769) | 0.074668 / 0.038508 (0.036160) | 0.035570 / 0.023109 (0.012461) | 0.348524 / 0.275898 (0.072626) | 0.378157 / 0.323480 (0.054677) | 0.006112 / 0.007986 (-0.001873) | 0.005641 / 0.004328 (0.001312) | 0.073536 / 0.004250 (0.069286) | 0.048651 / 0.037052 (0.011599) | 0.359282 / 0.258489 (0.100793) | 0.385961 / 0.293841 (0.092120) | 0.035417 / 0.128546 (-0.093129) | 0.012227 / 0.075646 (-0.063419) | 0.085725 / 0.419271 (-0.333546) | 0.049651 / 0.043533 (0.006118) | 0.344122 / 0.255139 (0.088983) | 0.364795 / 0.283200 (0.081595) | 0.112711 / 0.141683 (-0.028972) | 1.426823 / 1.452155 (-0.025332) | 1.534745 / 1.492716 (0.042029) |\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) | 0.201728 / 0.018006 (0.183721) | 0.448533 / 0.000490 (0.448043) | 0.003554 / 0.000200 (0.003354) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030917 / 0.037411 (-0.006494) | 0.117966 / 0.014526 (0.103440) | 0.125954 / 0.176557 (-0.050602) | 0.176382 / 0.737135 (-0.560753) | 0.130757 / 0.296338 (-0.165582) |\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) | 0.422167 / 0.215209 (0.206958) | 4.213948 / 2.077655 (2.136294) | 2.040049 / 1.504120 (0.535929) | 1.858317 / 1.541195 (0.317122) | 1.937108 / 1.468490 (0.468618) | 0.707797 / 4.584777 (-3.876979) | 3.831061 / 3.745712 (0.085349) | 3.373711 / 5.269862 (-1.896151) | 1.590343 / 4.565676 (-2.975333) | 0.086672 / 0.424275 (-0.337603) | 0.012429 / 0.007607 (0.004821) | 0.520269 / 0.226044 (0.294225) | 5.207285 / 2.268929 (2.938357) | 2.518107 / 55.444624 (-52.926517) | 2.230696 / 6.876477 (-4.645781) | 2.363164 / 2.142072 (0.221091) | 0.836749 / 4.805227 (-3.968479) | 0.169676 / 6.500664 (-6.330988) | 0.065766 / 0.075469 (-0.009703) |\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) | 1.251195 / 1.841788 (-0.590592) | 15.196091 / 8.074308 (7.121782) | 14.991600 / 10.191392 (4.800208) | 0.165335 / 0.680424 (-0.515089) | 0.017789 / 0.534201 (-0.516412) | 0.433863 / 0.579283 (-0.145420) | 0.428660 / 0.434364 (-0.005704) | 0.527385 / 0.540337 (-0.012952) | 0.628067 / 1.386936 (-0.758869) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d06b8c21ba98ae85971a2b1d135ac2ef035b59c9 \"CML watermark\")\n" ]

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[ "Looks like as long as the number of shards makes a batch lower than 1000 images it works. In my training set I have 40K images. If I use `num_shards=40` (batch of 1000 images) I get the error, but if I update it to `num_shards=50` (batch of 800 images) it works.\r\n\r\nI will be happy to share my dataset privately if it can help to better debug.", "Hi! I didn't manage to reproduce this behavior, so sharing the dataset with us would help a lot. \r\n\r\n> My dataset is around 50K images, is this error might be due to a bad image?\r\n\r\nThis shouldn't be the case as we save raw data to disk without decoding it.", "OK, thanks! The dataset is currently hosted on a gcs bucket. How would you like to proceed for sharing the link? ", "You could follow [this](https://cloud.google.com/storage/docs/collaboration#browser) procedure or upload the dataset to Google Drive (50K images is not that much unless high-res) and send me an email with the link.", "Thanks @mariosasko. I just sent you the GDrive link.", "Thanks @jplu! I managed to reproduce the `TypeError` - it stems from [this](https://github.com/huggingface/datasets/blob/e3f4f124a1b118a5bfff5bae76b25a68aedbebbc/src/datasets/features/image.py#L258-L264) line, which can return a `ChunkedArray` (its mask is also chunked then, which Arrow does not allow) when the embedded data is too big to fit in a standard `Array`.\r\n\r\nI'm working on a fix." ]

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[ "Hi! Can you share one of the problematic audio files with us?\r\n\r\nI tried to reproduce the error with the following code: \r\n```python\r\nimport soundfile as sf\r\nimport numpy as np\r\nfrom datasets import Audio\r\n\r\nsf.write(\"empty.wav\", np.array([]), 16000)\r\nAudio(sampling_rate=24000).decode_example({\"path\": \"empty.wav\", \"bytes\": None})\r\n```\r\nBut without success.\r\n\r\nAlso, what version of `librosa` is installed in your env? (You can get this info with `python -c \"import librosa; print(librosa.__version__)`)\r\n\r\n" ]

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[ "Hi! \r\n\r\nYou can use [`.set_format(\"np\")`](https://huggingface.co/docs/datasets/process#format) to get NumPy arrays (or Pytorch tensors with `.set_format(\"torch\")`) in `__getitem__`.\r\n\r\nAlso, have you been able to reproduce the linked PyTorch issue with a HF dataset?\r\n " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.004406 / 0.011008 (-0.006602) | 0.097136 / 0.038508 (0.058628) | 0.027711 / 0.023109 (0.004601) | 0.303092 / 0.275898 (0.027194) | 0.336804 / 0.323480 (0.013324) | 0.004838 / 0.007986 (-0.003148) | 0.004533 / 0.004328 (0.000204) | 0.075062 / 0.004250 (0.070812) | 0.035105 / 0.037052 (-0.001947) | 0.310245 / 0.258489 (0.051756) | 0.347086 / 0.293841 (0.053245) | 0.030867 / 0.128546 (-0.097679) | 0.011436 / 0.075646 (-0.064211) | 0.320728 / 0.419271 (-0.098544) | 0.042303 / 0.043533 (-0.001230) | 0.308177 / 0.255139 (0.053038) | 0.333673 / 0.283200 (0.050473) | 0.084736 / 0.141683 (-0.056947) | 1.477391 / 1.452155 (0.025237) | 1.530399 / 1.492716 (0.037682) |\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) | 0.212698 / 0.018006 (0.194692) | 0.409098 / 0.000490 (0.408608) | 0.004202 / 0.000200 (0.004002) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022725 / 0.037411 (-0.014686) | 0.095866 / 0.014526 (0.081340) | 0.104153 / 0.176557 (-0.072404) | 0.162964 / 0.737135 (-0.574171) | 0.106505 / 0.296338 (-0.189834) |\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) | 0.431336 / 0.215209 (0.216127) | 4.283290 / 2.077655 (2.205635) | 1.982418 / 1.504120 (0.478298) | 1.762104 / 1.541195 (0.220909) | 1.807528 / 1.468490 (0.339038) | 0.695507 / 4.584777 (-3.889270) | 3.376299 / 3.745712 (-0.369413) | 1.856642 / 5.269862 (-3.413219) | 1.154258 / 4.565676 (-3.411419) | 0.082749 / 0.424275 (-0.341526) | 0.012289 / 0.007607 (0.004682) | 0.525842 / 0.226044 (0.299798) | 5.285764 / 2.268929 (3.016835) | 2.389926 / 55.444624 (-53.054698) | 2.021830 / 6.876477 (-4.854646) | 2.107460 / 2.142072 (-0.034612) | 0.808118 / 4.805227 (-3.997109) | 0.150791 / 6.500664 (-6.349873) | 0.065825 / 0.075469 (-0.009644) |\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) | 1.206939 / 1.841788 (-0.634849) | 13.795902 / 8.074308 (5.721594) | 14.107950 / 10.191392 (3.916558) | 0.144300 / 0.680424 (-0.536124) | 0.016478 / 0.534201 (-0.517723) | 0.379395 / 0.579283 (-0.199888) | 0.388437 / 0.434364 (-0.045927) | 0.451443 / 0.540337 (-0.088894) | 0.523142 / 1.386936 (-0.863794) |\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) | 0.006503 / 0.011353 (-0.004850) | 0.004578 / 0.011008 (-0.006430) | 0.076278 / 0.038508 (0.037770) | 0.028052 / 0.023109 (0.004943) | 0.337873 / 0.275898 (0.061975) | 0.371368 / 0.323480 (0.047888) | 0.005086 / 0.007986 (-0.002899) | 0.003354 / 0.004328 (-0.000975) | 0.076876 / 0.004250 (0.072625) | 0.039146 / 0.037052 (0.002093) | 0.340299 / 0.258489 (0.081810) | 0.381209 / 0.293841 (0.087368) | 0.031771 / 0.128546 (-0.096775) | 0.011670 / 0.075646 (-0.063976) | 0.085156 / 0.419271 (-0.334116) | 0.041990 / 0.043533 (-0.001543) | 0.338644 / 0.255139 (0.083505) | 0.362461 / 0.283200 (0.079262) | 0.089772 / 0.141683 (-0.051911) | 1.480341 / 1.452155 (0.028187) | 1.562815 / 1.492716 (0.070099) |\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) | 0.205700 / 0.018006 (0.187694) | 0.402206 / 0.000490 (0.401716) | 0.001212 / 0.000200 (0.001012) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025172 / 0.037411 (-0.012240) | 0.100959 / 0.014526 (0.086433) | 0.108464 / 0.176557 (-0.068093) | 0.161321 / 0.737135 (-0.575814) | 0.114245 / 0.296338 (-0.182093) |\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) | 0.437425 / 0.215209 (0.222216) | 4.362212 / 2.077655 (2.284557) | 2.068815 / 1.504120 (0.564695) | 1.864089 / 1.541195 (0.322894) | 1.909038 / 1.468490 (0.440548) | 0.696097 / 4.584777 (-3.888680) | 3.358628 / 3.745712 (-0.387084) | 2.999085 / 5.269862 (-2.270777) | 1.533917 / 4.565676 (-3.031760) | 0.083010 / 0.424275 (-0.341266) | 0.012372 / 0.007607 (0.004765) | 0.539926 / 0.226044 (0.313882) | 5.438326 / 2.268929 (3.169397) | 2.498581 / 55.444624 (-52.946043) | 2.153359 / 6.876477 (-4.723117) | 2.177891 / 2.142072 (0.035819) | 0.803169 / 4.805227 (-4.002059) | 0.151079 / 6.500664 (-6.349585) | 0.065981 / 0.075469 (-0.009489) |\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) | 1.336682 / 1.841788 (-0.505106) | 14.133055 / 8.074308 (6.058747) | 14.033972 / 10.191392 (3.842580) | 0.152109 / 0.680424 (-0.528315) | 0.016475 / 0.534201 (-0.517726) | 0.387808 / 0.579283 (-0.191475) | 0.378347 / 0.434364 (-0.056017) | 0.484732 / 0.540337 (-0.055606) | 0.569907 / 1.386936 (-0.817029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1c4ec00511868bd881e84a6f7e0333648d833b8e \"CML watermark\")\n" ]

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[ "Hi Julien ! This sounds related to https://github.com/huggingface/datasets/issues/5695 - TL;DR: you need to have shards smaller than 2GB to avoid this issue\r\n\r\nThe number of rows per shard is computed using an estimated size of the full dataset, which can sometimes lead to shards bigger than `max_shard_size`. The estimation is currently done using the first samples of the dataset (which can surely be improved). We should probably open an issue to fix this once and for all.\r\n\r\nAnyway for your specific dataset I'd suggest you to pass `num_shards` instead of `max_shard_size` for now, and make sure to have enough shards to end up with shards smaller than 2GB", "Hi Quentin! Thanks a lot! Using `num_shards` instead of `max_shard_size` works as expected.\r\n\r\nIndeed the way you describe how the size is computed cannot really work with the dataset I'm building as all the image doesn't have the same resolution and then size. Opening an issue on this might be a good idea." ]

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[ "Closing since this is a duplicate of #5711", "> Closing since this is a duplicate of #5711\r\n\r\nSorry @mariosasko , my internet went down went submitting the issue, and somehow it ended up creating a duplicate" ]

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[ "It seems like https://github.com/huggingface/datasets/pull/5626 has introduced this error. \r\n\r\ncc @albertvillanova \r\n\r\nI think replacing:\r\nhttps://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/src/datasets/download/streaming_download_manager.py#L777-L778\r\nwith:\r\n```python\r\nreturn np.load(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), *args, **kwargs)\r\n```\r\nshould fix the issue.\r\n\r\n(Maybe this is also worth doing a patch release afterward)", "Thanks for reporting, @rcasero.\r\n\r\nI can have a look..." ]

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[ "Hi! This error means that PyArrow's internal [`mmap`](https://man7.org/linux/man-pages/man2/mmap.2.html) call failed to allocate memory, which can be tricky to debug. Since this error is more related to PyArrow than us, I think it's best to report this issue in their [repo](https://github.com/apache/arrow) (they are more experienced on this matter). Also, googling \"mmap cannot allocate memory\" returns some approaches to solving this problem." ]

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[ "hi @jplu ! Did I understand you correctly that you create the dataset, push it to the Hub with `.push_to_hub` and you see a `dataset_infos.json` file there, then you edit this file, load the dataset with `load_dataset` and you don't see any changes in `.info` attribute of a dataset object? \r\n\r\nThis is actually weird that when you push your dataset to the Hub, a `dataset_infos.json` file is created, because this file is deprecated and it should create `README.md` with the `dataset_info` field instead. Some keys are also deprecated, like \"supervised_keys\" and \"task_templates\".\r\n\r\nCan you please provide a toy reproducible example of how you create and push the dataset? And also why do you want to change this file, especially the number of bytes and examples?", "Hi @polinaeterna Yes I have created the dataset with `Dataset.from_dict` applied some updates afterward and when I pushed to the hub I had a `dataset_infos.json` file and there was a `README.md` file as well.\r\n\r\nI didn't know that the JSON file was deprecated. So I have built my dataset with `ImageBuilder` instead and now it works like a charm without having to touch anything.\r\n\r\nI haven't succeed to reproduce the creation of the JSON file with a toy example, hence, I certainly did some mistakes when I have manipulated my dataset manually at first. My bad." ]

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[ "Example of bulk edit: https://huggingface.co/datasets/aeslc/discussions/5", "looks great! \r\n\r\nDo you encode the fact that you've already converted a dataset? (to not convert it twice) or do you base yourself on the info contained in `dataset_info`", "I am only looping trough the dataset cards, assuming that all of them were created with MiB.\r\n\r\nI agree we should only run the bulk edit once for all canonical datasets: I'm using a for-loop over canonical datasets.", "yes, worst case, we have this in structured data:\r\n\r\n<img width=\"337\" alt=\"image\" src=\"https://user-images.githubusercontent.com/326577/230037051-06caddcb-08c8-4953-a710-f3d122917db3.png\">\r\n", "I have just included as well the conversion from MB to GB if necessary. See: \r\n- https://huggingface.co/datasets/bookcorpus/discussions/2/files\r\n- https://huggingface.co/datasets/asnq/discussions/2/files", "Nice. Is it another loop? Because in https://huggingface.co/datasets/amazon_us_reviews/discussions/2/files we have `32377.29 MB` for example", "First, I tested some batches to check the changes made. Then I incorporated the MB to GB conversion. Now I'm running the rest.", "The bulk edit parsed 751 canonical datasets and updated 166.", "Thanks a lot!\r\n\r\nThe sizes now match as expected!\r\n\r\n<img width=\"1446\" alt=\"Capture d’écran 2023-04-05 à 16 10 15\" src=\"https://user-images.githubusercontent.com/1676121/230107044-ac2a76ea-a4fe-4e81-a925-f464b85f5edd.png\">\r\n", "I made another bulk edit of ancient canonical datasets that were moved to community organization. I have parsed 11 datasets and opened a PR on 3 of them:\r\n- [ ] \"allenai/scicite\": https://huggingface.co/datasets/allenai/scicite/discussions/3\r\n- [ ] \"allenai/scifact\": https://huggingface.co/datasets/allenai/scifact/discussions/2\r\n- [x] \"dair-ai/emotion\": https://huggingface.co/datasets/dair-ai/emotion/discussions/6" ]

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[ "Hi ! We could definitely a type that holds the categories and uses a DictionaryType storage. There's a ClassLabel type that is similar with a 'names' parameter (similar to a id2label in deep learning frameworks) that uses an integer array as storage.\r\n\r\nIt can be added in `features.py`. Here are some pointers:\r\n- the conversion from HF type to PyArrow type is done in `get_nested_type`\r\n- the conversion from Pyarrow type to HF type is done in `generate_from_arrow_type`\r\n- `encode_nested_example` and `decode_nested_example` are used to do user's value (what users see) <-> storage value (what is in the pyarrow.array) if there's any conversion to do", "@kklemon did you implement this? Otherwise I would like to give it a try", "@mhattingpete no, I hadn't time for this so far. Feel free to work on this.", "#self-assign", "This would be super useful, so +1. \r\n\r\nAlso, these prior issues/PRs seem relevant: \r\nhttps://github.com/huggingface/datasets/issues/1906\r\nhttps://github.com/huggingface/datasets/pull/1936", "Hi, this is a really useful feature, has this been implemented yet? " ]

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[ "Hi! It can take some time to iterate over Parquet files as big as yours, convert the samples to Python, and find the first one that matches a filter predicate before yielding it...", "Thanks @mariosasko, I figured it was the filter operation. I'm closing this issue because it is not a bug, it is the expected beheaviour." ]

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