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DanielPFlorian
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Hugging-Face-Datasets-Github-Issues

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https://api.github.com/repos/huggingface/datasets/issues/6254
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Dataset.from_generator() cost much more time in vscode debugging mode then running mode
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[ "Answered on the forum: https://discuss.huggingface.co/t/dataset-from-generator-cost-much-more-time-in-vscode-debugging-mode-then-running-mode/56005/2" ]
2023-09-23T02:07:26
2023-10-03T14:42:53
2023-10-03T14:42:53
NONE
null
null
### Describe the bug Hey there, I’m using Dataset.from_generator() to convert a torch_dataset to the Huggingface Dataset. However, when I debug my code on vscode, I find that it runs really slow on Dataset.from_generator() which may even 20 times longer then run the script on terminal. ### Steps to reproduce the bug I write a simple test code : ```python import os from functools import partial from typing import Callable import torch import time from torch.utils.data import Dataset as TorchDataset from datasets import load_from_disk, Dataset as HFDataset import torch from torch.utils.data import Dataset class SimpleDataset(Dataset): def __init__(self, data): self.data = data self.keys = list(data[0].keys()) def __len__(self): return len(self.data) def __getitem__(self, index): sample = self.data[index] return {key: sample[key] for key in self.keys} def TorchDataset2HuggingfaceDataset(torch_dataset: TorchDataset, cache_dir: str = None ) -> HFDataset: """ convert torch dataset to huggingface dataset """ generator : Callable[[], TorchDataset] = lambda: (sample for sample in torch_dataset) return HFDataset.from_generator(generator, cache_dir=cache_dir) if __name__ == '__main__': data = [ {'id': 1, 'name': 'Alice'}, {'id': 2, 'name': 'Bob'}, {'id': 3, 'name': 'Charlie'} ] torch_dataset = SimpleDataset(data) start_time = time.time() huggingface_dataset = TorchDataset2HuggingfaceDataset(torch_dataset) end_time = time.time() print("time: ", end_time - start_time) print(huggingface_dataset) ``` ### Expected behavior this test on my machine report that the running time on terminal is 0.086, however the running time in debugging mode on vscode is 0.25, which I think is much longer than expected. I’d like to know is the anything wrong in the code or just because of debugging? I have traced the code and I find is this func which I get stuck. ```python def create_config_id( self, config_kwargs: dict, custom_features: Optional[Features] = None, ) -> str: ... # stuck in this line suffix = Hasher.hash(config_kwargs_to_add_to_suffix) ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31 - Python version: 3.11.3 - Huggingface_hub version: 0.17.2 - PyArrow version: 11.0.0 - Pandas version: 2.0.1
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Check builder cls default config name in inspect
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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) | 0.006591 / 0.011353 (-0.004762) | 0.003991 / 0.011008 (-0.007017) | 0.085197 / 0.038508 (0.046689) | 0.080312 / 0.023109 (0.057202) | 0.342026 / 0.275898 (0.066128) | 0.370749 / 0.323480 (0.047269) | 0.004124 / 0.007986 (-0.003861) | 0.003413 / 0.004328 (-0.000916) | 0.064363 / 0.004250 (0.060113) | 0.055920 / 0.037052 (0.018868) | 0.340667 / 0.258489 (0.082178) | 0.380138 / 0.293841 (0.086297) | 0.031115 / 0.128546 (-0.097431) | 0.008511 / 0.075646 (-0.067135) | 0.289065 / 0.419271 (-0.130207) | 0.052266 / 0.043533 (0.008734) | 0.343808 / 0.255139 (0.088669) | 0.353578 / 0.283200 (0.070378) | 0.024006 / 0.141683 (-0.117676) | 1.490322 / 1.452155 (0.038168) | 1.591133 / 1.492716 (0.098417) |\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) | 0.234718 / 0.018006 (0.216712) | 0.447023 / 0.000490 (0.446533) | 0.009343 / 0.000200 (0.009143) | 0.000259 / 0.000054 (0.000204) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030466 / 0.037411 (-0.006945) | 0.083367 / 0.014526 (0.068841) | 0.100532 / 0.176557 (-0.076024) | 0.158018 / 0.737135 (-0.579117) | 0.098280 / 0.296338 (-0.198059) |\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) | 0.408501 / 0.215209 (0.193292) | 4.066937 / 2.077655 (1.989282) | 2.034029 / 1.504120 (0.529909) | 1.842982 / 1.541195 (0.301788) | 1.987319 / 1.468490 (0.518829) | 0.492126 / 4.584777 (-4.092651) | 3.554027 / 3.745712 (-0.191685) | 3.289023 / 5.269862 (-1.980839) | 2.069796 / 4.565676 (-2.495880) | 0.057930 / 0.424275 (-0.366346) | 0.007308 / 0.007607 (-0.000299) | 0.482596 / 0.226044 (0.256552) | 4.830714 / 2.268929 (2.561785) | 2.506787 / 55.444624 (-52.937838) | 2.163498 / 6.876477 (-4.712979) | 2.389135 / 2.142072 (0.247062) | 0.597538 / 4.805227 (-4.207689) | 0.134268 / 6.500664 (-6.366396) | 0.061189 / 0.075469 (-0.014280) |\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) | 1.245328 / 1.841788 (-0.596460) | 19.145151 / 8.074308 (11.070843) | 14.742121 / 10.191392 (4.550729) | 0.144749 / 0.680424 (-0.535675) | 0.018433 / 0.534201 (-0.515768) | 0.391867 / 0.579283 (-0.187416) | 0.416555 / 0.434364 (-0.017809) | 0.454341 / 0.540337 (-0.085997) | 0.646833 / 1.386936 (-0.740103) |\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) | 0.006669 / 0.011353 (-0.004684) | 0.004031 / 0.011008 (-0.006978) | 0.064347 / 0.038508 (0.025839) | 0.076857 / 0.023109 (0.053748) | 0.415864 / 0.275898 (0.139966) | 0.468615 / 0.323480 (0.145135) | 0.005383 / 0.007986 (-0.002603) | 0.003314 / 0.004328 (-0.001015) | 0.064829 / 0.004250 (0.060578) | 0.057182 / 0.037052 (0.020129) | 0.417055 / 0.258489 (0.158566) | 0.472725 / 0.293841 (0.178884) | 0.031938 / 0.128546 (-0.096608) | 0.008564 / 0.075646 (-0.067082) | 0.070649 / 0.419271 (-0.348623) | 0.047439 / 0.043533 (0.003906) | 0.409589 / 0.255139 (0.154450) | 0.433700 / 0.283200 (0.150500) | 0.024132 / 0.141683 (-0.117551) | 1.500825 / 1.452155 (0.048670) | 1.592059 / 1.492716 (0.099343) |\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) | 0.225652 / 0.018006 (0.207646) | 0.444188 / 0.000490 (0.443698) | 0.004581 / 0.000200 (0.004381) | 0.000104 / 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) | 0.033272 / 0.037411 (-0.004139) | 0.096833 / 0.014526 (0.082307) | 0.107134 / 0.176557 (-0.069422) | 0.159299 / 0.737135 (-0.577836) | 0.107533 / 0.296338 (-0.188806) |\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) | 0.429100 / 0.215209 (0.213890) | 4.281051 / 2.077655 (2.203396) | 2.318713 / 1.504120 (0.814593) | 2.165645 / 1.541195 (0.624451) | 2.250224 / 1.468490 (0.781734) | 0.495791 / 4.584777 (-4.088986) | 3.591953 / 3.745712 (-0.153760) | 3.303426 / 5.269862 (-1.966436) | 2.076861 / 4.565676 (-2.488816) | 0.058369 / 0.424275 (-0.365906) | 0.007387 / 0.007607 (-0.000220) | 0.501270 / 0.226044 (0.275225) | 5.014987 / 2.268929 (2.746059) | 2.800951 / 55.444624 (-52.643673) | 2.464316 / 6.876477 (-4.412161) | 2.685259 / 2.142072 (0.543187) | 0.584797 / 4.805227 (-4.220430) | 0.131889 / 6.500664 (-6.368775) | 0.061021 / 0.075469 (-0.014448) |\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) | 1.366982 / 1.841788 (-0.474806) | 19.820376 / 8.074308 (11.746068) | 14.968664 / 10.191392 (4.777272) | 0.165344 / 0.680424 (-0.515080) | 0.019956 / 0.534201 (-0.514245) | 0.395843 / 0.579283 (-0.183441) | 0.420854 / 0.434364 (-0.013510) | 0.465065 / 0.540337 (-0.075272) | 0.651531 / 1.386936 (-0.735405) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ca0e13300f5392cd87189d5afd5942927afc7 \"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) | 0.005974 / 0.011353 (-0.005379) | 0.003714 / 0.011008 (-0.007294) | 0.080049 / 0.038508 (0.041541) | 0.061233 / 0.023109 (0.038124) | 0.317187 / 0.275898 (0.041289) | 0.352725 / 0.323480 (0.029245) | 0.004867 / 0.007986 (-0.003119) | 0.002953 / 0.004328 (-0.001376) | 0.063156 / 0.004250 (0.058905) | 0.046752 / 0.037052 (0.009700) | 0.320171 / 0.258489 (0.061682) | 0.367572 / 0.293841 (0.073731) | 0.027253 / 0.128546 (-0.101293) | 0.008100 / 0.075646 (-0.067546) | 0.261206 / 0.419271 (-0.158066) | 0.044581 / 0.043533 (0.001048) | 0.331169 / 0.255139 (0.076030) | 0.348719 / 0.283200 (0.065519) | 0.021397 / 0.141683 (-0.120286) | 1.528315 / 1.452155 (0.076160) | 1.533789 / 1.492716 (0.041073) |\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) | 0.233336 / 0.018006 (0.215329) | 0.416866 / 0.000490 (0.416376) | 0.008805 / 0.000200 (0.008605) | 0.000240 / 0.000054 (0.000186) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024754 / 0.037411 (-0.012657) | 0.073311 / 0.014526 (0.058785) | 0.085419 / 0.176557 (-0.091138) | 0.146380 / 0.737135 (-0.590756) | 0.085545 / 0.296338 (-0.210793) |\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) | 0.431426 / 0.215209 (0.216217) | 4.315899 / 2.077655 (2.238244) | 2.232492 / 1.504120 (0.728372) | 2.064174 / 1.541195 (0.522979) | 2.158982 / 1.468490 (0.690492) | 0.499375 / 4.584777 (-4.085402) | 3.093259 / 3.745712 (-0.652454) | 2.848260 / 5.269862 (-2.421601) | 1.853097 / 4.565676 (-2.712579) | 0.057143 / 0.424275 (-0.367132) | 0.006349 / 0.007607 (-0.001258) | 0.507747 / 0.226044 (0.281702) | 5.078872 / 2.268929 (2.809944) | 2.717697 / 55.444624 (-52.726927) | 2.363564 / 6.876477 (-4.512913) | 2.485756 / 2.142072 (0.343684) | 0.595888 / 4.805227 (-4.209340) | 0.127285 / 6.500664 (-6.373379) | 0.060639 / 0.075469 (-0.014830) |\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) | 1.219287 / 1.841788 (-0.622501) | 17.300038 / 8.074308 (9.225730) | 13.747230 / 10.191392 (3.555838) | 0.144841 / 0.680424 (-0.535583) | 0.016587 / 0.534201 (-0.517614) | 0.336891 / 0.579283 (-0.242392) | 0.376128 / 0.434364 (-0.058236) | 0.385749 / 0.540337 (-0.154588) | 0.552218 / 1.386936 (-0.834718) |\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) | 0.006477 / 0.011353 (-0.004876) | 0.003709 / 0.011008 (-0.007299) | 0.064708 / 0.038508 (0.026200) | 0.062627 / 0.023109 (0.039518) | 0.444721 / 0.275898 (0.168823) | 0.477825 / 0.323480 (0.154345) | 0.004890 / 0.007986 (-0.003096) | 0.002896 / 0.004328 (-0.001432) | 0.063781 / 0.004250 (0.059530) | 0.050488 / 0.037052 (0.013436) | 0.453466 / 0.258489 (0.194977) | 0.483303 / 0.293841 (0.189462) | 0.028814 / 0.128546 (-0.099732) | 0.008207 / 0.075646 (-0.067440) | 0.070140 / 0.419271 (-0.349131) | 0.041487 / 0.043533 (-0.002045) | 0.454599 / 0.255139 (0.199460) | 0.468374 / 0.283200 (0.185174) | 0.019758 / 0.141683 (-0.121925) | 1.437542 / 1.452155 (-0.014613) | 1.507965 / 1.492716 (0.015249) |\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) | 0.223358 / 0.018006 (0.205352) | 0.413824 / 0.000490 (0.413334) | 0.004593 / 0.000200 (0.004393) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026278 / 0.037411 (-0.011134) | 0.081992 / 0.014526 (0.067466) | 0.089969 / 0.176557 (-0.086587) | 0.143668 / 0.737135 (-0.593467) | 0.091273 / 0.296338 (-0.205066) |\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) | 0.461198 / 0.215209 (0.245989) | 4.615398 / 2.077655 (2.537743) | 2.552291 / 1.504120 (1.048171) | 2.373789 / 1.541195 (0.832595) | 2.431591 / 1.468490 (0.963101) | 0.507683 / 4.584777 (-4.077094) | 3.148771 / 3.745712 (-0.596941) | 2.849118 / 5.269862 (-2.420744) | 1.883001 / 4.565676 (-2.682675) | 0.059423 / 0.424275 (-0.364852) | 0.006463 / 0.007607 (-0.001144) | 0.535129 / 0.226044 (0.309085) | 5.362870 / 2.268929 (3.093941) | 3.016548 / 55.444624 (-52.428076) | 2.666205 / 6.876477 (-4.210271) | 2.821396 / 2.142072 (0.679324) | 0.606596 / 4.805227 (-4.198631) | 0.125991 / 6.500664 (-6.374673) | 0.063566 / 0.075469 (-0.011903) |\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) | 1.364771 / 1.841788 (-0.477017) | 18.000713 / 8.074308 (9.926404) | 14.840330 / 10.191392 (4.648937) | 0.144770 / 0.680424 (-0.535653) | 0.018060 / 0.534201 (-0.516141) | 0.334470 / 0.579283 (-0.244813) | 0.387386 / 0.434364 (-0.046978) | 0.398743 / 0.540337 (-0.141595) | 0.555437 / 1.386936 (-0.831499) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b974c9af6b45b6ebdbbf4b3418f25506c1c0618 \"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) | 0.006491 / 0.011353 (-0.004862) | 0.004058 / 0.011008 (-0.006950) | 0.084462 / 0.038508 (0.045954) | 0.072310 / 0.023109 (0.049201) | 0.352458 / 0.275898 (0.076560) | 0.385829 / 0.323480 (0.062350) | 0.003978 / 0.007986 (-0.004007) | 0.003455 / 0.004328 (-0.000873) | 0.064070 / 0.004250 (0.059819) | 0.055577 / 0.037052 (0.018525) | 0.361288 / 0.258489 (0.102799) | 0.400147 / 0.293841 (0.106306) | 0.030785 / 0.128546 (-0.097761) | 0.008676 / 0.075646 (-0.066971) | 0.287481 / 0.419271 (-0.131791) | 0.052643 / 0.043533 (0.009110) | 0.354670 / 0.255139 (0.099531) | 0.382322 / 0.283200 (0.099122) | 0.025657 / 0.141683 (-0.116026) | 1.486798 / 1.452155 (0.034643) | 1.588439 / 1.492716 (0.095723) |\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) | 0.240881 / 0.018006 (0.222875) | 0.463997 / 0.000490 (0.463507) | 0.009688 / 0.000200 (0.009488) | 0.000601 / 0.000054 (0.000546) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029071 / 0.037411 (-0.008340) | 0.083077 / 0.014526 (0.068551) | 0.119857 / 0.176557 (-0.056699) | 0.153387 / 0.737135 (-0.583749) | 0.132162 / 0.296338 (-0.164177) |\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) | 0.383822 / 0.215209 (0.168613) | 3.828572 / 2.077655 (1.750918) | 1.877629 / 1.504120 (0.373509) | 1.708757 / 1.541195 (0.167562) | 1.771658 / 1.468490 (0.303168) | 0.482439 / 4.584777 (-4.102338) | 3.496247 / 3.745712 (-0.249466) | 3.282055 / 5.269862 (-1.987807) | 2.053069 / 4.565676 (-2.512607) | 0.056626 / 0.424275 (-0.367649) | 0.007338 / 0.007607 (-0.000269) | 0.461257 / 0.226044 (0.235213) | 4.605326 / 2.268929 (2.336397) | 2.408365 / 55.444624 (-53.036260) | 1.986550 / 6.876477 (-4.889926) | 2.225220 / 2.142072 (0.083148) | 0.601301 / 4.805227 (-4.203927) | 0.132217 / 6.500664 (-6.368447) | 0.061217 / 0.075469 (-0.014252) |\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) | 1.268706 / 1.841788 (-0.573081) | 18.892026 / 8.074308 (10.817717) | 14.093892 / 10.191392 (3.902500) | 0.162483 / 0.680424 (-0.517941) | 0.018372 / 0.534201 (-0.515829) | 0.391901 / 0.579283 (-0.187382) | 0.401578 / 0.434364 (-0.032786) | 0.456741 / 0.540337 (-0.083596) | 0.646760 / 1.386936 (-0.740176) |\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) | 0.006657 / 0.011353 (-0.004696) | 0.003981 / 0.011008 (-0.007027) | 0.066126 / 0.038508 (0.027617) | 0.072673 / 0.023109 (0.049564) | 0.409970 / 0.275898 (0.134072) | 0.430797 / 0.323480 (0.107317) | 0.005477 / 0.007986 (-0.002508) | 0.003362 / 0.004328 (-0.000966) | 0.065532 / 0.004250 (0.061282) | 0.056018 / 0.037052 (0.018966) | 0.406676 / 0.258489 (0.148187) | 0.438516 / 0.293841 (0.144675) | 0.032795 / 0.128546 (-0.095751) | 0.008580 / 0.075646 (-0.067066) | 0.072692 / 0.419271 (-0.346579) | 0.048110 / 0.043533 (0.004577) | 0.396826 / 0.255139 (0.141687) | 0.418442 / 0.283200 (0.135242) | 0.023269 / 0.141683 (-0.118414) | 1.499438 / 1.452155 (0.047283) | 1.568842 / 1.492716 (0.076126) |\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) | 0.218729 / 0.018006 (0.200723) | 0.450771 / 0.000490 (0.450281) | 0.004996 / 0.000200 (0.004796) | 0.000086 / 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) | 0.031484 / 0.037411 (-0.005928) | 0.092927 / 0.014526 (0.078401) | 0.107849 / 0.176557 (-0.068707) | 0.156658 / 0.737135 (-0.580478) | 0.106373 / 0.296338 (-0.189965) |\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) | 0.434658 / 0.215209 (0.219449) | 4.336386 / 2.077655 (2.258731) | 2.322577 / 1.504120 (0.818457) | 2.149505 / 1.541195 (0.608310) | 2.201967 / 1.468490 (0.733476) | 0.496994 / 4.584777 (-4.087783) | 3.533065 / 3.745712 (-0.212647) | 3.235750 / 5.269862 (-2.034112) | 2.034854 / 4.565676 (-2.530823) | 0.058258 / 0.424275 (-0.366017) | 0.007260 / 0.007607 (-0.000347) | 0.509115 / 0.226044 (0.283071) | 5.088427 / 2.268929 (2.819499) | 2.793551 / 55.444624 (-52.651073) | 2.430588 / 6.876477 (-4.445889) | 2.625998 / 2.142072 (0.483926) | 0.611676 / 4.805227 (-4.193552) | 0.133343 / 6.500664 (-6.367321) | 0.059888 / 0.075469 (-0.015581) |\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) | 1.377292 / 1.841788 (-0.464496) | 19.214299 / 8.074308 (11.139991) | 14.629146 / 10.191392 (4.437754) | 0.171283 / 0.680424 (-0.509141) | 0.020348 / 0.534201 (-0.513853) | 0.397823 / 0.579283 (-0.181461) | 0.411590 / 0.434364 (-0.022774) | 0.470850 / 0.540337 (-0.069487) | 0.658667 / 1.386936 (-0.728269) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1e1867e932f14233244fb25713f3c94c46ff50a \"CML watermark\")\n" ]
2023-09-21T10:15:32
2023-09-21T14:16:44
2023-09-21T14:08:00
MEMBER
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Fix https://github.com/huggingface/datasets-server/issues/1812 this was causing this issue: ```ipython In [1]: from datasets import * In [2]: inspect.get_dataset_config_names("aakanksha/udpos") Out[2]: ['default'] In [3]: load_dataset_builder("aakanksha/udpos").config.name Out[3]: 'en' ```
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I_kwDODunzps5xoPrS
6,252
exif_transpose not done to Image (PIL problem)
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[ "Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndataset = dataset.with_transform(exif_transpose_transform)\r\n```", "This operation sets some `Image` attributes to `None` (`.format`, `.filename`, etc.), causing our tests to fail, so I think we should wait for Datasets 3.0 to make this change. In version 3.0, storing image paths will be replaced by embedding image bytes, so there will be fewer instances where we use the `.filename` attribute." ]
2023-09-21T08:11:46
2023-09-22T14:07:52
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### Feature request I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading. Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images). For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference): ``` from PIL import Image, ImageOps pil = ImageOps.exif_transpose(pil) ``` reference: https://stackoverflow.com/a/63950647/5720150 Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose? Thank you ### Motivation Prevent having inverted data related to exif metadata that may affect object detection tasks ### Your contribution Changing in datasets.featrues.Image I can help with that.
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Support streaming datasets with pyarrow.parquet.read_table
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[ "_The documentation is not available anymore as the PR was closed or merged._", "This function reads an entire Arrow table in one go, which is not ideal memory-wise, so I don't think we should encourage using this function, considering we want to keep RAM usage as low as possible in the streaming mode. \r\n\r\n(Note that Parquet files are compressed, meaning the loaded table can be significantly larger than the size in Parquet.)\r\n\r\nInstead, we should suggest the authors to use:\r\n```python\r\nwith open(doc_path, \"rb\") as f:\r\n parquet_file = pq.ParquetFile(f)\r\n for batch in parquet_file.iter_batches():\r\n pa_table = pa.Table.from_batches([batch])\r\n yield idx, pa_table\r\n idx += 1\r\n```", "@mariosasko I think the potential problem you evoke is independent of whether or not we support streaming mode:\r\n- if the user's script with `read_table` works in non-streaming mode, it will also work in streaming mode after this PR\r\n\r\nIn fact, what we should suggest instead is to follow the scriptless approach, so that our `parquet` packaged module is used, with all the optimizations implemented. But this approach is not possible in all cases, and some use cases need to implement a script. And if they have small Parquet files and use `read_table`, I think we should support streaming.\r\n\r\nIn summary, let me clarify the goal and the scope of this PR:\r\n- a user needs using a loading script\r\n- their files are small enough so that they use `read_table`\r\n- their loading script works in non-streaming mode\r\n- therefore, this PR allows loading their dataset in streaming mode as well", "Yes, the no-script approach with metadata configs makes the most sense.\r\n\r\n> their files are small enough so that they use read_table\r\n\r\nSome of the Parquet files in that repo are larger than 1 GB ...\r\n\r\nAlso, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.", "@mariosasko, yes, this solution is not specifically for the \"uonlp/CulturaX\" dataset, but for other use cases as I explained above: indeed, they finally removed the use of `read_table` because their data files are too large.\r\n\r\n> Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.\r\n\r\nDo you know how many datasets are currently using `read_table`?", "> Do you know how many datasets are currently using read_table?\r\n\r\nZero (based on the script that checks the script contents of the public Hub datasets). ", "I see... Thanks! :hugs: ", "@mariosasko thanks for pointing the script! :hugs: \r\n\r\nHowever, I have found some Hub datasets that are using `read_table`, e.g.:\r\n- https://huggingface.co/datasets/jglaser/protein_ligand_contacts\r\n- https://huggingface.co/datasets/AresEkb/prof_standards_sbert_large_mt_nlu_ru\r\n- https://huggingface.co/datasets/victorcosta/pt_legislation\r\n- https://huggingface.co/datasets/jglaser/binding_affinity\r\n- https://huggingface.co/datasets/jglaser/pdbbind_complexes\r\n- https://huggingface.co/datasets/victorcosta/ria_pt__proems_format", "I'm merging this PR as discussed in private.", "<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) | 0.008267 / 0.011353 (-0.003086) | 0.005813 / 0.011008 (-0.005195) | 0.108802 / 0.038508 (0.070294) | 0.093996 / 0.023109 (0.070886) | 0.403115 / 0.275898 (0.127217) | 0.457299 / 0.323480 (0.133819) | 0.006277 / 0.007986 (-0.001709) | 0.004701 / 0.004328 (0.000373) | 0.080700 / 0.004250 (0.076449) | 0.077906 / 0.037052 (0.040854) | 0.409972 / 0.258489 (0.151483) | 0.477707 / 0.293841 (0.183867) | 0.041816 / 0.128546 (-0.086731) | 0.011250 / 0.075646 (-0.064397) | 0.390634 / 0.419271 (-0.028637) | 0.065361 / 0.043533 (0.021828) | 0.404501 / 0.255139 (0.149362) | 0.448162 / 0.283200 (0.164962) | 0.032823 / 0.141683 (-0.108860) | 1.899892 / 1.452155 (0.447737) | 2.044561 / 1.492716 (0.551844) |\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) | 0.241093 / 0.018006 (0.223086) | 0.482111 / 0.000490 (0.481622) | 0.005505 / 0.000200 (0.005305) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034861 / 0.037411 (-0.002551) | 0.109296 / 0.014526 (0.094770) | 0.127594 / 0.176557 (-0.048962) | 0.191815 / 0.737135 (-0.545320) | 0.122630 / 0.296338 (-0.173709) |\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) | 0.452194 / 0.215209 (0.236985) | 4.486200 / 2.077655 (2.408545) | 2.155635 / 1.504120 (0.651515) | 2.004569 / 1.541195 (0.463374) | 2.142570 / 1.468490 (0.674080) | 0.561488 / 4.584777 (-4.023289) | 4.381102 / 3.745712 (0.635390) | 3.914920 / 5.269862 (-1.354942) | 2.474271 / 4.565676 (-2.091406) | 0.067528 / 0.424275 (-0.356747) | 0.008723 / 0.007607 (0.001116) | 0.536077 / 0.226044 (0.310033) | 5.342050 / 2.268929 (3.073122) | 2.735747 / 55.444624 (-52.708877) | 2.353938 / 6.876477 (-4.522539) | 2.442878 / 2.142072 (0.300805) | 0.685404 / 4.805227 (-4.119823) | 0.156657 / 6.500664 (-6.344007) | 0.071714 / 0.075469 (-0.003755) |\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) | 1.562852 / 1.841788 (-0.278935) | 24.538203 / 8.074308 (16.463895) | 16.857777 / 10.191392 (6.666385) | 0.184221 / 0.680424 (-0.496203) | 0.021688 / 0.534201 (-0.512513) | 0.470700 / 0.579283 (-0.108583) | 0.470593 / 0.434364 (0.036229) | 0.645066 / 0.540337 (0.104729) | 0.756075 / 1.386936 (-0.630861) |\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) | 0.009486 / 0.011353 (-0.001867) | 0.004694 / 0.011008 (-0.006314) | 0.080216 / 0.038508 (0.041708) | 0.093479 / 0.023109 (0.070369) | 0.537353 / 0.275898 (0.261455) | 0.551631 / 0.323480 (0.228151) | 0.007373 / 0.007986 (-0.000613) | 0.004044 / 0.004328 (-0.000285) | 0.075301 / 0.004250 (0.071051) | 0.069408 / 0.037052 (0.032355) | 0.527962 / 0.258489 (0.269473) | 0.559423 / 0.293841 (0.265582) | 0.039351 / 0.128546 (-0.089195) | 0.010801 / 0.075646 (-0.064845) | 0.092803 / 0.419271 (-0.326468) | 0.058876 / 0.043533 (0.015343) | 0.513742 / 0.255139 (0.258603) | 0.574666 / 0.283200 (0.291466) | 0.030277 / 0.141683 (-0.111406) | 1.884936 / 1.452155 (0.432782) | 2.008260 / 1.492716 (0.515543) |\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) | 0.242162 / 0.018006 (0.224156) | 0.467400 / 0.000490 (0.466910) | 0.005348 / 0.000200 (0.005148) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038022 / 0.037411 (0.000611) | 0.108239 / 0.014526 (0.093713) | 0.121514 / 0.176557 (-0.055042) | 0.184951 / 0.737135 (-0.552184) | 0.123138 / 0.296338 (-0.173200) |\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) | 0.558587 / 0.215209 (0.343377) | 5.740312 / 2.077655 (3.662657) | 3.172164 / 1.504120 (1.668044) | 2.852908 / 1.541195 (1.311713) | 2.894435 / 1.468490 (1.425945) | 0.586399 / 4.584777 (-3.998378) | 4.498342 / 3.745712 (0.752630) | 4.000569 / 5.269862 (-1.269292) | 2.610988 / 4.565676 (-1.954688) | 0.068415 / 0.424275 (-0.355860) | 0.008602 / 0.007607 (0.000994) | 0.614731 / 0.226044 (0.388686) | 6.068158 / 2.268929 (3.799229) | 3.301070 / 55.444624 (-52.143554) | 2.868034 / 6.876477 (-4.008443) | 2.959072 / 2.142072 (0.816999) | 0.684174 / 4.805227 (-4.121053) | 0.154099 / 6.500664 (-6.346565) | 0.070641 / 0.075469 (-0.004828) |\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) | 1.835667 / 1.841788 (-0.006120) | 24.981645 / 8.074308 (16.907337) | 17.218517 / 10.191392 (7.027125) | 0.197055 / 0.680424 (-0.483368) | 0.025465 / 0.534201 (-0.508736) | 0.523498 / 0.579283 (-0.055785) | 0.528268 / 0.434364 (0.093904) | 0.599518 / 0.540337 (0.059180) | 0.887206 / 1.386936 (-0.499730) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd786d3b8dc94f1ab717327e88f65879b525091d \"CML watermark\")\n" ]
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Support streaming datasets with `pyarrow.parquet.read_table`. See: https://huggingface.co/datasets/uonlp/CulturaX/discussions/2 CC: @AndreaFrancis
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Update create_dataset.mdx
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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) | 0.008892 / 0.011353 (-0.002461) | 0.005140 / 0.011008 (-0.005868) | 0.110951 / 0.038508 (0.072442) | 0.086159 / 0.023109 (0.063050) | 0.391117 / 0.275898 (0.115218) | 0.440884 / 0.323480 (0.117404) | 0.006562 / 0.007986 (-0.001423) | 0.003711 / 0.004328 (-0.000618) | 0.081848 / 0.004250 (0.077598) | 0.063187 / 0.037052 (0.026135) | 0.369771 / 0.258489 (0.111282) | 0.447685 / 0.293841 (0.153844) | 0.046623 / 0.128546 (-0.081923) | 0.014024 / 0.075646 (-0.061622) | 0.418556 / 0.419271 (-0.000715) | 0.064660 / 0.043533 (0.021127) | 0.379416 / 0.255139 (0.124277) | 0.415800 / 0.283200 (0.132600) | 0.036899 / 0.141683 (-0.104784) | 1.710280 / 1.452155 (0.258125) | 1.932326 / 1.492716 (0.439610) |\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) | 0.311351 / 0.018006 (0.293345) | 0.621121 / 0.000490 (0.620631) | 0.013677 / 0.000200 (0.013477) | 0.000543 / 0.000054 (0.000488) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031310 / 0.037411 (-0.006102) | 0.099546 / 0.014526 (0.085020) | 0.122100 / 0.176557 (-0.054457) | 0.186477 / 0.737135 (-0.550659) | 0.116634 / 0.296338 (-0.179704) |\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) | 0.574639 / 0.215209 (0.359430) | 5.976678 / 2.077655 (3.899023) | 2.535482 / 1.504120 (1.031362) | 2.248873 / 1.541195 (0.707678) | 2.361696 / 1.468490 (0.893205) | 0.866700 / 4.584777 (-3.718077) | 5.298018 / 3.745712 (1.552306) | 4.753240 / 5.269862 (-0.516622) | 3.124698 / 4.565676 (-1.440979) | 0.101852 / 0.424275 (-0.322423) | 0.009117 / 0.007607 (0.001510) | 0.723730 / 0.226044 (0.497685) | 7.172649 / 2.268929 (4.903720) | 3.400410 / 55.444624 (-52.044214) | 2.626619 / 6.876477 (-4.249857) | 2.948692 / 2.142072 (0.806620) | 0.991589 / 4.805227 (-3.813638) | 0.208902 / 6.500664 (-6.291762) | 0.076172 / 0.075469 (0.000703) |\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) | 1.621880 / 1.841788 (-0.219907) | 22.735673 / 8.074308 (14.661365) | 20.376990 / 10.191392 (10.185598) | 0.232219 / 0.680424 (-0.448204) | 0.028616 / 0.534201 (-0.505585) | 0.455725 / 0.579283 (-0.123558) | 0.562796 / 0.434364 (0.128432) | 0.545344 / 0.540337 (0.005007) | 0.759440 / 1.386936 (-0.627496) |\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) | 0.009845 / 0.011353 (-0.001508) | 0.005289 / 0.011008 (-0.005719) | 0.083117 / 0.038508 (0.044609) | 0.098467 / 0.023109 (0.075357) | 0.532345 / 0.275898 (0.256447) | 0.571000 / 0.323480 (0.247520) | 0.007223 / 0.007986 (-0.000763) | 0.004442 / 0.004328 (0.000114) | 0.081710 / 0.004250 (0.077459) | 0.071132 / 0.037052 (0.034080) | 0.540093 / 0.258489 (0.281604) | 0.582244 / 0.293841 (0.288403) | 0.048509 / 0.128546 (-0.080038) | 0.013897 / 0.075646 (-0.061749) | 0.092579 / 0.419271 (-0.326692) | 0.073409 / 0.043533 (0.029876) | 0.537369 / 0.255139 (0.282230) | 0.551403 / 0.283200 (0.268203) | 0.038847 / 0.141683 (-0.102835) | 1.940848 / 1.452155 (0.488693) | 2.045597 / 1.492716 (0.552881) |\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) | 0.303883 / 0.018006 (0.285877) | 0.600237 / 0.000490 (0.599748) | 0.006030 / 0.000200 (0.005830) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036633 / 0.037411 (-0.000778) | 0.105853 / 0.014526 (0.091327) | 0.126289 / 0.176557 (-0.050267) | 0.190022 / 0.737135 (-0.547113) | 0.123251 / 0.296338 (-0.173087) |\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) | 0.711893 / 0.215209 (0.496684) | 6.979781 / 2.077655 (4.902126) | 3.491514 / 1.504120 (1.987394) | 3.268077 / 1.541195 (1.726882) | 3.241777 / 1.468490 (1.773287) | 0.875913 / 4.584777 (-3.708864) | 5.458421 / 3.745712 (1.712709) | 4.818355 / 5.269862 (-0.451507) | 3.256046 / 4.565676 (-1.309631) | 0.095000 / 0.424275 (-0.329275) | 0.009072 / 0.007607 (0.001465) | 0.818468 / 0.226044 (0.592424) | 8.027702 / 2.268929 (5.758773) | 4.363234 / 55.444624 (-51.081390) | 3.695269 / 6.876477 (-3.181207) | 3.902601 / 2.142072 (1.760528) | 1.039007 / 4.805227 (-3.766220) | 0.212050 / 6.500664 (-6.288614) | 0.081438 / 0.075469 (0.005969) |\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) | 1.746945 / 1.841788 (-0.094842) | 25.274283 / 8.074308 (17.199975) | 23.514717 / 10.191392 (13.323325) | 0.232580 / 0.680424 (-0.447843) | 0.032083 / 0.534201 (-0.502118) | 0.482873 / 0.579283 (-0.096410) | 0.585730 / 0.434364 (0.151366) | 0.602066 / 0.540337 (0.061729) | 0.796391 / 1.386936 (-0.590546) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d7cb68fe37dbfd81e5f82e19d8f9847c337788d \"CML watermark\")\n" ]
2023-09-18T17:06:29
2023-09-19T18:51:49
2023-09-19T18:40:10
CONTRIBUTOR
false
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modified , as AudioFolder and ImageFolder not in Dataset Library. ``` from datasets import AudioFolder ``` and ```from datasets import ImageFolder``` to ```from datasets import load_dataset``` ``` cannot import name 'AudioFolder' from 'datasets' (/home/eswardivi/miniconda3/envs/Hugformers/lib/python3.10/site-packages/datasets/__init__.py) ```
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1,899,848,414
I_kwDODunzps5xPWLe
6,246
Add new column to dataset
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[ "I think it's an issue with the code.\r\n\r\nSpecifically:\r\n```python\r\ndataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nNow `dataset` is the train set with a new column. \r\nTo fix this, you can do:\r\n\r\n```python\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```", "> I think it's an issue with the code.\r\n> \r\n> Specifically:\r\n> \r\n> ```python\r\n> dataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> Now `dataset` is the train set with a new column. To fix this, you can do:\r\n> \r\n> ```python\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n\r\nThanks for your response, but i can not access mask images, please let me know why the problem still persists. Here is the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ", "I think there is a slight misunderstanding.\r\n```python\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nadds a column with the string `mask` to your dataset.\r\nIf you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n\r\n```\r\nfrom datasets import Image\r\n\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n```\r\n\r\nWhat this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n\r\nThis [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n\r\nHope this helps!", "> I think there is a slight misunderstanding.\r\n> \r\n> ```python\r\n> new_column = [\"mask\"] * len(dataset[\"train\"])\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> adds a column with the string `mask` to your dataset. If you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n> \r\n> ```\r\n> from datasets import Image\r\n> \r\n> dataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n> ```\r\n> \r\n> What this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n> \r\n> This [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n> \r\n> Hope this helps!\r\n\r\nThank you very much, this is really helpful...\r\ni made some changes for it to work:\r\n```\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/content/data/mask_{idx}.png\"}, with_indices=True).cast_column(\"mask\", Image())\r\n```\r\nThanks Again @Dref360 " ]
2023-09-17T16:59:48
2023-09-18T16:20:09
2023-09-18T16:20:09
NONE
null
null
### Describe the bug ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) [<ipython-input-9-bd197b36b6a0>](https://localhost:8080/#) in <cell line: 1>() ----> 1 dataset['train']['/workspace/data'] 3 frames [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_column_key(key, columns) 518 def _check_valid_column_key(key: str, columns: List[str]) -> None: 519 if key not in columns: --> 520 raise KeyError(f"Column {key} not in the dataset. Current columns in the dataset: {columns}") 521 522 KeyError: "Column train not in the dataset. Current columns in the dataset: ['image', '/workspace/data']" ``` ### Steps to reproduce the bug please find the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ### Expected behavior add column to the dataset ### Environment info colab pro
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1,898,861,422
PR_kwDODunzps5adtD3
6,244
Add support for `fsspec>=2023.9.0`
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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) | 0.006410 / 0.011353 (-0.004943) | 0.003995 / 0.011008 (-0.007013) | 0.083585 / 0.038508 (0.045076) | 0.074285 / 0.023109 (0.051176) | 0.307163 / 0.275898 (0.031265) | 0.344691 / 0.323480 (0.021212) | 0.004277 / 0.007986 (-0.003708) | 0.004192 / 0.004328 (-0.000136) | 0.065156 / 0.004250 (0.060905) | 0.056774 / 0.037052 (0.019721) | 0.315483 / 0.258489 (0.056994) | 0.361911 / 0.293841 (0.068070) | 0.030454 / 0.128546 (-0.098092) | 0.008600 / 0.075646 (-0.067047) | 0.286692 / 0.419271 (-0.132579) | 0.052354 / 0.043533 (0.008821) | 0.308997 / 0.255139 (0.053858) | 0.337847 / 0.283200 (0.054647) | 0.022459 / 0.141683 (-0.119224) | 1.482758 / 1.452155 (0.030604) | 1.572853 / 1.492716 (0.080137) |\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) | 0.288603 / 0.018006 (0.270597) | 0.632903 / 0.000490 (0.632413) | 0.013702 / 0.000200 (0.013502) | 0.000284 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028448 / 0.037411 (-0.008964) | 0.082441 / 0.014526 (0.067916) | 0.099048 / 0.176557 (-0.077508) | 0.154370 / 0.737135 (-0.582765) | 0.146143 / 0.296338 (-0.150195) |\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) | 0.399250 / 0.215209 (0.184040) | 3.986683 / 2.077655 (1.909028) | 1.962606 / 1.504120 (0.458486) | 1.782653 / 1.541195 (0.241459) | 1.830251 / 1.468490 (0.361761) | 0.492498 / 4.584777 (-4.092278) | 3.549581 / 3.745712 (-0.196131) | 3.200056 / 5.269862 (-2.069806) | 2.028109 / 4.565676 (-2.537568) | 0.058222 / 0.424275 (-0.366053) | 0.007629 / 0.007607 (0.000022) | 0.482083 / 0.226044 (0.256039) | 4.824728 / 2.268929 (2.555800) | 2.448772 / 55.444624 (-52.995852) | 2.079629 / 6.876477 (-4.796848) | 2.267739 / 2.142072 (0.125667) | 0.586712 / 4.805227 (-4.218515) | 0.134073 / 6.500664 (-6.366591) | 0.060565 / 0.075469 (-0.014904) |\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) | 1.263244 / 1.841788 (-0.578544) | 18.964498 / 8.074308 (10.890190) | 14.125062 / 10.191392 (3.933670) | 0.167635 / 0.680424 (-0.512789) | 0.018469 / 0.534201 (-0.515732) | 0.390395 / 0.579283 (-0.188888) | 0.406055 / 0.434364 (-0.028309) | 0.460717 / 0.540337 (-0.079620) | 0.642746 / 1.386936 (-0.744190) |\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) | 0.006637 / 0.011353 (-0.004716) | 0.003972 / 0.011008 (-0.007036) | 0.064569 / 0.038508 (0.026061) | 0.075450 / 0.023109 (0.052341) | 0.405250 / 0.275898 (0.129352) | 0.433530 / 0.323480 (0.110050) | 0.005625 / 0.007986 (-0.002361) | 0.004118 / 0.004328 (-0.000211) | 0.065092 / 0.004250 (0.060842) | 0.057979 / 0.037052 (0.020927) | 0.413732 / 0.258489 (0.155243) | 0.451983 / 0.293841 (0.158142) | 0.032170 / 0.128546 (-0.096377) | 0.008690 / 0.075646 (-0.066957) | 0.071792 / 0.419271 (-0.347479) | 0.048560 / 0.043533 (0.005027) | 0.410312 / 0.255139 (0.155173) | 0.427294 / 0.283200 (0.144095) | 0.023006 / 0.141683 (-0.118677) | 1.496319 / 1.452155 (0.044164) | 1.566744 / 1.492716 (0.074027) |\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) | 0.266812 / 0.018006 (0.248805) | 0.540277 / 0.000490 (0.539788) | 0.008998 / 0.000200 (0.008799) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032496 / 0.037411 (-0.004915) | 0.091387 / 0.014526 (0.076861) | 0.107516 / 0.176557 (-0.069041) | 0.160019 / 0.737135 (-0.577116) | 0.107686 / 0.296338 (-0.188652) |\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) | 0.433321 / 0.215209 (0.218111) | 4.330221 / 2.077655 (2.252566) | 2.367215 / 1.504120 (0.863095) | 2.192464 / 1.541195 (0.651269) | 2.200204 / 1.468490 (0.731714) | 0.488057 / 4.584777 (-4.096720) | 3.625429 / 3.745712 (-0.120283) | 3.282859 / 5.269862 (-1.987003) | 2.038716 / 4.565676 (-2.526960) | 0.057968 / 0.424275 (-0.366307) | 0.007753 / 0.007607 (0.000146) | 0.509133 / 0.226044 (0.283089) | 5.086445 / 2.268929 (2.817516) | 2.846017 / 55.444624 (-52.598607) | 2.469546 / 6.876477 (-4.406931) | 2.673218 / 2.142072 (0.531145) | 0.591228 / 4.805227 (-4.213999) | 0.131920 / 6.500664 (-6.368744) | 0.059967 / 0.075469 (-0.015502) |\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) | 1.375634 / 1.841788 (-0.466153) | 19.506752 / 8.074308 (11.432444) | 14.677876 / 10.191392 (4.486484) | 0.165071 / 0.680424 (-0.515353) | 0.020614 / 0.534201 (-0.513587) | 0.395967 / 0.579283 (-0.183316) | 0.424358 / 0.434364 (-0.010006) | 0.469954 / 0.540337 (-0.070384) | 0.643169 / 1.386936 (-0.743767) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887a854f03c4ac6d2e99b9ef4d89e6fe8c46d6f1 \"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) | 0.006072 / 0.011353 (-0.005281) | 0.003691 / 0.011008 (-0.007318) | 0.081683 / 0.038508 (0.043175) | 0.059114 / 0.023109 (0.036005) | 0.317053 / 0.275898 (0.041155) | 0.357672 / 0.323480 (0.034192) | 0.003577 / 0.007986 (-0.004408) | 0.003890 / 0.004328 (-0.000438) | 0.063667 / 0.004250 (0.059417) | 0.048233 / 0.037052 (0.011181) | 0.322854 / 0.258489 (0.064365) | 0.368014 / 0.293841 (0.074173) | 0.027750 / 0.128546 (-0.100796) | 0.008137 / 0.075646 (-0.067509) | 0.263906 / 0.419271 (-0.155366) | 0.045402 / 0.043533 (0.001870) | 0.315414 / 0.255139 (0.060275) | 0.340906 / 0.283200 (0.057707) | 0.023475 / 0.141683 (-0.118208) | 1.443922 / 1.452155 (-0.008233) | 1.550332 / 1.492716 (0.057616) |\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) | 0.211914 / 0.018006 (0.193908) | 0.423577 / 0.000490 (0.423088) | 0.003436 / 0.000200 (0.003236) | 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) | 0.024675 / 0.037411 (-0.012737) | 0.072550 / 0.014526 (0.058024) | 0.084533 / 0.176557 (-0.092024) | 0.146106 / 0.737135 (-0.591029) | 0.085523 / 0.296338 (-0.210816) |\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) | 0.403498 / 0.215209 (0.188289) | 4.019000 / 2.077655 (1.941345) | 1.984821 / 1.504120 (0.480701) | 1.805071 / 1.541195 (0.263876) | 1.860906 / 1.468490 (0.392416) | 0.499570 / 4.584777 (-4.085207) | 3.088424 / 3.745712 (-0.657288) | 2.833693 / 5.269862 (-2.436169) | 1.869731 / 4.565676 (-2.695945) | 0.057606 / 0.424275 (-0.366669) | 0.006960 / 0.007607 (-0.000647) | 0.476085 / 0.226044 (0.250040) | 4.774063 / 2.268929 (2.505134) | 2.458079 / 55.444624 (-52.986545) | 2.106075 / 6.876477 (-4.770402) | 2.248373 / 2.142072 (0.106301) | 0.589767 / 4.805227 (-4.215460) | 0.124382 / 6.500664 (-6.376282) | 0.060705 / 0.075469 (-0.014764) |\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) | 1.287031 / 1.841788 (-0.554756) | 17.662455 / 8.074308 (9.588147) | 14.288812 / 10.191392 (4.097420) | 0.156168 / 0.680424 (-0.524256) | 0.016795 / 0.534201 (-0.517406) | 0.333726 / 0.579283 (-0.245557) | 0.362327 / 0.434364 (-0.072037) | 0.387773 / 0.540337 (-0.152564) | 0.547232 / 1.386936 (-0.839704) |\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) | 0.006494 / 0.011353 (-0.004859) | 0.003762 / 0.011008 (-0.007247) | 0.062373 / 0.038508 (0.023864) | 0.066357 / 0.023109 (0.043247) | 0.448687 / 0.275898 (0.172789) | 0.482445 / 0.323480 (0.158965) | 0.004990 / 0.007986 (-0.002996) | 0.002945 / 0.004328 (-0.001384) | 0.062444 / 0.004250 (0.058194) | 0.051381 / 0.037052 (0.014329) | 0.449310 / 0.258489 (0.190821) | 0.483188 / 0.293841 (0.189347) | 0.029078 / 0.128546 (-0.099468) | 0.008146 / 0.075646 (-0.067501) | 0.067369 / 0.419271 (-0.351903) | 0.041732 / 0.043533 (-0.001801) | 0.451675 / 0.255139 (0.196536) | 0.470445 / 0.283200 (0.187246) | 0.021053 / 0.141683 (-0.120630) | 1.483627 / 1.452155 (0.031472) | 1.541594 / 1.492716 (0.048878) |\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) | 0.210247 / 0.018006 (0.192240) | 0.424663 / 0.000490 (0.424173) | 0.005394 / 0.000200 (0.005194) | 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) | 0.026894 / 0.037411 (-0.010517) | 0.081324 / 0.014526 (0.066798) | 0.091362 / 0.176557 (-0.085195) | 0.145602 / 0.737135 (-0.591533) | 0.091896 / 0.296338 (-0.204443) |\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) | 0.469662 / 0.215209 (0.254453) | 4.689495 / 2.077655 (2.611840) | 2.596462 / 1.504120 (1.092342) | 2.422584 / 1.541195 (0.881389) | 2.476710 / 1.468490 (1.008220) | 0.507049 / 4.584777 (-4.077728) | 3.185519 / 3.745712 (-0.560193) | 2.879842 / 5.269862 (-2.390019) | 1.882643 / 4.565676 (-2.683034) | 0.058046 / 0.424275 (-0.366229) | 0.006797 / 0.007607 (-0.000811) | 0.545245 / 0.226044 (0.319201) | 5.449248 / 2.268929 (3.180319) | 3.057341 / 55.444624 (-52.387283) | 2.728385 / 6.876477 (-4.148092) | 2.898945 / 2.142072 (0.756873) | 0.600035 / 4.805227 (-4.205192) | 0.126337 / 6.500664 (-6.374327) | 0.061333 / 0.075469 (-0.014136) |\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) | 1.332966 / 1.841788 (-0.508821) | 17.960805 / 8.074308 (9.886497) | 14.978838 / 10.191392 (4.787446) | 0.148852 / 0.680424 (-0.531572) | 0.018307 / 0.534201 (-0.515894) | 0.335234 / 0.579283 (-0.244050) | 0.389659 / 0.434364 (-0.044704) | 0.393259 / 0.540337 (-0.147078) | 0.549237 / 1.386936 (-0.837699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#278a5673172c30b915a9ebf64cc7aff9667b58fd \"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) | 0.008808 / 0.011353 (-0.002545) | 0.005001 / 0.011008 (-0.006008) | 0.110022 / 0.038508 (0.071514) | 0.078015 / 0.023109 (0.054906) | 0.384724 / 0.275898 (0.108826) | 0.441354 / 0.323480 (0.117874) | 0.005116 / 0.007986 (-0.002870) | 0.004308 / 0.004328 (-0.000020) | 0.081679 / 0.004250 (0.077429) | 0.061386 / 0.037052 (0.024333) | 0.398149 / 0.258489 (0.139660) | 0.464859 / 0.293841 (0.171018) | 0.047443 / 0.128546 (-0.081104) | 0.014693 / 0.075646 (-0.060954) | 0.365438 / 0.419271 (-0.053833) | 0.081689 / 0.043533 (0.038156) | 0.400458 / 0.255139 (0.145319) | 0.449958 / 0.283200 (0.166758) | 0.038266 / 0.141683 (-0.103417) | 1.795043 / 1.452155 (0.342888) | 1.908819 / 1.492716 (0.416102) |\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) | 0.297911 / 0.018006 (0.279905) | 0.601640 / 0.000490 (0.601150) | 0.015406 / 0.000200 (0.015206) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034520 / 0.037411 (-0.002891) | 0.092657 / 0.014526 (0.078131) | 0.113992 / 0.176557 (-0.062564) | 0.189075 / 0.737135 (-0.548061) | 0.106602 / 0.296338 (-0.189736) |\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) | 0.585838 / 0.215209 (0.370629) | 5.719281 / 2.077655 (3.641627) | 2.525914 / 1.504120 (1.021794) | 2.231908 / 1.541195 (0.690713) | 2.215272 / 1.468490 (0.746782) | 0.814425 / 4.584777 (-3.770352) | 5.243406 / 3.745712 (1.497694) | 4.476642 / 5.269862 (-0.793220) | 2.929438 / 4.565676 (-1.636239) | 0.092070 / 0.424275 (-0.332205) | 0.009358 / 0.007607 (0.001751) | 0.713975 / 0.226044 (0.487931) | 6.948846 / 2.268929 (4.679918) | 3.361125 / 55.444624 (-52.083500) | 2.575224 / 6.876477 (-4.301253) | 2.783082 / 2.142072 (0.641009) | 1.016205 / 4.805227 (-3.789022) | 0.202578 / 6.500664 (-6.298086) | 0.076696 / 0.075469 (0.001227) |\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) | 1.650889 / 1.841788 (-0.190898) | 23.358273 / 8.074308 (15.283965) | 19.882450 / 10.191392 (9.691058) | 0.228971 / 0.680424 (-0.451453) | 0.027736 / 0.534201 (-0.506465) | 0.472405 / 0.579283 (-0.106878) | 0.581799 / 0.434364 (0.147435) | 0.533000 / 0.540337 (-0.007338) | 0.815588 / 1.386936 (-0.571348) |\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) | 0.009151 / 0.011353 (-0.002202) | 0.005074 / 0.011008 (-0.005934) | 0.078709 / 0.038508 (0.040201) | 0.077696 / 0.023109 (0.054586) | 0.522356 / 0.275898 (0.246458) | 0.562345 / 0.323480 (0.238865) | 0.006411 / 0.007986 (-0.001575) | 0.004379 / 0.004328 (0.000051) | 0.082402 / 0.004250 (0.078151) | 0.064223 / 0.037052 (0.027170) | 0.518184 / 0.258489 (0.259695) | 0.566221 / 0.293841 (0.272380) | 0.046796 / 0.128546 (-0.081750) | 0.013987 / 0.075646 (-0.061659) | 0.094925 / 0.419271 (-0.324346) | 0.058810 / 0.043533 (0.015277) | 0.520252 / 0.255139 (0.265113) | 0.566403 / 0.283200 (0.283203) | 0.034720 / 0.141683 (-0.106963) | 1.796809 / 1.452155 (0.344654) | 1.913787 / 1.492716 (0.421070) |\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) | 0.317449 / 0.018006 (0.299443) | 0.620154 / 0.000490 (0.619664) | 0.007066 / 0.000200 (0.006866) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035252 / 0.037411 (-0.002160) | 0.111648 / 0.014526 (0.097122) | 0.120692 / 0.176557 (-0.055864) | 0.193202 / 0.737135 (-0.543933) | 0.127905 / 0.296338 (-0.168434) |\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) | 0.661012 / 0.215209 (0.445803) | 6.626680 / 2.077655 (4.549026) | 3.243065 / 1.504120 (1.738945) | 2.904053 / 1.541195 (1.362858) | 2.880516 / 1.468490 (1.412026) | 0.875650 / 4.584777 (-3.709127) | 5.381993 / 3.745712 (1.636281) | 4.743997 / 5.269862 (-0.525864) | 3.020736 / 4.565676 (-1.544940) | 0.106573 / 0.424275 (-0.317702) | 0.011151 / 0.007607 (0.003544) | 0.821990 / 0.226044 (0.595946) | 8.225383 / 2.268929 (5.956454) | 3.963232 / 55.444624 (-51.481392) | 3.288916 / 6.876477 (-3.587561) | 3.579435 / 2.142072 (1.437363) | 1.043379 / 4.805227 (-3.761848) | 0.207508 / 6.500664 (-6.293156) | 0.085109 / 0.075469 (0.009640) |\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) | 1.723798 / 1.841788 (-0.117990) | 24.709848 / 8.074308 (16.635540) | 22.484674 / 10.191392 (12.293282) | 0.260357 / 0.680424 (-0.420067) | 0.033539 / 0.534201 (-0.500662) | 0.487814 / 0.579283 (-0.091469) | 0.610171 / 0.434364 (0.175807) | 0.585012 / 0.540337 (0.044674) | 0.803764 / 1.386936 (-0.583172) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f611e5815ce1bdcb4fa8556f55d85a6739cba0ea \"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) | 0.006661 / 0.011353 (-0.004692) | 0.004022 / 0.011008 (-0.006987) | 0.084269 / 0.038508 (0.045760) | 0.070707 / 0.023109 (0.047598) | 0.315035 / 0.275898 (0.039137) | 0.339830 / 0.323480 (0.016350) | 0.003994 / 0.007986 (-0.003991) | 0.004129 / 0.004328 (-0.000199) | 0.065383 / 0.004250 (0.061133) | 0.055493 / 0.037052 (0.018441) | 0.320521 / 0.258489 (0.062032) | 0.354301 / 0.293841 (0.060460) | 0.031177 / 0.128546 (-0.097370) | 0.008724 / 0.075646 (-0.066922) | 0.288298 / 0.419271 (-0.130974) | 0.052418 / 0.043533 (0.008885) | 0.319122 / 0.255139 (0.063983) | 0.335859 / 0.283200 (0.052659) | 0.026260 / 0.141683 (-0.115423) | 1.450039 / 1.452155 (-0.002115) | 1.545172 / 1.492716 (0.052455) |\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) | 0.234232 / 0.018006 (0.216226) | 0.454983 / 0.000490 (0.454493) | 0.007590 / 0.000200 (0.007390) | 0.000550 / 0.000054 (0.000495) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028714 / 0.037411 (-0.008698) | 0.083686 / 0.014526 (0.069160) | 0.162986 / 0.176557 (-0.013570) | 0.167574 / 0.737135 (-0.569561) | 0.273158 / 0.296338 (-0.023180) |\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) | 0.388275 / 0.215209 (0.173066) | 3.862034 / 2.077655 (1.784379) | 1.843561 / 1.504120 (0.339441) | 1.675224 / 1.541195 (0.134029) | 1.730394 / 1.468490 (0.261904) | 0.495259 / 4.584777 (-4.089518) | 3.627155 / 3.745712 (-0.118557) | 3.290590 / 5.269862 (-1.979272) | 2.032432 / 4.565676 (-2.533245) | 0.058212 / 0.424275 (-0.366063) | 0.007815 / 0.007607 (0.000208) | 0.460625 / 0.226044 (0.234580) | 4.616845 / 2.268929 (2.347916) | 2.339280 / 55.444624 (-53.105344) | 1.957216 / 6.876477 (-4.919261) | 2.129511 / 2.142072 (-0.012562) | 0.591782 / 4.805227 (-4.213446) | 0.136391 / 6.500664 (-6.364273) | 0.059627 / 0.075469 (-0.015842) |\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) | 1.278998 / 1.841788 (-0.562789) | 18.485496 / 8.074308 (10.411188) | 14.161273 / 10.191392 (3.969881) | 0.164346 / 0.680424 (-0.516078) | 0.018144 / 0.534201 (-0.516057) | 0.391601 / 0.579283 (-0.187682) | 0.424391 / 0.434364 (-0.009973) | 0.458209 / 0.540337 (-0.082129) | 0.645124 / 1.386936 (-0.741812) |\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) | 0.006799 / 0.011353 (-0.004554) | 0.004023 / 0.011008 (-0.006985) | 0.065206 / 0.038508 (0.026698) | 0.074386 / 0.023109 (0.051277) | 0.437399 / 0.275898 (0.161501) | 0.467382 / 0.323480 (0.143903) | 0.005467 / 0.007986 (-0.002519) | 0.003324 / 0.004328 (-0.001005) | 0.064289 / 0.004250 (0.060039) | 0.057257 / 0.037052 (0.020205) | 0.440035 / 0.258489 (0.181546) | 0.477138 / 0.293841 (0.183298) | 0.032171 / 0.128546 (-0.096375) | 0.008400 / 0.075646 (-0.067247) | 0.070877 / 0.419271 (-0.348395) | 0.048180 / 0.043533 (0.004648) | 0.441274 / 0.255139 (0.186135) | 0.461386 / 0.283200 (0.178187) | 0.022576 / 0.141683 (-0.119106) | 1.520914 / 1.452155 (0.068759) | 1.575593 / 1.492716 (0.082877) |\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) | 0.221551 / 0.018006 (0.203545) | 0.447213 / 0.000490 (0.446723) | 0.004435 / 0.000200 (0.004235) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032123 / 0.037411 (-0.005288) | 0.091809 / 0.014526 (0.077283) | 0.103938 / 0.176557 (-0.072618) | 0.156878 / 0.737135 (-0.580258) | 0.105071 / 0.296338 (-0.191268) |\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) | 0.430389 / 0.215209 (0.215180) | 4.293496 / 2.077655 (2.215841) | 2.292801 / 1.504120 (0.788681) | 2.135320 / 1.541195 (0.594126) | 2.195720 / 1.468490 (0.727229) | 0.493277 / 4.584777 (-4.091500) | 3.685617 / 3.745712 (-0.060096) | 3.278897 / 5.269862 (-1.990965) | 2.036939 / 4.565676 (-2.528737) | 0.058766 / 0.424275 (-0.365509) | 0.007783 / 0.007607 (0.000176) | 0.511165 / 0.226044 (0.285120) | 5.126757 / 2.268929 (2.857829) | 2.756690 / 55.444624 (-52.687935) | 2.421745 / 6.876477 (-4.454732) | 2.597249 / 2.142072 (0.455177) | 0.647206 / 4.805227 (-4.158021) | 0.143392 / 6.500664 (-6.357273) | 0.060110 / 0.075469 (-0.015359) |\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) | 1.340289 / 1.841788 (-0.501499) | 19.057620 / 8.074308 (10.983312) | 14.832892 / 10.191392 (4.641500) | 0.167730 / 0.680424 (-0.512694) | 0.020178 / 0.534201 (-0.514023) | 0.394060 / 0.579283 (-0.185223) | 0.433976 / 0.434364 (-0.000388) | 0.474417 / 0.540337 (-0.065921) | 0.640653 / 1.386936 (-0.746283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0519c6a1988a3344ecae37f7348c208bcbc99d6 \"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) | 0.007661 / 0.011353 (-0.003692) | 0.004541 / 0.011008 (-0.006467) | 0.100547 / 0.038508 (0.062039) | 0.084257 / 0.023109 (0.061148) | 0.377627 / 0.275898 (0.101729) | 0.433764 / 0.323480 (0.110284) | 0.005995 / 0.007986 (-0.001990) | 0.003810 / 0.004328 (-0.000518) | 0.076409 / 0.004250 (0.072158) | 0.063411 / 0.037052 (0.026359) | 0.382504 / 0.258489 (0.124015) | 0.449721 / 0.293841 (0.155880) | 0.036499 / 0.128546 (-0.092047) | 0.009942 / 0.075646 (-0.065705) | 0.343839 / 0.419271 (-0.075433) | 0.062147 / 0.043533 (0.018614) | 0.383244 / 0.255139 (0.128105) | 0.415606 / 0.283200 (0.132406) | 0.027475 / 0.141683 (-0.114207) | 1.740413 / 1.452155 (0.288258) | 1.862210 / 1.492716 (0.369493) |\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) | 0.260064 / 0.018006 (0.242058) | 0.499001 / 0.000490 (0.498511) | 0.015811 / 0.000200 (0.015611) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033599 / 0.037411 (-0.003812) | 0.099354 / 0.014526 (0.084828) | 0.114693 / 0.176557 (-0.061864) | 0.180231 / 0.737135 (-0.556904) | 0.114715 / 0.296338 (-0.181623) |\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) | 0.459884 / 0.215209 (0.244675) | 4.580806 / 2.077655 (2.503151) | 2.270770 / 1.504120 (0.766650) | 2.077127 / 1.541195 (0.535932) | 2.167175 / 1.468490 (0.698685) | 0.570593 / 4.584777 (-4.014184) | 4.120926 / 3.745712 (0.375214) | 3.817595 / 5.269862 (-1.452267) | 2.404782 / 4.565676 (-2.160894) | 0.067972 / 0.424275 (-0.356304) | 0.009378 / 0.007607 (0.001771) | 0.549642 / 0.226044 (0.323597) | 5.490369 / 2.268929 (3.221440) | 2.905264 / 55.444624 (-52.539361) | 2.452935 / 6.876477 (-4.423542) | 2.700760 / 2.142072 (0.558688) | 0.700407 / 4.805227 (-4.104820) | 0.159349 / 6.500664 (-6.341315) | 0.074605 / 0.075469 (-0.000864) |\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) | 1.517803 / 1.841788 (-0.323985) | 22.343700 / 8.074308 (14.269392) | 16.411639 / 10.191392 (6.220247) | 0.169816 / 0.680424 (-0.510608) | 0.021532 / 0.534201 (-0.512668) | 0.470161 / 0.579283 (-0.109122) | 0.473412 / 0.434364 (0.039048) | 0.539690 / 0.540337 (-0.000647) | 0.774011 / 1.386936 (-0.612925) |\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) | 0.007629 / 0.011353 (-0.003724) | 0.004651 / 0.011008 (-0.006357) | 0.075162 / 0.038508 (0.036654) | 0.085365 / 0.023109 (0.062256) | 0.493272 / 0.275898 (0.217374) | 0.535776 / 0.323480 (0.212296) | 0.006323 / 0.007986 (-0.001663) | 0.003785 / 0.004328 (-0.000544) | 0.076161 / 0.004250 (0.071911) | 0.065982 / 0.037052 (0.028929) | 0.513355 / 0.258489 (0.254866) | 0.549219 / 0.293841 (0.255378) | 0.038052 / 0.128546 (-0.090494) | 0.010055 / 0.075646 (-0.065592) | 0.083744 / 0.419271 (-0.335527) | 0.056708 / 0.043533 (0.013175) | 0.496273 / 0.255139 (0.241135) | 0.523709 / 0.283200 (0.240509) | 0.026502 / 0.141683 (-0.115181) | 1.793032 / 1.452155 (0.340877) | 1.870534 / 1.492716 (0.377817) |\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) | 0.252288 / 0.018006 (0.234281) | 0.490380 / 0.000490 (0.489890) | 0.005884 / 0.000200 (0.005684) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038238 / 0.037411 (0.000827) | 0.110010 / 0.014526 (0.095485) | 0.125497 / 0.176557 (-0.051059) | 0.188154 / 0.737135 (-0.548981) | 0.126112 / 0.296338 (-0.170227) |\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) | 0.515837 / 0.215209 (0.300628) | 5.135153 / 2.077655 (3.057498) | 2.761740 / 1.504120 (1.257620) | 2.552718 / 1.541195 (1.011523) | 2.636425 / 1.468490 (1.167935) | 0.588442 / 4.584777 (-3.996335) | 4.220833 / 3.745712 (0.475120) | 3.874637 / 5.269862 (-1.395225) | 2.424668 / 4.565676 (-2.141009) | 0.069979 / 0.424275 (-0.354296) | 0.009349 / 0.007607 (0.001742) | 0.608936 / 0.226044 (0.382891) | 6.081209 / 2.268929 (3.812280) | 3.348067 / 55.444624 (-52.096557) | 2.919130 / 6.876477 (-3.957347) | 3.159093 / 2.142072 (1.017020) | 0.704059 / 4.805227 (-4.101169) | 0.158417 / 6.500664 (-6.342247) | 0.071321 / 0.075469 (-0.004148) |\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) | 1.595287 / 1.841788 (-0.246501) | 23.096619 / 8.074308 (15.022311) | 17.258041 / 10.191392 (7.066649) | 0.186197 / 0.680424 (-0.494227) | 0.023633 / 0.534201 (-0.510567) | 0.472181 / 0.579283 (-0.107102) | 0.493817 / 0.434364 (0.059453) | 0.567657 / 0.540337 (0.027320) | 0.793789 / 1.386936 (-0.593147) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0bd8444689c5d82344a62ddf79e5dc103fc67b8 \"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) | 0.007084 / 0.011353 (-0.004268) | 0.004093 / 0.011008 (-0.006915) | 0.086395 / 0.038508 (0.047887) | 0.087734 / 0.023109 (0.064625) | 0.356936 / 0.275898 (0.081038) | 0.386413 / 0.323480 (0.062933) | 0.005531 / 0.007986 (-0.002454) | 0.003462 / 0.004328 (-0.000866) | 0.065503 / 0.004250 (0.061252) | 0.058973 / 0.037052 (0.021920) | 0.354151 / 0.258489 (0.095662) | 0.398812 / 0.293841 (0.104971) | 0.031508 / 0.128546 (-0.097038) | 0.008537 / 0.075646 (-0.067109) | 0.290942 / 0.419271 (-0.128329) | 0.053537 / 0.043533 (0.010004) | 0.352067 / 0.255139 (0.096928) | 0.375142 / 0.283200 (0.091943) | 0.025658 / 0.141683 (-0.116025) | 1.468496 / 1.452155 (0.016341) | 1.556926 / 1.492716 (0.064210) |\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) | 0.238858 / 0.018006 (0.220852) | 0.460018 / 0.000490 (0.459528) | 0.009613 / 0.000200 (0.009414) | 0.000326 / 0.000054 (0.000272) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030333 / 0.037411 (-0.007078) | 0.088431 / 0.014526 (0.073905) | 0.098130 / 0.176557 (-0.078427) | 0.155160 / 0.737135 (-0.581975) | 0.099963 / 0.296338 (-0.196375) |\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) | 0.385769 / 0.215209 (0.170560) | 3.836723 / 2.077655 (1.759069) | 1.861065 / 1.504120 (0.356945) | 1.685159 / 1.541195 (0.143965) | 1.780679 / 1.468490 (0.312189) | 0.491865 / 4.584777 (-4.092912) | 3.581139 / 3.745712 (-0.164573) | 3.366278 / 5.269862 (-1.903584) | 2.093094 / 4.565676 (-2.472583) | 0.058063 / 0.424275 (-0.366212) | 0.007903 / 0.007607 (0.000296) | 0.464866 / 0.226044 (0.238821) | 4.647754 / 2.268929 (2.378825) | 2.316466 / 55.444624 (-53.128158) | 1.984079 / 6.876477 (-4.892398) | 2.235020 / 2.142072 (0.092948) | 0.592591 / 4.805227 (-4.212636) | 0.135586 / 6.500664 (-6.365078) | 0.061434 / 0.075469 (-0.014035) |\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) | 1.282940 / 1.841788 (-0.558848) | 19.635975 / 8.074308 (11.561667) | 14.426135 / 10.191392 (4.234743) | 0.166732 / 0.680424 (-0.513692) | 0.018438 / 0.534201 (-0.515763) | 0.393173 / 0.579283 (-0.186110) | 0.417291 / 0.434364 (-0.017073) | 0.459188 / 0.540337 (-0.081149) | 0.632568 / 1.386936 (-0.754368) |\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) | 0.007166 / 0.011353 (-0.004187) | 0.004254 / 0.011008 (-0.006754) | 0.064667 / 0.038508 (0.026159) | 0.085142 / 0.023109 (0.062033) | 0.410081 / 0.275898 (0.134183) | 0.445803 / 0.323480 (0.122323) | 0.005600 / 0.007986 (-0.002385) | 0.003520 / 0.004328 (-0.000809) | 0.064148 / 0.004250 (0.059897) | 0.059869 / 0.037052 (0.022817) | 0.407439 / 0.258489 (0.148950) | 0.451169 / 0.293841 (0.157329) | 0.032619 / 0.128546 (-0.095927) | 0.008706 / 0.075646 (-0.066940) | 0.071230 / 0.419271 (-0.348041) | 0.048499 / 0.043533 (0.004966) | 0.416401 / 0.255139 (0.161262) | 0.430737 / 0.283200 (0.147537) | 0.022511 / 0.141683 (-0.119172) | 1.517296 / 1.452155 (0.065141) | 1.581704 / 1.492716 (0.088988) |\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) | 0.220738 / 0.018006 (0.202732) | 0.454026 / 0.000490 (0.453536) | 0.004695 / 0.000200 (0.004495) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033202 / 0.037411 (-0.004209) | 0.097506 / 0.014526 (0.082980) | 0.106661 / 0.176557 (-0.069896) | 0.160554 / 0.737135 (-0.576581) | 0.109203 / 0.296338 (-0.187135) |\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) | 0.432013 / 0.215209 (0.216804) | 4.310399 / 2.077655 (2.232744) | 2.296529 / 1.504120 (0.792409) | 2.139929 / 1.541195 (0.598734) | 2.227432 / 1.468490 (0.758942) | 0.493697 / 4.584777 (-4.091080) | 3.639877 / 3.745712 (-0.105835) | 3.323165 / 5.269862 (-1.946697) | 2.084527 / 4.565676 (-2.481150) | 0.058812 / 0.424275 (-0.365463) | 0.007813 / 0.007607 (0.000206) | 0.512366 / 0.226044 (0.286321) | 5.119660 / 2.268929 (2.850732) | 2.783819 / 55.444624 (-52.660806) | 2.490669 / 6.876477 (-4.385808) | 2.696653 / 2.142072 (0.554581) | 0.627161 / 4.805227 (-4.178066) | 0.137032 / 6.500664 (-6.363632) | 0.064040 / 0.075469 (-0.011429) |\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) | 1.369578 / 1.841788 (-0.472210) | 20.421182 / 8.074308 (12.346873) | 15.719347 / 10.191392 (5.527955) | 0.166150 / 0.680424 (-0.514274) | 0.020262 / 0.534201 (-0.513939) | 0.395645 / 0.579283 (-0.183638) | 0.430363 / 0.434364 (-0.004001) | 0.477843 / 0.540337 (-0.062494) | 0.638501 / 1.386936 (-0.748435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c89e60cc50563dfc41ea039c6d3a1f6e43033e8e \"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) | 0.006141 / 0.011353 (-0.005211) | 0.003683 / 0.011008 (-0.007325) | 0.081127 / 0.038508 (0.042618) | 0.064285 / 0.023109 (0.041176) | 0.323038 / 0.275898 (0.047140) | 0.347280 / 0.323480 (0.023800) | 0.003518 / 0.007986 (-0.004467) | 0.002958 / 0.004328 (-0.001370) | 0.063093 / 0.004250 (0.058843) | 0.050682 / 0.037052 (0.013629) | 0.321222 / 0.258489 (0.062733) | 0.359266 / 0.293841 (0.065425) | 0.027515 / 0.128546 (-0.101032) | 0.007964 / 0.075646 (-0.067682) | 0.261305 / 0.419271 (-0.157966) | 0.044897 / 0.043533 (0.001365) | 0.320684 / 0.255139 (0.065545) | 0.335722 / 0.283200 (0.052522) | 0.023378 / 0.141683 (-0.118305) | 1.418211 / 1.452155 (-0.033943) | 1.523728 / 1.492716 (0.031011) |\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) | 0.222316 / 0.018006 (0.204310) | 0.426943 / 0.000490 (0.426454) | 0.008785 / 0.000200 (0.008585) | 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) | 0.024716 / 0.037411 (-0.012695) | 0.075341 / 0.014526 (0.060816) | 0.089532 / 0.176557 (-0.087024) | 0.147638 / 0.737135 (-0.589498) | 0.085697 / 0.296338 (-0.210641) |\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) | 0.396395 / 0.215209 (0.181186) | 3.947280 / 2.077655 (1.869625) | 1.894762 / 1.504120 (0.390642) | 1.712094 / 1.541195 (0.170899) | 1.779049 / 1.468490 (0.310559) | 0.509206 / 4.584777 (-4.075571) | 3.073951 / 3.745712 (-0.671761) | 2.886826 / 5.269862 (-2.383035) | 1.894444 / 4.565676 (-2.671232) | 0.059519 / 0.424275 (-0.364756) | 0.006951 / 0.007607 (-0.000656) | 0.468213 / 0.226044 (0.242169) | 4.667134 / 2.268929 (2.398206) | 2.342516 / 55.444624 (-53.102108) | 1.992047 / 6.876477 (-4.884430) | 2.142059 / 2.142072 (-0.000014) | 0.600507 / 4.805227 (-4.204720) | 0.128982 / 6.500664 (-6.371682) | 0.062100 / 0.075469 (-0.013369) |\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) | 1.234500 / 1.841788 (-0.607288) | 17.951646 / 8.074308 (9.877338) | 13.862763 / 10.191392 (3.671371) | 0.143133 / 0.680424 (-0.537291) | 0.016643 / 0.534201 (-0.517558) | 0.333174 / 0.579283 (-0.246109) | 0.366956 / 0.434364 (-0.067408) | 0.384569 / 0.540337 (-0.155769) | 0.546830 / 1.386936 (-0.840106) |\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) | 0.006146 / 0.011353 (-0.005207) | 0.003725 / 0.011008 (-0.007283) | 0.062099 / 0.038508 (0.023591) | 0.064117 / 0.023109 (0.041008) | 0.456100 / 0.275898 (0.180202) | 0.490794 / 0.323480 (0.167314) | 0.005652 / 0.007986 (-0.002334) | 0.002897 / 0.004328 (-0.001432) | 0.061909 / 0.004250 (0.057659) | 0.050634 / 0.037052 (0.013582) | 0.454422 / 0.258489 (0.195933) | 0.493208 / 0.293841 (0.199367) | 0.028822 / 0.128546 (-0.099724) | 0.008115 / 0.075646 (-0.067531) | 0.067214 / 0.419271 (-0.352058) | 0.041529 / 0.043533 (-0.002004) | 0.458016 / 0.255139 (0.202877) | 0.476059 / 0.283200 (0.192859) | 0.019926 / 0.141683 (-0.121757) | 1.465345 / 1.452155 (0.013190) | 1.533518 / 1.492716 (0.040802) |\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) | 0.218830 / 0.018006 (0.200823) | 0.418869 / 0.000490 (0.418380) | 0.005154 / 0.000200 (0.004954) | 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) | 0.027648 / 0.037411 (-0.009763) | 0.083842 / 0.014526 (0.069316) | 0.092300 / 0.176557 (-0.084257) | 0.146098 / 0.737135 (-0.591037) | 0.093441 / 0.296338 (-0.202898) |\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) | 0.464426 / 0.215209 (0.249217) | 4.632705 / 2.077655 (2.555051) | 2.642091 / 1.504120 (1.137971) | 2.461768 / 1.541195 (0.920573) | 2.535554 / 1.468490 (1.067064) | 0.507506 / 4.584777 (-4.077271) | 3.095485 / 3.745712 (-0.650227) | 2.884261 / 5.269862 (-2.385601) | 1.908943 / 4.565676 (-2.656734) | 0.058622 / 0.424275 (-0.365653) | 0.006892 / 0.007607 (-0.000715) | 0.536045 / 0.226044 (0.310001) | 5.377448 / 2.268929 (3.108519) | 3.076023 / 55.444624 (-52.368602) | 2.745586 / 6.876477 (-4.130890) | 2.939582 / 2.142072 (0.797510) | 0.595639 / 4.805227 (-4.209589) | 0.125086 / 6.500664 (-6.375578) | 0.061075 / 0.075469 (-0.014394) |\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) | 1.342820 / 1.841788 (-0.498968) | 18.326240 / 8.074308 (10.251932) | 15.007094 / 10.191392 (4.815702) | 0.133037 / 0.680424 (-0.547387) | 0.018702 / 0.534201 (-0.515499) | 0.330245 / 0.579283 (-0.249038) | 0.381494 / 0.434364 (-0.052870) | 0.393705 / 0.540337 (-0.146633) | 0.533676 / 1.386936 (-0.853260) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45291275d84448c235829fb62aa951070aa4061d \"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) | 0.007644 / 0.011353 (-0.003709) | 0.004759 / 0.011008 (-0.006249) | 0.100569 / 0.038508 (0.062061) | 0.089645 / 0.023109 (0.066536) | 0.376679 / 0.275898 (0.100781) | 0.413214 / 0.323480 (0.089735) | 0.006087 / 0.007986 (-0.001899) | 0.003832 / 0.004328 (-0.000496) | 0.075892 / 0.004250 (0.071641) | 0.064635 / 0.037052 (0.027582) | 0.376874 / 0.258489 (0.118385) | 0.436756 / 0.293841 (0.142915) | 0.036372 / 0.128546 (-0.092174) | 0.010047 / 0.075646 (-0.065599) | 0.345073 / 0.419271 (-0.074198) | 0.062092 / 0.043533 (0.018559) | 0.380503 / 0.255139 (0.125364) | 0.414800 / 0.283200 (0.131600) | 0.028274 / 0.141683 (-0.113409) | 1.732463 / 1.452155 (0.280308) | 1.859049 / 1.492716 (0.366333) |\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) | 0.267129 / 0.018006 (0.249123) | 0.509109 / 0.000490 (0.508619) | 0.012329 / 0.000200 (0.012130) | 0.000432 / 0.000054 (0.000377) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033773 / 0.037411 (-0.003638) | 0.102800 / 0.014526 (0.088274) | 0.114256 / 0.176557 (-0.062300) | 0.182048 / 0.737135 (-0.555087) | 0.118225 / 0.296338 (-0.178113) |\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) | 0.457553 / 0.215209 (0.242344) | 4.588212 / 2.077655 (2.510557) | 2.184138 / 1.504120 (0.680018) | 2.003570 / 1.541195 (0.462375) | 2.093217 / 1.468490 (0.624727) | 0.585679 / 4.584777 (-3.999098) | 4.175319 / 3.745712 (0.429607) | 3.914168 / 5.269862 (-1.355693) | 2.452992 / 4.565676 (-2.112684) | 0.068363 / 0.424275 (-0.355912) | 0.009314 / 0.007607 (0.001707) | 0.543640 / 0.226044 (0.317595) | 5.440853 / 2.268929 (3.171925) | 2.782415 / 55.444624 (-52.662210) | 2.332359 / 6.876477 (-4.544118) | 2.628520 / 2.142072 (0.486448) | 0.696838 / 4.805227 (-4.108389) | 0.160653 / 6.500664 (-6.340012) | 0.075599 / 0.075469 (0.000130) |\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) | 1.545305 / 1.841788 (-0.296483) | 23.073174 / 8.074308 (14.998866) | 16.974977 / 10.191392 (6.783585) | 0.183719 / 0.680424 (-0.496705) | 0.021633 / 0.534201 (-0.512568) | 0.471202 / 0.579283 (-0.108081) | 0.479385 / 0.434364 (0.045021) | 0.550872 / 0.540337 (0.010535) | 0.766825 / 1.386936 (-0.620111) |\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) | 0.007918 / 0.011353 (-0.003435) | 0.004793 / 0.011008 (-0.006215) | 0.077273 / 0.038508 (0.038765) | 0.092079 / 0.023109 (0.068969) | 0.483269 / 0.275898 (0.207371) | 0.524919 / 0.323480 (0.201439) | 0.006273 / 0.007986 (-0.001713) | 0.004018 / 0.004328 (-0.000310) | 0.077188 / 0.004250 (0.072937) | 0.067891 / 0.037052 (0.030839) | 0.478531 / 0.258489 (0.220042) | 0.526956 / 0.293841 (0.233115) | 0.038309 / 0.128546 (-0.090237) | 0.010133 / 0.075646 (-0.065513) | 0.083892 / 0.419271 (-0.335379) | 0.057369 / 0.043533 (0.013836) | 0.509427 / 0.255139 (0.254288) | 0.506574 / 0.283200 (0.223374) | 0.027987 / 0.141683 (-0.113696) | 1.897469 / 1.452155 (0.445314) | 1.893102 / 1.492716 (0.400385) |\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) | 0.243003 / 0.018006 (0.224997) | 0.500267 / 0.000490 (0.499777) | 0.007442 / 0.000200 (0.007242) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039266 / 0.037411 (0.001855) | 0.114438 / 0.014526 (0.099912) | 0.124528 / 0.176557 (-0.052029) | 0.189399 / 0.737135 (-0.547736) | 0.126703 / 0.296338 (-0.169635) |\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) | 0.518139 / 0.215209 (0.302930) | 5.162058 / 2.077655 (3.084403) | 2.835111 / 1.504120 (1.330991) | 2.640919 / 1.541195 (1.099724) | 2.736800 / 1.468490 (1.268310) | 0.582813 / 4.584777 (-4.001964) | 4.246269 / 3.745712 (0.500557) | 3.891161 / 5.269862 (-1.378701) | 2.445392 / 4.565676 (-2.120285) | 0.068943 / 0.424275 (-0.355332) | 0.009248 / 0.007607 (0.001641) | 0.604859 / 0.226044 (0.378815) | 6.030660 / 2.268929 (3.761731) | 3.409778 / 55.444624 (-52.034846) | 2.990488 / 6.876477 (-3.885988) | 3.281317 / 2.142072 (1.139245) | 0.697705 / 4.805227 (-4.107523) | 0.159502 / 6.500664 (-6.341162) | 0.072471 / 0.075469 (-0.002999) |\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) | 1.625428 / 1.841788 (-0.216360) | 23.602509 / 8.074308 (15.528201) | 18.091474 / 10.191392 (7.900082) | 0.172816 / 0.680424 (-0.507608) | 0.023708 / 0.534201 (-0.510493) | 0.473768 / 0.579283 (-0.105515) | 0.493713 / 0.434364 (0.059349) | 0.566326 / 0.540337 (0.025989) | 0.788670 / 1.386936 (-0.598266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ee2359c17ccb35b57e195f2bfe8478f49630039 \"CML watermark\")\n", "> Thanks. Any comment on my comment below?\r\n> \r\n> >Maybe we should update the docstring of get_data_patterns accordingly? Currently it only gives examples of outputs with ** not in a single path segment (i.e. not with a / as prefix or suffix).\r\n\r\nYea right we need to update it indeed, the outputs are the ones from older versions of fsspec, and from older patterns that we don't use anymore.\r\n\r\nIn general in docstrings I also think we should encourage users to use `**/*` instead of `**` (which has a behavior that is unique to fsspec)", "Also just noticed that `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` seems to include `KEYWORDS_IN_FILENAME_BASE_PATTERNS`. I guess we can try to remove the filename one in another PR to remove this redundancy \r\n\r\n(noticed this by checking that the data pattern is the same for both the dir name and filename examples in the get_data_patterns docstring)", "<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) | 0.006922 / 0.011353 (-0.004431) | 0.004459 / 0.011008 (-0.006549) | 0.084742 / 0.038508 (0.046234) | 0.089002 / 0.023109 (0.065893) | 0.310886 / 0.275898 (0.034988) | 0.340518 / 0.323480 (0.017038) | 0.007011 / 0.007986 (-0.000975) | 0.004566 / 0.004328 (0.000237) | 0.067260 / 0.004250 (0.063009) | 0.066349 / 0.037052 (0.029297) | 0.324029 / 0.258489 (0.065540) | 0.373785 / 0.293841 (0.079944) | 0.031780 / 0.128546 (-0.096766) | 0.009208 / 0.075646 (-0.066438) | 0.288871 / 0.419271 (-0.130401) | 0.054548 / 0.043533 (0.011015) | 0.313344 / 0.255139 (0.058205) | 0.336430 / 0.283200 (0.053231) | 0.029037 / 0.141683 (-0.112646) | 1.483797 / 1.452155 (0.031642) | 1.581884 / 1.492716 (0.089167) |\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) | 0.370520 / 0.018006 (0.352514) | 0.796720 / 0.000490 (0.796230) | 0.009329 / 0.000200 (0.009129) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033002 / 0.037411 (-0.004410) | 0.083442 / 0.014526 (0.068916) | 0.106468 / 0.176557 (-0.070088) | 0.165315 / 0.737135 (-0.571820) | 0.103048 / 0.296338 (-0.193291) |\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) | 0.386800 / 0.215209 (0.171591) | 3.843312 / 2.077655 (1.765658) | 1.848953 / 1.504120 (0.344834) | 1.679508 / 1.541195 (0.138313) | 1.733578 / 1.468490 (0.265088) | 0.488455 / 4.584777 (-4.096322) | 3.613594 / 3.745712 (-0.132118) | 3.533334 / 5.269862 (-1.736528) | 2.176216 / 4.565676 (-2.389460) | 0.056915 / 0.424275 (-0.367360) | 0.007349 / 0.007607 (-0.000258) | 0.465132 / 0.226044 (0.239088) | 4.638479 / 2.268929 (2.369550) | 2.354741 / 55.444624 (-53.089883) | 1.991777 / 6.876477 (-4.884700) | 2.249823 / 2.142072 (0.107751) | 0.582748 / 4.805227 (-4.222480) | 0.133829 / 6.500664 (-6.366835) | 0.060949 / 0.075469 (-0.014520) |\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) | 1.252027 / 1.841788 (-0.589760) | 20.660234 / 8.074308 (12.585926) | 14.328496 / 10.191392 (4.137104) | 0.164872 / 0.680424 (-0.515552) | 0.018867 / 0.534201 (-0.515334) | 0.392850 / 0.579283 (-0.186433) | 0.425684 / 0.434364 (-0.008679) | 0.461776 / 0.540337 (-0.078562) | 0.663688 / 1.386936 (-0.723248) |\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) | 0.007010 / 0.011353 (-0.004343) | 0.004791 / 0.011008 (-0.006217) | 0.064738 / 0.038508 (0.026230) | 0.088648 / 0.023109 (0.065539) | 0.418106 / 0.275898 (0.142208) | 0.446767 / 0.323480 (0.123287) | 0.006761 / 0.007986 (-0.001224) | 0.004649 / 0.004328 (0.000320) | 0.066345 / 0.004250 (0.062094) | 0.068326 / 0.037052 (0.031274) | 0.423426 / 0.258489 (0.164937) | 0.463160 / 0.293841 (0.169319) | 0.032689 / 0.128546 (-0.095858) | 0.009299 / 0.075646 (-0.066347) | 0.071321 / 0.419271 (-0.347951) | 0.048752 / 0.043533 (0.005219) | 0.418932 / 0.255139 (0.163793) | 0.440673 / 0.283200 (0.157473) | 0.027898 / 0.141683 (-0.113785) | 1.531860 / 1.452155 (0.079705) | 1.620456 / 1.492716 (0.127739) |\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) | 0.354917 / 0.018006 (0.336911) | 0.792432 / 0.000490 (0.791943) | 0.006626 / 0.000200 (0.006426) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036190 / 0.037411 (-0.001222) | 0.093052 / 0.014526 (0.078526) | 0.111927 / 0.176557 (-0.064629) | 0.165571 / 0.737135 (-0.571564) | 0.112159 / 0.296338 (-0.184180) |\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) | 0.437798 / 0.215209 (0.222589) | 4.367166 / 2.077655 (2.289511) | 2.343292 / 1.504120 (0.839172) | 2.169298 / 1.541195 (0.628103) | 2.224471 / 1.468490 (0.755981) | 0.487317 / 4.584777 (-4.097460) | 3.627825 / 3.745712 (-0.117887) | 3.500914 / 5.269862 (-1.768947) | 2.175862 / 4.565676 (-2.389815) | 0.057975 / 0.424275 (-0.366300) | 0.007509 / 0.007607 (-0.000098) | 0.517389 / 0.226044 (0.291345) | 5.169694 / 2.268929 (2.900766) | 2.850993 / 55.444624 (-52.593631) | 2.473111 / 6.876477 (-4.403366) | 2.746731 / 2.142072 (0.604659) | 0.586597 / 4.805227 (-4.218630) | 0.134082 / 6.500664 (-6.366582) | 0.061035 / 0.075469 (-0.014434) |\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) | 1.375186 / 1.841788 (-0.466602) | 20.960817 / 8.074308 (12.886509) | 15.035071 / 10.191392 (4.843679) | 0.169494 / 0.680424 (-0.510930) | 0.020654 / 0.534201 (-0.513547) | 0.398047 / 0.579283 (-0.181236) | 0.438117 / 0.434364 (0.003753) | 0.483896 / 0.540337 (-0.056441) | 0.690728 / 1.386936 (-0.696208) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e7fc64af912e5fcdcf949ed09d954332f0ae94a \"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) | 0.006892 / 0.011353 (-0.004461) | 0.004087 / 0.011008 (-0.006921) | 0.084695 / 0.038508 (0.046187) | 0.078084 / 0.023109 (0.054975) | 0.322976 / 0.275898 (0.047078) | 0.355332 / 0.323480 (0.031852) | 0.004235 / 0.007986 (-0.003750) | 0.003450 / 0.004328 (-0.000879) | 0.065355 / 0.004250 (0.061104) | 0.058593 / 0.037052 (0.021541) | 0.335761 / 0.258489 (0.077272) | 0.370392 / 0.293841 (0.076551) | 0.031720 / 0.128546 (-0.096827) | 0.008611 / 0.075646 (-0.067036) | 0.288213 / 0.419271 (-0.131059) | 0.053374 / 0.043533 (0.009842) | 0.321863 / 0.255139 (0.066724) | 0.341587 / 0.283200 (0.058387) | 0.025694 / 0.141683 (-0.115989) | 1.470502 / 1.452155 (0.018348) | 1.565068 / 1.492716 (0.072352) |\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) | 0.231063 / 0.018006 (0.213057) | 0.464996 / 0.000490 (0.464506) | 0.007316 / 0.000200 (0.007116) | 0.000288 / 0.000054 (0.000233) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029244 / 0.037411 (-0.008167) | 0.086303 / 0.014526 (0.071777) | 0.097281 / 0.176557 (-0.079276) | 0.153552 / 0.737135 (-0.583583) | 0.098488 / 0.296338 (-0.197850) |\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) | 0.382753 / 0.215209 (0.167544) | 3.826503 / 2.077655 (1.748848) | 1.848439 / 1.504120 (0.344319) | 1.688519 / 1.541195 (0.147324) | 1.787867 / 1.468490 (0.319377) | 0.489708 / 4.584777 (-4.095069) | 3.576780 / 3.745712 (-0.168932) | 3.341536 / 5.269862 (-1.928325) | 2.108787 / 4.565676 (-2.456889) | 0.057409 / 0.424275 (-0.366866) | 0.007325 / 0.007607 (-0.000282) | 0.459536 / 0.226044 (0.233492) | 4.590609 / 2.268929 (2.321681) | 2.313005 / 55.444624 (-53.131620) | 1.972389 / 6.876477 (-4.904087) | 2.218511 / 2.142072 (0.076439) | 0.613817 / 4.805227 (-4.191410) | 0.133846 / 6.500664 (-6.366818) | 0.062190 / 0.075469 (-0.013279) |\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) | 1.279860 / 1.841788 (-0.561928) | 19.549777 / 8.074308 (11.475469) | 14.225844 / 10.191392 (4.034452) | 0.164682 / 0.680424 (-0.515741) | 0.018321 / 0.534201 (-0.515880) | 0.389874 / 0.579283 (-0.189409) | 0.408597 / 0.434364 (-0.025767) | 0.454327 / 0.540337 (-0.086011) | 0.645571 / 1.386936 (-0.741365) |\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) | 0.007021 / 0.011353 (-0.004332) | 0.004119 / 0.011008 (-0.006889) | 0.065393 / 0.038508 (0.026885) | 0.085005 / 0.023109 (0.061896) | 0.412221 / 0.275898 (0.136323) | 0.438266 / 0.323480 (0.114786) | 0.005594 / 0.007986 (-0.002392) | 0.003499 / 0.004328 (-0.000829) | 0.065053 / 0.004250 (0.060802) | 0.060608 / 0.037052 (0.023555) | 0.413938 / 0.258489 (0.155449) | 0.446192 / 0.293841 (0.152351) | 0.032232 / 0.128546 (-0.096314) | 0.008617 / 0.075646 (-0.067029) | 0.071296 / 0.419271 (-0.347976) | 0.048756 / 0.043533 (0.005223) | 0.404977 / 0.255139 (0.149838) | 0.426801 / 0.283200 (0.143602) | 0.023650 / 0.141683 (-0.118033) | 1.526928 / 1.452155 (0.074773) | 1.627504 / 1.492716 (0.134787) |\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) | 0.224318 / 0.018006 (0.206312) | 0.469717 / 0.000490 (0.469227) | 0.005539 / 0.000200 (0.005339) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034240 / 0.037411 (-0.003171) | 0.096449 / 0.014526 (0.081923) | 0.107309 / 0.176557 (-0.069247) | 0.160246 / 0.737135 (-0.576889) | 0.107595 / 0.296338 (-0.188743) |\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) | 0.434266 / 0.215209 (0.219057) | 4.325571 / 2.077655 (2.247916) | 2.324066 / 1.504120 (0.819946) | 2.140238 / 1.541195 (0.599044) | 2.244593 / 1.468490 (0.776103) | 0.486259 / 4.584777 (-4.098518) | 3.644120 / 3.745712 (-0.101592) | 3.372330 / 5.269862 (-1.897531) | 2.074779 / 4.565676 (-2.490897) | 0.057154 / 0.424275 (-0.367121) | 0.007304 / 0.007607 (-0.000303) | 0.516944 / 0.226044 (0.290899) | 5.174300 / 2.268929 (2.905372) | 2.816269 / 55.444624 (-52.628356) | 2.462943 / 6.876477 (-4.413534) | 2.735851 / 2.142072 (0.593779) | 0.589028 / 4.805227 (-4.216200) | 0.131804 / 6.500664 (-6.368860) | 0.060173 / 0.075469 (-0.015296) |\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) | 1.354540 / 1.841788 (-0.487248) | 20.436511 / 8.074308 (12.362203) | 15.541981 / 10.191392 (5.350589) | 0.168399 / 0.680424 (-0.512025) | 0.020716 / 0.534201 (-0.513485) | 0.396275 / 0.579283 (-0.183008) | 0.427232 / 0.434364 (-0.007132) | 0.475121 / 0.540337 (-0.065216) | 0.648579 / 1.386936 (-0.738357) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4fa138fc0d9aa1536194fd46566840e698ccde03 \"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) | 0.009071 / 0.011353 (-0.002282) | 0.005820 / 0.011008 (-0.005188) | 0.119974 / 0.038508 (0.081466) | 0.092145 / 0.023109 (0.069036) | 0.445349 / 0.275898 (0.169451) | 0.442488 / 0.323480 (0.119008) | 0.005352 / 0.007986 (-0.002634) | 0.004332 / 0.004328 (0.000003) | 0.084397 / 0.004250 (0.080147) | 0.064624 / 0.037052 (0.027572) | 0.430938 / 0.258489 (0.172448) | 0.503574 / 0.293841 (0.209733) | 0.047900 / 0.128546 (-0.080647) | 0.014237 / 0.075646 (-0.061409) | 0.366145 / 0.419271 (-0.053127) | 0.066344 / 0.043533 (0.022811) | 0.424582 / 0.255139 (0.169443) | 0.451845 / 0.283200 (0.168646) | 0.041409 / 0.141683 (-0.100274) | 1.886998 / 1.452155 (0.434843) | 2.011676 / 1.492716 (0.518960) |\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) | 0.301008 / 0.018006 (0.283001) | 0.608670 / 0.000490 (0.608180) | 0.011963 / 0.000200 (0.011763) | 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) | 0.031996 / 0.037411 (-0.005415) | 0.102274 / 0.014526 (0.087748) | 0.121437 / 0.176557 (-0.055120) | 0.181647 / 0.737135 (-0.555489) | 0.121634 / 0.296338 (-0.174704) |\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) | 0.597070 / 0.215209 (0.381861) | 5.973808 / 2.077655 (3.896154) | 2.486345 / 1.504120 (0.982225) | 2.125395 / 1.541195 (0.584201) | 2.270864 / 1.468490 (0.802374) | 0.880031 / 4.584777 (-3.704746) | 5.396522 / 3.745712 (1.650809) | 4.702005 / 5.269862 (-0.567857) | 3.023087 / 4.565676 (-1.542589) | 0.097093 / 0.424275 (-0.327182) | 0.008457 / 0.007607 (0.000850) | 0.712164 / 0.226044 (0.486120) | 7.112867 / 2.268929 (4.843938) | 3.364509 / 55.444624 (-52.080115) | 2.646953 / 6.876477 (-4.229524) | 2.795967 / 2.142072 (0.653894) | 1.067182 / 4.805227 (-3.738046) | 0.218297 / 6.500664 (-6.282368) | 0.071720 / 0.075469 (-0.003750) |\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) | 1.640477 / 1.841788 (-0.201311) | 24.875163 / 8.074308 (16.800855) | 22.125706 / 10.191392 (11.934314) | 0.247267 / 0.680424 (-0.433157) | 0.033717 / 0.534201 (-0.500484) | 0.492422 / 0.579283 (-0.086862) | 0.578323 / 0.434364 (0.143959) | 0.579503 / 0.540337 (0.039165) | 0.816721 / 1.386936 (-0.570215) |\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) | 0.009372 / 0.011353 (-0.001981) | 0.005449 / 0.011008 (-0.005559) | 0.095371 / 0.038508 (0.056863) | 0.086320 / 0.023109 (0.063211) | 0.539573 / 0.275898 (0.263675) | 0.580338 / 0.323480 (0.256858) | 0.007028 / 0.007986 (-0.000958) | 0.004196 / 0.004328 (-0.000133) | 0.082710 / 0.004250 (0.078460) | 0.064336 / 0.037052 (0.027284) | 0.521490 / 0.258489 (0.263001) | 0.567942 / 0.293841 (0.274101) | 0.049659 / 0.128546 (-0.078887) | 0.017297 / 0.075646 (-0.058350) | 0.093874 / 0.419271 (-0.325398) | 0.061664 / 0.043533 (0.018131) | 0.524476 / 0.255139 (0.269337) | 0.563255 / 0.283200 (0.280055) | 0.039990 / 0.141683 (-0.101693) | 1.854438 / 1.452155 (0.402283) | 1.819321 / 1.492716 (0.326605) |\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) | 0.298817 / 0.018006 (0.280811) | 0.629381 / 0.000490 (0.628891) | 0.006259 / 0.000200 (0.006059) | 0.000690 / 0.000054 (0.000635) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.041009 / 0.037411 (0.003598) | 0.123845 / 0.014526 (0.109319) | 0.138606 / 0.176557 (-0.037951) | 0.215042 / 0.737135 (-0.522093) | 0.129572 / 0.296338 (-0.166767) |\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) | 0.668823 / 0.215209 (0.453614) | 6.596762 / 2.077655 (4.519108) | 3.275429 / 1.504120 (1.771309) | 2.921747 / 1.541195 (1.380553) | 2.963748 / 1.468490 (1.495258) | 0.897588 / 4.584777 (-3.687188) | 5.683618 / 3.745712 (1.937906) | 5.051102 / 5.269862 (-0.218760) | 3.178855 / 4.565676 (-1.386822) | 0.107446 / 0.424275 (-0.316829) | 0.008967 / 0.007607 (0.001360) | 0.785577 / 0.226044 (0.559532) | 8.236556 / 2.268929 (5.967628) | 3.914725 / 55.444624 (-51.529899) | 3.129068 / 6.876477 (-3.747409) | 3.368383 / 2.142072 (1.226310) | 1.004307 / 4.805227 (-3.800920) | 0.204788 / 6.500664 (-6.295876) | 0.078250 / 0.075469 (0.002780) |\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) | 1.778574 / 1.841788 (-0.063213) | 25.583659 / 8.074308 (17.509351) | 23.505866 / 10.191392 (13.314474) | 0.228759 / 0.680424 (-0.451665) | 0.038348 / 0.534201 (-0.495853) | 0.468980 / 0.579283 (-0.110303) | 0.630194 / 0.434364 (0.195830) | 0.587535 / 0.540337 (0.047198) | 0.831761 / 1.386936 (-0.555175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68f4f847f3248f02fc99458310d9d786906d7a6f \"CML watermark\")\n", "I've addressed the comments. Let me know if it looks all good now :)", "Actually just found out that the current `**/*[-._ 0-9/]train[-._ 0-9/]**` doesn't match `data/train.csv` in bash (but does match in fsspec right now).\r\n\r\nSo there might be a risk that this pattern breaks in the future no ?", "@lhoestq `fsspec` has tests to check their specific (non-posix) behavior, so I think merging in the current state is fine. And if they make a breaking change in the future, we can align the patterns once again :) ", "Yea after more thoughts I also think it's fine. Feel free to merge !", "<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) | 0.006920 / 0.011353 (-0.004433) | 0.004182 / 0.011008 (-0.006826) | 0.084629 / 0.038508 (0.046121) | 0.086052 / 0.023109 (0.062943) | 0.326062 / 0.275898 (0.050164) | 0.344190 / 0.323480 (0.020710) | 0.005393 / 0.007986 (-0.002593) | 0.003410 / 0.004328 (-0.000918) | 0.064327 / 0.004250 (0.060076) | 0.056556 / 0.037052 (0.019504) | 0.319255 / 0.258489 (0.060766) | 0.357943 / 0.293841 (0.064102) | 0.032097 / 0.128546 (-0.096450) | 0.008778 / 0.075646 (-0.066868) | 0.291057 / 0.419271 (-0.128215) | 0.053225 / 0.043533 (0.009692) | 0.307713 / 0.255139 (0.052574) | 0.350058 / 0.283200 (0.066858) | 0.024380 / 0.141683 (-0.117303) | 1.459482 / 1.452155 (0.007328) | 1.555711 / 1.492716 (0.062994) |\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) | 0.239487 / 0.018006 (0.221480) | 0.467604 / 0.000490 (0.467114) | 0.010742 / 0.000200 (0.010542) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029394 / 0.037411 (-0.008018) | 0.087404 / 0.014526 (0.072879) | 0.098701 / 0.176557 (-0.077855) | 0.154145 / 0.737135 (-0.582990) | 0.099726 / 0.296338 (-0.196612) |\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) | 0.389008 / 0.215209 (0.173799) | 3.873165 / 2.077655 (1.795510) | 1.860676 / 1.504120 (0.356556) | 1.679668 / 1.541195 (0.138474) | 1.782347 / 1.468490 (0.313857) | 0.489469 / 4.584777 (-4.095308) | 3.678706 / 3.745712 (-0.067006) | 3.404076 / 5.269862 (-1.865785) | 2.110972 / 4.565676 (-2.454704) | 0.057478 / 0.424275 (-0.366797) | 0.007443 / 0.007607 (-0.000164) | 0.464780 / 0.226044 (0.238736) | 4.643606 / 2.268929 (2.374678) | 2.355744 / 55.444624 (-53.088881) | 1.993992 / 6.876477 (-4.882485) | 2.245520 / 2.142072 (0.103447) | 0.592773 / 4.805227 (-4.212454) | 0.135369 / 6.500664 (-6.365295) | 0.062478 / 0.075469 (-0.012991) |\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) | 1.257537 / 1.841788 (-0.584251) | 19.828010 / 8.074308 (11.753702) | 14.709260 / 10.191392 (4.517868) | 0.168359 / 0.680424 (-0.512065) | 0.018907 / 0.534201 (-0.515294) | 0.397223 / 0.579283 (-0.182060) | 0.421760 / 0.434364 (-0.012604) | 0.464597 / 0.540337 (-0.075740) | 0.665905 / 1.386936 (-0.721031) |\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) | 0.007247 / 0.011353 (-0.004106) | 0.004104 / 0.011008 (-0.006904) | 0.065008 / 0.038508 (0.026500) | 0.083485 / 0.023109 (0.060376) | 0.399808 / 0.275898 (0.123910) | 0.433374 / 0.323480 (0.109894) | 0.005453 / 0.007986 (-0.002532) | 0.003479 / 0.004328 (-0.000850) | 0.065126 / 0.004250 (0.060876) | 0.059945 / 0.037052 (0.022893) | 0.402018 / 0.258489 (0.143529) | 0.437927 / 0.293841 (0.144086) | 0.032654 / 0.128546 (-0.095892) | 0.008717 / 0.075646 (-0.066929) | 0.071737 / 0.419271 (-0.347534) | 0.048903 / 0.043533 (0.005370) | 0.402107 / 0.255139 (0.146968) | 0.417602 / 0.283200 (0.134402) | 0.024821 / 0.141683 (-0.116862) | 1.474471 / 1.452155 (0.022316) | 1.559571 / 1.492716 (0.066855) |\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) | 0.232010 / 0.018006 (0.214003) | 0.460768 / 0.000490 (0.460278) | 0.005250 / 0.000200 (0.005050) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033839 / 0.037411 (-0.003573) | 0.101617 / 0.014526 (0.087091) | 0.107984 / 0.176557 (-0.068573) | 0.160923 / 0.737135 (-0.576212) | 0.110367 / 0.296338 (-0.185971) |\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) | 0.433087 / 0.215209 (0.217878) | 4.324100 / 2.077655 (2.246445) | 2.312937 / 1.504120 (0.808817) | 2.159903 / 1.541195 (0.618708) | 2.240235 / 1.468490 (0.771745) | 0.500659 / 4.584777 (-4.084118) | 3.743801 / 3.745712 (-0.001911) | 3.441350 / 5.269862 (-1.828512) | 2.141370 / 4.565676 (-2.424306) | 0.059078 / 0.424275 (-0.365197) | 0.007468 / 0.007607 (-0.000139) | 0.508108 / 0.226044 (0.282064) | 5.076738 / 2.268929 (2.807809) | 2.825939 / 55.444624 (-52.618685) | 2.467762 / 6.876477 (-4.408715) | 2.705079 / 2.142072 (0.563006) | 0.603363 / 4.805227 (-4.201864) | 0.136267 / 6.500664 (-6.364397) | 0.062887 / 0.075469 (-0.012582) |\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) | 1.359344 / 1.841788 (-0.482443) | 20.581510 / 8.074308 (12.507202) | 15.534489 / 10.191392 (5.343097) | 0.192068 / 0.680424 (-0.488356) | 0.020831 / 0.534201 (-0.513370) | 0.403330 / 0.579283 (-0.175953) | 0.429536 / 0.434364 (-0.004828) | 0.479906 / 0.540337 (-0.060431) | 0.674170 / 1.386936 (-0.712766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ac74c2df928dece49ca2cf25e14172896b442e \"CML watermark\")\n" ]
2023-09-15T17:58:25
2023-09-26T15:41:38
2023-09-26T15:32:51
CONTRIBUTOR
false
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Fix #6214
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6,243
Fix cast from fixed size list to variable size list
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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) | 0.006784 / 0.011353 (-0.004569) | 0.004051 / 0.011008 (-0.006957) | 0.083790 / 0.038508 (0.045282) | 0.081219 / 0.023109 (0.058110) | 0.313195 / 0.275898 (0.037297) | 0.336954 / 0.323480 (0.013475) | 0.004324 / 0.007986 (-0.003662) | 0.004516 / 0.004328 (0.000188) | 0.065051 / 0.004250 (0.060801) | 0.057647 / 0.037052 (0.020595) | 0.316675 / 0.258489 (0.058186) | 0.357936 / 0.293841 (0.064095) | 0.030980 / 0.128546 (-0.097566) | 0.008844 / 0.075646 (-0.066802) | 0.287027 / 0.419271 (-0.132245) | 0.052130 / 0.043533 (0.008597) | 0.308125 / 0.255139 (0.052986) | 0.337345 / 0.283200 (0.054145) | 0.025781 / 0.141683 (-0.115902) | 1.466161 / 1.452155 (0.014006) | 1.565824 / 1.492716 (0.073108) |\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) | 0.299112 / 0.018006 (0.281106) | 0.640520 / 0.000490 (0.640030) | 0.008846 / 0.000200 (0.008647) | 0.000273 / 0.000054 (0.000219) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029853 / 0.037411 (-0.007559) | 0.081697 / 0.014526 (0.067172) | 0.099110 / 0.176557 (-0.077447) | 0.155864 / 0.737135 (-0.581271) | 0.098749 / 0.296338 (-0.197590) |\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) | 0.385722 / 0.215209 (0.170512) | 3.851490 / 2.077655 (1.773835) | 1.851995 / 1.504120 (0.347875) | 1.660398 / 1.541195 (0.119204) | 1.769370 / 1.468490 (0.300879) | 0.481523 / 4.584777 (-4.103254) | 3.550449 / 3.745712 (-0.195263) | 3.424782 / 5.269862 (-1.845079) | 2.106470 / 4.565676 (-2.459206) | 0.056500 / 0.424275 (-0.367775) | 0.007891 / 0.007607 (0.000284) | 0.465564 / 0.226044 (0.239520) | 4.662892 / 2.268929 (2.393964) | 2.305424 / 55.444624 (-53.139201) | 1.980524 / 6.876477 (-4.895953) | 2.218423 / 2.142072 (0.076350) | 0.584662 / 4.805227 (-4.220565) | 0.132325 / 6.500664 (-6.368340) | 0.060773 / 0.075469 (-0.014696) |\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) | 1.254261 / 1.841788 (-0.587527) | 19.479805 / 8.074308 (11.405497) | 14.222687 / 10.191392 (4.031295) | 0.149829 / 0.680424 (-0.530595) | 0.018630 / 0.534201 (-0.515571) | 0.395284 / 0.579283 (-0.183999) | 0.413385 / 0.434364 (-0.020978) | 0.462931 / 0.540337 (-0.077406) | 0.645359 / 1.386936 (-0.741577) |\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) | 0.006991 / 0.011353 (-0.004362) | 0.004306 / 0.011008 (-0.006702) | 0.065213 / 0.038508 (0.026705) | 0.082442 / 0.023109 (0.059332) | 0.411294 / 0.275898 (0.135396) | 0.452176 / 0.323480 (0.128696) | 0.005802 / 0.007986 (-0.002183) | 0.003556 / 0.004328 (-0.000772) | 0.066163 / 0.004250 (0.061913) | 0.060680 / 0.037052 (0.023628) | 0.416975 / 0.258489 (0.158486) | 0.456353 / 0.293841 (0.162512) | 0.033584 / 0.128546 (-0.094963) | 0.008687 / 0.075646 (-0.066959) | 0.071300 / 0.419271 (-0.347972) | 0.049382 / 0.043533 (0.005849) | 0.409329 / 0.255139 (0.154190) | 0.434829 / 0.283200 (0.151629) | 0.022966 / 0.141683 (-0.118716) | 1.493847 / 1.452155 (0.041692) | 1.582372 / 1.492716 (0.089656) |\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) | 0.280578 / 0.018006 (0.262572) | 0.538122 / 0.000490 (0.537632) | 0.004515 / 0.000200 (0.004315) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033383 / 0.037411 (-0.004028) | 0.093426 / 0.014526 (0.078901) | 0.109314 / 0.176557 (-0.067242) | 0.162349 / 0.737135 (-0.574786) | 0.109849 / 0.296338 (-0.186490) |\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) | 0.431073 / 0.215209 (0.215864) | 4.311942 / 2.077655 (2.234287) | 2.291170 / 1.504120 (0.787051) | 2.132266 / 1.541195 (0.591072) | 2.236526 / 1.468490 (0.768036) | 0.492001 / 4.584777 (-4.092776) | 3.523013 / 3.745712 (-0.222699) | 3.413481 / 5.269862 (-1.856381) | 2.112979 / 4.565676 (-2.452698) | 0.058654 / 0.424275 (-0.365621) | 0.007729 / 0.007607 (0.000121) | 0.512027 / 0.226044 (0.285982) | 5.125264 / 2.268929 (2.856336) | 2.836281 / 55.444624 (-52.608344) | 2.447253 / 6.876477 (-4.429224) | 2.711908 / 2.142072 (0.569835) | 0.592598 / 4.805227 (-4.212629) | 0.134837 / 6.500664 (-6.365827) | 0.059813 / 0.075469 (-0.015656) |\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) | 1.373464 / 1.841788 (-0.468323) | 20.548983 / 8.074308 (12.474675) | 14.799833 / 10.191392 (4.608441) | 0.168601 / 0.680424 (-0.511823) | 0.020358 / 0.534201 (-0.513843) | 0.398790 / 0.579283 (-0.180494) | 0.416921 / 0.434364 (-0.017443) | 0.480542 / 0.540337 (-0.059795) | 0.645062 / 1.386936 (-0.741874) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#afd6fc193a91cb0461c8bf3b64db6943c23de846 \"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) | 0.008616 / 0.011353 (-0.002737) | 0.004957 / 0.011008 (-0.006051) | 0.102629 / 0.038508 (0.064121) | 0.080492 / 0.023109 (0.057383) | 0.461817 / 0.275898 (0.185919) | 0.487964 / 0.323480 (0.164484) | 0.006336 / 0.007986 (-0.001649) | 0.004607 / 0.004328 (0.000278) | 0.074311 / 0.004250 (0.070061) | 0.060368 / 0.037052 (0.023315) | 0.458076 / 0.258489 (0.199587) | 0.493028 / 0.293841 (0.199187) | 0.044153 / 0.128546 (-0.084394) | 0.014066 / 0.075646 (-0.061581) | 0.369848 / 0.419271 (-0.049424) | 0.061690 / 0.043533 (0.018157) | 0.439728 / 0.255139 (0.184590) | 0.484706 / 0.283200 (0.201506) | 0.034657 / 0.141683 (-0.107026) | 1.710591 / 1.452155 (0.258437) | 1.900225 / 1.492716 (0.407509) |\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) | 0.308837 / 0.018006 (0.290831) | 0.579561 / 0.000490 (0.579072) | 0.010163 / 0.000200 (0.009963) | 0.000613 / 0.000054 (0.000558) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028108 / 0.037411 (-0.009303) | 0.085072 / 0.014526 (0.070546) | 0.103375 / 0.176557 (-0.073182) | 0.173765 / 0.737135 (-0.563371) | 0.102460 / 0.296338 (-0.193879) |\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) | 0.602642 / 0.215209 (0.387433) | 5.582537 / 2.077655 (3.504882) | 2.405553 / 1.504120 (0.901434) | 2.057298 / 1.541195 (0.516103) | 2.223787 / 1.468490 (0.755297) | 0.846138 / 4.584777 (-3.738638) | 5.290306 / 3.745712 (1.544594) | 4.836066 / 5.269862 (-0.433795) | 2.951901 / 4.565676 (-1.613775) | 0.099432 / 0.424275 (-0.324843) | 0.009198 / 0.007607 (0.001591) | 0.731370 / 0.226044 (0.505325) | 6.663026 / 2.268929 (4.394098) | 3.200932 / 55.444624 (-52.243692) | 2.486654 / 6.876477 (-4.389823) | 2.833195 / 2.142072 (0.691123) | 0.989481 / 4.805227 (-3.815746) | 0.205176 / 6.500664 (-6.295488) | 0.073760 / 0.075469 (-0.001709) |\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) | 1.745494 / 1.841788 (-0.096294) | 24.649294 / 8.074308 (16.574986) | 22.312182 / 10.191392 (12.120790) | 0.245207 / 0.680424 (-0.435217) | 0.031971 / 0.534201 (-0.502230) | 0.495179 / 0.579283 (-0.084104) | 0.603233 / 0.434364 (0.168869) | 0.560906 / 0.540337 (0.020569) | 0.788292 / 1.386936 (-0.598644) |\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) | 0.008922 / 0.011353 (-0.002431) | 0.005203 / 0.011008 (-0.005805) | 0.074414 / 0.038508 (0.035906) | 0.077552 / 0.023109 (0.054443) | 0.547217 / 0.275898 (0.271319) | 0.625298 / 0.323480 (0.301818) | 0.006135 / 0.007986 (-0.001851) | 0.004163 / 0.004328 (-0.000165) | 0.078014 / 0.004250 (0.073764) | 0.064484 / 0.037052 (0.027431) | 0.562356 / 0.258489 (0.303867) | 0.643613 / 0.293841 (0.349772) | 0.050155 / 0.128546 (-0.078391) | 0.013665 / 0.075646 (-0.061981) | 0.090224 / 0.419271 (-0.329048) | 0.063852 / 0.043533 (0.020319) | 0.560914 / 0.255139 (0.305775) | 0.591531 / 0.283200 (0.308331) | 0.036491 / 0.141683 (-0.105192) | 1.670898 / 1.452155 (0.218743) | 1.783924 / 1.492716 (0.291208) |\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) | 0.312764 / 0.018006 (0.294758) | 0.611116 / 0.000490 (0.610626) | 0.006367 / 0.000200 (0.006167) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033967 / 0.037411 (-0.003445) | 0.101550 / 0.014526 (0.087025) | 0.116953 / 0.176557 (-0.059604) | 0.180061 / 0.737135 (-0.557075) | 0.115220 / 0.296338 (-0.181118) |\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) | 0.642110 / 0.215209 (0.426901) | 6.361381 / 2.077655 (4.283727) | 2.948175 / 1.504120 (1.444055) | 2.633935 / 1.541195 (1.092740) | 2.822150 / 1.468490 (1.353660) | 0.931412 / 4.584777 (-3.653365) | 5.428540 / 3.745712 (1.682828) | 4.672920 / 5.269862 (-0.596941) | 3.102046 / 4.565676 (-1.463630) | 0.100825 / 0.424275 (-0.323450) | 0.009464 / 0.007607 (0.001857) | 0.774102 / 0.226044 (0.548058) | 7.715003 / 2.268929 (5.446074) | 3.987807 / 55.444624 (-51.456817) | 3.089129 / 6.876477 (-3.787347) | 3.333247 / 2.142072 (1.191174) | 1.012427 / 4.805227 (-3.792800) | 0.200662 / 6.500664 (-6.300002) | 0.072422 / 0.075469 (-0.003047) |\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) | 1.680364 / 1.841788 (-0.161424) | 24.484576 / 8.074308 (16.410268) | 21.920990 / 10.191392 (11.729598) | 0.218604 / 0.680424 (-0.461820) | 0.035818 / 0.534201 (-0.498383) | 0.470648 / 0.579283 (-0.108635) | 0.585108 / 0.434364 (0.150744) | 0.539152 / 0.540337 (-0.001185) | 0.763999 / 1.386936 (-0.622937) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfed1d09ed6c680085624d96eb99bfb2b0b27599 \"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) | 0.006304 / 0.011353 (-0.005049) | 0.003884 / 0.011008 (-0.007125) | 0.084847 / 0.038508 (0.046339) | 0.069372 / 0.023109 (0.046263) | 0.318876 / 0.275898 (0.042978) | 0.344733 / 0.323480 (0.021253) | 0.005139 / 0.007986 (-0.002847) | 0.003203 / 0.004328 (-0.001125) | 0.065758 / 0.004250 (0.061507) | 0.054189 / 0.037052 (0.017137) | 0.317475 / 0.258489 (0.058986) | 0.359310 / 0.293841 (0.065469) | 0.030639 / 0.128546 (-0.097908) | 0.008657 / 0.075646 (-0.066989) | 0.289127 / 0.419271 (-0.130144) | 0.052344 / 0.043533 (0.008811) | 0.316122 / 0.255139 (0.060983) | 0.338339 / 0.283200 (0.055140) | 0.022677 / 0.141683 (-0.119006) | 1.551629 / 1.452155 (0.099474) | 1.617917 / 1.492716 (0.125201) |\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) | 0.231067 / 0.018006 (0.213061) | 0.450559 / 0.000490 (0.450070) | 0.008484 / 0.000200 (0.008284) | 0.000234 / 0.000054 (0.000179) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.081560 / 0.014526 (0.067034) | 0.094162 / 0.176557 (-0.082395) | 0.148583 / 0.737135 (-0.588552) | 0.093596 / 0.296338 (-0.202742) |\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) | 0.388616 / 0.215209 (0.173407) | 3.874905 / 2.077655 (1.797251) | 1.915845 / 1.504120 (0.411725) | 1.746410 / 1.541195 (0.205215) | 1.828789 / 1.468490 (0.360299) | 0.483270 / 4.584777 (-4.101506) | 3.489157 / 3.745712 (-0.256555) | 3.190086 / 5.269862 (-2.079776) | 1.978023 / 4.565676 (-2.587653) | 0.056290 / 0.424275 (-0.367985) | 0.007585 / 0.007607 (-0.000022) | 0.467051 / 0.226044 (0.241007) | 4.665971 / 2.268929 (2.397043) | 2.418550 / 55.444624 (-53.026075) | 2.048338 / 6.876477 (-4.828139) | 2.225275 / 2.142072 (0.083203) | 0.576601 / 4.805227 (-4.228626) | 0.131960 / 6.500664 (-6.368704) | 0.060177 / 0.075469 (-0.015292) |\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) | 1.249797 / 1.841788 (-0.591991) | 18.552939 / 8.074308 (10.478631) | 14.016616 / 10.191392 (3.825224) | 0.162869 / 0.680424 (-0.517555) | 0.018105 / 0.534201 (-0.516096) | 0.394838 / 0.579283 (-0.184445) | 0.403378 / 0.434364 (-0.030986) | 0.460931 / 0.540337 (-0.079407) | 0.637365 / 1.386936 (-0.749571) |\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) | 0.006497 / 0.011353 (-0.004856) | 0.003928 / 0.011008 (-0.007080) | 0.063958 / 0.038508 (0.025450) | 0.069609 / 0.023109 (0.046500) | 0.401599 / 0.275898 (0.125701) | 0.428128 / 0.323480 (0.104648) | 0.005296 / 0.007986 (-0.002689) | 0.003332 / 0.004328 (-0.000996) | 0.063903 / 0.004250 (0.059652) | 0.056303 / 0.037052 (0.019250) | 0.400704 / 0.258489 (0.142214) | 0.435982 / 0.293841 (0.142141) | 0.032434 / 0.128546 (-0.096112) | 0.008570 / 0.075646 (-0.067077) | 0.070788 / 0.419271 (-0.348483) | 0.048252 / 0.043533 (0.004719) | 0.403269 / 0.255139 (0.148130) | 0.419796 / 0.283200 (0.136596) | 0.022598 / 0.141683 (-0.119085) | 1.481627 / 1.452155 (0.029472) | 1.578388 / 1.492716 (0.085672) |\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) | 0.224552 / 0.018006 (0.206546) | 0.444059 / 0.000490 (0.443570) | 0.003757 / 0.000200 (0.003557) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032173 / 0.037411 (-0.005239) | 0.092562 / 0.014526 (0.078036) | 0.104972 / 0.176557 (-0.071584) | 0.156467 / 0.737135 (-0.580669) | 0.104274 / 0.296338 (-0.192065) |\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) | 0.441693 / 0.215209 (0.226484) | 4.400217 / 2.077655 (2.322562) | 2.393862 / 1.504120 (0.889742) | 2.281178 / 1.541195 (0.739983) | 2.339895 / 1.468490 (0.871405) | 0.488734 / 4.584777 (-4.096043) | 3.523352 / 3.745712 (-0.222360) | 3.216761 / 5.269862 (-2.053101) | 2.007553 / 4.565676 (-2.558123) | 0.058050 / 0.424275 (-0.366225) | 0.007566 / 0.007607 (-0.000041) | 0.515439 / 0.226044 (0.289394) | 5.155086 / 2.268929 (2.886157) | 2.864958 / 55.444624 (-52.579666) | 2.592460 / 6.876477 (-4.284016) | 2.800449 / 2.142072 (0.658376) | 0.588441 / 4.805227 (-4.216786) | 0.131589 / 6.500664 (-6.369075) | 0.059075 / 0.075469 (-0.016394) |\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) | 1.353889 / 1.841788 (-0.487898) | 18.938285 / 8.074308 (10.863977) | 14.937141 / 10.191392 (4.745749) | 0.168811 / 0.680424 (-0.511613) | 0.020118 / 0.534201 (-0.514083) | 0.394791 / 0.579283 (-0.184492) | 0.414434 / 0.434364 (-0.019930) | 0.466821 / 0.540337 (-0.073517) | 0.629894 / 1.386936 (-0.757042) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23921b08390db7dbb3186a8de40dc49a4066da76 \"CML watermark\")\n", "CI failures are unrelated", "<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) | 0.005959 / 0.011353 (-0.005394) | 0.004164 / 0.011008 (-0.006844) | 0.082336 / 0.038508 (0.043828) | 0.070344 / 0.023109 (0.047234) | 0.348032 / 0.275898 (0.072134) | 0.366328 / 0.323480 (0.042848) | 0.003882 / 0.007986 (-0.004104) | 0.003619 / 0.004328 (-0.000709) | 0.063343 / 0.004250 (0.059093) | 0.056617 / 0.037052 (0.019564) | 0.351625 / 0.258489 (0.093136) | 0.395839 / 0.293841 (0.101998) | 0.030842 / 0.128546 (-0.097704) | 0.008363 / 0.075646 (-0.067284) | 0.300535 / 0.419271 (-0.118737) | 0.053303 / 0.043533 (0.009770) | 0.354782 / 0.255139 (0.099643) | 0.364918 / 0.283200 (0.081719) | 0.025365 / 0.141683 (-0.116318) | 1.555009 / 1.452155 (0.102854) | 1.597443 / 1.492716 (0.104727) |\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) | 0.239808 / 0.018006 (0.221801) | 0.488164 / 0.000490 (0.487675) | 0.013183 / 0.000200 (0.012983) | 0.000483 / 0.000054 (0.000429) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027938 / 0.037411 (-0.009473) | 0.078521 / 0.014526 (0.063995) | 0.095498 / 0.176557 (-0.081059) | 0.150884 / 0.737135 (-0.586251) | 0.097577 / 0.296338 (-0.198762) |\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) | 0.384546 / 0.215209 (0.169337) | 4.037707 / 2.077655 (1.960053) | 1.940321 / 1.504120 (0.436201) | 1.716741 / 1.541195 (0.175546) | 1.837200 / 1.468490 (0.368710) | 0.502112 / 4.584777 (-4.082665) | 3.770452 / 3.745712 (0.024740) | 3.325691 / 5.269862 (-1.944171) | 2.015622 / 4.565676 (-2.550055) | 0.056246 / 0.424275 (-0.368029) | 0.007320 / 0.007607 (-0.000287) | 0.445553 / 0.226044 (0.219509) | 4.567233 / 2.268929 (2.298304) | 2.319531 / 55.444624 (-53.125093) | 1.968664 / 6.876477 (-4.907813) | 2.122349 / 2.142072 (-0.019724) | 0.573688 / 4.805227 (-4.231540) | 0.131410 / 6.500664 (-6.369254) | 0.062767 / 0.075469 (-0.012702) |\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) | 1.255244 / 1.841788 (-0.586543) | 19.042480 / 8.074308 (10.968172) | 13.935342 / 10.191392 (3.743950) | 0.161259 / 0.680424 (-0.519165) | 0.020582 / 0.534201 (-0.513619) | 0.391365 / 0.579283 (-0.187918) | 0.417462 / 0.434364 (-0.016902) | 0.473121 / 0.540337 (-0.067216) | 0.674768 / 1.386936 (-0.712168) |\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) | 0.006299 / 0.011353 (-0.005054) | 0.003969 / 0.011008 (-0.007040) | 0.063558 / 0.038508 (0.025050) | 0.073847 / 0.023109 (0.050738) | 0.407064 / 0.275898 (0.131166) | 0.440695 / 0.323480 (0.117215) | 0.005783 / 0.007986 (-0.002203) | 0.003517 / 0.004328 (-0.000812) | 0.065721 / 0.004250 (0.061470) | 0.056390 / 0.037052 (0.019338) | 0.419019 / 0.258489 (0.160530) | 0.450721 / 0.293841 (0.156880) | 0.034094 / 0.128546 (-0.094452) | 0.008594 / 0.075646 (-0.067052) | 0.069254 / 0.419271 (-0.350017) | 0.049218 / 0.043533 (0.005685) | 0.413312 / 0.255139 (0.158173) | 0.439454 / 0.283200 (0.156255) | 0.021481 / 0.141683 (-0.120202) | 1.517536 / 1.452155 (0.065382) | 1.530532 / 1.492716 (0.037815) |\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) | 0.235392 / 0.018006 (0.217386) | 0.477371 / 0.000490 (0.476881) | 0.007070 / 0.000200 (0.006870) | 0.000132 / 0.000054 (0.000077) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031909 / 0.037411 (-0.005502) | 0.092459 / 0.014526 (0.077933) | 0.105795 / 0.176557 (-0.070761) | 0.157745 / 0.737135 (-0.579390) | 0.104187 / 0.296338 (-0.192152) |\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) | 0.424385 / 0.215209 (0.209176) | 4.445371 / 2.077655 (2.367716) | 2.423639 / 1.504120 (0.919519) | 2.188167 / 1.541195 (0.646972) | 2.171023 / 1.468490 (0.702532) | 0.483566 / 4.584777 (-4.101211) | 3.825702 / 3.745712 (0.079990) | 3.276350 / 5.269862 (-1.993512) | 2.063075 / 4.565676 (-2.502602) | 0.061628 / 0.424275 (-0.362647) | 0.008176 / 0.007607 (0.000569) | 0.506697 / 0.226044 (0.280653) | 5.067924 / 2.268929 (2.798995) | 2.785567 / 55.444624 (-52.659057) | 2.457340 / 6.876477 (-4.419137) | 2.599646 / 2.142072 (0.457574) | 0.581550 / 4.805227 (-4.223677) | 0.131712 / 6.500664 (-6.368952) | 0.058776 / 0.075469 (-0.016693) |\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) | 1.356639 / 1.841788 (-0.485148) | 20.103463 / 8.074308 (12.029155) | 14.481010 / 10.191392 (4.289618) | 0.162870 / 0.680424 (-0.517554) | 0.023197 / 0.534201 (-0.511004) | 0.413042 / 0.579283 (-0.166241) | 0.427494 / 0.434364 (-0.006870) | 0.508457 / 0.540337 (-0.031880) | 0.662412 / 1.386936 (-0.724524) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05fe5c06d42f84408b933c2809acb9b7449cbbb3 \"CML watermark\")\n" ]
2023-09-15T14:23:33
2023-09-19T18:02:21
2023-09-19T17:53:17
CONTRIBUTOR
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Fix #6242
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6,242
Data alteration when loading dataset with unspecified inner sequence length
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[ "While this issue may seem specific, it led to a silent problem in my workflow that took days to diagnose. If this feature is not intended to be supported, an error should be raised when encountering this configuration to prevent such issues.", "Thanks for reporting! This is a MRE:\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.table import cast_array_to_feature\r\nfrom datasets import Sequence, Value\r\ndata = [\r\n [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],\r\n [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],\r\n]\r\narr = pa.array(data, pa.list_(pa.list_(pa.float32(), 3)))\r\ncast_array_to_feature(arr, Sequence(Sequence(Value(\"float32\"))))\r\n```\r\n\r\nI've opened a PR with a fix." ]
2023-09-14T16:12:45
2023-09-19T17:53:18
2023-09-19T17:53:18
CONTRIBUTOR
null
null
### Describe the bug When a dataset saved with a specified inner sequence length is loaded without specifying that length, the original data is altered and becomes inconsistent. ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Value, Sequence, load_dataset # Repository ID repo_id = "my_repo_id" # Define features with a specific length of 3 for each inner sequence specified_features = Features({"key": Sequence(Sequence(Value("float32"), length=3))}) # Create a dataset with the specified features data = [ [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]], ] dataset = Dataset.from_dict({"key": data}, features=specified_features) # Push the dataset to the hub dataset.push_to_hub(repo_id) # Define features without specifying the length unspecified_features = Features({"key": Sequence(Sequence(Value("float32")))}) # Load the dataset from the hub with this new feature definition dataset = load_dataset(f"qgallouedec/{repo_id}", split="train", features=unspecified_features) # The obtained data is altered print(dataset.to_dict()) # {'key': [[[1.0], [2.0]], [[3.0], [4.0]]]} ``` ### Expected behavior ```python print(dataset.to_dict()) # {'key': [[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]]} ``` ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-32-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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PR_kwDODunzps5aVfl-
6,241
Remove unused global variables in `audio.py`
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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) | 0.006753 / 0.011353 (-0.004600) | 0.004027 / 0.011008 (-0.006982) | 0.084200 / 0.038508 (0.045692) | 0.072233 / 0.023109 (0.049124) | 0.361535 / 0.275898 (0.085637) | 0.386196 / 0.323480 (0.062716) | 0.004047 / 0.007986 (-0.003939) | 0.003416 / 0.004328 (-0.000912) | 0.064724 / 0.004250 (0.060474) | 0.055740 / 0.037052 (0.018688) | 0.360422 / 0.258489 (0.101933) | 0.399230 / 0.293841 (0.105389) | 0.031537 / 0.128546 (-0.097009) | 0.008630 / 0.075646 (-0.067016) | 0.289652 / 0.419271 (-0.129620) | 0.052881 / 0.043533 (0.009348) | 0.359538 / 0.255139 (0.104399) | 0.379410 / 0.283200 (0.096211) | 0.024539 / 0.141683 (-0.117144) | 1.470891 / 1.452155 (0.018736) | 1.578879 / 1.492716 (0.086163) |\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) | 0.239200 / 0.018006 (0.221194) | 0.462100 / 0.000490 (0.461610) | 0.009055 / 0.000200 (0.008856) | 0.000406 / 0.000054 (0.000352) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028736 / 0.037411 (-0.008675) | 0.088051 / 0.014526 (0.073525) | 0.098101 / 0.176557 (-0.078456) | 0.152399 / 0.737135 (-0.584737) | 0.098776 / 0.296338 (-0.197563) |\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) | 0.401761 / 0.215209 (0.186552) | 4.014143 / 2.077655 (1.936488) | 2.033255 / 1.504120 (0.529135) | 1.855347 / 1.541195 (0.314152) | 1.996144 / 1.468490 (0.527654) | 0.488545 / 4.584777 (-4.096232) | 3.712030 / 3.745712 (-0.033682) | 3.439725 / 5.269862 (-1.830137) | 2.119289 / 4.565676 (-2.446388) | 0.057523 / 0.424275 (-0.366752) | 0.007780 / 0.007607 (0.000173) | 0.479522 / 0.226044 (0.253477) | 4.798218 / 2.268929 (2.529290) | 2.543816 / 55.444624 (-52.900809) | 2.180392 / 6.876477 (-4.696085) | 2.427195 / 2.142072 (0.285122) | 0.602071 / 4.805227 (-4.203156) | 0.133450 / 6.500664 (-6.367214) | 0.061975 / 0.075469 (-0.013494) |\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) | 1.250040 / 1.841788 (-0.591748) | 19.532327 / 8.074308 (11.458019) | 14.200298 / 10.191392 (4.008906) | 0.165165 / 0.680424 (-0.515259) | 0.018326 / 0.534201 (-0.515875) | 0.389788 / 0.579283 (-0.189495) | 0.419301 / 0.434364 (-0.015063) | 0.452645 / 0.540337 (-0.087693) | 0.643409 / 1.386936 (-0.743527) |\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) | 0.007040 / 0.011353 (-0.004313) | 0.004157 / 0.011008 (-0.006851) | 0.065439 / 0.038508 (0.026931) | 0.083210 / 0.023109 (0.060101) | 0.406707 / 0.275898 (0.130809) | 0.442759 / 0.323480 (0.119279) | 0.006321 / 0.007986 (-0.001665) | 0.003684 / 0.004328 (-0.000645) | 0.064517 / 0.004250 (0.060266) | 0.060676 / 0.037052 (0.023624) | 0.413395 / 0.258489 (0.154906) | 0.446776 / 0.293841 (0.152935) | 0.032542 / 0.128546 (-0.096004) | 0.008614 / 0.075646 (-0.067033) | 0.071760 / 0.419271 (-0.347511) | 0.049646 / 0.043533 (0.006113) | 0.402409 / 0.255139 (0.147270) | 0.422775 / 0.283200 (0.139575) | 0.024846 / 0.141683 (-0.116836) | 1.522915 / 1.452155 (0.070761) | 1.566518 / 1.492716 (0.073802) |\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) | 0.234478 / 0.018006 (0.216472) | 0.461318 / 0.000490 (0.460828) | 0.006304 / 0.000200 (0.006105) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036904 / 0.037411 (-0.000508) | 0.102144 / 0.014526 (0.087619) | 0.108985 / 0.176557 (-0.067572) | 0.162609 / 0.737135 (-0.574526) | 0.110295 / 0.296338 (-0.186044) |\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) | 0.438735 / 0.215209 (0.223526) | 4.377602 / 2.077655 (2.299948) | 2.375305 / 1.504120 (0.871185) | 2.215877 / 1.541195 (0.674682) | 2.317468 / 1.468490 (0.848978) | 0.495137 / 4.584777 (-4.089640) | 3.726323 / 3.745712 (-0.019389) | 3.493785 / 5.269862 (-1.776077) | 2.177891 / 4.565676 (-2.387785) | 0.058975 / 0.424275 (-0.365300) | 0.007897 / 0.007607 (0.000290) | 0.514063 / 0.226044 (0.288019) | 5.132714 / 2.268929 (2.863786) | 2.914125 / 55.444624 (-52.530499) | 2.532912 / 6.876477 (-4.343564) | 2.776438 / 2.142072 (0.634365) | 0.624831 / 4.805227 (-4.180396) | 0.135023 / 6.500664 (-6.365641) | 0.062040 / 0.075469 (-0.013429) |\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) | 1.359970 / 1.841788 (-0.481818) | 20.816464 / 8.074308 (12.742156) | 16.103544 / 10.191392 (5.912152) | 0.149120 / 0.680424 (-0.531304) | 0.020279 / 0.534201 (-0.513922) | 0.408727 / 0.579283 (-0.170556) | 0.436191 / 0.434364 (0.001827) | 0.485056 / 0.540337 (-0.055281) | 0.737727 / 1.386936 (-0.649209) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d15280f435b7e27c9350a0cc37a07dbc5e2ea9ca \"CML watermark\")\n", "CI failures are unrelated", "<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) | 0.008102 / 0.011353 (-0.003251) | 0.004886 / 0.011008 (-0.006123) | 0.090482 / 0.038508 (0.051974) | 0.071594 / 0.023109 (0.048485) | 0.428678 / 0.275898 (0.152780) | 0.442179 / 0.323480 (0.118699) | 0.004329 / 0.007986 (-0.003657) | 0.003756 / 0.004328 (-0.000573) | 0.087125 / 0.004250 (0.082874) | 0.055159 / 0.037052 (0.018107) | 0.437646 / 0.258489 (0.179157) | 0.446665 / 0.293841 (0.152824) | 0.046402 / 0.128546 (-0.082145) | 0.014248 / 0.075646 (-0.061398) | 0.331401 / 0.419271 (-0.087871) | 0.062010 / 0.043533 (0.018478) | 0.434774 / 0.255139 (0.179635) | 0.441063 / 0.283200 (0.157863) | 0.037424 / 0.141683 (-0.104258) | 1.720276 / 1.452155 (0.268121) | 1.731491 / 1.492716 (0.238775) |\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) | 0.302935 / 0.018006 (0.284929) | 0.590556 / 0.000490 (0.590067) | 0.014473 / 0.000200 (0.014274) | 0.000712 / 0.000054 (0.000658) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031289 / 0.037411 (-0.006122) | 0.091175 / 0.014526 (0.076649) | 0.112895 / 0.176557 (-0.063661) | 0.199558 / 0.737135 (-0.537577) | 0.113397 / 0.296338 (-0.182942) |\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) | 0.571586 / 0.215209 (0.356377) | 5.706894 / 2.077655 (3.629240) | 2.512701 / 1.504120 (1.008581) | 2.151705 / 1.541195 (0.610510) | 2.252738 / 1.468490 (0.784248) | 0.857524 / 4.584777 (-3.727253) | 5.189027 / 3.745712 (1.443315) | 4.464979 / 5.269862 (-0.804882) | 2.787486 / 4.565676 (-1.778190) | 0.090161 / 0.424275 (-0.334115) | 0.008649 / 0.007607 (0.001042) | 0.703367 / 0.226044 (0.477322) | 7.128971 / 2.268929 (4.860043) | 3.437475 / 55.444624 (-52.007149) | 2.562291 / 6.876477 (-4.314186) | 2.753419 / 2.142072 (0.611346) | 0.981964 / 4.805227 (-3.823263) | 0.194533 / 6.500664 (-6.306131) | 0.069659 / 0.075469 (-0.005810) |\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) | 1.510356 / 1.841788 (-0.331431) | 22.414117 / 8.074308 (14.339809) | 20.325418 / 10.191392 (10.134025) | 0.226823 / 0.680424 (-0.453601) | 0.029123 / 0.534201 (-0.505078) | 0.454656 / 0.579283 (-0.124627) | 0.559588 / 0.434364 (0.125224) | 0.547386 / 0.540337 (0.007048) | 0.770169 / 1.386936 (-0.616767) |\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) | 0.010167 / 0.011353 (-0.001186) | 0.005164 / 0.011008 (-0.005844) | 0.094897 / 0.038508 (0.056388) | 0.078027 / 0.023109 (0.054918) | 0.474442 / 0.275898 (0.198544) | 0.503362 / 0.323480 (0.179882) | 0.006988 / 0.007986 (-0.000998) | 0.005369 / 0.004328 (0.001041) | 0.079547 / 0.004250 (0.075297) | 0.059382 / 0.037052 (0.022329) | 0.468759 / 0.258489 (0.210270) | 0.566780 / 0.293841 (0.272939) | 0.050791 / 0.128546 (-0.077755) | 0.013191 / 0.075646 (-0.062455) | 0.086086 / 0.419271 (-0.333186) | 0.060399 / 0.043533 (0.016866) | 0.492985 / 0.255139 (0.237846) | 0.509139 / 0.283200 (0.225940) | 0.034537 / 0.141683 (-0.107146) | 1.699166 / 1.452155 (0.247011) | 1.789781 / 1.492716 (0.297065) |\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) | 0.278776 / 0.018006 (0.260769) | 0.615877 / 0.000490 (0.615387) | 0.009062 / 0.000200 (0.008862) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032931 / 0.037411 (-0.004481) | 0.094796 / 0.014526 (0.080270) | 0.126697 / 0.176557 (-0.049859) | 0.168172 / 0.737135 (-0.568963) | 0.113906 / 0.296338 (-0.182433) |\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) | 0.602378 / 0.215209 (0.387169) | 5.987708 / 2.077655 (3.910054) | 2.800339 / 1.504120 (1.296219) | 2.474127 / 1.541195 (0.932932) | 2.502387 / 1.468490 (1.033897) | 0.808147 / 4.584777 (-3.776630) | 5.212691 / 3.745712 (1.466979) | 4.479452 / 5.269862 (-0.790409) | 2.831960 / 4.565676 (-1.733717) | 0.086777 / 0.424275 (-0.337498) | 0.009492 / 0.007607 (0.001885) | 0.716848 / 0.226044 (0.490803) | 7.099904 / 2.268929 (4.830975) | 3.794708 / 55.444624 (-51.649916) | 2.859826 / 6.876477 (-4.016650) | 3.109673 / 2.142072 (0.967600) | 0.936776 / 4.805227 (-3.868451) | 0.195152 / 6.500664 (-6.305512) | 0.074184 / 0.075469 (-0.001285) |\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) | 1.585419 / 1.841788 (-0.256369) | 22.420377 / 8.074308 (14.346068) | 20.761533 / 10.191392 (10.570141) | 0.228480 / 0.680424 (-0.451943) | 0.030944 / 0.534201 (-0.503257) | 0.444717 / 0.579283 (-0.134566) | 0.579632 / 0.434364 (0.145268) | 0.521669 / 0.540337 (-0.018669) | 0.748274 / 1.386936 (-0.638662) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94e07965a400e6901f12e6f0f25c7090656c828c \"CML watermark\")\n" ]
2023-09-14T12:06:32
2023-09-15T15:57:10
2023-09-15T15:46:07
CONTRIBUTOR
false
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1,895,723,888
I_kwDODunzps5w_nNw
6,240
Dataloader stuck on multiple GPUs
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[ "What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.", "Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issue and repo the issue in the appropriate place." ]
2023-09-14T05:30:30
2023-09-14T23:54:42
2023-09-14T23:54:42
NONE
null
null
### Describe the bug I am trying to get CLIP to fine-tuning with my code. When I tried to run it on multiple GPUs using accelerate, I encountered the following phenomenon. - Validation dataloader stuck in 2nd epoch only on multi-GPU Specifically, when the "for inputs in valid_loader:" process is finished, it does not proceed to the next step. train_loader process is completed. Also, both train and valid are working correctly in the first epoch. The accelerate command at that time is as follows. `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` - This will not happen when single GPU is used. `CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` - Setting num_workers=0 in dataloader did not change the result. ### Steps to reproduce the bug 1. The codes for fine-tuning the regular CLIP were updated for accelerate. 2. Run the code with the accelerate command as `accelerate launch --multi_gpu --num_processes=2 {script_name.py} {--arg1} {--arg2} ...` and the above problem will occur. 3. CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...` , it works fine. ### Expected behavior It Should end normally as if it was run on a single GPU. ### Environment info Since `datasets-cli env` did not work, the environment is described below. - OS: Ubuntu 22.04 with Docker - Docker: 24.0.5, build ced0996 - Python: 3.10.12 - torch==2.0.1 - accelerate==0.21.0 - transformers==4.33.1
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1,895,349,382
I_kwDODunzps5w-LyG
6,239
Load local audio data doesn't work
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[ "I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.", "@mariosasko \r\nThanks for your help" ]
2023-09-13T22:30:01
2023-09-15T14:32:10
2023-09-15T14:32:10
NONE
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### Describe the bug I get a RuntimeError from the following code: ```python audio_dataset = Dataset.from_dict({"audio": ["/kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3"]}).cast_column("audio", Audio()) audio_dataset[0] ``` ### Traceback <details> ```python RuntimeError Traceback (most recent call last) Cell In[33], line 1 ----> 1 train_dataset[0] File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1764, in Dataset.__getitem__(self, key) 1762 def __getitem__(self, key): # noqa: F811 1763 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 1764 return self._getitem( 1765 key, 1766 ) File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:1749, in Dataset._getitem(self, key, decoded, **kwargs) 1747 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs) 1748 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 1749 formatted_output = format_table( 1750 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1751 ) 1752 return formatted_output File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row File /opt/conda/lib/python3.10/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row) 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1386, in Features.decode_example(self, example) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ -> 1386 return { 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1387, in <dictcomp>(.0) 1376 def decode_example(self, example: dict): 1377 """Decode example with custom feature decoding. 1378 1379 Args: (...) 1383 :obj:`dict[str, Any]` 1384 """ 1386 return { -> 1387 column_name: decode_nested_example(feature, value) 1388 if self._column_requires_decoding[column_name] 1389 else value 1390 for column_name, (feature, value) in zip_dict( 1391 {key: value for key, value in self.items() if key in example}, example 1392 ) 1393 } File /opt/conda/lib/python3.10/site-packages/datasets/features/features.py:1087, in decode_nested_example(schema, obj) 1085 # Object with special decoding: 1086 elif isinstance(schema, (Audio, Image)): -> 1087 return schema.decode_example(obj) if obj is not None else None 1088 return obj File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:103, in Audio.decode_example(self, value) 101 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.") 102 elif path is not None and path.endswith("mp3"): --> 103 array, sampling_rate = self._decode_mp3(file if file else path) 104 elif path is not None and path.endswith("opus"): 105 if file: File /opt/conda/lib/python3.10/site-packages/datasets/features/audio.py:241, in Audio._decode_mp3(self, path_or_file) 238 except RuntimeError as err: 239 raise ImportError("To support decoding 'mp3' audio files, please install 'sox'.") from err --> 241 array, sampling_rate = torchaudio.load(path_or_file, format="mp3") 242 if self.sampling_rate and self.sampling_rate != sampling_rate: 243 if not hasattr(self, "_resampler") or self._resampler.orig_freq != sampling_rate: File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:256, in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 254 if ret is not None: 255 return ret --> 256 return _fallback_load(filepath, frame_offset, num_frames, normalize, channels_first, format) File /opt/conda/lib/python3.10/site-packages/torchaudio/backend/sox_io_backend.py:30, in _fail_load(filepath, frame_offset, num_frames, normalize, channels_first, format) 22 def _fail_load( 23 filepath: str, 24 frame_offset: int = 0, (...) 28 format: Optional[str] = None, 29 ) -> Tuple[torch.Tensor, int]: ---> 30 raise RuntimeError("Failed to load audio from {}".format(filepath)) RuntimeError: Failed to load audio from /kaggle/input/bengaliai-speech/train_mp3s/000005f3362c.mp3 ``` </details> ### Steps to reproduce the bug 1. - Create a custom dataset using Local files of type mp3. 3. - Try to read the first audio item. ### Expected behavior Expected output ```python audio_dataset[0]["audio"] {'array': array([ 0. , 0.00024414, -0.00024414, ..., -0.00024414, 0. , 0. ], dtype=float32), 'path': 'path/to/audio_1', 'sampling_rate': 16000} ``` ### Environment info N/A
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I_kwDODunzps5w9pOU
6,238
`dataset.filter` ALWAYS removes the first item from the dataset when using batched=True
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[ "`filter` treats the function's output as a (selection) mask - `True` keeps the sample, and `False` drops it. In your case, `bool(0)` evaluates to `False`, so dropping the first sample is the correct behavior.", "Oh gosh! 🤦 I totally misunderstood the API! My apologies!" ]
2023-09-13T20:20:37
2023-09-17T07:05:07
2023-09-17T07:05:07
NONE
null
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### Describe the bug If you call batched=True when calling `filter`, the first item is _always_ filtered out, regardless of the filter condition. ### Steps to reproduce the bug Here's a minimal example: ```python def filter_batch_always_true(batch, indices): print("First index being passed into this filter function: ", indices[0]) return indices # Keep all indices data = {"value": list(range(10))} dataset = Dataset.from_dict(data) filtered_dataset = dataset.filter(filter_batch_always_true, with_indices=True, batched=True) print("Length of original dataset: ", len(dataset)) print("Length of filtered_dataset: ", len(filtered_dataset)) print("Is equal to original? ", len(filtered_dataset) == len(dataset)) print("First item of filtered dataset: ", filtered_dataset[0]) print("Last item of filtered dataset: ", filtered_dataset[-1]) ``` prints: ``` First index being passed into this filter function: 0 Length of original dataset: 10 Length of filtered_dataset: 9 Is equal to original? False First item of filtered dataset: {'value': 1} Last item of filtered dataset: {'value': 9} ``` ### Expected behavior Filter should respect the filter condition. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.18 - Huggingface_hub version: 0.17.1 - PyArrow version: 10.0.1 - Pandas version: 2.0.2
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I_kwDODunzps5w4W9x
6,237
Tokenization with multiple workers is too slow
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[ "[This](https://huggingface.co/docs/datasets/nlp_process#map) is the most performant way to tokenize a dataset (`batched=True, num_proc=None, return_tensors=\"np\"`) \r\n\r\nIf`tokenizer.is_fast` returns `True`, `num_proc` must be `None/1` to benefit from the fast tokenizers' parallelism (the fast tokenizers are implemented in Rust, and Rust multi-threading doesn't work well with Python multi-processing)" ]
2023-09-13T06:18:34
2023-09-19T21:54:58
2023-09-19T21:54:58
NONE
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I am trying to tokenize a few million documents with multiple workers but the tokenization process is taking forever. Code snippet: ``` raw_datasets.map( encode_function, batched=False, num_proc=args.preprocessing_num_workers, load_from_cache_file=not args.overwrite_cache, remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]], desc="Tokenizing data", ) ``` Details: ``` transformers==4.28.0.dev0 datasets==4.28.0.dev0 preprocessing_num_workers==48 ``` tokenizer == decapoda-research/llama-7b-hf
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1,893,648,480
I_kwDODunzps5w3shg
6,236
Support buffer shuffle for to_tf_dataset
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[ "cc @Rocketknight1 ", "Hey! You can implement this yourself, just:\r\n\r\n1) Create the dataset with `to_tf_dataset()` with `shuffle=False`\r\n2) Add an `unbatch()` at the end (or use batch_size=1)\r\n3) Add a `shuffle()` to the resulting dataset with your desired buffer size\r\n4) Add a `batch()` at the end again to re-batch your dataset.\r\n\r\nNote that the way we construct datasets in `to_tf_dataset()`, we don't actually shuffle the entire dataset in-memory, using `tf.data.Dataset.shuffle()`! Instead, we shuffle an index array and then load from the dataset with that. This means that shuffling with `tf.data.Dataset.shuffle()` will probably be slower and use more memory than our approach - I don't think adding the option for smaller shuffle buffers will actually save you memory on this!", "Thanks for your reply! @Rocketknight1 \r\n\"We don't actually shuffle the entire dataset in-memory, using tf.data.Dataset.shuffle()! Instead, we shuffle an index array and then load from the dataset with that.\"\r\nIn such case, there will be random access to dataset data during shuffling. When the dataset is large, the performance can be X10 times slow. I have tried many ways with to_tf_dataset() trying to achieve comparable performance with tf.data.Dataset().shuffle(buffer_size).batch(). But the performance with to_tf_dataset() is still slow. \r\n" ]
2023-09-13T03:19:44
2023-09-18T01:11:21
null
NONE
null
null
### Feature request I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model. Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset. tf.data.Dataset support buffer shuffle by default. shuffle( buffer_size, seed=None, reshuffle_each_iteration=None, name=None ) ### Motivation I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset. ### Your contribution NA
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1,893,337,083
I_kwDODunzps5w2gf7
6,235
Support multiprocessing for download/extract nestedly
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2023-09-12T21:51:08
2023-09-12T21:51:08
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### Feature request Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders ``` Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s] Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s] Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s] Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s] ``` ### Motivation speedup dataset loading ### Your contribution I can help test the feature
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6,233
Update README.md
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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) | 0.008370 / 0.011353 (-0.002983) | 0.004674 / 0.011008 (-0.006334) | 0.103912 / 0.038508 (0.065404) | 0.101668 / 0.023109 (0.078559) | 0.417945 / 0.275898 (0.142047) | 0.454805 / 0.323480 (0.131325) | 0.004763 / 0.007986 (-0.003223) | 0.003934 / 0.004328 (-0.000394) | 0.078446 / 0.004250 (0.074196) | 0.068383 / 0.037052 (0.031331) | 0.415100 / 0.258489 (0.156611) | 0.475272 / 0.293841 (0.181431) | 0.036884 / 0.128546 (-0.091662) | 0.010097 / 0.075646 (-0.065549) | 0.354962 / 0.419271 (-0.064309) | 0.062688 / 0.043533 (0.019155) | 0.420643 / 0.255139 (0.165504) | 0.446504 / 0.283200 (0.163304) | 0.029075 / 0.141683 (-0.112608) | 1.791517 / 1.452155 (0.339363) | 1.859820 / 1.492716 (0.367104) |\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) | 0.246929 / 0.018006 (0.228923) | 0.519593 / 0.000490 (0.519103) | 0.006848 / 0.000200 (0.006648) | 0.000168 / 0.000054 (0.000114) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035179 / 0.037411 (-0.002232) | 0.115582 / 0.014526 (0.101057) | 0.128235 / 0.176557 (-0.048321) | 0.187123 / 0.737135 (-0.550012) | 0.120862 / 0.296338 (-0.175477) |\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) | 0.463406 / 0.215209 (0.248197) | 4.615517 / 2.077655 (2.537863) | 2.250513 / 1.504120 (0.746393) | 2.061226 / 1.541195 (0.520032) | 2.189938 / 1.468490 (0.721448) | 0.582984 / 4.584777 (-4.001793) | 4.299464 / 3.745712 (0.553751) | 4.037274 / 5.269862 (-1.232588) | 2.608967 / 4.565676 (-1.956710) | 0.068944 / 0.424275 (-0.355331) | 0.009501 / 0.007607 (0.001894) | 0.567436 / 0.226044 (0.341392) | 5.662738 / 2.268929 (3.393809) | 2.849094 / 55.444624 (-52.595530) | 2.461013 / 6.876477 (-4.415464) | 2.663245 / 2.142072 (0.521172) | 0.704528 / 4.805227 (-4.100699) | 0.163583 / 6.500664 (-6.337081) | 0.075719 / 0.075469 (0.000250) |\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) | 1.604743 / 1.841788 (-0.237044) | 24.512054 / 8.074308 (16.437746) | 17.870939 / 10.191392 (7.679547) | 0.199188 / 0.680424 (-0.481236) | 0.023820 / 0.534201 (-0.510381) | 0.487520 / 0.579283 (-0.091763) | 0.512543 / 0.434364 (0.078179) | 0.575138 / 0.540337 (0.034801) | 0.759863 / 1.386936 (-0.627073) |\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) | 0.010516 / 0.011353 (-0.000837) | 0.004779 / 0.011008 (-0.006229) | 0.078482 / 0.038508 (0.039974) | 0.108533 / 0.023109 (0.085424) | 0.498692 / 0.275898 (0.222794) | 0.534698 / 0.323480 (0.211218) | 0.007624 / 0.007986 (-0.000362) | 0.003938 / 0.004328 (-0.000391) | 0.077317 / 0.004250 (0.073067) | 0.078056 / 0.037052 (0.041004) | 0.493648 / 0.258489 (0.235159) | 0.540891 / 0.293841 (0.247050) | 0.040377 / 0.128546 (-0.088169) | 0.010155 / 0.075646 (-0.065491) | 0.084384 / 0.419271 (-0.334888) | 0.061419 / 0.043533 (0.017886) | 0.494474 / 0.255139 (0.239335) | 0.524656 / 0.283200 (0.241456) | 0.029052 / 0.141683 (-0.112631) | 1.794584 / 1.452155 (0.342429) | 1.939987 / 1.492716 (0.447270) |\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) | 0.377404 / 0.018006 (0.359398) | 0.516562 / 0.000490 (0.516072) | 0.109555 / 0.000200 (0.109356) | 0.001126 / 0.000054 (0.001071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039793 / 0.037411 (0.002382) | 0.123001 / 0.014526 (0.108475) | 0.127536 / 0.176557 (-0.049021) | 0.191681 / 0.737135 (-0.545455) | 0.128590 / 0.296338 (-0.167748) |\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) | 0.513689 / 0.215209 (0.298480) | 5.135114 / 2.077655 (3.057459) | 2.797885 / 1.504120 (1.293765) | 2.715332 / 1.541195 (1.174137) | 2.746437 / 1.468490 (1.277947) | 0.596480 / 4.584777 (-3.988297) | 4.382013 / 3.745712 (0.636301) | 3.965956 / 5.269862 (-1.303906) | 2.545206 / 4.565676 (-2.020471) | 0.069620 / 0.424275 (-0.354655) | 0.009321 / 0.007607 (0.001714) | 0.612424 / 0.226044 (0.386379) | 6.107037 / 2.268929 (3.838109) | 3.447246 / 55.444624 (-51.997379) | 3.073262 / 6.876477 (-3.803215) | 3.280185 / 2.142072 (1.138113) | 0.704776 / 4.805227 (-4.100451) | 0.160488 / 6.500664 (-6.340176) | 0.075730 / 0.075469 (0.000261) |\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) | 1.697035 / 1.841788 (-0.144753) | 24.766118 / 8.074308 (16.691809) | 18.476699 / 10.191392 (8.285307) | 0.176594 / 0.680424 (-0.503830) | 0.024249 / 0.534201 (-0.509952) | 0.478743 / 0.579283 (-0.100541) | 0.518774 / 0.434364 (0.084410) | 0.581498 / 0.540337 (0.041161) | 0.797784 / 1.386936 (-0.589152) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#722cea0f4929ff4ffcdbb7ca6b72cba229b9701a \"CML watermark\")\n" ]
2023-09-12T06:53:06
2023-09-13T18:20:50
2023-09-13T18:10:04
CONTRIBUTOR
false
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fixed a typo
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https://api.github.com/repos/huggingface/datasets/issues/6232
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6,232
Improve error message for missing function parameters
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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI errors are unrelated", "<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) | 0.006681 / 0.011353 (-0.004672) | 0.004132 / 0.011008 (-0.006876) | 0.085045 / 0.038508 (0.046536) | 0.077680 / 0.023109 (0.054571) | 0.382042 / 0.275898 (0.106144) | 0.412932 / 0.323480 (0.089452) | 0.005339 / 0.007986 (-0.002646) | 0.003408 / 0.004328 (-0.000921) | 0.065280 / 0.004250 (0.061030) | 0.055732 / 0.037052 (0.018680) | 0.400231 / 0.258489 (0.141742) | 0.432497 / 0.293841 (0.138656) | 0.031532 / 0.128546 (-0.097014) | 0.008721 / 0.075646 (-0.066925) | 0.289612 / 0.419271 (-0.129660) | 0.053089 / 0.043533 (0.009556) | 0.383300 / 0.255139 (0.128161) | 0.401204 / 0.283200 (0.118004) | 0.023582 / 0.141683 (-0.118100) | 1.493854 / 1.452155 (0.041699) | 1.583497 / 1.492716 (0.090781) |\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) | 0.239163 / 0.018006 (0.221157) | 0.469555 / 0.000490 (0.469065) | 0.008325 / 0.000200 (0.008125) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028975 / 0.037411 (-0.008436) | 0.084195 / 0.014526 (0.069669) | 0.189394 / 0.176557 (0.012837) | 0.158010 / 0.737135 (-0.579125) | 0.097502 / 0.296338 (-0.198837) |\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) | 0.383085 / 0.215209 (0.167876) | 3.827030 / 2.077655 (1.749375) | 1.872279 / 1.504120 (0.368159) | 1.705808 / 1.541195 (0.164613) | 1.833706 / 1.468490 (0.365216) | 0.484744 / 4.584777 (-4.100033) | 3.658221 / 3.745712 (-0.087491) | 3.398462 / 5.269862 (-1.871399) | 2.064974 / 4.565676 (-2.500703) | 0.057740 / 0.424275 (-0.366535) | 0.007926 / 0.007607 (0.000319) | 0.465358 / 0.226044 (0.239314) | 4.652951 / 2.268929 (2.384022) | 2.328390 / 55.444624 (-53.116235) | 2.000606 / 6.876477 (-4.875870) | 2.268391 / 2.142072 (0.126318) | 0.586537 / 4.805227 (-4.218690) | 0.134749 / 6.500664 (-6.365915) | 0.061276 / 0.075469 (-0.014193) |\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) | 1.337913 / 1.841788 (-0.503875) | 20.232122 / 8.074308 (12.157814) | 14.478579 / 10.191392 (4.287187) | 0.167545 / 0.680424 (-0.512878) | 0.018745 / 0.534201 (-0.515456) | 0.401209 / 0.579283 (-0.178074) | 0.425748 / 0.434364 (-0.008616) | 0.462539 / 0.540337 (-0.077798) | 0.652446 / 1.386936 (-0.734490) |\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) | 0.007159 / 0.011353 (-0.004194) | 0.004091 / 0.011008 (-0.006917) | 0.066202 / 0.038508 (0.027694) | 0.083096 / 0.023109 (0.059987) | 0.402160 / 0.275898 (0.126261) | 0.440565 / 0.323480 (0.117085) | 0.005757 / 0.007986 (-0.002228) | 0.003445 / 0.004328 (-0.000884) | 0.065498 / 0.004250 (0.061248) | 0.059787 / 0.037052 (0.022735) | 0.407017 / 0.258489 (0.148528) | 0.448270 / 0.293841 (0.154429) | 0.033606 / 0.128546 (-0.094941) | 0.008744 / 0.075646 (-0.066902) | 0.072902 / 0.419271 (-0.346369) | 0.050144 / 0.043533 (0.006611) | 0.401069 / 0.255139 (0.145930) | 0.426389 / 0.283200 (0.143189) | 0.023297 / 0.141683 (-0.118386) | 1.506152 / 1.452155 (0.053998) | 1.570211 / 1.492716 (0.077495) |\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) | 0.235759 / 0.018006 (0.217753) | 0.488410 / 0.000490 (0.487921) | 0.004587 / 0.000200 (0.004387) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034123 / 0.037411 (-0.003289) | 0.102163 / 0.014526 (0.087638) | 0.110892 / 0.176557 (-0.065664) | 0.166000 / 0.737135 (-0.571135) | 0.110845 / 0.296338 (-0.185494) |\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) | 0.431397 / 0.215209 (0.216188) | 4.291540 / 2.077655 (2.213885) | 2.298248 / 1.504120 (0.794128) | 2.134752 / 1.541195 (0.593557) | 2.207913 / 1.468490 (0.739423) | 0.490607 / 4.584777 (-4.094170) | 3.683078 / 3.745712 (-0.062635) | 3.314266 / 5.269862 (-1.955596) | 2.059488 / 4.565676 (-2.506188) | 0.057876 / 0.424275 (-0.366399) | 0.007696 / 0.007607 (0.000089) | 0.512186 / 0.226044 (0.286142) | 5.124071 / 2.268929 (2.855142) | 2.803913 / 55.444624 (-52.640711) | 2.428558 / 6.876477 (-4.447919) | 2.655207 / 2.142072 (0.513135) | 0.584589 / 4.805227 (-4.220638) | 0.133518 / 6.500664 (-6.367146) | 0.060729 / 0.075469 (-0.014740) |\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) | 1.352916 / 1.841788 (-0.488872) | 20.249632 / 8.074308 (12.175323) | 15.283079 / 10.191392 (5.091686) | 0.157601 / 0.680424 (-0.522823) | 0.019650 / 0.534201 (-0.514551) | 0.396398 / 0.579283 (-0.182885) | 0.430111 / 0.434364 (-0.004252) | 0.480627 / 0.540337 (-0.059710) | 0.642165 / 1.386936 (-0.744771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9b21e181b642bd55b3ef68c1948bfbcd388136d6 \"CML watermark\")\n" ]
2023-09-11T19:11:58
2023-09-15T18:07:56
2023-09-15T17:59:02
CONTRIBUTOR
false
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The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature." This has been fixed.
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1,890,863,249
PR_kwDODunzps5aCr8_
6,231
Overwrite legacy default config name in `dataset_infos.json` in packaged datasets
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6231). All of your documentation changes will be reflected on that endpoint.", "realized that this pr is still not merged, @lhoestq maybe you can take a look at it? ", "I think https://github.com/huggingface/datasets/pull/6218 fixed the issue (a bit differently though)", "ah actually nope, let me check", "@lhoestq yeah the pr you're referencing doesn't fix the problem when two semantically analogous configs occur in datasets_info.json, i suggest to rewrite the legacy one if it exists during .push_to_hub", "Only the old versions of `datasets` use the JSON file over the README and they can only load one config so the name doesn't really matter.\r\n\r\nThat's why I chose to load the info from the JSON no matter the name (no check to see if it's \"username--dataset_name\") in my previous PR.\r\n\r\nI think you can remove the old info without even checking the name. In this case maybe no need to update load.py ", "(also minor: not checking the name makes it more robust to dataset renaming)", "@lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with `dataset_infos.json` having two keys in it?", "> @lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with dataset_infos.json having two keys in it?\r\n\r\nIdeally they should have only one config no ? Since old versions of `datasets` simply load the first config in the JSON.\r\nWe can overwrite it with the new default one (and no matter the name of the outdated config in the JSON)\r\n\r\n" ]
2023-09-11T16:27:09
2023-09-26T11:19:36
null
CONTRIBUTOR
false
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Currently if we push data as default config with `.push_to_hub` to a repo that has a legacy `dataset_infos.json` file containing a legacy default config name like `{username}--{dataset_name}`, new key `"default"` is added to `dataset_infos.json` along with the legacy one. I think the legacy one should be dropped in this case. Also, in `load.py` I suggest to check if a legacy config name is indeed a legacy config name because after this fix it might not be the case (this check was first introduced in https://github.com/huggingface/datasets/pull/6218)
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PR_kwDODunzps5aBh6L
6,230
Don't skip hidden files in `dl_manager.iter_files` when they are given as input
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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) | 0.005894 / 0.011353 (-0.005459) | 0.003621 / 0.011008 (-0.007387) | 0.080446 / 0.038508 (0.041938) | 0.056800 / 0.023109 (0.033691) | 0.326485 / 0.275898 (0.050587) | 0.376207 / 0.323480 (0.052727) | 0.004640 / 0.007986 (-0.003346) | 0.002795 / 0.004328 (-0.001533) | 0.062815 / 0.004250 (0.058565) | 0.045761 / 0.037052 (0.008709) | 0.341417 / 0.258489 (0.082928) | 0.373129 / 0.293841 (0.079288) | 0.027226 / 0.128546 (-0.101321) | 0.007873 / 0.075646 (-0.067774) | 0.261737 / 0.419271 (-0.157535) | 0.044648 / 0.043533 (0.001115) | 0.320195 / 0.255139 (0.065056) | 0.381892 / 0.283200 (0.098692) | 0.020431 / 0.141683 (-0.121252) | 1.405332 / 1.452155 (-0.046823) | 1.455592 / 1.492716 (-0.037125) |\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) | 0.191539 / 0.018006 (0.173533) | 0.423655 / 0.000490 (0.423165) | 0.002741 / 0.000200 (0.002541) | 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) | 0.023952 / 0.037411 (-0.013459) | 0.073387 / 0.014526 (0.058861) | 0.083746 / 0.176557 (-0.092810) | 0.144977 / 0.737135 (-0.592159) | 0.083808 / 0.296338 (-0.212530) |\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) | 0.436228 / 0.215209 (0.221019) | 4.370510 / 2.077655 (2.292855) | 2.340426 / 1.504120 (0.836306) | 2.202215 / 1.541195 (0.661021) | 2.258528 / 1.468490 (0.790037) | 0.503455 / 4.584777 (-4.081322) | 3.043695 / 3.745712 (-0.702017) | 2.784033 / 5.269862 (-2.485829) | 1.847956 / 4.565676 (-2.717721) | 0.057702 / 0.424275 (-0.366573) | 0.006703 / 0.007607 (-0.000904) | 0.510628 / 0.226044 (0.284583) | 5.101890 / 2.268929 (2.832961) | 2.816469 / 55.444624 (-52.628155) | 2.474220 / 6.876477 (-4.402257) | 2.617851 / 2.142072 (0.475779) | 0.593585 / 4.805227 (-4.211642) | 0.125895 / 6.500664 (-6.374769) | 0.062170 / 0.075469 (-0.013299) |\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) | 1.238792 / 1.841788 (-0.602996) | 18.096417 / 8.074308 (10.022108) | 13.548778 / 10.191392 (3.357386) | 0.144878 / 0.680424 (-0.535546) | 0.016644 / 0.534201 (-0.517557) | 0.334556 / 0.579283 (-0.244728) | 0.343680 / 0.434364 (-0.090684) | 0.383093 / 0.540337 (-0.157244) | 0.525075 / 1.386936 (-0.861861) |\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) | 0.006125 / 0.011353 (-0.005228) | 0.003668 / 0.011008 (-0.007340) | 0.062650 / 0.038508 (0.024142) | 0.058882 / 0.023109 (0.035772) | 0.454643 / 0.275898 (0.178745) | 0.486659 / 0.323480 (0.163179) | 0.005558 / 0.007986 (-0.002427) | 0.002858 / 0.004328 (-0.001471) | 0.062603 / 0.004250 (0.058353) | 0.049701 / 0.037052 (0.012649) | 0.455903 / 0.258489 (0.197413) | 0.491544 / 0.293841 (0.197703) | 0.028581 / 0.128546 (-0.099965) | 0.008040 / 0.075646 (-0.067607) | 0.068314 / 0.419271 (-0.350957) | 0.040637 / 0.043533 (-0.002896) | 0.450288 / 0.255139 (0.195149) | 0.476330 / 0.283200 (0.193131) | 0.018989 / 0.141683 (-0.122693) | 1.455122 / 1.452155 (0.002967) | 1.496941 / 1.492716 (0.004225) |\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) | 0.227382 / 0.018006 (0.209376) | 0.432637 / 0.000490 (0.432147) | 0.002727 / 0.000200 (0.002527) | 0.000073 / 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) | 0.026125 / 0.037411 (-0.011286) | 0.081342 / 0.014526 (0.066817) | 0.091227 / 0.176557 (-0.085329) | 0.145175 / 0.737135 (-0.591960) | 0.091988 / 0.296338 (-0.204351) |\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) | 0.454293 / 0.215209 (0.239083) | 4.537912 / 2.077655 (2.460257) | 2.489146 / 1.504120 (0.985026) | 2.307166 / 1.541195 (0.765971) | 2.380866 / 1.468490 (0.912376) | 0.509015 / 4.584777 (-4.075762) | 3.111069 / 3.745712 (-0.634644) | 2.839181 / 5.269862 (-2.430681) | 1.874630 / 4.565676 (-2.691047) | 0.058540 / 0.424275 (-0.365735) | 0.006693 / 0.007607 (-0.000914) | 0.528408 / 0.226044 (0.302363) | 5.285802 / 2.268929 (3.016874) | 2.952090 / 55.444624 (-52.492534) | 2.591496 / 6.876477 (-4.284980) | 2.741080 / 2.142072 (0.599007) | 0.595610 / 4.805227 (-4.209617) | 0.124387 / 6.500664 (-6.376277) | 0.061032 / 0.075469 (-0.014437) |\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) | 1.365816 / 1.841788 (-0.475972) | 18.684534 / 8.074308 (10.610226) | 14.540438 / 10.191392 (4.349046) | 0.146793 / 0.680424 (-0.533631) | 0.018165 / 0.534201 (-0.516036) | 0.333794 / 0.579283 (-0.245489) | 0.345533 / 0.434364 (-0.088830) | 0.384453 / 0.540337 (-0.155885) | 0.529104 / 1.386936 (-0.857832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6c884967dd5f4e8aa3d1f3c2e3a414ae53afe261 \"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) | 0.006121 / 0.011353 (-0.005232) | 0.003683 / 0.011008 (-0.007325) | 0.083329 / 0.038508 (0.044821) | 0.063350 / 0.023109 (0.040241) | 0.329959 / 0.275898 (0.054061) | 0.396111 / 0.323480 (0.072631) | 0.003554 / 0.007986 (-0.004432) | 0.002907 / 0.004328 (-0.001421) | 0.064152 / 0.004250 (0.059902) | 0.049182 / 0.037052 (0.012130) | 0.343862 / 0.258489 (0.085373) | 0.414568 / 0.293841 (0.120727) | 0.027157 / 0.128546 (-0.101389) | 0.007957 / 0.075646 (-0.067689) | 0.261868 / 0.419271 (-0.157404) | 0.044938 / 0.043533 (0.001405) | 0.318470 / 0.255139 (0.063331) | 0.393319 / 0.283200 (0.110119) | 0.022848 / 0.141683 (-0.118835) | 1.419916 / 1.452155 (-0.032238) | 1.508783 / 1.492716 (0.016067) |\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) | 0.200530 / 0.018006 (0.182523) | 0.433586 / 0.000490 (0.433097) | 0.002063 / 0.000200 (0.001863) | 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) | 0.024803 / 0.037411 (-0.012609) | 0.075894 / 0.014526 (0.061368) | 0.086488 / 0.176557 (-0.090069) | 0.149058 / 0.737135 (-0.588077) | 0.087046 / 0.296338 (-0.209292) |\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) | 0.390771 / 0.215209 (0.175562) | 3.886178 / 2.077655 (1.808523) | 1.868626 / 1.504120 (0.364506) | 1.708532 / 1.541195 (0.167338) | 1.788491 / 1.468490 (0.320001) | 0.505706 / 4.584777 (-4.079071) | 3.062094 / 3.745712 (-0.683618) | 2.898559 / 5.269862 (-2.371302) | 1.901225 / 4.565676 (-2.664452) | 0.058366 / 0.424275 (-0.365909) | 0.006851 / 0.007607 (-0.000756) | 0.465382 / 0.226044 (0.239337) | 4.650187 / 2.268929 (2.381258) | 2.316152 / 55.444624 (-53.128472) | 1.989597 / 6.876477 (-4.886879) | 2.169266 / 2.142072 (0.027194) | 0.593257 / 4.805227 (-4.211970) | 0.126440 / 6.500664 (-6.374224) | 0.062227 / 0.075469 (-0.013242) |\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) | 1.283591 / 1.841788 (-0.558197) | 18.384667 / 8.074308 (10.310358) | 14.079611 / 10.191392 (3.888219) | 0.150453 / 0.680424 (-0.529971) | 0.017100 / 0.534201 (-0.517101) | 0.330503 / 0.579283 (-0.248780) | 0.348134 / 0.434364 (-0.086230) | 0.385726 / 0.540337 (-0.154612) | 0.529147 / 1.386936 (-0.857789) |\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) | 0.006168 / 0.011353 (-0.005185) | 0.003801 / 0.011008 (-0.007208) | 0.063168 / 0.038508 (0.024660) | 0.062331 / 0.023109 (0.039221) | 0.448321 / 0.275898 (0.172423) | 0.484416 / 0.323480 (0.160937) | 0.004827 / 0.007986 (-0.003159) | 0.002848 / 0.004328 (-0.001480) | 0.062736 / 0.004250 (0.058486) | 0.049128 / 0.037052 (0.012075) | 0.449276 / 0.258489 (0.190787) | 0.499035 / 0.293841 (0.205194) | 0.028577 / 0.128546 (-0.099969) | 0.008114 / 0.075646 (-0.067532) | 0.068297 / 0.419271 (-0.350974) | 0.040835 / 0.043533 (-0.002698) | 0.453556 / 0.255139 (0.198417) | 0.475420 / 0.283200 (0.192220) | 0.020292 / 0.141683 (-0.121390) | 1.472226 / 1.452155 (0.020071) | 1.523809 / 1.492716 (0.031093) |\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) | 0.230662 / 0.018006 (0.212655) | 0.439697 / 0.000490 (0.439207) | 0.009899 / 0.000200 (0.009699) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026418 / 0.037411 (-0.010993) | 0.082188 / 0.014526 (0.067662) | 0.091039 / 0.176557 (-0.085518) | 0.146646 / 0.737135 (-0.590489) | 0.091693 / 0.296338 (-0.204645) |\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) | 0.462086 / 0.215209 (0.246877) | 4.620925 / 2.077655 (2.543271) | 2.539234 / 1.504120 (1.035114) | 2.371178 / 1.541195 (0.829983) | 2.440538 / 1.468490 (0.972048) | 0.511047 / 4.584777 (-4.073730) | 3.082088 / 3.745712 (-0.663624) | 2.918162 / 5.269862 (-2.351700) | 1.899651 / 4.565676 (-2.666025) | 0.059003 / 0.424275 (-0.365272) | 0.006746 / 0.007607 (-0.000861) | 0.537863 / 0.226044 (0.311819) | 5.382355 / 2.268929 (3.113426) | 3.060091 / 55.444624 (-52.384534) | 2.754969 / 6.876477 (-4.121507) | 2.863156 / 2.142072 (0.721084) | 0.606888 / 4.805227 (-4.198339) | 0.127448 / 6.500664 (-6.373216) | 0.062975 / 0.075469 (-0.012494) |\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) | 1.336065 / 1.841788 (-0.505722) | 19.019902 / 8.074308 (10.945594) | 15.057979 / 10.191392 (4.866587) | 0.160646 / 0.680424 (-0.519778) | 0.018340 / 0.534201 (-0.515861) | 0.341664 / 0.579283 (-0.237619) | 0.356536 / 0.434364 (-0.077828) | 0.393974 / 0.540337 (-0.146363) | 0.546036 / 1.386936 (-0.840900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd04e445bd36d7eb4af4d5a6b8519ab8e306ecf5 \"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) | 0.007220 / 0.011353 (-0.004132) | 0.004537 / 0.011008 (-0.006471) | 0.087333 / 0.038508 (0.048825) | 0.095637 / 0.023109 (0.072528) | 0.323819 / 0.275898 (0.047921) | 0.358838 / 0.323480 (0.035358) | 0.005910 / 0.007986 (-0.002076) | 0.003781 / 0.004328 (-0.000548) | 0.064565 / 0.004250 (0.060315) | 0.062818 / 0.037052 (0.025766) | 0.322595 / 0.258489 (0.064106) | 0.371865 / 0.293841 (0.078024) | 0.031667 / 0.128546 (-0.096880) | 0.009068 / 0.075646 (-0.066579) | 0.290574 / 0.419271 (-0.128697) | 0.054618 / 0.043533 (0.011085) | 0.314708 / 0.255139 (0.059569) | 0.336647 / 0.283200 (0.053447) | 0.027070 / 0.141683 (-0.114613) | 1.500640 / 1.452155 (0.048485) | 1.586775 / 1.492716 (0.094059) |\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) | 0.294461 / 0.018006 (0.276455) | 0.580125 / 0.000490 (0.579635) | 0.008165 / 0.000200 (0.007965) | 0.000320 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032352 / 0.037411 (-0.005059) | 0.092187 / 0.014526 (0.077661) | 0.104993 / 0.176557 (-0.071564) | 0.162738 / 0.737135 (-0.574397) | 0.103242 / 0.296338 (-0.193096) |\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) | 0.396732 / 0.215209 (0.181523) | 3.955049 / 2.077655 (1.877394) | 1.876762 / 1.504120 (0.372642) | 1.698477 / 1.541195 (0.157282) | 1.847086 / 1.468490 (0.378596) | 0.488306 / 4.584777 (-4.096471) | 3.658922 / 3.745712 (-0.086790) | 3.559050 / 5.269862 (-1.710812) | 2.187363 / 4.565676 (-2.378313) | 0.059795 / 0.424275 (-0.364480) | 0.008966 / 0.007607 (0.001359) | 0.474212 / 0.226044 (0.248168) | 4.732540 / 2.268929 (2.463611) | 2.466370 / 55.444624 (-52.978254) | 2.112105 / 6.876477 (-4.764372) | 2.414624 / 2.142072 (0.272552) | 0.595447 / 4.805227 (-4.209780) | 0.136705 / 6.500664 (-6.363959) | 0.062267 / 0.075469 (-0.013202) |\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) | 1.266518 / 1.841788 (-0.575270) | 21.009975 / 8.074308 (12.935666) | 14.823960 / 10.191392 (4.632568) | 0.165630 / 0.680424 (-0.514793) | 0.018499 / 0.534201 (-0.515702) | 0.396720 / 0.579283 (-0.182563) | 0.424807 / 0.434364 (-0.009557) | 0.463326 / 0.540337 (-0.077011) | 0.653132 / 1.386936 (-0.733804) |\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) | 0.007789 / 0.011353 (-0.003564) | 0.004720 / 0.011008 (-0.006288) | 0.066656 / 0.038508 (0.028148) | 0.094219 / 0.023109 (0.071109) | 0.414965 / 0.275898 (0.139067) | 0.454808 / 0.323480 (0.131328) | 0.006088 / 0.007986 (-0.001898) | 0.003980 / 0.004328 (-0.000349) | 0.066048 / 0.004250 (0.061797) | 0.065875 / 0.037052 (0.028823) | 0.419994 / 0.258489 (0.161505) | 0.462001 / 0.293841 (0.168160) | 0.033534 / 0.128546 (-0.095013) | 0.009010 / 0.075646 (-0.066636) | 0.072778 / 0.419271 (-0.346493) | 0.049834 / 0.043533 (0.006301) | 0.411003 / 0.255139 (0.155864) | 0.430918 / 0.283200 (0.147718) | 0.025664 / 0.141683 (-0.116019) | 1.526771 / 1.452155 (0.074616) | 1.634767 / 1.492716 (0.142051) |\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) | 0.271180 / 0.018006 (0.253174) | 0.576704 / 0.000490 (0.576214) | 0.004362 / 0.000200 (0.004162) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035648 / 0.037411 (-0.001763) | 0.102407 / 0.014526 (0.087881) | 0.111613 / 0.176557 (-0.064944) | 0.166173 / 0.737135 (-0.570962) | 0.113371 / 0.296338 (-0.182967) |\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) | 0.436031 / 0.215209 (0.220822) | 4.347071 / 2.077655 (2.269416) | 2.366937 / 1.504120 (0.862817) | 2.216356 / 1.541195 (0.675161) | 2.335933 / 1.468490 (0.867443) | 0.490484 / 4.584777 (-4.094293) | 3.730656 / 3.745712 (-0.015056) | 3.497248 / 5.269862 (-1.772613) | 2.215729 / 4.565676 (-2.349947) | 0.057905 / 0.424275 (-0.366370) | 0.007983 / 0.007607 (0.000376) | 0.510413 / 0.226044 (0.284369) | 5.114502 / 2.268929 (2.845574) | 2.871599 / 55.444624 (-52.573026) | 2.537514 / 6.876477 (-4.338962) | 2.819135 / 2.142072 (0.677063) | 0.588397 / 4.805227 (-4.216830) | 0.134665 / 6.500664 (-6.365999) | 0.063349 / 0.075469 (-0.012120) |\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) | 1.352962 / 1.841788 (-0.488826) | 21.628664 / 8.074308 (13.554356) | 15.962105 / 10.191392 (5.770713) | 0.167781 / 0.680424 (-0.512643) | 0.020965 / 0.534201 (-0.513236) | 0.402809 / 0.579283 (-0.176474) | 0.435153 / 0.434364 (0.000789) | 0.481394 / 0.540337 (-0.058944) | 0.658068 / 1.386936 (-0.728868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12adf38b90fde8e2a4e46fcbb023ee23b5c4e98c \"CML watermark\")\n" ]
2023-09-11T13:29:19
2023-09-13T18:21:28
2023-09-13T18:12:09
CONTRIBUTOR
false
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Required for `load_dataset(<format>, data_files=["path/to/.hidden_file"])` to work as expected
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1,889,050,954
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6,229
Apply inference on all images in the dataset
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[ "From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object). ", "> From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object).\r\n\r\nThanks @mariosasko for your reply...\r\ni tried :\r\n```\r\n# Define a function to apply the code to each image in the dataset\r\ndef process_image(image_path):\r\n print(\"Processing image:\", image_path)\r\n result = inferencer(image_path)['predictions']\r\n mask = np.where(result == 12, 255, 0).astype('uint8')\r\n return Image.fromarray(mask)\r\n\r\n# Process and save masks for each image in the dataset\r\nfor idx, example in enumerate(dataset['train']):\r\n image_path = np.array(example['image'])\r\n mask_image = process_image(image_path)\r\n mask_image.save(f\"mask_{idx}.png\")\r\n```\r\nand got\r\n```\r\nProcessing image: [[[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [243 205 122]\r\n [244 202 120]\r\n [242 200 118]]\r\n\r\n [[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [244 206 123]\r\n [245 203 121]\r\n [243 201 119]]\r\n\r\n [[203 164 87]\r\n [204 165 88]\r\n [207 168 91]\r\n ...\r\n [245 207 126]\r\n [246 204 122]\r\n [245 203 121]]\r\n\r\n ...\r\n\r\n [[154 123 56]\r\n [155 124 57]\r\n [158 125 56]\r\n ...\r\n [ 3 3 1]\r\n [ 3 3 1]\r\n [ 3 3 1]]\r\n\r\n [[154 123 56]\r\n [154 123 56]\r\n [155 124 57]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]\r\n\r\n [[152 121 54]\r\n [152 121 54]\r\n [153 122 55]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 39 44 40]\r\n [ 39 44 40]\r\n [ 39 43 44]\r\n ...\r\n [187 185 164]\r\n [208 204 175]\r\n [203 198 166]]\r\n\r\n [[ 42 47 43]\r\n [ 40 45 41]\r\n [ 40 44 45]\r\n ...\r\n [188 186 165]\r\n [202 198 169]\r\n [201 196 164]]\r\n\r\n [[ 41 46 42]\r\n [ 39 44 40]\r\n [ 40 44 45]\r\n ...\r\n [187 184 165]\r\n [197 193 166]\r\n [201 196 166]]\r\n\r\n ...\r\n\r\n [[ 29 27 30]\r\n [ 28 26 29]\r\n [ 25 23 26]\r\n ...\r\n [ 48 33 28]\r\n [ 44 31 25]\r\n [ 39 26 20]]\r\n\r\n [[ 34 29 33]\r\n [ 32 27 31]\r\n [ 29 24 28]\r\n ...\r\n [ 30 17 11]\r\n [ 36 23 15]\r\n [ 41 28 20]]\r\n\r\n [[ 35 30 34]\r\n [ 33 28 32]\r\n [ 28 23 27]\r\n ...\r\n [ 28 15 9]\r\n [ 41 28 20]\r\n [ 46 33 25]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 65 53 55]\r\n [ 65 53 55]\r\n [ 51 39 41]\r\n ...\r\n [133 127 111]\r\n [150 141 124]\r\n [133 124 107]]\r\n\r\n [[ 58 45 52]\r\n [ 61 48 55]\r\n [ 51 38 45]\r\n ...\r\n [148 141 123]\r\n [178 169 152]\r\n [144 135 118]]\r\n\r\n [[ 79 66 83]\r\n [ 73 60 77]\r\n [ 65 51 66]\r\n ...\r\n [140 131 114]\r\n [142 133 116]\r\n [147 136 118]]\r\n\r\n ...\r\n\r\n [[132 122 133]\r\n [ 95 85 94]\r\n [ 61 51 60]\r\n ...\r\n [ 39 28 42]\r\n [ 46 36 45]\r\n [ 25 16 21]]\r\n\r\n [[150 143 151]\r\n [114 107 115]\r\n [ 64 54 63]\r\n ...\r\n [ 47 35 47]\r\n [ 38 27 35]\r\n [140 129 133]]\r\n\r\n [[145 138 146]\r\n [115 108 116]\r\n [ 69 59 67]\r\n ...\r\n [ 31 19 31]\r\n [128 117 123]\r\n [196 185 189]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[159 151 140]\r\n [171 163 152]\r\n [161 148 142]\r\n ...\r\n [198 184 171]\r\n [189 175 162]\r\n [183 169 156]]\r\n\r\n [[128 118 106]\r\n [138 128 116]\r\n [138 125 116]\r\n ...\r\n [200 186 173]\r\n [190 176 163]\r\n [187 173 160]]\r\n\r\n [[165 153 137]\r\n [170 158 142]\r\n [174 162 148]\r\n ...\r\n [200 187 171]\r\n [188 175 159]\r\n [182 169 153]]\r\n```\r\nHowever , when trying to add to:\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('Andyrasika/cat_kingdom')\r\ndataset\r\n```\r\ni did \r\n```\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\nnew_column\r\ndataset = dataset.add_column(\"/workspace/data\", new_column)\r\n\r\nprint(dataset)\r\n```\r\ngot error:\r\n```\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[11], line 3\r\n 1 new_column = [\"mask\"] * len(dataset[\"train\"])\r\n 2 new_column\r\n----> 3 dataset = dataset.add_column(\"/workspace/data\", new_column)\r\n 5 print(dataset)\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'add_column'\r\n```", "https://github.com/huggingface/datasets/issues/6246 resolved the `add_column` error, so I'm closing this issue :) " ]
2023-09-10T08:36:12
2023-09-20T16:11:53
2023-09-20T16:11:52
NONE
null
null
### Describe the bug ``` --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[14], line 11 9 for idx, example in enumerate(dataset['train']): 10 image_path = example['image'] ---> 11 mask_image = process_image(image_path) 12 mask_image.save(f"mask_{idx}.png") Cell In[14], line 4, in process_image(image_path) 2 def process_image(image_path): 3 print("Processing image:", image_path) ----> 4 result = inferencer(image_path)['predictions'] 5 mask = np.where(result == 12, 255, 0).astype('uint8') 6 return Image.fromarray(mask) File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, **kwargs) 180 pred_out_dir = '' 181 img_out_dir = '' --> 183 return super().__call__( 184 inputs=inputs, 185 return_datasamples=return_datasamples, 186 batch_size=batch_size, 187 show=show, 188 wait_time=wait_time, 189 img_out_dir=img_out_dir, 190 pred_out_dir=pred_out_dir, 191 **kwargs) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, **kwargs) 218 inputs = self.preprocess( 219 ori_inputs, batch_size=batch_size, **preprocess_kwargs) 220 preds = [] --> 221 for data in (track(inputs, description='Inference') 222 if self.show_progress else inputs): 223 preds.extend(self.forward(data, **forward_kwargs)) 224 visualization = self.visualize( 225 ori_inputs, preds, 226 **visualize_kwargs) # type: ignore # noqa: E501 File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed) 157 progress = Progress( 158 *columns, 159 auto_refresh=auto_refresh, (...) 164 disable=disable, 165 ) 167 with progress: --> 168 yield from progress.track( 169 sequence, total=total, description=description, update_period=update_period 170 ) File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period) 1208 if self.live.auto_refresh: 1209 with _TrackThread(self, task_id, update_period) as track_thread: -> 1210 for value in sequence: 1211 yield value 1212 track_thread.completed += 1 File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, **kwargs) 266 """Process the inputs into a model-feedable format. 267 268 Customize your preprocess by overriding this method. Preprocess should (...) 287 Any: Data processed by the ``pipeline`` and ``collate_fn``. 288 """ 289 chunked_data = self._get_chunk_data( 290 map(self.pipeline, inputs), batch_size) --> 291 yield from map(self.collate_fn, chunked_data) File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size) 586 chunk_data = [] 587 for _ in range(chunk_size): --> 588 processed_data = next(inputs_iter) 589 chunk_data.append(processed_data) 590 yield chunk_data File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results) 79 """Call function to apply transforms sequentially. 80 81 Args: (...) 85 dict or None: Transformed results. 86 """ 87 for t in self.transforms: ---> 88 results = t(results) # type: ignore 89 if results is None: 90 return None File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results) 9 def __call__(self, 10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]: ---> 12 return self.transform(results) File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input) 494 inputs = single_input 495 else: --> 496 raise NotImplementedError 498 if 'img' in inputs: 499 return self.from_ndarray(inputs) NotImplementedError: ```` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset('Andyrasika/cat_kingdom') dataset from mmseg.apis import MMSegInferencer checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640' inferencer = MMSegInferencer(model=checkpoint_name) # Define a function to apply the code to each image in the dataset def process_image(image_path): print("Processing image:", image_path) result = inferencer(image_path)['predictions'] mask = np.where(result == 12, 255, 0).astype('uint8') return Image.fromarray(mask) # Process and save masks for each image in the dataset for idx, example in enumerate(dataset['train']): image_path = example['image'] mask_image = process_image(image_path) mask_image.save(f"mask_{idx}.png") ``` ### Expected behavior create a separate column with masks in the dataset and further shows as a separate column in hub ### Environment info jupyter notebook RTX 3090
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6,228
Remove RGB -> BGR image conversion in Object Detection tutorial
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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) | 0.009443 / 0.011353 (-0.001910) | 0.005274 / 0.011008 (-0.005734) | 0.105950 / 0.038508 (0.067441) | 0.079947 / 0.023109 (0.056837) | 0.414248 / 0.275898 (0.138350) | 0.440611 / 0.323480 (0.117131) | 0.006779 / 0.007986 (-0.001206) | 0.004301 / 0.004328 (-0.000028) | 0.080616 / 0.004250 (0.076366) | 0.061425 / 0.037052 (0.024372) | 0.418460 / 0.258489 (0.159971) | 0.468108 / 0.293841 (0.174267) | 0.051090 / 0.128546 (-0.077456) | 0.014133 / 0.075646 (-0.061513) | 0.376121 / 0.419271 (-0.043151) | 0.070715 / 0.043533 (0.027182) | 0.415435 / 0.255139 (0.160296) | 0.457925 / 0.283200 (0.174725) | 0.053653 / 0.141683 (-0.088030) | 1.872681 / 1.452155 (0.420527) | 1.961187 / 1.492716 (0.468470) |\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) | 0.255829 / 0.018006 (0.237823) | 0.574224 / 0.000490 (0.573735) | 0.007597 / 0.000200 (0.007397) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032562 / 0.037411 (-0.004849) | 0.097528 / 0.014526 (0.083003) | 0.113487 / 0.176557 (-0.063070) | 0.185670 / 0.737135 (-0.551465) | 0.118909 / 0.296338 (-0.177430) |\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) | 0.611441 / 0.215209 (0.396232) | 5.908576 / 2.077655 (3.830921) | 2.586758 / 1.504120 (1.082638) | 2.310199 / 1.541195 (0.769004) | 2.333396 / 1.468490 (0.864906) | 0.900884 / 4.584777 (-3.683893) | 5.438304 / 3.745712 (1.692591) | 4.806611 / 5.269862 (-0.463250) | 2.970631 / 4.565676 (-1.595046) | 0.097861 / 0.424275 (-0.326414) | 0.009873 / 0.007607 (0.002266) | 0.739553 / 0.226044 (0.513509) | 7.104953 / 2.268929 (4.836024) | 3.150128 / 55.444624 (-52.294497) | 2.469552 / 6.876477 (-4.406924) | 2.709206 / 2.142072 (0.567133) | 0.983081 / 4.805227 (-3.822147) | 0.205150 / 6.500664 (-6.295514) | 0.075947 / 0.075469 (0.000478) |\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) | 1.631255 / 1.841788 (-0.210532) | 24.213679 / 8.074308 (16.139370) | 21.514481 / 10.191392 (11.323089) | 0.220360 / 0.680424 (-0.460063) | 0.031663 / 0.534201 (-0.502538) | 0.516029 / 0.579283 (-0.063254) | 0.591461 / 0.434364 (0.157097) | 0.612398 / 0.540337 (0.072061) | 0.807609 / 1.386936 (-0.579328) |\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) | 0.009443 / 0.011353 (-0.001910) | 0.005510 / 0.011008 (-0.005498) | 0.085722 / 0.038508 (0.047214) | 0.076256 / 0.023109 (0.053146) | 0.604248 / 0.275898 (0.328349) | 0.596222 / 0.323480 (0.272742) | 0.006786 / 0.007986 (-0.001200) | 0.004135 / 0.004328 (-0.000193) | 0.085934 / 0.004250 (0.081683) | 0.065890 / 0.037052 (0.028838) | 0.592080 / 0.258489 (0.333591) | 0.624560 / 0.293841 (0.330719) | 0.048200 / 0.128546 (-0.080346) | 0.015477 / 0.075646 (-0.060169) | 0.097042 / 0.419271 (-0.322230) | 0.060513 / 0.043533 (0.016981) | 0.557171 / 0.255139 (0.302032) | 0.582057 / 0.283200 (0.298858) | 0.035678 / 0.141683 (-0.106005) | 1.894947 / 1.452155 (0.442792) | 1.956652 / 1.492716 (0.463936) |\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) | 0.268927 / 0.018006 (0.250921) | 0.566086 / 0.000490 (0.565597) | 0.007190 / 0.000200 (0.006990) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042090 / 0.037411 (0.004679) | 0.109618 / 0.014526 (0.095092) | 0.126588 / 0.176557 (-0.049968) | 0.200426 / 0.737135 (-0.536709) | 0.127032 / 0.296338 (-0.169306) |\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) | 0.669773 / 0.215209 (0.454564) | 6.453417 / 2.077655 (4.375763) | 3.119147 / 1.504120 (1.615027) | 2.818632 / 1.541195 (1.277437) | 2.930880 / 1.468490 (1.462390) | 0.922164 / 4.584777 (-3.662612) | 5.769564 / 3.745712 (2.023852) | 4.885108 / 5.269862 (-0.384754) | 3.041640 / 4.565676 (-1.524037) | 0.100186 / 0.424275 (-0.324090) | 0.009417 / 0.007607 (0.001810) | 0.783138 / 0.226044 (0.557094) | 8.113361 / 2.268929 (5.844432) | 4.018630 / 55.444624 (-51.425995) | 3.246772 / 6.876477 (-3.629704) | 3.520690 / 2.142072 (1.378618) | 1.063686 / 4.805227 (-3.741541) | 0.218667 / 6.500664 (-6.281997) | 0.084169 / 0.075469 (0.008700) |\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) | 1.791949 / 1.841788 (-0.049839) | 23.148341 / 8.074308 (15.074033) | 23.321125 / 10.191392 (13.129733) | 0.245391 / 0.680424 (-0.435032) | 0.031911 / 0.534201 (-0.502290) | 0.470707 / 0.579283 (-0.108576) | 0.608195 / 0.434364 (0.173832) | 0.559590 / 0.540337 (0.019253) | 0.786007 / 1.386936 (-0.600929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8e071f565cc0801f73f7f34fba92dc30a43946a9 \"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) | 0.008428 / 0.011353 (-0.002925) | 0.004064 / 0.011008 (-0.006944) | 0.088421 / 0.038508 (0.049913) | 0.078042 / 0.023109 (0.054933) | 0.306356 / 0.275898 (0.030458) | 0.349766 / 0.323480 (0.026286) | 0.004086 / 0.007986 (-0.003900) | 0.003900 / 0.004328 (-0.000428) | 0.068379 / 0.004250 (0.064129) | 0.056214 / 0.037052 (0.019161) | 0.310211 / 0.258489 (0.051722) | 0.363692 / 0.293841 (0.069851) | 0.050421 / 0.128546 (-0.078125) | 0.011661 / 0.075646 (-0.063985) | 0.298400 / 0.419271 (-0.120871) | 0.063503 / 0.043533 (0.019970) | 0.339799 / 0.255139 (0.084660) | 0.359479 / 0.283200 (0.076279) | 0.039265 / 0.141683 (-0.102418) | 1.390578 / 1.452155 (-0.061576) | 1.573333 / 1.492716 (0.080617) |\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) | 0.260442 / 0.018006 (0.242436) | 0.560390 / 0.000490 (0.559900) | 0.003926 / 0.000200 (0.003726) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025809 / 0.037411 (-0.011602) | 0.081902 / 0.014526 (0.067376) | 0.093655 / 0.176557 (-0.082901) | 0.149432 / 0.737135 (-0.587703) | 0.099059 / 0.296338 (-0.197279) |\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) | 0.505644 / 0.215209 (0.290435) | 5.108292 / 2.077655 (3.030638) | 2.121689 / 1.504120 (0.617569) | 1.846576 / 1.541195 (0.305381) | 1.836587 / 1.468490 (0.368097) | 0.708088 / 4.584777 (-3.876689) | 4.562630 / 3.745712 (0.816918) | 3.934747 / 5.269862 (-1.335115) | 2.453409 / 4.565676 (-2.112267) | 0.081908 / 0.424275 (-0.342367) | 0.012996 / 0.007607 (0.005389) | 0.636588 / 0.226044 (0.410544) | 6.361086 / 2.268929 (4.092157) | 2.911681 / 55.444624 (-52.532943) | 2.271809 / 6.876477 (-4.604667) | 2.670327 / 2.142072 (0.528254) | 0.943688 / 4.805227 (-3.861539) | 0.191677 / 6.500664 (-6.308988) | 0.066008 / 0.075469 (-0.009461) |\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) | 1.400139 / 1.841788 (-0.441648) | 21.896198 / 8.074308 (13.821890) | 17.853604 / 10.191392 (7.662212) | 0.226603 / 0.680424 (-0.453821) | 0.026682 / 0.534201 (-0.507518) | 0.460131 / 0.579283 (-0.119152) | 0.536790 / 0.434364 (0.102427) | 0.492913 / 0.540337 (-0.047424) | 0.724290 / 1.386936 (-0.662646) |\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) | 0.007795 / 0.011353 (-0.003557) | 0.009045 / 0.011008 (-0.001963) | 0.085480 / 0.038508 (0.046972) | 0.071881 / 0.023109 (0.048772) | 0.514520 / 0.275898 (0.238622) | 0.569762 / 0.323480 (0.246282) | 0.006126 / 0.007986 (-0.001859) | 0.004153 / 0.004328 (-0.000175) | 0.072150 / 0.004250 (0.067900) | 0.056511 / 0.037052 (0.019458) | 0.484097 / 0.258489 (0.225607) | 0.532673 / 0.293841 (0.238832) | 0.040974 / 0.128546 (-0.087572) | 0.012071 / 0.075646 (-0.063575) | 0.102608 / 0.419271 (-0.316663) | 0.052893 / 0.043533 (0.009360) | 0.485832 / 0.255139 (0.230693) | 0.530479 / 0.283200 (0.247280) | 0.031556 / 0.141683 (-0.110127) | 1.737508 / 1.452155 (0.285354) | 1.834637 / 1.492716 (0.341921) |\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) | 0.423314 / 0.018006 (0.405308) | 0.614163 / 0.000490 (0.613673) | 0.052784 / 0.000200 (0.052584) | 0.000206 / 0.000054 (0.000151) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031728 / 0.037411 (-0.005684) | 0.088048 / 0.014526 (0.073522) | 0.105759 / 0.176557 (-0.070798) | 0.181433 / 0.737135 (-0.555703) | 0.103133 / 0.296338 (-0.193205) |\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) | 0.659710 / 0.215209 (0.444501) | 5.876378 / 2.077655 (3.798723) | 2.899444 / 1.504120 (1.395324) | 2.871592 / 1.541195 (1.330397) | 2.861205 / 1.468490 (1.392715) | 0.879452 / 4.584777 (-3.705325) | 5.395988 / 3.745712 (1.650275) | 4.548359 / 5.269862 (-0.721502) | 2.946601 / 4.565676 (-1.619076) | 0.099832 / 0.424275 (-0.324443) | 0.008958 / 0.007607 (0.001351) | 0.778480 / 0.226044 (0.552435) | 7.672282 / 2.268929 (5.403354) | 3.963701 / 55.444624 (-51.480923) | 3.154950 / 6.876477 (-3.721527) | 3.351070 / 2.142072 (1.208997) | 1.059459 / 4.805227 (-3.745768) | 0.212035 / 6.500664 (-6.288629) | 0.076941 / 0.075469 (0.001472) |\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) | 1.639813 / 1.841788 (-0.201975) | 24.807517 / 8.074308 (16.733208) | 20.662500 / 10.191392 (10.471108) | 0.244486 / 0.680424 (-0.435937) | 0.032335 / 0.534201 (-0.501866) | 0.470896 / 0.579283 (-0.108387) | 0.581561 / 0.434364 (0.147197) | 0.495158 / 0.540337 (-0.045179) | 0.788350 / 1.386936 (-0.598586) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99641ced2e08a28cb876f483babcdd43f7dd76d2 \"CML watermark\")\n" ]
2023-09-08T16:09:13
2023-09-08T18:02:49
2023-09-08T17:52:16
CONTRIBUTOR
false
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Fix #6225
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6,226
Add push_to_hub with multiple configs docs
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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) | 0.005920 / 0.011353 (-0.005433) | 0.003623 / 0.011008 (-0.007385) | 0.079283 / 0.038508 (0.040775) | 0.058325 / 0.023109 (0.035216) | 0.313733 / 0.275898 (0.037835) | 0.360790 / 0.323480 (0.037310) | 0.004653 / 0.007986 (-0.003332) | 0.002876 / 0.004328 (-0.001452) | 0.062137 / 0.004250 (0.057886) | 0.045084 / 0.037052 (0.008031) | 0.328569 / 0.258489 (0.070079) | 0.368965 / 0.293841 (0.075124) | 0.027085 / 0.128546 (-0.101461) | 0.008051 / 0.075646 (-0.067595) | 0.260222 / 0.419271 (-0.159050) | 0.045477 / 0.043533 (0.001944) | 0.315344 / 0.255139 (0.060205) | 0.348215 / 0.283200 (0.065015) | 0.021352 / 0.141683 (-0.120331) | 1.432200 / 1.452155 (-0.019955) | 1.509217 / 1.492716 (0.016501) |\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) | 0.199843 / 0.018006 (0.181837) | 0.427925 / 0.000490 (0.427435) | 0.002903 / 0.000200 (0.002703) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023121 / 0.037411 (-0.014291) | 0.072451 / 0.014526 (0.057925) | 0.083260 / 0.176557 (-0.093296) | 0.142879 / 0.737135 (-0.594257) | 0.084053 / 0.296338 (-0.212286) |\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) | 0.394922 / 0.215209 (0.179713) | 3.956111 / 2.077655 (1.878456) | 1.926411 / 1.504120 (0.422291) | 1.743840 / 1.541195 (0.202646) | 1.776957 / 1.468490 (0.308467) | 0.502134 / 4.584777 (-4.082643) | 3.001721 / 3.745712 (-0.743991) | 2.852496 / 5.269862 (-2.417365) | 1.862794 / 4.565676 (-2.702883) | 0.057544 / 0.424275 (-0.366731) | 0.006751 / 0.007607 (-0.000856) | 0.470619 / 0.226044 (0.244575) | 4.696674 / 2.268929 (2.427746) | 2.326545 / 55.444624 (-53.118080) | 1.980888 / 6.876477 (-4.895589) | 2.139172 / 2.142072 (-0.002901) | 0.590256 / 4.805227 (-4.214971) | 0.125815 / 6.500664 (-6.374849) | 0.061000 / 0.075469 (-0.014469) |\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) | 1.261948 / 1.841788 (-0.579839) | 18.317473 / 8.074308 (10.243165) | 13.810883 / 10.191392 (3.619491) | 0.146180 / 0.680424 (-0.534244) | 0.016701 / 0.534201 (-0.517500) | 0.330731 / 0.579283 (-0.248552) | 0.345103 / 0.434364 (-0.089261) | 0.374449 / 0.540337 (-0.165889) | 0.522463 / 1.386936 (-0.864473) |\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) | 0.006217 / 0.011353 (-0.005136) | 0.003678 / 0.011008 (-0.007331) | 0.062321 / 0.038508 (0.023813) | 0.059256 / 0.023109 (0.036147) | 0.444501 / 0.275898 (0.168603) | 0.475881 / 0.323480 (0.152401) | 0.004863 / 0.007986 (-0.003123) | 0.002916 / 0.004328 (-0.001412) | 0.062197 / 0.004250 (0.057946) | 0.048449 / 0.037052 (0.011396) | 0.443680 / 0.258489 (0.185191) | 0.484570 / 0.293841 (0.190729) | 0.028694 / 0.128546 (-0.099852) | 0.008096 / 0.075646 (-0.067550) | 0.068347 / 0.419271 (-0.350924) | 0.041031 / 0.043533 (-0.002502) | 0.443907 / 0.255139 (0.188768) | 0.469888 / 0.283200 (0.186689) | 0.020237 / 0.141683 (-0.121445) | 1.438484 / 1.452155 (-0.013671) | 1.512652 / 1.492716 (0.019936) |\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) | 0.243118 / 0.018006 (0.225111) | 0.416797 / 0.000490 (0.416308) | 0.010421 / 0.000200 (0.010221) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026191 / 0.037411 (-0.011220) | 0.080881 / 0.014526 (0.066355) | 0.093207 / 0.176557 (-0.083349) | 0.146708 / 0.737135 (-0.590428) | 0.091676 / 0.296338 (-0.204663) |\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) | 0.461475 / 0.215209 (0.246266) | 4.617351 / 2.077655 (2.539696) | 2.564369 / 1.504120 (1.060249) | 2.393263 / 1.541195 (0.852068) | 2.447343 / 1.468490 (0.978853) | 0.508764 / 4.584777 (-4.076013) | 3.075460 / 3.745712 (-0.670252) | 2.884683 / 5.269862 (-2.385179) | 1.866432 / 4.565676 (-2.699244) | 0.058759 / 0.424275 (-0.365516) | 0.006591 / 0.007607 (-0.001016) | 0.537718 / 0.226044 (0.311674) | 5.378709 / 2.268929 (3.109781) | 3.006751 / 55.444624 (-52.437873) | 2.666653 / 6.876477 (-4.209824) | 2.847559 / 2.142072 (0.705486) | 0.596878 / 4.805227 (-4.208350) | 0.125073 / 6.500664 (-6.375591) | 0.061345 / 0.075469 (-0.014124) |\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) | 1.349066 / 1.841788 (-0.492721) | 18.684735 / 8.074308 (10.610427) | 15.128142 / 10.191392 (4.936750) | 0.149254 / 0.680424 (-0.531170) | 0.017911 / 0.534201 (-0.516290) | 0.344057 / 0.579283 (-0.235226) | 0.363474 / 0.434364 (-0.070890) | 0.399425 / 0.540337 (-0.140912) | 0.549329 / 1.386936 (-0.837607) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e675a2396efb5204a4553721001f3b46aa4cc334 \"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) | 0.005843 / 0.011353 (-0.005510) | 0.003549 / 0.011008 (-0.007460) | 0.082318 / 0.038508 (0.043810) | 0.056835 / 0.023109 (0.033726) | 0.312968 / 0.275898 (0.037070) | 0.345918 / 0.323480 (0.022438) | 0.003239 / 0.007986 (-0.004747) | 0.002762 / 0.004328 (-0.001567) | 0.062362 / 0.004250 (0.058111) | 0.045934 / 0.037052 (0.008882) | 0.317035 / 0.258489 (0.058546) | 0.358473 / 0.293841 (0.064632) | 0.027311 / 0.128546 (-0.101235) | 0.007994 / 0.075646 (-0.067652) | 0.261565 / 0.419271 (-0.157706) | 0.044942 / 0.043533 (0.001410) | 0.313092 / 0.255139 (0.057953) | 0.339021 / 0.283200 (0.055821) | 0.021555 / 0.141683 (-0.120127) | 1.421232 / 1.452155 (-0.030923) | 1.487597 / 1.492716 (-0.005119) |\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) | 0.206432 / 0.018006 (0.188425) | 0.421932 / 0.000490 (0.421442) | 0.002825 / 0.000200 (0.002625) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022795 / 0.037411 (-0.014616) | 0.072666 / 0.014526 (0.058140) | 0.082779 / 0.176557 (-0.093778) | 0.142320 / 0.737135 (-0.594815) | 0.083343 / 0.296338 (-0.212995) |\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) | 0.394227 / 0.215209 (0.179018) | 3.931858 / 2.077655 (1.854203) | 1.909953 / 1.504120 (0.405833) | 1.711298 / 1.541195 (0.170104) | 1.745816 / 1.468490 (0.277326) | 0.503670 / 4.584777 (-4.081107) | 3.053677 / 3.745712 (-0.692035) | 2.802597 / 5.269862 (-2.467264) | 1.825315 / 4.565676 (-2.740362) | 0.057741 / 0.424275 (-0.366534) | 0.006581 / 0.007607 (-0.001027) | 0.463597 / 0.226044 (0.237552) | 4.638821 / 2.268929 (2.369893) | 2.301266 / 55.444624 (-53.143358) | 1.967111 / 6.876477 (-4.909365) | 2.097756 / 2.142072 (-0.044317) | 0.589840 / 4.805227 (-4.215387) | 0.125538 / 6.500664 (-6.375126) | 0.061203 / 0.075469 (-0.014266) |\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) | 1.291815 / 1.841788 (-0.549973) | 17.997040 / 8.074308 (9.922732) | 13.616252 / 10.191392 (3.424860) | 0.137349 / 0.680424 (-0.543075) | 0.016626 / 0.534201 (-0.517575) | 0.329611 / 0.579283 (-0.249672) | 0.346592 / 0.434364 (-0.087772) | 0.379521 / 0.540337 (-0.160817) | 0.528058 / 1.386936 (-0.858878) |\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) | 0.006073 / 0.011353 (-0.005280) | 0.003594 / 0.011008 (-0.007414) | 0.062537 / 0.038508 (0.024029) | 0.057503 / 0.023109 (0.034394) | 0.449427 / 0.275898 (0.173529) | 0.482729 / 0.323480 (0.159249) | 0.004690 / 0.007986 (-0.003295) | 0.002901 / 0.004328 (-0.001428) | 0.062421 / 0.004250 (0.058171) | 0.046405 / 0.037052 (0.009353) | 0.456578 / 0.258489 (0.198089) | 0.492268 / 0.293841 (0.198427) | 0.028283 / 0.128546 (-0.100263) | 0.008028 / 0.075646 (-0.067618) | 0.067885 / 0.419271 (-0.351387) | 0.041273 / 0.043533 (-0.002260) | 0.449870 / 0.255139 (0.194731) | 0.472305 / 0.283200 (0.189106) | 0.018556 / 0.141683 (-0.123127) | 1.449016 / 1.452155 (-0.003138) | 1.490839 / 1.492716 (-0.001877) |\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) | 0.226569 / 0.018006 (0.208563) | 0.417106 / 0.000490 (0.416616) | 0.002784 / 0.000200 (0.002584) | 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) | 0.025803 / 0.037411 (-0.011608) | 0.081084 / 0.014526 (0.066559) | 0.091851 / 0.176557 (-0.084706) | 0.143982 / 0.737135 (-0.593153) | 0.090511 / 0.296338 (-0.205827) |\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) | 0.463664 / 0.215209 (0.248454) | 4.634528 / 2.077655 (2.556874) | 2.574739 / 1.504120 (1.070619) | 2.412857 / 1.541195 (0.871662) | 2.442858 / 1.468490 (0.974368) | 0.511990 / 4.584777 (-4.072787) | 3.070345 / 3.745712 (-0.675367) | 2.842290 / 5.269862 (-2.427571) | 1.846727 / 4.565676 (-2.718950) | 0.058852 / 0.424275 (-0.365424) | 0.006624 / 0.007607 (-0.000983) | 0.539616 / 0.226044 (0.313571) | 5.410784 / 2.268929 (3.141856) | 3.065593 / 55.444624 (-52.379031) | 2.677930 / 6.876477 (-4.198547) | 2.817548 / 2.142072 (0.675476) | 0.602672 / 4.805227 (-4.202555) | 0.125689 / 6.500664 (-6.374975) | 0.062007 / 0.075469 (-0.013462) |\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) | 1.335336 / 1.841788 (-0.506452) | 18.310099 / 8.074308 (10.235791) | 14.818452 / 10.191392 (4.627060) | 0.154001 / 0.680424 (-0.526423) | 0.017892 / 0.534201 (-0.516309) | 0.345989 / 0.579283 (-0.233294) | 0.352108 / 0.434364 (-0.082256) | 0.394333 / 0.540337 (-0.146004) | 0.547680 / 1.386936 (-0.839256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d058d6e9b849acb5bc61d7df597a94253b487eb6 \"CML watermark\")\n" ]
2023-09-08T11:08:55
2023-09-08T12:29:21
2023-09-08T12:20:51
MEMBER
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1,887,054,320
I_kwDODunzps5weinw
6,225
Conversion from RGB to BGR in Object Detection tutorial
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[ "Good catch!" ]
2023-09-08T06:49:19
2023-09-08T17:52:18
2023-09-08T17:52:17
NONE
null
null
The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB > albumentations expects the image to be in BGR format, not RGB, so you’ll have to convert the image before applying the transform. [Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77) However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more. I suggest removing this outdated conversion from the tutorial.
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PR_kwDODunzps5Zym3j
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Ignore `dataset_info.json` in data files resolution
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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) | 0.009450 / 0.011353 (-0.001903) | 0.007339 / 0.011008 (-0.003669) | 0.110150 / 0.038508 (0.071641) | 0.087794 / 0.023109 (0.064685) | 0.472099 / 0.275898 (0.196201) | 0.476622 / 0.323480 (0.153142) | 0.005057 / 0.007986 (-0.002929) | 0.005262 / 0.004328 (0.000933) | 0.103059 / 0.004250 (0.098808) | 0.069815 / 0.037052 (0.032763) | 0.489377 / 0.258489 (0.230888) | 0.547087 / 0.293841 (0.253247) | 0.048883 / 0.128546 (-0.079663) | 0.019192 / 0.075646 (-0.056454) | 0.410865 / 0.419271 (-0.008407) | 0.076215 / 0.043533 (0.032682) | 0.484825 / 0.255139 (0.229686) | 0.519035 / 0.283200 (0.235835) | 0.042030 / 0.141683 (-0.099653) | 1.909630 / 1.452155 (0.457475) | 2.120869 / 1.492716 (0.628153) |\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) | 0.267600 / 0.018006 (0.249594) | 0.619135 / 0.000490 (0.618645) | 0.005897 / 0.000200 (0.005697) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033265 / 0.037411 (-0.004146) | 0.104476 / 0.014526 (0.089950) | 0.129199 / 0.176557 (-0.047358) | 0.196898 / 0.737135 (-0.540238) | 0.118852 / 0.296338 (-0.177487) |\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) | 0.598908 / 0.215209 (0.383699) | 6.263096 / 2.077655 (4.185441) | 2.672134 / 1.504120 (1.168014) | 2.428706 / 1.541195 (0.887511) | 2.431651 / 1.468490 (0.963161) | 0.918465 / 4.584777 (-3.666312) | 5.667857 / 3.745712 (1.922145) | 5.113696 / 5.269862 (-0.156166) | 3.276805 / 4.565676 (-1.288872) | 0.101829 / 0.424275 (-0.322446) | 0.010224 / 0.007607 (0.002617) | 0.741547 / 0.226044 (0.515502) | 7.517002 / 2.268929 (5.248073) | 3.546353 / 55.444624 (-51.898272) | 2.845956 / 6.876477 (-4.030521) | 3.172777 / 2.142072 (1.030705) | 1.153485 / 4.805227 (-3.651742) | 0.225758 / 6.500664 (-6.274906) | 0.084333 / 0.075469 (0.008864) |\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) | 1.704645 / 1.841788 (-0.137143) | 27.044110 / 8.074308 (18.969801) | 24.653837 / 10.191392 (14.462445) | 0.235452 / 0.680424 (-0.444971) | 0.029285 / 0.534201 (-0.504916) | 0.576122 / 0.579283 (-0.003161) | 0.626263 / 0.434364 (0.191899) | 0.600201 / 0.540337 (0.059864) | 0.838406 / 1.386936 (-0.548530) |\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) | 0.013754 / 0.011353 (0.002401) | 0.005954 / 0.011008 (-0.005054) | 0.089766 / 0.038508 (0.051258) | 0.096126 / 0.023109 (0.073017) | 0.556455 / 0.275898 (0.280557) | 0.579302 / 0.323480 (0.255822) | 0.009222 / 0.007986 (0.001236) | 0.006128 / 0.004328 (0.001800) | 0.099725 / 0.004250 (0.095475) | 0.075642 / 0.037052 (0.038589) | 0.556645 / 0.258489 (0.298156) | 0.615898 / 0.293841 (0.322057) | 0.057728 / 0.128546 (-0.070818) | 0.016746 / 0.075646 (-0.058900) | 0.098053 / 0.419271 (-0.321219) | 0.066676 / 0.043533 (0.023143) | 0.534156 / 0.255139 (0.279017) | 0.590020 / 0.283200 (0.306820) | 0.038782 / 0.141683 (-0.102901) | 1.952301 / 1.452155 (0.500146) | 2.104255 / 1.492716 (0.611539) |\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) | 0.305945 / 0.018006 (0.287939) | 0.643915 / 0.000490 (0.643426) | 0.006268 / 0.000200 (0.006068) | 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) | 0.039891 / 0.037411 (0.002479) | 0.117888 / 0.014526 (0.103363) | 0.134230 / 0.176557 (-0.042326) | 0.212544 / 0.737135 (-0.524591) | 0.128858 / 0.296338 (-0.167480) |\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) | 0.718165 / 0.215209 (0.502955) | 7.023867 / 2.077655 (4.946212) | 3.391344 / 1.504120 (1.887224) | 3.021248 / 1.541195 (1.480053) | 3.010217 / 1.468490 (1.541727) | 0.932608 / 4.584777 (-3.652169) | 5.787536 / 3.745712 (2.041824) | 5.221305 / 5.269862 (-0.048557) | 3.282552 / 4.565676 (-1.283125) | 0.105486 / 0.424275 (-0.318789) | 0.009800 / 0.007607 (0.002193) | 0.839358 / 0.226044 (0.613314) | 8.279712 / 2.268929 (6.010784) | 4.118466 / 55.444624 (-51.326158) | 3.407738 / 6.876477 (-3.468739) | 3.632538 / 2.142072 (1.490466) | 1.109673 / 4.805227 (-3.695555) | 0.216541 / 6.500664 (-6.284123) | 0.094031 / 0.075469 (0.018562) |\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) | 1.983979 / 1.841788 (0.142191) | 27.125882 / 8.074308 (19.051573) | 24.714002 / 10.191392 (14.522610) | 0.264417 / 0.680424 (-0.416007) | 0.034783 / 0.534201 (-0.499418) | 0.533304 / 0.579283 (-0.045979) | 0.647798 / 0.434364 (0.213434) | 0.588680 / 0.540337 (0.048343) | 0.854250 / 1.386936 (-0.532686) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#491604b46b1fd8d6fd1b7531f7917ccd657665a6 \"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) | 0.006664 / 0.011353 (-0.004689) | 0.004164 / 0.011008 (-0.006844) | 0.085192 / 0.038508 (0.046684) | 0.073578 / 0.023109 (0.050469) | 0.356379 / 0.275898 (0.080481) | 0.389381 / 0.323480 (0.065902) | 0.005527 / 0.007986 (-0.002459) | 0.003488 / 0.004328 (-0.000840) | 0.065640 / 0.004250 (0.061390) | 0.055013 / 0.037052 (0.017960) | 0.358002 / 0.258489 (0.099513) | 0.400663 / 0.293841 (0.106822) | 0.030937 / 0.128546 (-0.097609) | 0.008838 / 0.075646 (-0.066808) | 0.287488 / 0.419271 (-0.131784) | 0.051503 / 0.043533 (0.007971) | 0.353945 / 0.255139 (0.098806) | 0.388778 / 0.283200 (0.105579) | 0.023346 / 0.141683 (-0.118337) | 1.479621 / 1.452155 (0.027466) | 1.559164 / 1.492716 (0.066448) |\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) | 0.245160 / 0.018006 (0.227154) | 0.561890 / 0.000490 (0.561400) | 0.004339 / 0.000200 (0.004139) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028460 / 0.037411 (-0.008952) | 0.082046 / 0.014526 (0.067520) | 0.098005 / 0.176557 (-0.078552) | 0.154171 / 0.737135 (-0.582965) | 0.097632 / 0.296338 (-0.198707) |\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) | 0.389993 / 0.215209 (0.174784) | 3.893287 / 2.077655 (1.815632) | 1.885668 / 1.504120 (0.381549) | 1.715055 / 1.541195 (0.173860) | 1.778008 / 1.468490 (0.309518) | 0.482818 / 4.584777 (-4.101959) | 3.572153 / 3.745712 (-0.173559) | 3.267666 / 5.269862 (-2.002196) | 2.088394 / 4.565676 (-2.477282) | 0.056961 / 0.424275 (-0.367314) | 0.007784 / 0.007607 (0.000177) | 0.466586 / 0.226044 (0.240542) | 4.652505 / 2.268929 (2.383576) | 2.491392 / 55.444624 (-52.953233) | 2.127600 / 6.876477 (-4.748877) | 2.296778 / 2.142072 (0.154705) | 0.582332 / 4.805227 (-4.222895) | 0.134372 / 6.500664 (-6.366292) | 0.061737 / 0.075469 (-0.013732) |\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) | 1.253647 / 1.841788 (-0.588140) | 19.802353 / 8.074308 (11.728045) | 14.262815 / 10.191392 (4.071423) | 0.169489 / 0.680424 (-0.510935) | 0.018108 / 0.534201 (-0.516093) | 0.391711 / 0.579283 (-0.187572) | 0.406169 / 0.434364 (-0.028195) | 0.456728 / 0.540337 (-0.083609) | 0.633538 / 1.386936 (-0.753398) |\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) | 0.006661 / 0.011353 (-0.004692) | 0.004181 / 0.011008 (-0.006827) | 0.064945 / 0.038508 (0.026437) | 0.073965 / 0.023109 (0.050856) | 0.406549 / 0.275898 (0.130651) | 0.441568 / 0.323480 (0.118089) | 0.005579 / 0.007986 (-0.002407) | 0.003523 / 0.004328 (-0.000805) | 0.065270 / 0.004250 (0.061019) | 0.055596 / 0.037052 (0.018544) | 0.407701 / 0.258489 (0.149212) | 0.444609 / 0.293841 (0.150768) | 0.031749 / 0.128546 (-0.096797) | 0.008680 / 0.075646 (-0.066966) | 0.071154 / 0.419271 (-0.348117) | 0.047376 / 0.043533 (0.003843) | 0.406409 / 0.255139 (0.151270) | 0.420477 / 0.283200 (0.137278) | 0.023707 / 0.141683 (-0.117976) | 1.484516 / 1.452155 (0.032361) | 1.568493 / 1.492716 (0.075777) |\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) | 0.266534 / 0.018006 (0.248528) | 0.573806 / 0.000490 (0.573316) | 0.006247 / 0.000200 (0.006048) | 0.000165 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033436 / 0.037411 (-0.003976) | 0.091947 / 0.014526 (0.077421) | 0.105556 / 0.176557 (-0.071000) | 0.162094 / 0.737135 (-0.575041) | 0.107879 / 0.296338 (-0.188459) |\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) | 0.429126 / 0.215209 (0.213917) | 4.281329 / 2.077655 (2.203675) | 2.295406 / 1.504120 (0.791286) | 2.123336 / 1.541195 (0.582141) | 2.190804 / 1.468490 (0.722314) | 0.492972 / 4.584777 (-4.091805) | 3.638485 / 3.745712 (-0.107227) | 3.304576 / 5.269862 (-1.965285) | 2.063694 / 4.565676 (-2.501983) | 0.058549 / 0.424275 (-0.365726) | 0.007591 / 0.007607 (-0.000016) | 0.504268 / 0.226044 (0.278223) | 5.031990 / 2.268929 (2.763061) | 2.773173 / 55.444624 (-52.671451) | 2.430789 / 6.876477 (-4.445688) | 2.699900 / 2.142072 (0.557828) | 0.593220 / 4.805227 (-4.212007) | 0.133710 / 6.500664 (-6.366954) | 0.059840 / 0.075469 (-0.015629) |\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) | 1.351158 / 1.841788 (-0.490629) | 20.176310 / 8.074308 (12.102002) | 14.933202 / 10.191392 (4.741810) | 0.169920 / 0.680424 (-0.510503) | 0.020156 / 0.534201 (-0.514045) | 0.397440 / 0.579283 (-0.181843) | 0.409395 / 0.434364 (-0.024969) | 0.471066 / 0.540337 (-0.069271) | 0.642670 / 1.386936 (-0.744266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf90ca7fbfd9c4639cc3faf0a349eb26490e38fc \"CML watermark\")\n" ]
2023-09-07T14:43:51
2023-09-07T15:46:10
2023-09-07T15:37:20
CONTRIBUTOR
false
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`save_to_disk` creates this file, but also [`HugginFaceDatasetSever`](https://github.com/gradio-app/gradio/blob/26fef8c7f85a006c7e25cdbed1792df19c512d02/gradio/flagging.py#L214), so this is needed to avoid issues such as [this one](https://discord.com/channels/879548962464493619/1149295819938349107/1149295819938349107).
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Update README.md
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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) | 0.006757 / 0.011353 (-0.004596) | 0.004233 / 0.011008 (-0.006775) | 0.084123 / 0.038508 (0.045614) | 0.077513 / 0.023109 (0.054404) | 0.357024 / 0.275898 (0.081126) | 0.392956 / 0.323480 (0.069476) | 0.005408 / 0.007986 (-0.002577) | 0.003363 / 0.004328 (-0.000966) | 0.064395 / 0.004250 (0.060145) | 0.054711 / 0.037052 (0.017659) | 0.367287 / 0.258489 (0.108798) | 0.402934 / 0.293841 (0.109093) | 0.031845 / 0.128546 (-0.096701) | 0.008646 / 0.075646 (-0.067000) | 0.288740 / 0.419271 (-0.130531) | 0.053171 / 0.043533 (0.009638) | 0.360711 / 0.255139 (0.105572) | 0.388707 / 0.283200 (0.105507) | 0.025321 / 0.141683 (-0.116361) | 1.500684 / 1.452155 (0.048529) | 1.585747 / 1.492716 (0.093030) |\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) | 0.207329 / 0.018006 (0.189323) | 0.465304 / 0.000490 (0.464814) | 0.003229 / 0.000200 (0.003029) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028752 / 0.037411 (-0.008659) | 0.085327 / 0.014526 (0.070802) | 0.332210 / 0.176557 (0.155653) | 0.178779 / 0.737135 (-0.558356) | 0.097765 / 0.296338 (-0.198573) |\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) | 0.403710 / 0.215209 (0.188501) | 4.027069 / 2.077655 (1.949414) | 2.053451 / 1.504120 (0.549331) | 1.906647 / 1.541195 (0.365452) | 1.992507 / 1.468490 (0.524017) | 0.490203 / 4.584777 (-4.094574) | 3.696569 / 3.745712 (-0.049143) | 3.319919 / 5.269862 (-1.949943) | 2.072794 / 4.565676 (-2.492883) | 0.057893 / 0.424275 (-0.366383) | 0.007723 / 0.007607 (0.000116) | 0.485400 / 0.226044 (0.259355) | 4.842891 / 2.268929 (2.573963) | 2.578949 / 55.444624 (-52.865675) | 2.229217 / 6.876477 (-4.647259) | 2.468017 / 2.142072 (0.325945) | 0.595236 / 4.805227 (-4.209992) | 0.135641 / 6.500664 (-6.365023) | 0.061232 / 0.075469 (-0.014237) |\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) | 1.307059 / 1.841788 (-0.534729) | 20.108581 / 8.074308 (12.034273) | 14.438985 / 10.191392 (4.247593) | 0.168878 / 0.680424 (-0.511545) | 0.018208 / 0.534201 (-0.515993) | 0.395986 / 0.579283 (-0.183297) | 0.427440 / 0.434364 (-0.006924) | 0.459917 / 0.540337 (-0.080421) | 0.631379 / 1.386936 (-0.755557) |\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) | 0.007002 / 0.011353 (-0.004351) | 0.004120 / 0.011008 (-0.006888) | 0.064817 / 0.038508 (0.026309) | 0.081297 / 0.023109 (0.058188) | 0.405598 / 0.275898 (0.129700) | 0.442360 / 0.323480 (0.118880) | 0.005475 / 0.007986 (-0.002511) | 0.003483 / 0.004328 (-0.000845) | 0.064750 / 0.004250 (0.060499) | 0.058111 / 0.037052 (0.021059) | 0.410154 / 0.258489 (0.151665) | 0.445137 / 0.293841 (0.151296) | 0.033314 / 0.128546 (-0.095232) | 0.008747 / 0.075646 (-0.066899) | 0.071595 / 0.419271 (-0.347676) | 0.048894 / 0.043533 (0.005361) | 0.409162 / 0.255139 (0.154023) | 0.428877 / 0.283200 (0.145677) | 0.024127 / 0.141683 (-0.117556) | 1.521369 / 1.452155 (0.069214) | 1.573505 / 1.492716 (0.080789) |\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) | 0.233199 / 0.018006 (0.215193) | 0.455619 / 0.000490 (0.455129) | 0.003688 / 0.000200 (0.003488) | 0.000081 / 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) | 0.033186 / 0.037411 (-0.004225) | 0.100528 / 0.014526 (0.086003) | 0.105617 / 0.176557 (-0.070940) | 0.159437 / 0.737135 (-0.577698) | 0.108064 / 0.296338 (-0.188274) |\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) | 0.435509 / 0.215209 (0.220300) | 4.339920 / 2.077655 (2.262265) | 2.368983 / 1.504120 (0.864863) | 2.211761 / 1.541195 (0.670566) | 2.301701 / 1.468490 (0.833211) | 0.495144 / 4.584777 (-4.089633) | 3.768882 / 3.745712 (0.023170) | 3.348940 / 5.269862 (-1.920922) | 2.081142 / 4.565676 (-2.484534) | 0.058184 / 0.424275 (-0.366091) | 0.007597 / 0.007607 (-0.000010) | 0.508806 / 0.226044 (0.282762) | 5.089226 / 2.268929 (2.820297) | 2.851930 / 55.444624 (-52.592694) | 2.512144 / 6.876477 (-4.364332) | 2.724461 / 2.142072 (0.582388) | 0.593446 / 4.805227 (-4.211781) | 0.134908 / 6.500664 (-6.365756) | 0.060811 / 0.075469 (-0.014658) |\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) | 1.362279 / 1.841788 (-0.479508) | 20.548216 / 8.074308 (12.473908) | 15.179181 / 10.191392 (4.987789) | 0.170249 / 0.680424 (-0.510175) | 0.020772 / 0.534201 (-0.513429) | 0.398737 / 0.579283 (-0.180546) | 0.441487 / 0.434364 (0.007124) | 0.480096 / 0.540337 (-0.060242) | 0.645825 / 1.386936 (-0.741111) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6fb8b9a833afb25311da395c6e0d9bf770ca2c7 \"CML watermark\")\n" ]
2023-09-07T11:33:20
2023-09-13T22:32:31
2023-09-13T22:23:42
CONTRIBUTOR
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fixed a few typos
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fix typo in Audio dataset documentation
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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) | 0.006655 / 0.011353 (-0.004698) | 0.004115 / 0.011008 (-0.006893) | 0.083895 / 0.038508 (0.045387) | 0.072770 / 0.023109 (0.049661) | 0.311401 / 0.275898 (0.035503) | 0.341079 / 0.323480 (0.017599) | 0.005488 / 0.007986 (-0.002497) | 0.003530 / 0.004328 (-0.000799) | 0.064691 / 0.004250 (0.060441) | 0.053096 / 0.037052 (0.016044) | 0.314969 / 0.258489 (0.056480) | 0.358245 / 0.293841 (0.064404) | 0.030789 / 0.128546 (-0.097757) | 0.008868 / 0.075646 (-0.066779) | 0.288022 / 0.419271 (-0.131249) | 0.052092 / 0.043533 (0.008559) | 0.310061 / 0.255139 (0.054922) | 0.345369 / 0.283200 (0.062170) | 0.024100 / 0.141683 (-0.117582) | 1.520573 / 1.452155 (0.068418) | 1.593750 / 1.492716 (0.101033) |\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) | 0.242520 / 0.018006 (0.224514) | 0.567963 / 0.000490 (0.567473) | 0.003183 / 0.000200 (0.002983) | 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) | 0.029473 / 0.037411 (-0.007939) | 0.083012 / 0.014526 (0.068486) | 0.262386 / 0.176557 (0.085830) | 0.155131 / 0.737135 (-0.582004) | 0.099880 / 0.296338 (-0.196458) |\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) | 0.382388 / 0.215209 (0.167179) | 3.816538 / 2.077655 (1.738884) | 1.863422 / 1.504120 (0.359302) | 1.694652 / 1.541195 (0.153457) | 1.738738 / 1.468490 (0.270248) | 0.477073 / 4.584777 (-4.107704) | 3.539244 / 3.745712 (-0.206468) | 3.238469 / 5.269862 (-2.031392) | 2.026154 / 4.565676 (-2.539523) | 0.056111 / 0.424275 (-0.368164) | 0.007615 / 0.007607 (0.000008) | 0.460620 / 0.226044 (0.234576) | 4.596383 / 2.268929 (2.327455) | 2.348645 / 55.444624 (-53.095979) | 1.977465 / 6.876477 (-4.899011) | 2.222828 / 2.142072 (0.080755) | 0.588065 / 4.805227 (-4.217162) | 0.132175 / 6.500664 (-6.368489) | 0.061322 / 0.075469 (-0.014147) |\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) | 1.260623 / 1.841788 (-0.581164) | 19.976475 / 8.074308 (11.902167) | 14.346488 / 10.191392 (4.155096) | 0.145614 / 0.680424 (-0.534810) | 0.018309 / 0.534201 (-0.515892) | 0.393644 / 0.579283 (-0.185639) | 0.405355 / 0.434364 (-0.029009) | 0.458355 / 0.540337 (-0.081982) | 0.630147 / 1.386936 (-0.756789) |\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) | 0.006769 / 0.011353 (-0.004584) | 0.004172 / 0.011008 (-0.006836) | 0.064863 / 0.038508 (0.026355) | 0.076831 / 0.023109 (0.053722) | 0.419391 / 0.275898 (0.143493) | 0.439912 / 0.323480 (0.116432) | 0.006249 / 0.007986 (-0.001737) | 0.003571 / 0.004328 (-0.000757) | 0.064877 / 0.004250 (0.060626) | 0.056023 / 0.037052 (0.018971) | 0.419899 / 0.258489 (0.161410) | 0.459334 / 0.293841 (0.165493) | 0.032217 / 0.128546 (-0.096329) | 0.008628 / 0.075646 (-0.067019) | 0.071089 / 0.419271 (-0.348183) | 0.047463 / 0.043533 (0.003930) | 0.414961 / 0.255139 (0.159822) | 0.431408 / 0.283200 (0.148209) | 0.022406 / 0.141683 (-0.119277) | 1.511890 / 1.452155 (0.059735) | 1.580268 / 1.492716 (0.087551) |\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) | 0.280805 / 0.018006 (0.262799) | 0.553766 / 0.000490 (0.553276) | 0.006155 / 0.000200 (0.005955) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032980 / 0.037411 (-0.004431) | 0.092981 / 0.014526 (0.078456) | 0.108820 / 0.176557 (-0.067737) | 0.161709 / 0.737135 (-0.575426) | 0.109772 / 0.296338 (-0.186566) |\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) | 0.433659 / 0.215209 (0.218450) | 4.328577 / 2.077655 (2.250923) | 2.316899 / 1.504120 (0.812779) | 2.142645 / 1.541195 (0.601451) | 2.245518 / 1.468490 (0.777028) | 0.489448 / 4.584777 (-4.095329) | 3.630074 / 3.745712 (-0.115638) | 3.322749 / 5.269862 (-1.947112) | 2.062307 / 4.565676 (-2.503370) | 0.058153 / 0.424275 (-0.366122) | 0.007453 / 0.007607 (-0.000154) | 0.507234 / 0.226044 (0.281190) | 5.071830 / 2.268929 (2.802902) | 2.839374 / 55.444624 (-52.605250) | 2.429583 / 6.876477 (-4.446893) | 2.671940 / 2.142072 (0.529868) | 0.588256 / 4.805227 (-4.216972) | 0.135135 / 6.500664 (-6.365530) | 0.060963 / 0.075469 (-0.014506) |\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) | 1.337462 / 1.841788 (-0.504326) | 20.292912 / 8.074308 (12.218604) | 14.871809 / 10.191392 (4.680417) | 0.169214 / 0.680424 (-0.511209) | 0.020450 / 0.534201 (-0.513751) | 0.397094 / 0.579283 (-0.182189) | 0.411623 / 0.434364 (-0.022741) | 0.471560 / 0.540337 (-0.068777) | 0.647293 / 1.386936 (-0.739643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a068dbf3b446417ffd89d32857608394ec699e6 \"CML watermark\")\n" ]
2023-09-06T23:17:24
2023-10-03T14:18:41
2023-09-07T15:39:09
CONTRIBUTOR
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There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config` https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59
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1,884,324,631
I_kwDODunzps5wUIMX
6,221
Support saving datasets with custom formatting
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[ "Not a fan of pickling this sort of stuff either.\r\nNote that users can also share the code in their dataset documentation." ]
2023-09-06T16:03:32
2023-09-06T18:32:07
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CONTRIBUTOR
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Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036. I am not sure if supporting this is the best idea for the following reasons: >For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea. @lhoestq WDYT?
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Set dev version
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6220). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.005950 / 0.011353 (-0.005403) | 0.003578 / 0.011008 (-0.007431) | 0.079327 / 0.038508 (0.040819) | 0.057862 / 0.023109 (0.034752) | 0.317288 / 0.275898 (0.041390) | 0.358210 / 0.323480 (0.034730) | 0.004685 / 0.007986 (-0.003301) | 0.002879 / 0.004328 (-0.001450) | 0.062355 / 0.004250 (0.058105) | 0.045093 / 0.037052 (0.008041) | 0.322520 / 0.258489 (0.064031) | 0.367114 / 0.293841 (0.073273) | 0.027233 / 0.128546 (-0.101313) | 0.007941 / 0.075646 (-0.067705) | 0.260511 / 0.419271 (-0.158761) | 0.044355 / 0.043533 (0.000822) | 0.332993 / 0.255139 (0.077854) | 0.351363 / 0.283200 (0.068163) | 0.020784 / 0.141683 (-0.120899) | 1.429044 / 1.452155 (-0.023111) | 1.489355 / 1.492716 (-0.003362) |\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) | 0.180903 / 0.018006 (0.162897) | 0.421566 / 0.000490 (0.421077) | 0.003259 / 0.000200 (0.003059) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023765 / 0.037411 (-0.013646) | 0.072815 / 0.014526 (0.058289) | 0.084592 / 0.176557 (-0.091965) | 0.143556 / 0.737135 (-0.593579) | 0.083591 / 0.296338 (-0.212748) |\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) | 0.401896 / 0.215209 (0.186687) | 4.006344 / 2.077655 (1.928689) | 2.092280 / 1.504120 (0.588160) | 1.937828 / 1.541195 (0.396633) | 2.026901 / 1.468490 (0.558411) | 0.499999 / 4.584777 (-4.084778) | 3.008715 / 3.745712 (-0.736997) | 2.789735 / 5.269862 (-2.480127) | 1.827319 / 4.565676 (-2.738358) | 0.057413 / 0.424275 (-0.366862) | 0.006716 / 0.007607 (-0.000891) | 0.473061 / 0.226044 (0.247016) | 4.733256 / 2.268929 (2.464327) | 2.403922 / 55.444624 (-53.040702) | 2.017466 / 6.876477 (-4.859011) | 2.209710 / 2.142072 (0.067638) | 0.590813 / 4.805227 (-4.214414) | 0.124760 / 6.500664 (-6.375904) | 0.060976 / 0.075469 (-0.014494) |\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) | 1.229172 / 1.841788 (-0.612616) | 17.924644 / 8.074308 (9.850336) | 13.697347 / 10.191392 (3.505955) | 0.128258 / 0.680424 (-0.552166) | 0.016780 / 0.534201 (-0.517421) | 0.329301 / 0.579283 (-0.249982) | 0.344527 / 0.434364 (-0.089837) | 0.379482 / 0.540337 (-0.160855) | 0.513851 / 1.386936 (-0.873085) |\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) | 0.005962 / 0.011353 (-0.005391) | 0.003613 / 0.011008 (-0.007396) | 0.062428 / 0.038508 (0.023920) | 0.058151 / 0.023109 (0.035042) | 0.452926 / 0.275898 (0.177027) | 0.489740 / 0.323480 (0.166260) | 0.006137 / 0.007986 (-0.001848) | 0.002890 / 0.004328 (-0.001438) | 0.062880 / 0.004250 (0.058629) | 0.046175 / 0.037052 (0.009123) | 0.452416 / 0.258489 (0.193927) | 0.486047 / 0.293841 (0.192206) | 0.028517 / 0.128546 (-0.100029) | 0.008102 / 0.075646 (-0.067544) | 0.068251 / 0.419271 (-0.351020) | 0.040569 / 0.043533 (-0.002964) | 0.461306 / 0.255139 (0.206167) | 0.477675 / 0.283200 (0.194475) | 0.020944 / 0.141683 (-0.120739) | 1.414300 / 1.452155 (-0.037855) | 1.502108 / 1.492716 (0.009391) |\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) | 0.217786 / 0.018006 (0.199780) | 0.410757 / 0.000490 (0.410267) | 0.002981 / 0.000200 (0.002781) | 0.000081 / 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) | 0.026846 / 0.037411 (-0.010565) | 0.080098 / 0.014526 (0.065572) | 0.090591 / 0.176557 (-0.085965) | 0.144674 / 0.737135 (-0.592461) | 0.091287 / 0.296338 (-0.205052) |\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) | 0.458224 / 0.215209 (0.243015) | 4.590541 / 2.077655 (2.512886) | 2.511251 / 1.504120 (1.007131) | 2.329165 / 1.541195 (0.787970) | 2.379187 / 1.468490 (0.910696) | 0.507485 / 4.584777 (-4.077292) | 3.135011 / 3.745712 (-0.610701) | 2.805913 / 5.269862 (-2.463948) | 1.851382 / 4.565676 (-2.714295) | 0.057981 / 0.424275 (-0.366294) | 0.006557 / 0.007607 (-0.001050) | 0.532496 / 0.226044 (0.306452) | 5.348802 / 2.268929 (3.079874) | 2.993379 / 55.444624 (-52.451245) | 2.636372 / 6.876477 (-4.240104) | 2.753219 / 2.142072 (0.611147) | 0.591989 / 4.805227 (-4.213238) | 0.126691 / 6.500664 (-6.373973) | 0.062359 / 0.075469 (-0.013110) |\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) | 1.345498 / 1.841788 (-0.496290) | 18.335767 / 8.074308 (10.261458) | 15.115449 / 10.191392 (4.924057) | 0.147382 / 0.680424 (-0.533041) | 0.017729 / 0.534201 (-0.516472) | 0.334337 / 0.579283 (-0.244946) | 0.359035 / 0.434364 (-0.075329) | 0.386319 / 0.540337 (-0.154019) | 0.536378 / 1.386936 (-0.850558) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b028fd83d74e7701e7b8f2d87e740a989505a7 \"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) | 0.009136 / 0.011353 (-0.002216) | 0.005567 / 0.011008 (-0.005442) | 0.120320 / 0.038508 (0.081812) | 0.078082 / 0.023109 (0.054973) | 0.405579 / 0.275898 (0.129681) | 0.459714 / 0.323480 (0.136234) | 0.006327 / 0.007986 (-0.001659) | 0.007187 / 0.004328 (0.002859) | 0.084373 / 0.004250 (0.080122) | 0.059727 / 0.037052 (0.022675) | 0.418918 / 0.258489 (0.160429) | 0.486767 / 0.293841 (0.192927) | 0.047715 / 0.128546 (-0.080831) | 0.014417 / 0.075646 (-0.061229) | 0.379847 / 0.419271 (-0.039425) | 0.067472 / 0.043533 (0.023939) | 0.419304 / 0.255139 (0.164166) | 0.466260 / 0.283200 (0.183060) | 0.036872 / 0.141683 (-0.104811) | 1.876273 / 1.452155 (0.424119) | 2.043856 / 1.492716 (0.551140) |\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) | 0.296266 / 0.018006 (0.278260) | 0.601843 / 0.000490 (0.601354) | 0.005663 / 0.000200 (0.005463) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033272 / 0.037411 (-0.004139) | 0.098839 / 0.014526 (0.084313) | 0.124658 / 0.176557 (-0.051899) | 0.190226 / 0.737135 (-0.546909) | 0.119288 / 0.296338 (-0.177051) |\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) | 0.600878 / 0.215209 (0.385668) | 6.011749 / 2.077655 (3.934095) | 2.611809 / 1.504120 (1.107689) | 2.314985 / 1.541195 (0.773790) | 2.398988 / 1.468490 (0.930498) | 0.835577 / 4.584777 (-3.749200) | 5.482848 / 3.745712 (1.737136) | 4.965393 / 5.269862 (-0.304469) | 3.082420 / 4.565676 (-1.483256) | 0.098048 / 0.424275 (-0.326227) | 0.009148 / 0.007607 (0.001541) | 0.725721 / 0.226044 (0.499676) | 7.297429 / 2.268929 (5.028501) | 3.558050 / 55.444624 (-51.886575) | 2.815884 / 6.876477 (-4.060593) | 3.094103 / 2.142072 (0.952031) | 1.023617 / 4.805227 (-3.781610) | 0.222453 / 6.500664 (-6.278211) | 0.081707 / 0.075469 (0.006238) |\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) | 1.788327 / 1.841788 (-0.053461) | 25.285829 / 8.074308 (17.211521) | 21.878811 / 10.191392 (11.687419) | 0.215494 / 0.680424 (-0.464930) | 0.032050 / 0.534201 (-0.502151) | 0.505210 / 0.579283 (-0.074073) | 0.623545 / 0.434364 (0.189181) | 0.583342 / 0.540337 (0.043005) | 0.826497 / 1.386936 (-0.560439) |\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) | 0.009640 / 0.011353 (-0.001713) | 0.005479 / 0.011008 (-0.005529) | 0.088940 / 0.038508 (0.050432) | 0.084186 / 0.023109 (0.061077) | 0.552290 / 0.275898 (0.276392) | 0.583296 / 0.323480 (0.259816) | 0.006999 / 0.007986 (-0.000987) | 0.004597 / 0.004328 (0.000269) | 0.089407 / 0.004250 (0.085157) | 0.067210 / 0.037052 (0.030157) | 0.554968 / 0.258489 (0.296479) | 0.595635 / 0.293841 (0.301794) | 0.052245 / 0.128546 (-0.076301) | 0.015914 / 0.075646 (-0.059733) | 0.097037 / 0.419271 (-0.322235) | 0.063954 / 0.043533 (0.020421) | 0.533752 / 0.255139 (0.278614) | 0.573789 / 0.283200 (0.290589) | 0.036526 / 0.141683 (-0.105157) | 1.867713 / 1.452155 (0.415558) | 1.996901 / 1.492716 (0.504185) |\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) | 0.414967 / 0.018006 (0.396961) | 0.632367 / 0.000490 (0.631877) | 0.064061 / 0.000200 (0.063861) | 0.000565 / 0.000054 (0.000510) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035953 / 0.037411 (-0.001458) | 0.112603 / 0.014526 (0.098077) | 0.126227 / 0.176557 (-0.050330) | 0.196881 / 0.737135 (-0.540255) | 0.127635 / 0.296338 (-0.168704) |\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) | 0.674735 / 0.215209 (0.459526) | 6.614578 / 2.077655 (4.536923) | 3.208198 / 1.504120 (1.704078) | 2.870412 / 1.541195 (1.329217) | 2.979358 / 1.468490 (1.510868) | 0.872589 / 4.584777 (-3.712187) | 5.501771 / 3.745712 (1.756059) | 4.865191 / 5.269862 (-0.404671) | 3.075281 / 4.565676 (-1.490396) | 0.098048 / 0.424275 (-0.326227) | 0.009121 / 0.007607 (0.001514) | 0.801639 / 0.226044 (0.575595) | 8.062040 / 2.268929 (5.793111) | 3.996693 / 55.444624 (-51.447931) | 3.343770 / 6.876477 (-3.532706) | 3.555977 / 2.142072 (1.413904) | 1.035050 / 4.805227 (-3.770177) | 0.227552 / 6.500664 (-6.273112) | 0.097733 / 0.075469 (0.022264) |\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) | 1.897210 / 1.841788 (0.055422) | 25.762459 / 8.074308 (17.688151) | 22.771290 / 10.191392 (12.579898) | 0.252650 / 0.680424 (-0.427773) | 0.032534 / 0.534201 (-0.501667) | 0.521047 / 0.579283 (-0.058236) | 0.620850 / 0.434364 (0.186486) | 0.612750 / 0.540337 (0.072413) | 0.837486 / 1.386936 (-0.549451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f522e5bdd73c45f7ba0a03f2ecd4e7de7351f2e \"CML watermark\")\n" ]
2023-09-06T15:40:33
2023-09-06T15:52:33
2023-09-06T15:41:13
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6219). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.009523 / 0.011353 (-0.001830) | 0.005105 / 0.011008 (-0.005903) | 0.122664 / 0.038508 (0.084156) | 0.084688 / 0.023109 (0.061579) | 0.412057 / 0.275898 (0.136159) | 0.449690 / 0.323480 (0.126210) | 0.006627 / 0.007986 (-0.001358) | 0.004150 / 0.004328 (-0.000178) | 0.082079 / 0.004250 (0.077829) | 0.065289 / 0.037052 (0.028237) | 0.432934 / 0.258489 (0.174445) | 0.492068 / 0.293841 (0.198227) | 0.048317 / 0.128546 (-0.080229) | 0.015582 / 0.075646 (-0.060064) | 0.372050 / 0.419271 (-0.047222) | 0.070649 / 0.043533 (0.027116) | 0.431754 / 0.255139 (0.176615) | 0.473349 / 0.283200 (0.190149) | 0.037293 / 0.141683 (-0.104390) | 1.807537 / 1.452155 (0.355382) | 1.923073 / 1.492716 (0.430357) |\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) | 0.271214 / 0.018006 (0.253208) | 0.592961 / 0.000490 (0.592471) | 0.004062 / 0.000200 (0.003862) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034766 / 0.037411 (-0.002645) | 0.093014 / 0.014526 (0.078488) | 0.131332 / 0.176557 (-0.045225) | 0.188110 / 0.737135 (-0.549025) | 0.117617 / 0.296338 (-0.178722) |\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) | 0.668223 / 0.215209 (0.453013) | 6.707031 / 2.077655 (4.629376) | 3.040178 / 1.504120 (1.536058) | 2.641776 / 1.541195 (1.100581) | 2.524057 / 1.468490 (1.055567) | 0.893592 / 4.584777 (-3.691185) | 5.535848 / 3.745712 (1.790136) | 4.867067 / 5.269862 (-0.402794) | 2.999933 / 4.565676 (-1.565743) | 0.103602 / 0.424275 (-0.320673) | 0.008887 / 0.007607 (0.001280) | 0.822214 / 0.226044 (0.596169) | 8.028476 / 2.268929 (5.759547) | 3.708895 / 55.444624 (-51.735730) | 2.858314 / 6.876477 (-4.018163) | 3.101727 / 2.142072 (0.959655) | 1.083136 / 4.805227 (-3.722091) | 0.219588 / 6.500664 (-6.281076) | 0.080151 / 0.075469 (0.004682) |\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) | 1.645819 / 1.841788 (-0.195969) | 24.407887 / 8.074308 (16.333579) | 22.371901 / 10.191392 (12.180509) | 0.219557 / 0.680424 (-0.460867) | 0.037867 / 0.534201 (-0.496334) | 0.484136 / 0.579283 (-0.095147) | 0.620546 / 0.434364 (0.186182) | 0.562272 / 0.540337 (0.021934) | 0.774256 / 1.386936 (-0.612680) |\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) | 0.009381 / 0.011353 (-0.001972) | 0.005565 / 0.011008 (-0.005444) | 0.091057 / 0.038508 (0.052549) | 0.078085 / 0.023109 (0.054975) | 0.538929 / 0.275898 (0.263031) | 0.555155 / 0.323480 (0.231675) | 0.007007 / 0.007986 (-0.000978) | 0.004268 / 0.004328 (-0.000060) | 0.086618 / 0.004250 (0.082368) | 0.064117 / 0.037052 (0.027065) | 0.523788 / 0.258489 (0.265299) | 0.586451 / 0.293841 (0.292610) | 0.050804 / 0.128546 (-0.077742) | 0.013964 / 0.075646 (-0.061682) | 0.096008 / 0.419271 (-0.323263) | 0.062242 / 0.043533 (0.018709) | 0.530398 / 0.255139 (0.275259) | 0.568527 / 0.283200 (0.285327) | 0.032456 / 0.141683 (-0.109227) | 1.894975 / 1.452155 (0.442820) | 2.084172 / 1.492716 (0.591455) |\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) | 0.295539 / 0.018006 (0.277533) | 0.588804 / 0.000490 (0.588314) | 0.006445 / 0.000200 (0.006245) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033965 / 0.037411 (-0.003447) | 0.111743 / 0.014526 (0.097217) | 0.128805 / 0.176557 (-0.047752) | 0.185013 / 0.737135 (-0.552123) | 0.129400 / 0.296338 (-0.166938) |\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) | 0.749784 / 0.215209 (0.534575) | 7.091075 / 2.077655 (5.013420) | 3.424517 / 1.504120 (1.920397) | 3.069103 / 1.541195 (1.527908) | 3.122431 / 1.468490 (1.653941) | 0.949277 / 4.584777 (-3.635500) | 5.648731 / 3.745712 (1.903019) | 4.937684 / 5.269862 (-0.332178) | 3.198027 / 4.565676 (-1.367650) | 0.100289 / 0.424275 (-0.323987) | 0.009411 / 0.007607 (0.001803) | 0.862604 / 0.226044 (0.636559) | 8.615410 / 2.268929 (6.346482) | 4.306428 / 55.444624 (-51.138196) | 3.591404 / 6.876477 (-3.285073) | 3.823899 / 2.142072 (1.681827) | 1.108006 / 4.805227 (-3.697221) | 0.215330 / 6.500664 (-6.285334) | 0.080755 / 0.075469 (0.005286) |\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) | 1.774914 / 1.841788 (-0.066873) | 25.360983 / 8.074308 (17.286675) | 23.624044 / 10.191392 (13.432652) | 0.226887 / 0.680424 (-0.453537) | 0.032625 / 0.534201 (-0.501576) | 0.499730 / 0.579283 (-0.079553) | 0.647819 / 0.434364 (0.213455) | 0.592239 / 0.540337 (0.051901) | 0.805751 / 1.386936 (-0.581185) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0daa82428a0529478801574bcc68e1ed32051f3a \"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) | 0.008656 / 0.011353 (-0.002697) | 0.005545 / 0.011008 (-0.005463) | 0.107936 / 0.038508 (0.069428) | 0.077436 / 0.023109 (0.054327) | 0.391412 / 0.275898 (0.115514) | 0.452811 / 0.323480 (0.129331) | 0.004883 / 0.007986 (-0.003103) | 0.005125 / 0.004328 (0.000796) | 0.080006 / 0.004250 (0.075755) | 0.054425 / 0.037052 (0.017373) | 0.399667 / 0.258489 (0.141178) | 0.458099 / 0.293841 (0.164258) | 0.047302 / 0.128546 (-0.081244) | 0.014153 / 0.075646 (-0.061493) | 0.337281 / 0.419271 (-0.081991) | 0.062153 / 0.043533 (0.018620) | 0.399927 / 0.255139 (0.144788) | 0.407186 / 0.283200 (0.123987) | 0.036759 / 0.141683 (-0.104924) | 1.825935 / 1.452155 (0.373780) | 1.852238 / 1.492716 (0.359522) |\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) | 0.274163 / 0.018006 (0.256157) | 0.615624 / 0.000490 (0.615134) | 0.003782 / 0.000200 (0.003582) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011026) | 0.101151 / 0.014526 (0.086625) | 0.106115 / 0.176557 (-0.070442) | 0.161253 / 0.737135 (-0.575882) | 0.108861 / 0.296338 (-0.187478) |\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) | 0.587079 / 0.215209 (0.371870) | 6.141743 / 2.077655 (4.064089) | 2.727199 / 1.504120 (1.223079) | 2.526827 / 1.541195 (0.985632) | 2.598321 / 1.468490 (1.129831) | 0.904706 / 4.584777 (-3.680071) | 5.227742 / 3.745712 (1.482030) | 4.621627 / 5.269862 (-0.648234) | 2.931792 / 4.565676 (-1.633885) | 0.089538 / 0.424275 (-0.334737) | 0.008281 / 0.007607 (0.000674) | 0.675773 / 0.226044 (0.449729) | 7.212869 / 2.268929 (4.943941) | 3.541569 / 55.444624 (-51.903056) | 2.804034 / 6.876477 (-4.072443) | 3.080192 / 2.142072 (0.938120) | 1.034577 / 4.805227 (-3.770650) | 0.218727 / 6.500664 (-6.281937) | 0.084548 / 0.075469 (0.009079) |\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) | 1.528974 / 1.841788 (-0.312814) | 21.754329 / 8.074308 (13.680021) | 20.359808 / 10.191392 (10.168416) | 0.234719 / 0.680424 (-0.445705) | 0.026182 / 0.534201 (-0.508019) | 0.448956 / 0.579283 (-0.130327) | 0.577015 / 0.434364 (0.142651) | 0.513675 / 0.540337 (-0.026662) | 0.729780 / 1.386936 (-0.657156) |\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) | 0.010427 / 0.011353 (-0.000926) | 0.005126 / 0.011008 (-0.005882) | 0.082759 / 0.038508 (0.044251) | 0.084892 / 0.023109 (0.061783) | 0.543826 / 0.275898 (0.267927) | 0.603050 / 0.323480 (0.279570) | 0.006667 / 0.007986 (-0.001319) | 0.004036 / 0.004328 (-0.000292) | 0.079534 / 0.004250 (0.075283) | 0.067523 / 0.037052 (0.030471) | 0.544845 / 0.258489 (0.286356) | 0.578823 / 0.293841 (0.284982) | 0.054786 / 0.128546 (-0.073760) | 0.014888 / 0.075646 (-0.060759) | 0.095696 / 0.419271 (-0.323576) | 0.064908 / 0.043533 (0.021375) | 0.558087 / 0.255139 (0.302948) | 0.593919 / 0.283200 (0.310719) | 0.039190 / 0.141683 (-0.102493) | 1.828680 / 1.452155 (0.376526) | 1.908891 / 1.492716 (0.416174) |\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) | 0.298926 / 0.018006 (0.280920) | 0.589467 / 0.000490 (0.588977) | 0.005276 / 0.000200 (0.005076) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034300 / 0.037411 (-0.003111) | 0.096990 / 0.014526 (0.082464) | 0.109347 / 0.176557 (-0.067209) | 0.171312 / 0.737135 (-0.565823) | 0.121736 / 0.296338 (-0.174603) |\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) | 0.641619 / 0.215209 (0.426410) | 6.365556 / 2.077655 (4.287901) | 2.947989 / 1.504120 (1.443869) | 2.631680 / 1.541195 (1.090485) | 2.602762 / 1.468490 (1.134272) | 0.812767 / 4.584777 (-3.772010) | 5.185753 / 3.745712 (1.440041) | 4.589897 / 5.269862 (-0.679964) | 2.833020 / 4.565676 (-1.732656) | 0.097782 / 0.424275 (-0.326493) | 0.008625 / 0.007607 (0.001018) | 0.741613 / 0.226044 (0.515568) | 7.662905 / 2.268929 (5.393976) | 3.533753 / 55.444624 (-51.910871) | 2.898929 / 6.876477 (-3.977547) | 3.042616 / 2.142072 (0.900544) | 0.933932 / 4.805227 (-3.871296) | 0.195710 / 6.500664 (-6.304954) | 0.066954 / 0.075469 (-0.008515) |\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) | 1.745353 / 1.841788 (-0.096434) | 23.820840 / 8.074308 (15.746532) | 20.892645 / 10.191392 (10.701253) | 0.234853 / 0.680424 (-0.445571) | 0.029149 / 0.534201 (-0.505051) | 0.458953 / 0.579283 (-0.120330) | 0.594278 / 0.434364 (0.159914) | 0.522929 / 0.540337 (-0.017409) | 0.753731 / 1.386936 (-0.633205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"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) | 0.005976 / 0.011353 (-0.005377) | 0.003636 / 0.011008 (-0.007372) | 0.079946 / 0.038508 (0.041437) | 0.060143 / 0.023109 (0.037034) | 0.314752 / 0.275898 (0.038854) | 0.353714 / 0.323480 (0.030234) | 0.004706 / 0.007986 (-0.003280) | 0.002862 / 0.004328 (-0.001466) | 0.061988 / 0.004250 (0.057737) | 0.045907 / 0.037052 (0.008855) | 0.316118 / 0.258489 (0.057629) | 0.358488 / 0.293841 (0.064647) | 0.027377 / 0.128546 (-0.101170) | 0.007970 / 0.075646 (-0.067677) | 0.261677 / 0.419271 (-0.157594) | 0.045289 / 0.043533 (0.001757) | 0.307931 / 0.255139 (0.052792) | 0.341364 / 0.283200 (0.058165) | 0.021021 / 0.141683 (-0.120662) | 1.440002 / 1.452155 (-0.012153) | 1.502904 / 1.492716 (0.010187) |\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) | 0.201746 / 0.018006 (0.183740) | 0.451114 / 0.000490 (0.450624) | 0.003351 / 0.000200 (0.003151) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024233 / 0.037411 (-0.013178) | 0.075042 / 0.014526 (0.060516) | 0.085636 / 0.176557 (-0.090920) | 0.144699 / 0.737135 (-0.592436) | 0.085222 / 0.296338 (-0.211117) |\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) | 0.389464 / 0.215209 (0.174255) | 3.889072 / 2.077655 (1.811417) | 1.908307 / 1.504120 (0.404187) | 1.738914 / 1.541195 (0.197719) | 1.866869 / 1.468490 (0.398379) | 0.500536 / 4.584777 (-4.084240) | 3.050155 / 3.745712 (-0.695557) | 2.832259 / 5.269862 (-2.437602) | 1.886657 / 4.565676 (-2.679020) | 0.059214 / 0.424275 (-0.365062) | 0.006711 / 0.007607 (-0.000896) | 0.467753 / 0.226044 (0.241709) | 4.666939 / 2.268929 (2.398011) | 2.471168 / 55.444624 (-52.973456) | 2.223508 / 6.876477 (-4.652968) | 2.176543 / 2.142072 (0.034470) | 0.593461 / 4.805227 (-4.211766) | 0.126216 / 6.500664 (-6.374448) | 0.061495 / 0.075469 (-0.013974) |\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) | 1.301279 / 1.841788 (-0.540509) | 18.317461 / 8.074308 (10.243153) | 13.877813 / 10.191392 (3.686421) | 0.143510 / 0.680424 (-0.536914) | 0.016826 / 0.534201 (-0.517375) | 0.328735 / 0.579283 (-0.250548) | 0.342272 / 0.434364 (-0.092092) | 0.375768 / 0.540337 (-0.164570) | 0.517600 / 1.386936 (-0.869336) |\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) | 0.006215 / 0.011353 (-0.005138) | 0.003587 / 0.011008 (-0.007422) | 0.062248 / 0.038508 (0.023740) | 0.059830 / 0.023109 (0.036721) | 0.443278 / 0.275898 (0.167380) | 0.481279 / 0.323480 (0.157799) | 0.004773 / 0.007986 (-0.003213) | 0.002870 / 0.004328 (-0.001459) | 0.062730 / 0.004250 (0.058480) | 0.049422 / 0.037052 (0.012369) | 0.444196 / 0.258489 (0.185707) | 0.498614 / 0.293841 (0.204773) | 0.028477 / 0.128546 (-0.100069) | 0.008009 / 0.075646 (-0.067638) | 0.067919 / 0.419271 (-0.351352) | 0.040416 / 0.043533 (-0.003117) | 0.439460 / 0.255139 (0.184321) | 0.470529 / 0.283200 (0.187329) | 0.020767 / 0.141683 (-0.120916) | 1.478223 / 1.452155 (0.026068) | 1.538580 / 1.492716 (0.045863) |\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) | 0.271321 / 0.018006 (0.253315) | 0.456436 / 0.000490 (0.455946) | 0.011817 / 0.000200 (0.011617) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026355 / 0.037411 (-0.011056) | 0.081681 / 0.014526 (0.067155) | 0.091699 / 0.176557 (-0.084858) | 0.146115 / 0.737135 (-0.591021) | 0.094376 / 0.296338 (-0.201963) |\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) | 0.471677 / 0.215209 (0.256468) | 4.702909 / 2.077655 (2.625254) | 2.664882 / 1.504120 (1.160762) | 2.504106 / 1.541195 (0.962911) | 2.573226 / 1.468490 (1.104736) | 0.509679 / 4.584777 (-4.075097) | 3.034970 / 3.745712 (-0.710742) | 2.894704 / 5.269862 (-2.375157) | 1.915148 / 4.565676 (-2.650528) | 0.058312 / 0.424275 (-0.365963) | 0.006615 / 0.007607 (-0.000993) | 0.545339 / 0.226044 (0.319295) | 5.462261 / 2.268929 (3.193332) | 3.101482 / 55.444624 (-52.343143) | 2.755417 / 6.876477 (-4.121060) | 2.931440 / 2.142072 (0.789368) | 0.597521 / 4.805227 (-4.207707) | 0.125676 / 6.500664 (-6.374988) | 0.061798 / 0.075469 (-0.013671) |\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) | 1.356208 / 1.841788 (-0.485579) | 18.912492 / 8.074308 (10.838184) | 14.830128 / 10.191392 (4.638736) | 0.145992 / 0.680424 (-0.534432) | 0.019121 / 0.534201 (-0.515080) | 0.331534 / 0.579283 (-0.247749) | 0.361712 / 0.434364 (-0.072652) | 0.387532 / 0.540337 (-0.152805) | 0.536075 / 1.386936 (-0.850861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n" ]
2023-09-06T15:17:10
2023-09-06T15:46:20
2023-09-06T15:18:51
MEMBER
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Rename old push_to_hub configs to "default" in dataset_infos
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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) | 0.006529 / 0.011353 (-0.004823) | 0.004010 / 0.011008 (-0.006998) | 0.086258 / 0.038508 (0.047750) | 0.073775 / 0.023109 (0.050666) | 0.307573 / 0.275898 (0.031675) | 0.337091 / 0.323480 (0.013611) | 0.004251 / 0.007986 (-0.003735) | 0.003886 / 0.004328 (-0.000443) | 0.068238 / 0.004250 (0.063987) | 0.057000 / 0.037052 (0.019948) | 0.321751 / 0.258489 (0.063262) | 0.359227 / 0.293841 (0.065386) | 0.030841 / 0.128546 (-0.097705) | 0.008569 / 0.075646 (-0.067078) | 0.299523 / 0.419271 (-0.119748) | 0.052563 / 0.043533 (0.009030) | 0.312806 / 0.255139 (0.057667) | 0.342273 / 0.283200 (0.059074) | 0.025725 / 0.141683 (-0.115958) | 1.479263 / 1.452155 (0.027108) | 1.554975 / 1.492716 (0.062259) |\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) | 0.316328 / 0.018006 (0.298322) | 0.598993 / 0.000490 (0.598503) | 0.004548 / 0.000200 (0.004348) | 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) | 0.027399 / 0.037411 (-0.010013) | 0.081683 / 0.014526 (0.067157) | 0.096968 / 0.176557 (-0.079589) | 0.151559 / 0.737135 (-0.585576) | 0.096558 / 0.296338 (-0.199781) |\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) | 0.383117 / 0.215209 (0.167908) | 3.818634 / 2.077655 (1.740979) | 1.878112 / 1.504120 (0.373992) | 1.729031 / 1.541195 (0.187836) | 1.770259 / 1.468490 (0.301769) | 0.484061 / 4.584777 (-4.100716) | 3.596998 / 3.745712 (-0.148715) | 3.246846 / 5.269862 (-2.023016) | 2.019481 / 4.565676 (-2.546195) | 0.057279 / 0.424275 (-0.366996) | 0.007455 / 0.007607 (-0.000152) | 0.465002 / 0.226044 (0.238958) | 4.644669 / 2.268929 (2.375741) | 2.346415 / 55.444624 (-53.098209) | 2.039686 / 6.876477 (-4.836791) | 2.172822 / 2.142072 (0.030750) | 0.582925 / 4.805227 (-4.222302) | 0.134246 / 6.500664 (-6.366418) | 0.060093 / 0.075469 (-0.015376) |\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) | 1.249033 / 1.841788 (-0.592755) | 19.585949 / 8.074308 (11.511641) | 14.100681 / 10.191392 (3.909289) | 0.147138 / 0.680424 (-0.533286) | 0.018307 / 0.534201 (-0.515894) | 0.397939 / 0.579283 (-0.181344) | 0.413916 / 0.434364 (-0.020448) | 0.465688 / 0.540337 (-0.074650) | 0.642140 / 1.386936 (-0.744797) |\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) | 0.006627 / 0.011353 (-0.004726) | 0.004173 / 0.011008 (-0.006835) | 0.063850 / 0.038508 (0.025342) | 0.074733 / 0.023109 (0.051623) | 0.398111 / 0.275898 (0.122213) | 0.426344 / 0.323480 (0.102864) | 0.006261 / 0.007986 (-0.001725) | 0.003507 / 0.004328 (-0.000822) | 0.064511 / 0.004250 (0.060260) | 0.056508 / 0.037052 (0.019456) | 0.401750 / 0.258489 (0.143261) | 0.437081 / 0.293841 (0.143240) | 0.031815 / 0.128546 (-0.096732) | 0.008703 / 0.075646 (-0.066943) | 0.071411 / 0.419271 (-0.347861) | 0.048153 / 0.043533 (0.004620) | 0.399221 / 0.255139 (0.144082) | 0.429312 / 0.283200 (0.146112) | 0.022157 / 0.141683 (-0.119526) | 1.485656 / 1.452155 (0.033502) | 1.550967 / 1.492716 (0.058250) |\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) | 0.330575 / 0.018006 (0.312569) | 0.525553 / 0.000490 (0.525064) | 0.004574 / 0.000200 (0.004374) | 0.000093 / 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) | 0.031871 / 0.037411 (-0.005541) | 0.091819 / 0.014526 (0.077293) | 0.105542 / 0.176557 (-0.071015) | 0.158210 / 0.737135 (-0.578926) | 0.107167 / 0.296338 (-0.189172) |\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) | 0.430226 / 0.215209 (0.215017) | 4.293456 / 2.077655 (2.215801) | 2.289538 / 1.504120 (0.785418) | 2.122255 / 1.541195 (0.581060) | 2.181840 / 1.468490 (0.713350) | 0.498529 / 4.584777 (-4.086248) | 3.686636 / 3.745712 (-0.059077) | 3.287279 / 5.269862 (-1.982582) | 2.068397 / 4.565676 (-2.497280) | 0.058775 / 0.424275 (-0.365500) | 0.007583 / 0.007607 (-0.000024) | 0.507165 / 0.226044 (0.281121) | 5.072330 / 2.268929 (2.803401) | 2.796396 / 55.444624 (-52.648228) | 2.409946 / 6.876477 (-4.466531) | 2.657322 / 2.142072 (0.515250) | 0.597744 / 4.805227 (-4.207483) | 0.133803 / 6.500664 (-6.366861) | 0.060231 / 0.075469 (-0.015238) |\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) | 1.333130 / 1.841788 (-0.508658) | 20.545936 / 8.074308 (12.471627) | 14.875020 / 10.191392 (4.683628) | 0.168873 / 0.680424 (-0.511551) | 0.020316 / 0.534201 (-0.513885) | 0.397203 / 0.579283 (-0.182080) | 0.412412 / 0.434364 (-0.021952) | 0.479952 / 0.540337 (-0.060385) | 0.657155 / 1.386936 (-0.729781) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13fbee4ca8742460e9baab86a89d9100a294df3e \"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) | 0.007885 / 0.011353 (-0.003468) | 0.005221 / 0.011008 (-0.005787) | 0.099457 / 0.038508 (0.060949) | 0.085867 / 0.023109 (0.062758) | 0.359922 / 0.275898 (0.084024) | 0.406479 / 0.323480 (0.082999) | 0.005001 / 0.007986 (-0.002985) | 0.003678 / 0.004328 (-0.000650) | 0.075647 / 0.004250 (0.071396) | 0.064318 / 0.037052 (0.027265) | 0.372180 / 0.258489 (0.113691) | 0.419206 / 0.293841 (0.125365) | 0.040438 / 0.128546 (-0.088108) | 0.010008 / 0.075646 (-0.065638) | 0.340911 / 0.419271 (-0.078360) | 0.063326 / 0.043533 (0.019793) | 0.359015 / 0.255139 (0.103876) | 0.408601 / 0.283200 (0.125402) | 0.029828 / 0.141683 (-0.111855) | 1.767822 / 1.452155 (0.315667) | 1.829079 / 1.492716 (0.336363) |\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) | 0.234455 / 0.018006 (0.216449) | 0.507786 / 0.000490 (0.507297) | 0.004009 / 0.000200 (0.003809) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033374 / 0.037411 (-0.004038) | 0.100817 / 0.014526 (0.086291) | 0.113415 / 0.176557 (-0.063141) | 0.180368 / 0.737135 (-0.556768) | 0.115446 / 0.296338 (-0.180893) |\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) | 0.488976 / 0.215209 (0.273767) | 4.911354 / 2.077655 (2.833699) | 2.623525 / 1.504120 (1.119405) | 2.424400 / 1.541195 (0.883206) | 2.497580 / 1.468490 (1.029089) | 0.561106 / 4.584777 (-4.023671) | 4.265649 / 3.745712 (0.519937) | 3.830267 / 5.269862 (-1.439595) | 2.404727 / 4.565676 (-2.160949) | 0.067303 / 0.424275 (-0.356972) | 0.009177 / 0.007607 (0.001570) | 0.588433 / 0.226044 (0.362388) | 5.871573 / 2.268929 (3.602645) | 3.087845 / 55.444624 (-52.356779) | 2.765381 / 6.876477 (-4.111096) | 3.007863 / 2.142072 (0.865791) | 0.687327 / 4.805227 (-4.117901) | 0.157687 / 6.500664 (-6.342977) | 0.071291 / 0.075469 (-0.004178) |\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) | 1.510931 / 1.841788 (-0.330857) | 22.129590 / 8.074308 (14.055282) | 16.780479 / 10.191392 (6.589087) | 0.168297 / 0.680424 (-0.512127) | 0.021294 / 0.534201 (-0.512907) | 0.464535 / 0.579283 (-0.114748) | 0.480041 / 0.434364 (0.045677) | 0.549185 / 0.540337 (0.008848) | 0.739438 / 1.386936 (-0.647498) |\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) | 0.007834 / 0.011353 (-0.003518) | 0.004576 / 0.011008 (-0.006432) | 0.073331 / 0.038508 (0.034823) | 0.084688 / 0.023109 (0.061579) | 0.486367 / 0.275898 (0.210469) | 0.523127 / 0.323480 (0.199647) | 0.006278 / 0.007986 (-0.001708) | 0.003792 / 0.004328 (-0.000537) | 0.075416 / 0.004250 (0.071166) | 0.064053 / 0.037052 (0.027001) | 0.491908 / 0.258489 (0.233419) | 0.529177 / 0.293841 (0.235336) | 0.038483 / 0.128546 (-0.090063) | 0.009560 / 0.075646 (-0.066087) | 0.083431 / 0.419271 (-0.335841) | 0.057114 / 0.043533 (0.013581) | 0.486316 / 0.255139 (0.231177) | 0.512384 / 0.283200 (0.229185) | 0.028452 / 0.141683 (-0.113231) | 1.788886 / 1.452155 (0.336731) | 1.893834 / 1.492716 (0.401118) |\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) | 0.343018 / 0.018006 (0.325011) | 0.513673 / 0.000490 (0.513183) | 0.056778 / 0.000200 (0.056578) | 0.001799 / 0.000054 (0.001745) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038530 / 0.037411 (0.001119) | 0.109286 / 0.014526 (0.094760) | 0.122812 / 0.176557 (-0.053745) | 0.187780 / 0.737135 (-0.549355) | 0.124083 / 0.296338 (-0.172255) |\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) | 0.509839 / 0.215209 (0.294630) | 5.085840 / 2.077655 (3.008186) | 2.746695 / 1.504120 (1.242575) | 2.542283 / 1.541195 (1.001088) | 2.650243 / 1.468490 (1.181753) | 0.592801 / 4.584777 (-3.991976) | 4.316721 / 3.745712 (0.571009) | 3.811672 / 5.269862 (-1.458189) | 2.433982 / 4.565676 (-2.131695) | 0.066861 / 0.424275 (-0.357414) | 0.008633 / 0.007607 (0.001026) | 0.590482 / 0.226044 (0.364437) | 5.923484 / 2.268929 (3.654556) | 3.282293 / 55.444624 (-52.162332) | 2.882716 / 6.876477 (-3.993761) | 3.139581 / 2.142072 (0.997509) | 0.690702 / 4.805227 (-4.114525) | 0.156781 / 6.500664 (-6.343883) | 0.071487 / 0.075469 (-0.003982) |\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) | 1.604557 / 1.841788 (-0.237231) | 24.000026 / 8.074308 (15.925718) | 17.548685 / 10.191392 (7.357293) | 0.174883 / 0.680424 (-0.505541) | 0.023812 / 0.534201 (-0.510389) | 0.473522 / 0.579283 (-0.105761) | 0.494683 / 0.434364 (0.060319) | 0.593352 / 0.540337 (0.053015) | 0.771852 / 1.386936 (-0.615084) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b61c96a806fa97800bc8a66607fb0c78a5d04146 \"CML watermark\")\n", "thanks! i wonder if we should also fix (change config name) all the old `dataset_infos.json` on the Hub?", "<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) | 0.006388 / 0.011353 (-0.004965) | 0.003876 / 0.011008 (-0.007132) | 0.083960 / 0.038508 (0.045452) | 0.068328 / 0.023109 (0.045219) | 0.337958 / 0.275898 (0.062060) | 0.370783 / 0.323480 (0.047303) | 0.003925 / 0.007986 (-0.004060) | 0.004221 / 0.004328 (-0.000107) | 0.064198 / 0.004250 (0.059947) | 0.052681 / 0.037052 (0.015629) | 0.348890 / 0.258489 (0.090401) | 0.389038 / 0.293841 (0.095197) | 0.031133 / 0.128546 (-0.097413) | 0.008566 / 0.075646 (-0.067080) | 0.288169 / 0.419271 (-0.131102) | 0.053290 / 0.043533 (0.009757) | 0.344654 / 0.255139 (0.089515) | 0.381287 / 0.283200 (0.098087) | 0.022350 / 0.141683 (-0.119333) | 1.459933 / 1.452155 (0.007778) | 1.543097 / 1.492716 (0.050380) |\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) | 0.212592 / 0.018006 (0.194586) | 0.461863 / 0.000490 (0.461373) | 0.003468 / 0.000200 (0.003268) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026849 / 0.037411 (-0.010563) | 0.081059 / 0.014526 (0.066533) | 0.093986 / 0.176557 (-0.082571) | 0.150328 / 0.737135 (-0.586807) | 0.094253 / 0.296338 (-0.202085) |\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) | 0.382198 / 0.215209 (0.166989) | 3.813878 / 2.077655 (1.736224) | 1.855686 / 1.504120 (0.351566) | 1.672995 / 1.541195 (0.131800) | 1.697705 / 1.468490 (0.229215) | 0.479920 / 4.584777 (-4.104857) | 3.608305 / 3.745712 (-0.137407) | 3.216712 / 5.269862 (-2.053149) | 1.984781 / 4.565676 (-2.580896) | 0.056801 / 0.424275 (-0.367475) | 0.007499 / 0.007607 (-0.000108) | 0.454155 / 0.226044 (0.228110) | 4.531147 / 2.268929 (2.262218) | 2.296149 / 55.444624 (-53.148475) | 1.968701 / 6.876477 (-4.907775) | 2.144286 / 2.142072 (0.002213) | 0.599254 / 4.805227 (-4.205973) | 0.138150 / 6.500664 (-6.362514) | 0.060118 / 0.075469 (-0.015351) |\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) | 1.282486 / 1.841788 (-0.559301) | 19.127792 / 8.074308 (11.053483) | 14.116521 / 10.191392 (3.925129) | 0.163792 / 0.680424 (-0.516632) | 0.018116 / 0.534201 (-0.516085) | 0.390789 / 0.579283 (-0.188494) | 0.409241 / 0.434364 (-0.025123) | 0.457824 / 0.540337 (-0.082513) | 0.624390 / 1.386936 (-0.762546) |\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) | 0.006637 / 0.011353 (-0.004716) | 0.003932 / 0.011008 (-0.007076) | 0.063456 / 0.038508 (0.024948) | 0.070062 / 0.023109 (0.046953) | 0.410570 / 0.275898 (0.134672) | 0.436700 / 0.323480 (0.113220) | 0.005324 / 0.007986 (-0.002662) | 0.003263 / 0.004328 (-0.001065) | 0.063590 / 0.004250 (0.059340) | 0.054823 / 0.037052 (0.017770) | 0.408720 / 0.258489 (0.150231) | 0.441493 / 0.293841 (0.147652) | 0.031655 / 0.128546 (-0.096891) | 0.008421 / 0.075646 (-0.067225) | 0.070657 / 0.419271 (-0.348614) | 0.047370 / 0.043533 (0.003837) | 0.408217 / 0.255139 (0.153078) | 0.422178 / 0.283200 (0.138978) | 0.022282 / 0.141683 (-0.119401) | 1.511417 / 1.452155 (0.059262) | 1.570337 / 1.492716 (0.077620) |\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) | 0.224334 / 0.018006 (0.206327) | 0.447589 / 0.000490 (0.447099) | 0.004227 / 0.000200 (0.004027) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030797 / 0.037411 (-0.006615) | 0.091276 / 0.014526 (0.076750) | 0.102665 / 0.176557 (-0.073892) | 0.155423 / 0.737135 (-0.581712) | 0.103779 / 0.296338 (-0.192560) |\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) | 0.434509 / 0.215209 (0.219300) | 4.328910 / 2.077655 (2.251255) | 2.311424 / 1.504120 (0.807304) | 2.138380 / 1.541195 (0.597185) | 2.196293 / 1.468490 (0.727803) | 0.482123 / 4.584777 (-4.102654) | 3.597870 / 3.745712 (-0.147842) | 3.222426 / 5.269862 (-2.047435) | 1.994467 / 4.565676 (-2.571210) | 0.057517 / 0.424275 (-0.366758) | 0.007336 / 0.007607 (-0.000271) | 0.504968 / 0.226044 (0.278923) | 5.047940 / 2.268929 (2.779012) | 2.824014 / 55.444624 (-52.620610) | 2.457762 / 6.876477 (-4.418714) | 2.606970 / 2.142072 (0.464897) | 0.580758 / 4.805227 (-4.224469) | 0.132584 / 6.500664 (-6.368080) | 0.059258 / 0.075469 (-0.016211) |\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) | 1.354386 / 1.841788 (-0.487402) | 19.738147 / 8.074308 (11.663839) | 14.858001 / 10.191392 (4.666609) | 0.166074 / 0.680424 (-0.514350) | 0.020181 / 0.534201 (-0.514020) | 0.398333 / 0.579283 (-0.180950) | 0.406969 / 0.434364 (-0.027395) | 0.474515 / 0.540337 (-0.065822) | 0.649571 / 1.386936 (-0.737365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3ac3b3a9c5f40a29fae71504574cfdeebefe349 \"CML watermark\")\n", "I would say we should delete all `dataset_infos.json` on the Hub...", "@albertvillanova @lhoestq @mariosasko should we really stop supporting it and delete from everywhere?\r\n(bc if not, I've found a bug in updating `dataset_infos.json` with `.push_to_hub` and I'd open a PR to fix it)", "We can only delete them for the datasets without namespace and open PRs for the others, so we need to keep supporting them for now" ]
2023-09-06T10:40:05
2023-09-07T08:31:29
2023-09-06T11:23:56
MEMBER
false
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Fix ```python from datasets import load_dataset_builder b = load_dataset_builder("lambdalabs/pokemon-blip-captions", "default") print(b.info) ``` which should return ``` DatasetInfo( features={'image': Image(decode=True, id=None), 'text': Value(dtype='string', id=None)}, dataset_name='pokemon-blip-captions', config_name='default', version=0.0.0, splits={'train': SplitInfo(name='train', num_bytes=119417410.0, num_examples=833, shard_lengths=None, dataset_name='pokemon-blip-captions')}, download_checksums=None, download_size=99672355, dataset_size=119417410.0, size_in_bytes=219089765.0, ... ) ``` instead of and empty dataset info. The dataset has a dataset_infos.json file with a deprecated config name "lambdalabs--pokemon-blip-captions". We switched those config names to "default" in 2.14, so the builder.info should take this into account.
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https://api.github.com/repos/huggingface/datasets/issues/6217
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`Dataset.to_dict()` ignore `decode=True` with Image feature
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[ "We need to implement the `Image` type as a PyArrow extension type (to allow us to override the Python conversion) for this to work as expected. For now, it's best to use your approach indeed." ]
2023-09-06T09:26:16
2023-09-08T17:08:52
null
CONTRIBUTOR
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### Describe the bug `Dataset.to_dict` seems to ignore the decoding instruction passed in features. ### Steps to reproduce the bug ```python import datasets import numpy as np from PIL import Image img = np.random.randint(0, 256, (5, 5, 3), dtype=np.uint8) img = Image.fromarray(img) features = datasets.Features({"image": datasets.Image(decode=True)}) dataset = datasets.Dataset.from_dict({"image": [img]}, features=features) print({key: dataset[key] for key in dataset.column_names}) # {'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=5x5 at 0x7EFBC80E15B0>]} print(dataset.to_dict()) # {'image': [{'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x05\x00\x00\x00\x05\x08\x02\x00\x00\x00\x02\r\xb1\xb2\x00\x00\x00[IDATx\x9c\x01P\x00\xaf\xff\x01\x13\x1b<7\xe7\xe0\xdc^6\xed\x04\xc7M\xd2\x9f\x00X\x1b\xb0?\x1ba\x15\xc5 o\xd0\x80\xbe\x19/\x01\xec\x95\x1f\x9f\xffj\xfa1\xa7\xc4X\xea\xbe\xa4g\x00\xc4\x15\xdeC\xc7 \xbbaqe\xc8\xb9\xa9q\xe7\x00,?M\xc0)\xdaD`}\xb1\xdci\x1e\xafC\xa9]%.@\xa6\xf0\xb3\x00\x00\x00\x00IEND\xaeB`\x82', 'path': None}]} ``` ### Expected behavior I would expect `{key: dataset[key] for key in dataset.column_names}` and `dataset.to_dict()` to be equivalent. If the previous behavior is expected, then it should be stated [in the doc](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.to_dict). ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.9.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Pillow 9.5.0 - numpy 1.25.2
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Release: 2.13.2
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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) | 0.007801 / 0.011353 (-0.003552) | 0.004831 / 0.011008 (-0.006177) | 0.123101 / 0.038508 (0.084593) | 0.053246 / 0.023109 (0.030137) | 0.381787 / 0.275898 (0.105889) | 0.461822 / 0.323480 (0.138342) | 0.004655 / 0.007986 (-0.003331) | 0.004818 / 0.004328 (0.000490) | 0.090865 / 0.004250 (0.086614) | 0.070626 / 0.037052 (0.033574) | 0.409122 / 0.258489 (0.150633) | 0.449627 / 0.293841 (0.155787) | 0.037477 / 0.128546 (-0.091069) | 0.010677 / 0.075646 (-0.064970) | 0.419970 / 0.419271 (0.000699) | 0.064626 / 0.043533 (0.021093) | 0.379536 / 0.255139 (0.124397) | 0.405790 / 0.283200 (0.122590) | 0.027290 / 0.141683 (-0.114393) | 1.884973 / 1.452155 (0.432819) | 1.960547 / 1.492716 (0.467831) |\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) | 0.259393 / 0.018006 (0.241386) | 0.502130 / 0.000490 (0.501640) | 0.013053 / 0.000200 (0.012853) | 0.000336 / 0.000054 (0.000281) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033459 / 0.037411 (-0.003953) | 0.135888 / 0.014526 (0.121362) | 0.145354 / 0.176557 (-0.031203) | 0.213289 / 0.737135 (-0.523847) | 0.151239 / 0.296338 (-0.145100) |\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) | 0.510817 / 0.215209 (0.295608) | 5.077888 / 2.077655 (3.000234) | 2.502991 / 1.504120 (0.998871) | 2.275566 / 1.541195 (0.734371) | 2.353025 / 1.468490 (0.884535) | 0.659062 / 4.584777 (-3.925715) | 4.411399 / 3.745712 (0.665686) | 2.227395 / 5.269862 (-3.042467) | 1.306771 / 4.565676 (-3.258905) | 0.081121 / 0.424275 (-0.343154) | 0.014252 / 0.007607 (0.006645) | 0.635040 / 0.226044 (0.408996) | 6.357500 / 2.268929 (4.088572) | 3.056647 / 55.444624 (-52.387977) | 2.671997 / 6.876477 (-4.204480) | 2.847955 / 2.142072 (0.705883) | 0.808163 / 4.805227 (-3.997064) | 0.177176 / 6.500664 (-6.323488) | 0.079984 / 0.075469 (0.004515) |\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) | 1.490471 / 1.841788 (-0.351317) | 17.927433 / 8.074308 (9.853124) | 17.744967 / 10.191392 (7.553575) | 0.171034 / 0.680424 (-0.509390) | 0.021432 / 0.534201 (-0.512769) | 0.515745 / 0.579283 (-0.063538) | 0.504746 / 0.434364 (0.070382) | 0.630862 / 0.540337 (0.090524) | 0.755275 / 1.386936 (-0.631662) |\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) | 0.008227 / 0.011353 (-0.003126) | 0.004864 / 0.011008 (-0.006144) | 0.092801 / 0.038508 (0.054293) | 0.054996 / 0.023109 (0.031887) | 0.500348 / 0.275898 (0.224450) | 0.565028 / 0.323480 (0.241548) | 0.004792 / 0.007986 (-0.003194) | 0.005052 / 0.004328 (0.000723) | 0.090640 / 0.004250 (0.086390) | 0.074427 / 0.037052 (0.037374) | 0.499908 / 0.258489 (0.241419) | 0.566260 / 0.293841 (0.272419) | 0.040011 / 0.128546 (-0.088536) | 0.010438 / 0.075646 (-0.065208) | 0.099385 / 0.419271 (-0.319887) | 0.060485 / 0.043533 (0.016952) | 0.480603 / 0.255139 (0.225464) | 0.508807 / 0.283200 (0.225607) | 0.025976 / 0.141683 (-0.115707) | 1.870860 / 1.452155 (0.418705) | 1.943460 / 1.492716 (0.450744) |\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) | 0.227753 / 0.018006 (0.209747) | 0.501859 / 0.000490 (0.501369) | 0.008211 / 0.000200 (0.008011) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038329 / 0.037411 (0.000918) | 0.148214 / 0.014526 (0.133688) | 0.162704 / 0.176557 (-0.013852) | 0.218543 / 0.737135 (-0.518592) | 0.162992 / 0.296338 (-0.133347) |\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) | 0.553195 / 0.215209 (0.337986) | 5.568080 / 2.077655 (3.490425) | 2.936616 / 1.504120 (1.432496) | 2.712624 / 1.541195 (1.171429) | 2.713245 / 1.468490 (1.244755) | 0.648593 / 4.584777 (-3.936184) | 4.641361 / 3.745712 (0.895648) | 2.207064 / 5.269862 (-3.062798) | 1.315325 / 4.565676 (-3.250351) | 0.080285 / 0.424275 (-0.343990) | 0.014143 / 0.007607 (0.006536) | 0.672467 / 0.226044 (0.446423) | 6.730262 / 2.268929 (4.461333) | 3.344468 / 55.444624 (-52.100157) | 2.927837 / 6.876477 (-3.948640) | 3.124735 / 2.142072 (0.982662) | 0.795894 / 4.805227 (-4.009333) | 0.170985 / 6.500664 (-6.329679) | 0.077406 / 0.075469 (0.001937) |\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) | 1.598059 / 1.841788 (-0.243729) | 18.531854 / 8.074308 (10.457546) | 18.394895 / 10.191392 (8.203503) | 0.195702 / 0.680424 (-0.484722) | 0.023633 / 0.534201 (-0.510568) | 0.518110 / 0.579283 (-0.061173) | 0.517773 / 0.434364 (0.083409) | 0.617902 / 0.540337 (0.077565) | 0.736459 / 1.386936 (-0.650477) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d4bb03237b74c0009043d50c5b4e4339cb98b2b \"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) | 0.006943 / 0.011353 (-0.004410) | 0.004524 / 0.011008 (-0.006485) | 0.121603 / 0.038508 (0.083095) | 0.047462 / 0.023109 (0.024353) | 0.362393 / 0.275898 (0.086495) | 0.440577 / 0.323480 (0.117098) | 0.004153 / 0.007986 (-0.003832) | 0.003778 / 0.004328 (-0.000550) | 0.090402 / 0.004250 (0.086152) | 0.066268 / 0.037052 (0.029216) | 0.380721 / 0.258489 (0.122232) | 0.442959 / 0.293841 (0.149118) | 0.035228 / 0.128546 (-0.093318) | 0.010217 / 0.075646 (-0.065429) | 0.408587 / 0.419271 (-0.010684) | 0.062609 / 0.043533 (0.019076) | 0.372682 / 0.255139 (0.117543) | 0.389270 / 0.283200 (0.106070) | 0.026699 / 0.141683 (-0.114984) | 1.760476 / 1.452155 (0.308321) | 1.795081 / 1.492716 (0.302365) |\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) | 0.229912 / 0.018006 (0.211906) | 0.476837 / 0.000490 (0.476348) | 0.008178 / 0.000200 (0.007978) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031116 / 0.037411 (-0.006296) | 0.126767 / 0.014526 (0.112241) | 0.134242 / 0.176557 (-0.042315) | 0.202120 / 0.737135 (-0.535016) | 0.142777 / 0.296338 (-0.153561) |\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) | 0.470690 / 0.215209 (0.255481) | 4.723198 / 2.077655 (2.645543) | 2.163870 / 1.504120 (0.659750) | 1.914177 / 1.541195 (0.372982) | 2.034529 / 1.468490 (0.566038) | 0.620472 / 4.584777 (-3.964305) | 4.391008 / 3.745712 (0.645296) | 2.100966 / 5.269862 (-3.168896) | 1.225945 / 4.565676 (-3.339732) | 0.076279 / 0.424275 (-0.347996) | 0.013551 / 0.007607 (0.005944) | 0.600989 / 0.226044 (0.374945) | 5.946715 / 2.268929 (3.677787) | 2.665117 / 55.444624 (-52.779508) | 2.320004 / 6.876477 (-4.556473) | 2.413131 / 2.142072 (0.271059) | 0.771908 / 4.805227 (-4.033320) | 0.165438 / 6.500664 (-6.335226) | 0.074512 / 0.075469 (-0.000957) |\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) | 1.432728 / 1.841788 (-0.409060) | 17.398133 / 8.074308 (9.323824) | 16.819152 / 10.191392 (6.627760) | 0.191849 / 0.680424 (-0.488575) | 0.021557 / 0.534201 (-0.512644) | 0.514380 / 0.579283 (-0.064903) | 0.501453 / 0.434364 (0.067089) | 0.634091 / 0.540337 (0.093753) | 0.756786 / 1.386936 (-0.630150) |\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) | 0.007946 / 0.011353 (-0.003407) | 0.004751 / 0.011008 (-0.006257) | 0.090190 / 0.038508 (0.051682) | 0.052841 / 0.023109 (0.029732) | 0.480150 / 0.275898 (0.204252) | 0.537509 / 0.323480 (0.214029) | 0.004833 / 0.007986 (-0.003153) | 0.004796 / 0.004328 (0.000467) | 0.090616 / 0.004250 (0.086366) | 0.074325 / 0.037052 (0.037273) | 0.483776 / 0.258489 (0.225287) | 0.552094 / 0.293841 (0.258254) | 0.039240 / 0.128546 (-0.089307) | 0.010416 / 0.075646 (-0.065230) | 0.100275 / 0.419271 (-0.318996) | 0.058086 / 0.043533 (0.014553) | 0.468989 / 0.255139 (0.213850) | 0.485502 / 0.283200 (0.202302) | 0.027514 / 0.141683 (-0.114169) | 1.849625 / 1.452155 (0.397470) | 1.919515 / 1.492716 (0.426798) |\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) | 0.248061 / 0.018006 (0.230055) | 0.475630 / 0.000490 (0.475141) | 0.006248 / 0.000200 (0.006048) | 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) | 0.037746 / 0.037411 (0.000335) | 0.141638 / 0.014526 (0.127112) | 0.149530 / 0.176557 (-0.027026) | 0.209255 / 0.737135 (-0.527880) | 0.156447 / 0.296338 (-0.139892) |\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) | 0.544640 / 0.215209 (0.329431) | 5.493152 / 2.077655 (3.415497) | 2.869733 / 1.504120 (1.365613) | 2.624216 / 1.541195 (1.083022) | 2.710818 / 1.468490 (1.242328) | 0.640626 / 4.584777 (-3.944151) | 4.516130 / 3.745712 (0.770418) | 2.128097 / 5.269862 (-3.141765) | 1.278990 / 4.565676 (-3.286686) | 0.077114 / 0.424275 (-0.347161) | 0.013280 / 0.007607 (0.005673) | 0.655552 / 0.226044 (0.429507) | 6.526875 / 2.268929 (4.257947) | 3.347072 / 55.444624 (-52.097553) | 2.992435 / 6.876477 (-3.884041) | 3.124351 / 2.142072 (0.982278) | 0.778523 / 4.805227 (-4.026704) | 0.161873 / 6.500664 (-6.338791) | 0.072897 / 0.075469 (-0.002572) |\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) | 1.587058 / 1.841788 (-0.254730) | 18.170612 / 8.074308 (10.096304) | 17.220483 / 10.191392 (7.029091) | 0.207863 / 0.680424 (-0.472561) | 0.023746 / 0.534201 (-0.510455) | 0.512607 / 0.579283 (-0.066676) | 0.513258 / 0.434364 (0.078894) | 0.597880 / 0.540337 (0.057543) | 0.714974 / 1.386936 (-0.671962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) | 0.006224 / 0.011353 (-0.005128) | 0.003857 / 0.011008 (-0.007151) | 0.099786 / 0.038508 (0.061278) | 0.037919 / 0.023109 (0.014810) | 0.315294 / 0.275898 (0.039396) | 0.390178 / 0.323480 (0.066698) | 0.005358 / 0.007986 (-0.002628) | 0.002989 / 0.004328 (-0.001340) | 0.077834 / 0.004250 (0.073583) | 0.053315 / 0.037052 (0.016263) | 0.325155 / 0.258489 (0.066666) | 0.374712 / 0.293841 (0.080871) | 0.029176 / 0.128546 (-0.099370) | 0.008658 / 0.075646 (-0.066988) | 0.314245 / 0.419271 (-0.105027) | 0.046684 / 0.043533 (0.003151) | 0.316473 / 0.255139 (0.061334) | 0.346119 / 0.283200 (0.062919) | 0.022452 / 0.141683 (-0.119230) | 1.540497 / 1.452155 (0.088343) | 1.594888 / 1.492716 (0.102172) |\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) | 0.204349 / 0.018006 (0.186343) | 0.426842 / 0.000490 (0.426353) | 0.003060 / 0.000200 (0.002860) | 0.000073 / 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) | 0.023611 / 0.037411 (-0.013801) | 0.100247 / 0.014526 (0.085721) | 0.107824 / 0.176557 (-0.068733) | 0.166845 / 0.737135 (-0.570291) | 0.112782 / 0.296338 (-0.183556) |\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) | 0.423053 / 0.215209 (0.207844) | 4.235553 / 2.077655 (2.157899) | 1.936589 / 1.504120 (0.432469) | 1.738519 / 1.541195 (0.197325) | 1.787905 / 1.468490 (0.319415) | 0.573362 / 4.584777 (-4.011414) | 3.395272 / 3.745712 (-0.350440) | 1.765977 / 5.269862 (-3.503884) | 1.049596 / 4.565676 (-3.516081) | 0.068868 / 0.424275 (-0.355407) | 0.011028 / 0.007607 (0.003421) | 0.532835 / 0.226044 (0.306791) | 5.314890 / 2.268929 (3.045962) | 2.368733 / 55.444624 (-53.075891) | 2.033959 / 6.876477 (-4.842518) | 2.130481 / 2.142072 (-0.011591) | 0.689360 / 4.805227 (-4.115867) | 0.140271 / 6.500664 (-6.360393) | 0.068198 / 0.075469 (-0.007271) |\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) | 1.237212 / 1.841788 (-0.604576) | 14.182215 / 8.074308 (6.107907) | 14.972608 / 10.191392 (4.781216) | 0.133977 / 0.680424 (-0.546447) | 0.016759 / 0.534201 (-0.517442) | 0.361552 / 0.579283 (-0.217731) | 0.394932 / 0.434364 (-0.039432) | 0.442601 / 0.540337 (-0.097736) | 0.535709 / 1.386936 (-0.851227) |\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) | 0.006327 / 0.011353 (-0.005026) | 0.003780 / 0.011008 (-0.007228) | 0.078358 / 0.038508 (0.039850) | 0.037271 / 0.023109 (0.014162) | 0.456766 / 0.275898 (0.180868) | 0.515721 / 0.323480 (0.192241) | 0.004770 / 0.007986 (-0.003216) | 0.002942 / 0.004328 (-0.001387) | 0.077383 / 0.004250 (0.073132) | 0.051773 / 0.037052 (0.014721) | 0.460722 / 0.258489 (0.202233) | 0.519997 / 0.293841 (0.226157) | 0.030461 / 0.128546 (-0.098085) | 0.008622 / 0.075646 (-0.067024) | 0.083271 / 0.419271 (-0.336000) | 0.042242 / 0.043533 (-0.001291) | 0.447691 / 0.255139 (0.192552) | 0.481965 / 0.283200 (0.198765) | 0.019510 / 0.141683 (-0.122173) | 1.536718 / 1.452155 (0.084563) | 1.588433 / 1.492716 (0.095717) |\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) | 0.215880 / 0.018006 (0.197874) | 0.426102 / 0.000490 (0.425612) | 0.003976 / 0.000200 (0.003776) | 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) | 0.026168 / 0.037411 (-0.011243) | 0.105786 / 0.014526 (0.091260) | 0.113772 / 0.176557 (-0.062785) | 0.166576 / 0.737135 (-0.570559) | 0.117560 / 0.296338 (-0.178779) |\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) | 0.490485 / 0.215209 (0.275276) | 4.890105 / 2.077655 (2.812450) | 2.515099 / 1.504120 (1.010979) | 2.306591 / 1.541195 (0.765396) | 2.383634 / 1.468490 (0.915144) | 0.573780 / 4.584777 (-4.010997) | 3.474394 / 3.745712 (-0.271318) | 1.746795 / 5.269862 (-3.523067) | 1.044678 / 4.565676 (-3.520998) | 0.069176 / 0.424275 (-0.355099) | 0.011045 / 0.007607 (0.003438) | 0.597234 / 0.226044 (0.371189) | 5.979614 / 2.268929 (3.710685) | 3.024203 / 55.444624 (-52.420422) | 2.687502 / 6.876477 (-4.188975) | 2.781637 / 2.142072 (0.639565) | 0.690482 / 4.805227 (-4.114745) | 0.150138 / 6.500664 (-6.350526) | 0.077076 / 0.075469 (0.001607) |\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) | 1.307501 / 1.841788 (-0.534287) | 14.366780 / 8.074308 (6.292471) | 14.966981 / 10.191392 (4.775589) | 0.153829 / 0.680424 (-0.526594) | 0.018047 / 0.534201 (-0.516154) | 0.361391 / 0.579283 (-0.217892) | 0.398345 / 0.434364 (-0.036019) | 0.424574 / 0.540337 (-0.115764) | 0.517165 / 1.386936 (-0.869771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"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) | 0.006944 / 0.011353 (-0.004409) | 0.004504 / 0.011008 (-0.006504) | 0.105224 / 0.038508 (0.066716) | 0.047830 / 0.023109 (0.024721) | 0.339723 / 0.275898 (0.063825) | 0.419249 / 0.323480 (0.095769) | 0.005510 / 0.007986 (-0.002476) | 0.003574 / 0.004328 (-0.000754) | 0.079879 / 0.004250 (0.075628) | 0.066610 / 0.037052 (0.029557) | 0.353818 / 0.258489 (0.095329) | 0.397992 / 0.293841 (0.104151) | 0.031551 / 0.128546 (-0.096995) | 0.009037 / 0.075646 (-0.066610) | 0.355310 / 0.419271 (-0.063961) | 0.054931 / 0.043533 (0.011398) | 0.335153 / 0.255139 (0.080014) | 0.357460 / 0.283200 (0.074260) | 0.026031 / 0.141683 (-0.115652) | 1.546705 / 1.452155 (0.094550) | 1.627324 / 1.492716 (0.134608) |\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) | 0.276708 / 0.018006 (0.258701) | 0.589402 / 0.000490 (0.588912) | 0.009560 / 0.000200 (0.009360) | 0.000095 / 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) | 0.031041 / 0.037411 (-0.006370) | 0.117219 / 0.014526 (0.102693) | 0.125200 / 0.176557 (-0.051356) | 0.181528 / 0.737135 (-0.555607) | 0.131898 / 0.296338 (-0.164440) |\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) | 0.409965 / 0.215209 (0.194756) | 4.102700 / 2.077655 (2.025045) | 1.887578 / 1.504120 (0.383458) | 1.696490 / 1.541195 (0.155295) | 1.821352 / 1.468490 (0.352862) | 0.545422 / 4.584777 (-4.039355) | 3.933784 / 3.745712 (0.188071) | 1.934254 / 5.269862 (-3.335607) | 1.114935 / 4.565676 (-3.450742) | 0.067615 / 0.424275 (-0.356660) | 0.012004 / 0.007607 (0.004397) | 0.522048 / 0.226044 (0.296004) | 5.209224 / 2.268929 (2.940296) | 2.369911 / 55.444624 (-53.074714) | 2.032960 / 6.876477 (-4.843517) | 2.228874 / 2.142072 (0.086802) | 0.673172 / 4.805227 (-4.132055) | 0.147017 / 6.500664 (-6.353647) | 0.067020 / 0.075469 (-0.008449) |\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) | 1.281490 / 1.841788 (-0.560298) | 16.129701 / 8.074308 (8.055393) | 15.474730 / 10.191392 (5.283338) | 0.143934 / 0.680424 (-0.536490) | 0.018311 / 0.534201 (-0.515890) | 0.435940 / 0.579283 (-0.143343) | 0.446846 / 0.434364 (0.012482) | 0.543943 / 0.540337 (0.003605) | 0.648041 / 1.386936 (-0.738895) |\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) | 0.007380 / 0.011353 (-0.003973) | 0.004510 / 0.011008 (-0.006499) | 0.080741 / 0.038508 (0.042233) | 0.050907 / 0.023109 (0.027797) | 0.425548 / 0.275898 (0.149650) | 0.487959 / 0.323480 (0.164479) | 0.005887 / 0.007986 (-0.002099) | 0.003689 / 0.004328 (-0.000639) | 0.079588 / 0.004250 (0.075338) | 0.071841 / 0.037052 (0.034788) | 0.425172 / 0.258489 (0.166683) | 0.471185 / 0.293841 (0.177344) | 0.035768 / 0.128546 (-0.092779) | 0.009229 / 0.075646 (-0.066418) | 0.086021 / 0.419271 (-0.333250) | 0.052424 / 0.043533 (0.008891) | 0.413634 / 0.255139 (0.158495) | 0.422310 / 0.283200 (0.139111) | 0.026019 / 0.141683 (-0.115664) | 1.616861 / 1.452155 (0.164707) | 1.653660 / 1.492716 (0.160943) |\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) | 0.280096 / 0.018006 (0.262090) | 0.587853 / 0.000490 (0.587363) | 0.006560 / 0.000200 (0.006360) | 0.000181 / 0.000054 (0.000127) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033747 / 0.037411 (-0.003665) | 0.125089 / 0.014526 (0.110564) | 0.137995 / 0.176557 (-0.038561) | 0.188192 / 0.737135 (-0.548943) | 0.141438 / 0.296338 (-0.154900) |\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) | 0.471524 / 0.215209 (0.256315) | 4.713988 / 2.077655 (2.636334) | 2.414785 / 1.504120 (0.910665) | 2.226815 / 1.541195 (0.685620) | 2.259222 / 1.468490 (0.790732) | 0.551663 / 4.584777 (-4.033114) | 4.031399 / 3.745712 (0.285686) | 1.966917 / 5.269862 (-3.302945) | 1.154487 / 4.565676 (-3.411190) | 0.068500 / 0.424275 (-0.355775) | 0.012127 / 0.007607 (0.004520) | 0.579342 / 0.226044 (0.353298) | 5.757415 / 2.268929 (3.488486) | 2.820012 / 55.444624 (-52.624613) | 2.521783 / 6.876477 (-4.354694) | 2.699994 / 2.142072 (0.557921) | 0.686152 / 4.805227 (-4.119075) | 0.148521 / 6.500664 (-6.352143) | 0.068478 / 0.075469 (-0.006991) |\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) | 1.336260 / 1.841788 (-0.505528) | 17.016935 / 8.074308 (8.942627) | 16.406951 / 10.191392 (6.215559) | 0.166907 / 0.680424 (-0.513517) | 0.020166 / 0.534201 (-0.514035) | 0.437690 / 0.579283 (-0.141593) | 0.480337 / 0.434364 (0.045973) | 0.518065 / 0.540337 (-0.022272) | 0.625904 / 1.386936 (-0.761032) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98b1bdd492df953ca7139bb8c9a1771d5c603797 \"CML watermark\")\n" ]
2023-09-06T08:15:32
2023-09-06T08:52:18
2023-09-06T08:22:43
MEMBER
false
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PR_kwDODunzps5ZlcqC
6,215
Fix checking patterns to infer packaged builder
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[ "_The documentation is not available anymore as the PR was closed or merged._", "oh wow good catch", "<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) | 0.006681 / 0.011353 (-0.004672) | 0.003967 / 0.011008 (-0.007041) | 0.085590 / 0.038508 (0.047082) | 0.079285 / 0.023109 (0.056176) | 0.311583 / 0.275898 (0.035685) | 0.345578 / 0.323480 (0.022098) | 0.004115 / 0.007986 (-0.003871) | 0.004286 / 0.004328 (-0.000043) | 0.064405 / 0.004250 (0.060155) | 0.055084 / 0.037052 (0.018032) | 0.316117 / 0.258489 (0.057628) | 0.354737 / 0.293841 (0.060896) | 0.031280 / 0.128546 (-0.097266) | 0.008395 / 0.075646 (-0.067251) | 0.288910 / 0.419271 (-0.130362) | 0.051291 / 0.043533 (0.007759) | 0.309125 / 0.255139 (0.053986) | 0.349673 / 0.283200 (0.066473) | 0.025016 / 0.141683 (-0.116667) | 1.475577 / 1.452155 (0.023422) | 1.558967 / 1.492716 (0.066251) |\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) | 0.208504 / 0.018006 (0.190498) | 0.462270 / 0.000490 (0.461780) | 0.003476 / 0.000200 (0.003276) | 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) | 0.030371 / 0.037411 (-0.007041) | 0.086157 / 0.014526 (0.071631) | 0.098162 / 0.176557 (-0.078395) | 0.154649 / 0.737135 (-0.582486) | 0.098697 / 0.296338 (-0.197642) |\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) | 0.405883 / 0.215209 (0.190674) | 4.049614 / 2.077655 (1.971959) | 2.075047 / 1.504120 (0.570927) | 1.917782 / 1.541195 (0.376587) | 2.030268 / 1.468490 (0.561778) | 0.483974 / 4.584777 (-4.100803) | 3.542147 / 3.745712 (-0.203566) | 3.305999 / 5.269862 (-1.963863) | 2.052287 / 4.565676 (-2.513390) | 0.057246 / 0.424275 (-0.367029) | 0.007631 / 0.007607 (0.000024) | 0.488189 / 0.226044 (0.262144) | 4.884784 / 2.268929 (2.615856) | 2.576304 / 55.444624 (-52.868320) | 2.241249 / 6.876477 (-4.635228) | 2.490512 / 2.142072 (0.348440) | 0.584495 / 4.805227 (-4.220733) | 0.134741 / 6.500664 (-6.365923) | 0.061639 / 0.075469 (-0.013830) |\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) | 1.317717 / 1.841788 (-0.524071) | 20.098594 / 8.074308 (12.024286) | 14.641051 / 10.191392 (4.449659) | 0.165291 / 0.680424 (-0.515133) | 0.019179 / 0.534201 (-0.515022) | 0.399506 / 0.579283 (-0.179777) | 0.407662 / 0.434364 (-0.026701) | 0.457965 / 0.540337 (-0.082372) | 0.626401 / 1.386936 (-0.760536) |\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) | 0.007076 / 0.011353 (-0.004277) | 0.004125 / 0.011008 (-0.006884) | 0.064861 / 0.038508 (0.026353) | 0.082390 / 0.023109 (0.059281) | 0.423227 / 0.275898 (0.147329) | 0.452229 / 0.323480 (0.128750) | 0.005594 / 0.007986 (-0.002392) | 0.003465 / 0.004328 (-0.000863) | 0.064661 / 0.004250 (0.060411) | 0.057945 / 0.037052 (0.020892) | 0.424572 / 0.258489 (0.166083) | 0.465349 / 0.293841 (0.171509) | 0.032687 / 0.128546 (-0.095859) | 0.008573 / 0.075646 (-0.067074) | 0.073020 / 0.419271 (-0.346251) | 0.048423 / 0.043533 (0.004891) | 0.413425 / 0.255139 (0.158286) | 0.433778 / 0.283200 (0.150578) | 0.023942 / 0.141683 (-0.117741) | 1.495190 / 1.452155 (0.043036) | 1.586526 / 1.492716 (0.093810) |\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) | 0.271805 / 0.018006 (0.253799) | 0.454922 / 0.000490 (0.454432) | 0.015386 / 0.000200 (0.015186) | 0.000129 / 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) | 0.033804 / 0.037411 (-0.003607) | 0.099317 / 0.014526 (0.084791) | 0.107207 / 0.176557 (-0.069349) | 0.160926 / 0.737135 (-0.576210) | 0.108669 / 0.296338 (-0.187670) |\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) | 0.430776 / 0.215209 (0.215567) | 4.297622 / 2.077655 (2.219967) | 2.285918 / 1.504120 (0.781798) | 2.109608 / 1.541195 (0.568413) | 2.208326 / 1.468490 (0.739836) | 0.490016 / 4.584777 (-4.094761) | 3.570609 / 3.745712 (-0.175103) | 3.406335 / 5.269862 (-1.863526) | 2.070664 / 4.565676 (-2.495012) | 0.058089 / 0.424275 (-0.366186) | 0.007425 / 0.007607 (-0.000182) | 0.506972 / 0.226044 (0.280927) | 5.078643 / 2.268929 (2.809714) | 2.858973 / 55.444624 (-52.585651) | 2.457344 / 6.876477 (-4.419132) | 2.687727 / 2.142072 (0.545654) | 0.592134 / 4.805227 (-4.213093) | 0.133966 / 6.500664 (-6.366698) | 0.061800 / 0.075469 (-0.013669) |\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) | 1.337167 / 1.841788 (-0.504620) | 20.743951 / 8.074308 (12.669643) | 15.402686 / 10.191392 (5.211294) | 0.164548 / 0.680424 (-0.515876) | 0.020244 / 0.534201 (-0.513957) | 0.399044 / 0.579283 (-0.180239) | 0.414036 / 0.434364 (-0.020328) | 0.474141 / 0.540337 (-0.066197) | 0.654455 / 1.386936 (-0.732482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de930c45a81a6dff1805bf45f59170e9f953eeb \"CML watermark\")\n" ]
2023-09-05T15:10:47
2023-09-06T10:34:00
2023-09-06T10:25:00
CONTRIBUTOR
false
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Don't ignore results of pattern resolving if `self.data_files` is not None. Otherwise lines 854 and 1037 make no sense.
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I_kwDODunzps5wKQUV
6,214
Unpin fsspec < 2023.9.0
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[]
2023-09-05T11:02:58
2023-09-26T15:32:52
2023-09-26T15:32:52
MEMBER
null
null
Once root issue is fixed, remove temporary pin of fsspec < 2023.9.0 introduced by: - #6210 Related to issue: - #6209 After investigation, I think the root issue is related to the new glob behavior with double asterisk `**` they have introduced in: - https://github.com/fsspec/filesystem_spec/pull/1329
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https://api.github.com/repos/huggingface/datasets/issues/6214/timeline
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https://api.github.com/repos/huggingface/datasets/issues/6213
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https://github.com/huggingface/datasets/pull/6213
1,880,592,987
PR_kwDODunzps5ZgHLO
6,213
Better list array values handling in cast/embed storage
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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) | 0.008451 / 0.011353 (-0.002902) | 0.005056 / 0.011008 (-0.005952) | 0.086367 / 0.038508 (0.047859) | 0.068030 / 0.023109 (0.044920) | 0.358812 / 0.275898 (0.082914) | 0.385790 / 0.323480 (0.062310) | 0.005608 / 0.007986 (-0.002378) | 0.004262 / 0.004328 (-0.000067) | 0.066618 / 0.004250 (0.062368) | 0.053901 / 0.037052 (0.016849) | 0.398456 / 0.258489 (0.139967) | 0.391681 / 0.293841 (0.097840) | 0.046743 / 0.128546 (-0.081804) | 0.014118 / 0.075646 (-0.061528) | 0.308479 / 0.419271 (-0.110793) | 0.064214 / 0.043533 (0.020681) | 0.367940 / 0.255139 (0.112801) | 0.387204 / 0.283200 (0.104004) | 0.036093 / 0.141683 (-0.105590) | 1.534182 / 1.452155 (0.082027) | 1.598357 / 1.492716 (0.105641) |\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) | 0.265910 / 0.018006 (0.247904) | 0.589453 / 0.000490 (0.588963) | 0.004881 / 0.000200 (0.004681) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032540 / 0.037411 (-0.004872) | 0.083153 / 0.014526 (0.068627) | 0.098960 / 0.176557 (-0.077597) | 0.162044 / 0.737135 (-0.575091) | 0.093602 / 0.296338 (-0.202736) |\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) | 0.517056 / 0.215209 (0.301847) | 5.167908 / 2.077655 (3.090253) | 2.359856 / 1.504120 (0.855736) | 2.092448 / 1.541195 (0.551253) | 2.100270 / 1.468490 (0.631780) | 0.742321 / 4.584777 (-3.842456) | 4.845010 / 3.745712 (1.099298) | 4.361808 / 5.269862 (-0.908054) | 2.621941 / 4.565676 (-1.943736) | 0.094907 / 0.424275 (-0.329369) | 0.009357 / 0.007607 (0.001750) | 0.719859 / 0.226044 (0.493814) | 6.929731 / 2.268929 (4.660802) | 3.240862 / 55.444624 (-52.203763) | 2.700817 / 6.876477 (-4.175659) | 2.904600 / 2.142072 (0.762527) | 0.924930 / 4.805227 (-3.880298) | 0.194390 / 6.500664 (-6.306274) | 0.078331 / 0.075469 (0.002862) |\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) | 1.539347 / 1.841788 (-0.302441) | 22.696358 / 8.074308 (14.622050) | 18.791692 / 10.191392 (8.600300) | 0.221376 / 0.680424 (-0.459048) | 0.029824 / 0.534201 (-0.504377) | 0.455604 / 0.579283 (-0.123679) | 0.573169 / 0.434364 (0.138805) | 0.507109 / 0.540337 (-0.033228) | 0.730986 / 1.386936 (-0.655950) |\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) | 0.009308 / 0.011353 (-0.002045) | 0.005027 / 0.011008 (-0.005982) | 0.074094 / 0.038508 (0.035586) | 0.068277 / 0.023109 (0.045168) | 0.412716 / 0.275898 (0.136818) | 0.446883 / 0.323480 (0.123403) | 0.005864 / 0.007986 (-0.002122) | 0.003753 / 0.004328 (-0.000575) | 0.072575 / 0.004250 (0.068325) | 0.064434 / 0.037052 (0.027382) | 0.445395 / 0.258489 (0.186906) | 0.464520 / 0.293841 (0.170679) | 0.045303 / 0.128546 (-0.083243) | 0.013120 / 0.075646 (-0.062527) | 0.077830 / 0.419271 (-0.341441) | 0.057303 / 0.043533 (0.013770) | 0.420845 / 0.255139 (0.165706) | 0.431308 / 0.283200 (0.148109) | 0.033908 / 0.141683 (-0.107775) | 1.577667 / 1.452155 (0.125512) | 1.677321 / 1.492716 (0.184604) |\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) | 0.305855 / 0.018006 (0.287849) | 0.601442 / 0.000490 (0.600953) | 0.010722 / 0.000200 (0.010522) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029202 / 0.037411 (-0.008209) | 0.094576 / 0.014526 (0.080050) | 0.106734 / 0.176557 (-0.069822) | 0.168114 / 0.737135 (-0.569021) | 0.107241 / 0.296338 (-0.189098) |\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) | 0.643634 / 0.215209 (0.428425) | 6.391757 / 2.077655 (4.314103) | 3.011679 / 1.504120 (1.507559) | 2.379711 / 1.541195 (0.838517) | 2.387444 / 1.468490 (0.918954) | 0.823460 / 4.584777 (-3.761317) | 4.882240 / 3.745712 (1.136528) | 4.091170 / 5.269862 (-1.178691) | 2.688761 / 4.565676 (-1.876915) | 0.094555 / 0.424275 (-0.329720) | 0.008464 / 0.007607 (0.000857) | 0.665949 / 0.226044 (0.439905) | 6.948237 / 2.268929 (4.679309) | 3.384894 / 55.444624 (-52.059730) | 2.675570 / 6.876477 (-4.200907) | 3.073045 / 2.142072 (0.930973) | 0.969780 / 4.805227 (-3.835447) | 0.205859 / 6.500664 (-6.294805) | 0.072548 / 0.075469 (-0.002922) |\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) | 1.563869 / 1.841788 (-0.277919) | 22.431392 / 8.074308 (14.357084) | 19.434811 / 10.191392 (9.243419) | 0.255135 / 0.680424 (-0.425289) | 0.027799 / 0.534201 (-0.506402) | 0.427713 / 0.579283 (-0.151570) | 0.527030 / 0.434364 (0.092666) | 0.503660 / 0.540337 (-0.036678) | 0.730996 / 1.386936 (-0.655940) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06c1940953807dbde4bc18af64bd3d87234edf00 \"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) | 0.007597 / 0.011353 (-0.003756) | 0.004492 / 0.011008 (-0.006516) | 0.103779 / 0.038508 (0.065271) | 0.079287 / 0.023109 (0.056178) | 0.389651 / 0.275898 (0.113753) | 0.421955 / 0.323480 (0.098475) | 0.006023 / 0.007986 (-0.001963) | 0.003727 / 0.004328 (-0.000602) | 0.078604 / 0.004250 (0.074354) | 0.060810 / 0.037052 (0.023758) | 0.412170 / 0.258489 (0.153681) | 0.436218 / 0.293841 (0.142377) | 0.037282 / 0.128546 (-0.091264) | 0.010341 / 0.075646 (-0.065305) | 0.357652 / 0.419271 (-0.061620) | 0.063320 / 0.043533 (0.019788) | 0.389454 / 0.255139 (0.134315) | 0.433073 / 0.283200 (0.149874) | 0.028449 / 0.141683 (-0.113234) | 1.894107 / 1.452155 (0.441952) | 1.954190 / 1.492716 (0.461474) |\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) | 0.224477 / 0.018006 (0.206471) | 0.510878 / 0.000490 (0.510388) | 0.005013 / 0.000200 (0.004813) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032976 / 0.037411 (-0.004436) | 0.101073 / 0.014526 (0.086547) | 0.113990 / 0.176557 (-0.062566) | 0.183499 / 0.737135 (-0.553636) | 0.114283 / 0.296338 (-0.182056) |\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) | 0.473242 / 0.215209 (0.258033) | 4.719800 / 2.077655 (2.642146) | 2.318732 / 1.504120 (0.814612) | 2.102336 / 1.541195 (0.561141) | 2.143618 / 1.468490 (0.675128) | 0.594122 / 4.584777 (-3.990654) | 4.265961 / 3.745712 (0.520249) | 3.794635 / 5.269862 (-1.475226) | 2.394506 / 4.565676 (-2.171170) | 0.070091 / 0.424275 (-0.354184) | 0.009222 / 0.007607 (0.001614) | 0.564496 / 0.226044 (0.338452) | 5.644348 / 2.268929 (3.375419) | 2.934395 / 55.444624 (-52.510229) | 2.429076 / 6.876477 (-4.447401) | 2.592010 / 2.142072 (0.449937) | 0.713371 / 4.805227 (-4.091856) | 0.165019 / 6.500664 (-6.335646) | 0.075913 / 0.075469 (0.000444) |\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) | 1.570836 / 1.841788 (-0.270951) | 22.569763 / 8.074308 (14.495455) | 17.159658 / 10.191392 (6.968266) | 0.185716 / 0.680424 (-0.494708) | 0.021938 / 0.534201 (-0.512263) | 0.487204 / 0.579283 (-0.092079) | 0.472776 / 0.434364 (0.038412) | 0.565052 / 0.540337 (0.024714) | 0.763322 / 1.386936 (-0.623614) |\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) | 0.007810 / 0.011353 (-0.003543) | 0.005140 / 0.011008 (-0.005869) | 0.079018 / 0.038508 (0.040510) | 0.080899 / 0.023109 (0.057790) | 0.489213 / 0.275898 (0.213315) | 0.525334 / 0.323480 (0.201854) | 0.006992 / 0.007986 (-0.000994) | 0.003729 / 0.004328 (-0.000599) | 0.079277 / 0.004250 (0.075026) | 0.064883 / 0.037052 (0.027831) | 0.496718 / 0.258489 (0.238229) | 0.534976 / 0.293841 (0.241135) | 0.038790 / 0.128546 (-0.089756) | 0.010122 / 0.075646 (-0.065524) | 0.087669 / 0.419271 (-0.331603) | 0.057959 / 0.043533 (0.014426) | 0.490611 / 0.255139 (0.235472) | 0.518376 / 0.283200 (0.235176) | 0.026561 / 0.141683 (-0.115122) | 1.843241 / 1.452155 (0.391086) | 1.952367 / 1.492716 (0.459651) |\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) | 0.289799 / 0.018006 (0.271792) | 0.486999 / 0.000490 (0.486509) | 0.017481 / 0.000200 (0.017281) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037662 / 0.037411 (0.000250) | 0.113238 / 0.014526 (0.098712) | 0.123918 / 0.176557 (-0.052638) | 0.190484 / 0.737135 (-0.546652) | 0.126473 / 0.296338 (-0.169865) |\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) | 0.530622 / 0.215209 (0.315413) | 5.292093 / 2.077655 (3.214438) | 2.819354 / 1.504120 (1.315234) | 2.609821 / 1.541195 (1.068626) | 2.680090 / 1.468490 (1.211600) | 0.603490 / 4.584777 (-3.981287) | 4.344541 / 3.745712 (0.598828) | 3.874001 / 5.269862 (-1.395861) | 2.445302 / 4.565676 (-2.120375) | 0.071173 / 0.424275 (-0.353102) | 0.009131 / 0.007607 (0.001524) | 0.627273 / 0.226044 (0.401229) | 6.278637 / 2.268929 (4.009709) | 3.433762 / 55.444624 (-52.010862) | 2.973400 / 6.876477 (-3.903077) | 3.188165 / 2.142072 (1.046093) | 0.722824 / 4.805227 (-4.082404) | 0.165154 / 6.500664 (-6.335510) | 0.075268 / 0.075469 (-0.000202) |\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) | 1.652994 / 1.841788 (-0.188794) | 23.309030 / 8.074308 (15.234722) | 18.135649 / 10.191392 (7.944257) | 0.177543 / 0.680424 (-0.502881) | 0.024784 / 0.534201 (-0.509417) | 0.489952 / 0.579283 (-0.089331) | 0.485368 / 0.434364 (0.051004) | 0.580583 / 0.540337 (0.040246) | 0.787843 / 1.386936 (-0.599093) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5982039f7814a204fe532240ca6aabe72430d834 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "A bug in `FixedSizeArray.flatten` in `PyArrow<10.0.0` makes CI fail. Colab installs 9.0.0 by default, so we should be able to set the minimal version to `10.0.0` soon. Keeping this PR as a draft in the meantime.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6283" ]
2023-09-04T16:21:23
2024-01-11T06:32:20
2023-10-05T15:24:34
CONTRIBUTOR
true
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Use [`array.flatten`](https://arrow.apache.org/docs/python/generated/pyarrow.ListArray.html#pyarrow.ListArray.flatten) that takes `.offset` into account instead of `array.values` in array cast/embed.
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1,880,399,516
I_kwDODunzps5wFJ6c
6,212
Tilde (~) is not supported for data_files
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[ "Hi @exs-avianello, is it really needed? Note you can alternatively use `pathlib.Path` among others as it follows:\r\n\r\n```python\r\nimport datasets\r\nfrom pathlib import Path\r\n\r\n# save a parquet file at ~/path/to/data.parquet\r\n\r\ndata_files = Path.home() / \"path/to/data.parquet\"\r\ndataset = datasets.load_dataset(\"parquet\", data_files=data_files)\r\n```", "Hi @alvarobartt ! \r\n\r\nThis is definitely just a \"nice to have\" and I am personally more than happy to just use absolute paths client-side. I just wanted to flag it up in case it can help improve the package even more 🙌 It might not be immediately obvious from the stack trace that the error is triggered by the `~` in the path" ]
2023-09-04T14:23:49
2023-09-05T08:28:39
null
NONE
null
null
### Describe the bug Attempting to `load_dataset` from a path starting with `~` (as a shorthand for the user's home directory) seems not to be fully working - at least as far as the `parquet` dataset builder is concerned. (the same file can be loaded correctly if providing its absolute path instead) I think that this is very similar to https://github.com/huggingface/datasets/issues/5757, but for `data_files` rather than `data_dir` ### Steps to reproduce the bug ```python import datasets # save a parquet file at ~/path/to/data.parquet data_files = "~/path/to/data.parquet" dataset = datasets.load_dataset("parquet", data_files=data_files) ``` ``` Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 12671.61it/s] Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22671.91it/s] Generating train split: 0 examples [00:00, ? examples/s] Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1949, in _prepare_split_single num_examples, num_bytes = writer.finalize() ^^^^^^^^^^^^^^^^^ File ".venv/lib/python3.11/site-packages/datasets/arrow_writer.py", line 598, in finalize raise SchemaInferenceError("Please pass `features` or at least one example when writing data") datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: Traceback (most recent call last): File ".venv/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1813, in _prepare_split for job_id, done, content in self._prepare_split_single( File ".venv/lib/python3.11/site-packages/datasets/builder.py", line 1958, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Can use `~` shorthand in paths when loading local (parquet) datasets. ### Environment info `datasets 2.14.3`
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PR_kwDODunzps5Ze-pv
6,211
Fix empty splitinfo json
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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) | 0.007756 / 0.011353 (-0.003597) | 0.004733 / 0.011008 (-0.006275) | 0.095874 / 0.038508 (0.057366) | 0.081957 / 0.023109 (0.058848) | 0.426430 / 0.275898 (0.150532) | 0.457670 / 0.323480 (0.134190) | 0.004448 / 0.007986 (-0.003537) | 0.004956 / 0.004328 (0.000627) | 0.074195 / 0.004250 (0.069945) | 0.061101 / 0.037052 (0.024048) | 0.435134 / 0.258489 (0.176645) | 0.457245 / 0.293841 (0.163404) | 0.034945 / 0.128546 (-0.093601) | 0.010028 / 0.075646 (-0.065618) | 0.350724 / 0.419271 (-0.068548) | 0.064433 / 0.043533 (0.020901) | 0.417882 / 0.255139 (0.162743) | 0.445087 / 0.283200 (0.161887) | 0.027576 / 0.141683 (-0.114107) | 1.824066 / 1.452155 (0.371912) | 1.957568 / 1.492716 (0.464852) |\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) | 0.238568 / 0.018006 (0.220562) | 0.505289 / 0.000490 (0.504799) | 0.003527 / 0.000200 (0.003327) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032839 / 0.037411 (-0.004572) | 0.096708 / 0.014526 (0.082182) | 0.112100 / 0.176557 (-0.064456) | 0.177215 / 0.737135 (-0.559920) | 0.111273 / 0.296338 (-0.185066) |\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) | 0.475200 / 0.215209 (0.259991) | 4.725737 / 2.077655 (2.648082) | 2.414672 / 1.504120 (0.910552) | 2.196357 / 1.541195 (0.655162) | 2.329298 / 1.468490 (0.860808) | 0.575258 / 4.584777 (-4.009519) | 4.343630 / 3.745712 (0.597918) | 3.837665 / 5.269862 (-1.432196) | 2.497970 / 4.565676 (-2.067706) | 0.066467 / 0.424275 (-0.357808) | 0.008680 / 0.007607 (0.001073) | 0.569923 / 0.226044 (0.343878) | 5.634230 / 2.268929 (3.365302) | 2.959222 / 55.444624 (-52.485402) | 2.535954 / 6.876477 (-4.340523) | 2.804844 / 2.142072 (0.662771) | 0.682000 / 4.805227 (-4.123227) | 0.158193 / 6.500664 (-6.342471) | 0.072315 / 0.075469 (-0.003154) |\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) | 1.578148 / 1.841788 (-0.263639) | 22.993419 / 8.074308 (14.919110) | 16.524477 / 10.191392 (6.333085) | 0.169415 / 0.680424 (-0.511009) | 0.021520 / 0.534201 (-0.512681) | 0.455970 / 0.579283 (-0.123313) | 0.489022 / 0.434364 (0.054658) | 0.535656 / 0.540337 (-0.004682) | 0.802341 / 1.386936 (-0.584595) |\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) | 0.008002 / 0.011353 (-0.003351) | 0.005577 / 0.011008 (-0.005431) | 0.087803 / 0.038508 (0.049295) | 0.091285 / 0.023109 (0.068176) | 0.500514 / 0.275898 (0.224616) | 0.549770 / 0.323480 (0.226290) | 0.006125 / 0.007986 (-0.001861) | 0.004031 / 0.004328 (-0.000297) | 0.077941 / 0.004250 (0.073691) | 0.071419 / 0.037052 (0.034367) | 0.497570 / 0.258489 (0.239081) | 0.542454 / 0.293841 (0.248613) | 0.040827 / 0.128546 (-0.087719) | 0.011029 / 0.075646 (-0.064617) | 0.088788 / 0.419271 (-0.330484) | 0.056970 / 0.043533 (0.013438) | 0.523934 / 0.255139 (0.268795) | 0.552507 / 0.283200 (0.269308) | 0.029794 / 0.141683 (-0.111889) | 1.817778 / 1.452155 (0.365623) | 1.955843 / 1.492716 (0.463126) |\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) | 0.246992 / 0.018006 (0.228986) | 0.467879 / 0.000490 (0.467390) | 0.005439 / 0.000200 (0.005239) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037774 / 0.037411 (0.000363) | 0.109332 / 0.014526 (0.094806) | 0.120103 / 0.176557 (-0.056454) | 0.185259 / 0.737135 (-0.551876) | 0.126189 / 0.296338 (-0.170149) |\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) | 0.492856 / 0.215209 (0.277646) | 5.033209 / 2.077655 (2.955554) | 2.885551 / 1.504120 (1.381431) | 2.480304 / 1.541195 (0.939109) | 2.579092 / 1.468490 (1.110602) | 0.557671 / 4.584777 (-4.027106) | 4.352765 / 3.745712 (0.607053) | 4.039124 / 5.269862 (-1.230738) | 2.534342 / 4.565676 (-2.031335) | 0.067267 / 0.424275 (-0.357008) | 0.008891 / 0.007607 (0.001284) | 0.591592 / 0.226044 (0.365547) | 5.939982 / 2.268929 (3.671053) | 3.258389 / 55.444624 (-52.186235) | 2.843899 / 6.876477 (-4.032578) | 3.074217 / 2.142072 (0.932144) | 0.695065 / 4.805227 (-4.110162) | 0.156917 / 6.500664 (-6.343747) | 0.070185 / 0.075469 (-0.005284) |\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) | 1.586716 / 1.841788 (-0.255072) | 23.405837 / 8.074308 (15.331529) | 17.200851 / 10.191392 (7.009459) | 0.170073 / 0.680424 (-0.510351) | 0.023345 / 0.534201 (-0.510856) | 0.459192 / 0.579283 (-0.120091) | 0.477419 / 0.434364 (0.043055) | 0.558581 / 0.540337 (0.018244) | 0.814373 / 1.386936 (-0.572563) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#28bbe5667e6eaa1bb21685791fcf1a4ed1ef1777 \"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) | 0.006050 / 0.011353 (-0.005303) | 0.003661 / 0.011008 (-0.007348) | 0.081753 / 0.038508 (0.043245) | 0.061275 / 0.023109 (0.038166) | 0.316278 / 0.275898 (0.040380) | 0.350783 / 0.323480 (0.027303) | 0.004694 / 0.007986 (-0.003291) | 0.003003 / 0.004328 (-0.001326) | 0.062877 / 0.004250 (0.058627) | 0.046985 / 0.037052 (0.009933) | 0.315698 / 0.258489 (0.057208) | 0.364607 / 0.293841 (0.070766) | 0.027365 / 0.128546 (-0.101181) | 0.008016 / 0.075646 (-0.067631) | 0.261379 / 0.419271 (-0.157893) | 0.045173 / 0.043533 (0.001640) | 0.313499 / 0.255139 (0.058360) | 0.339383 / 0.283200 (0.056184) | 0.020855 / 0.141683 (-0.120828) | 1.429851 / 1.452155 (-0.022303) | 1.506112 / 1.492716 (0.013396) |\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) | 0.194872 / 0.018006 (0.176866) | 0.451951 / 0.000490 (0.451462) | 0.002790 / 0.000200 (0.002590) | 0.000070 / 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) | 0.024331 / 0.037411 (-0.013081) | 0.073156 / 0.014526 (0.058630) | 0.084054 / 0.176557 (-0.092502) | 0.145656 / 0.737135 (-0.591480) | 0.084998 / 0.296338 (-0.211340) |\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) | 0.391324 / 0.215209 (0.176115) | 3.898406 / 2.077655 (1.820751) | 1.891175 / 1.504120 (0.387055) | 1.698738 / 1.541195 (0.157543) | 1.774324 / 1.468490 (0.305834) | 0.495129 / 4.584777 (-4.089648) | 3.027027 / 3.745712 (-0.718685) | 2.821423 / 5.269862 (-2.448439) | 1.870761 / 4.565676 (-2.694915) | 0.057029 / 0.424275 (-0.367246) | 0.006715 / 0.007607 (-0.000892) | 0.465801 / 0.226044 (0.239757) | 4.650891 / 2.268929 (2.381962) | 2.425097 / 55.444624 (-53.019527) | 2.134731 / 6.876477 (-4.741745) | 2.312854 / 2.142072 (0.170781) | 0.589668 / 4.805227 (-4.215559) | 0.124673 / 6.500664 (-6.375991) | 0.060887 / 0.075469 (-0.014582) |\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) | 1.243622 / 1.841788 (-0.598166) | 18.501640 / 8.074308 (10.427332) | 13.853099 / 10.191392 (3.661707) | 0.130255 / 0.680424 (-0.550168) | 0.016824 / 0.534201 (-0.517377) | 0.332297 / 0.579283 (-0.246986) | 0.360346 / 0.434364 (-0.074018) | 0.388598 / 0.540337 (-0.151739) | 0.527551 / 1.386936 (-0.859385) |\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) | 0.006181 / 0.011353 (-0.005172) | 0.003688 / 0.011008 (-0.007320) | 0.063395 / 0.038508 (0.024887) | 0.062531 / 0.023109 (0.039422) | 0.446565 / 0.275898 (0.170667) | 0.485224 / 0.323480 (0.161744) | 0.004982 / 0.007986 (-0.003004) | 0.002961 / 0.004328 (-0.001367) | 0.063124 / 0.004250 (0.058874) | 0.050234 / 0.037052 (0.013182) | 0.449731 / 0.258489 (0.191242) | 0.487293 / 0.293841 (0.193452) | 0.028528 / 0.128546 (-0.100018) | 0.008210 / 0.075646 (-0.067436) | 0.069520 / 0.419271 (-0.349751) | 0.041026 / 0.043533 (-0.002507) | 0.451370 / 0.255139 (0.196231) | 0.469151 / 0.283200 (0.185951) | 0.021076 / 0.141683 (-0.120607) | 1.439185 / 1.452155 (-0.012970) | 1.492634 / 1.492716 (-0.000082) |\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) | 0.235932 / 0.018006 (0.217926) | 0.430070 / 0.000490 (0.429581) | 0.007347 / 0.000200 (0.007147) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026102 / 0.037411 (-0.011309) | 0.081333 / 0.014526 (0.066807) | 0.090111 / 0.176557 (-0.086446) | 0.144578 / 0.737135 (-0.592557) | 0.091961 / 0.296338 (-0.204378) |\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) | 0.455761 / 0.215209 (0.240552) | 4.536345 / 2.077655 (2.458690) | 2.496833 / 1.504120 (0.992713) | 2.323325 / 1.541195 (0.782130) | 2.388364 / 1.468490 (0.919873) | 0.512010 / 4.584777 (-4.072767) | 3.106268 / 3.745712 (-0.639444) | 2.879224 / 5.269862 (-2.390637) | 1.893859 / 4.565676 (-2.671818) | 0.059131 / 0.424275 (-0.365144) | 0.006763 / 0.007607 (-0.000844) | 0.528205 / 0.226044 (0.302161) | 5.296649 / 2.268929 (3.027720) | 2.933787 / 55.444624 (-52.510838) | 2.598258 / 6.876477 (-4.278218) | 2.768195 / 2.142072 (0.626123) | 0.597430 / 4.805227 (-4.207797) | 0.125865 / 6.500664 (-6.374799) | 0.061684 / 0.075469 (-0.013785) |\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) | 1.341194 / 1.841788 (-0.500594) | 18.948225 / 8.074308 (10.873917) | 14.912340 / 10.191392 (4.720948) | 0.146905 / 0.680424 (-0.533519) | 0.017952 / 0.534201 (-0.516249) | 0.332299 / 0.579283 (-0.246984) | 0.362733 / 0.434364 (-0.071631) | 0.388278 / 0.540337 (-0.152060) | 0.546436 / 1.386936 (-0.840500) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb4f8357de001df656f2ea7af27625e189c3995b \"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) | 0.008314 / 0.011353 (-0.003038) | 0.004904 / 0.011008 (-0.006105) | 0.097486 / 0.038508 (0.058978) | 0.074627 / 0.023109 (0.051518) | 0.396395 / 0.275898 (0.120497) | 0.440519 / 0.323480 (0.117039) | 0.005964 / 0.007986 (-0.002022) | 0.004203 / 0.004328 (-0.000126) | 0.079998 / 0.004250 (0.075747) | 0.055158 / 0.037052 (0.018106) | 0.415439 / 0.258489 (0.156950) | 0.476101 / 0.293841 (0.182260) | 0.044761 / 0.128546 (-0.083785) | 0.013966 / 0.075646 (-0.061680) | 0.351279 / 0.419271 (-0.067993) | 0.067250 / 0.043533 (0.023717) | 0.414310 / 0.255139 (0.159171) | 0.458104 / 0.283200 (0.174904) | 0.033678 / 0.141683 (-0.108005) | 1.730539 / 1.452155 (0.278385) | 1.840013 / 1.492716 (0.347297) |\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) | 0.272708 / 0.018006 (0.254702) | 0.593563 / 0.000490 (0.593074) | 0.005153 / 0.000200 (0.004953) | 0.000179 / 0.000054 (0.000125) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029595 / 0.037411 (-0.007816) | 0.087994 / 0.014526 (0.073469) | 0.106066 / 0.176557 (-0.070491) | 0.180491 / 0.737135 (-0.556644) | 0.103707 / 0.296338 (-0.192631) |\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) | 0.566711 / 0.215209 (0.351502) | 5.589034 / 2.077655 (3.511380) | 2.364034 / 1.504120 (0.859914) | 2.119050 / 1.541195 (0.577855) | 2.103823 / 1.468490 (0.635333) | 0.819906 / 4.584777 (-3.764871) | 5.178464 / 3.745712 (1.432752) | 4.433986 / 5.269862 (-0.835875) | 2.825470 / 4.565676 (-1.740207) | 0.096907 / 0.424275 (-0.327368) | 0.008573 / 0.007607 (0.000966) | 0.677607 / 0.226044 (0.451563) | 6.811090 / 2.268929 (4.542162) | 3.140923 / 55.444624 (-52.303701) | 2.492251 / 6.876477 (-4.384225) | 2.660231 / 2.142072 (0.518158) | 0.980573 / 4.805227 (-3.824655) | 0.209028 / 6.500664 (-6.291636) | 0.079413 / 0.075469 (0.003944) |\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) | 1.578861 / 1.841788 (-0.262926) | 22.518269 / 8.074308 (14.443961) | 21.335916 / 10.191392 (11.144524) | 0.211311 / 0.680424 (-0.469113) | 0.033216 / 0.534201 (-0.500985) | 0.473266 / 0.579283 (-0.106017) | 0.581650 / 0.434364 (0.147286) | 0.522442 / 0.540337 (-0.017895) | 0.729039 / 1.386936 (-0.657897) |\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) | 0.008349 / 0.011353 (-0.003003) | 0.005856 / 0.011008 (-0.005152) | 0.077855 / 0.038508 (0.039347) | 0.080608 / 0.023109 (0.057499) | 0.512533 / 0.275898 (0.236635) | 0.551862 / 0.323480 (0.228382) | 0.007004 / 0.007986 (-0.000982) | 0.004147 / 0.004328 (-0.000181) | 0.086625 / 0.004250 (0.082374) | 0.065962 / 0.037052 (0.028910) | 0.545590 / 0.258489 (0.287101) | 0.586313 / 0.293841 (0.292472) | 0.048719 / 0.128546 (-0.079827) | 0.014997 / 0.075646 (-0.060649) | 0.089510 / 0.419271 (-0.329761) | 0.060936 / 0.043533 (0.017404) | 0.498455 / 0.255139 (0.243316) | 0.535460 / 0.283200 (0.252260) | 0.034624 / 0.141683 (-0.107059) | 1.717401 / 1.452155 (0.265246) | 1.808772 / 1.492716 (0.316056) |\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) | 0.379504 / 0.018006 (0.361497) | 0.601756 / 0.000490 (0.601266) | 0.061740 / 0.000200 (0.061540) | 0.000497 / 0.000054 (0.000442) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031215 / 0.037411 (-0.006196) | 0.097501 / 0.014526 (0.082975) | 0.117434 / 0.176557 (-0.059122) | 0.166014 / 0.737135 (-0.571121) | 0.116466 / 0.296338 (-0.179873) |\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) | 0.699444 / 0.215209 (0.484235) | 6.329332 / 2.077655 (4.251678) | 3.072812 / 1.504120 (1.568693) | 2.729878 / 1.541195 (1.188683) | 2.933785 / 1.468490 (1.465295) | 0.935858 / 4.584777 (-3.648919) | 5.532532 / 3.745712 (1.786820) | 4.677139 / 5.269862 (-0.592722) | 2.963527 / 4.565676 (-1.602149) | 0.099661 / 0.424275 (-0.324614) | 0.009095 / 0.007607 (0.001488) | 0.751158 / 0.226044 (0.525114) | 7.652588 / 2.268929 (5.383660) | 3.802005 / 55.444624 (-51.642619) | 3.163126 / 6.876477 (-3.713351) | 3.401125 / 2.142072 (1.259052) | 0.998627 / 4.805227 (-3.806600) | 0.203310 / 6.500664 (-6.297354) | 0.073827 / 0.075469 (-0.001642) |\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) | 1.662989 / 1.841788 (-0.178799) | 23.777818 / 8.074308 (15.703510) | 20.855378 / 10.191392 (10.663986) | 0.279892 / 0.680424 (-0.400532) | 0.029303 / 0.534201 (-0.504898) | 0.473681 / 0.579283 (-0.105602) | 0.579148 / 0.434364 (0.144784) | 0.546931 / 0.540337 (0.006593) | 0.769740 / 1.386936 (-0.617196) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63114e9cb78fe02dc145f923dec13d545a8d0327 \"CML watermark\")\n" ]
2023-09-04T13:13:53
2023-09-04T14:58:34
2023-09-04T14:47:17
MEMBER
false
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If a split is empty, then the JSON split info should mention num_bytes = 0 and num_examples = 0. Until now they were omited because the JSON dumps ignore the fields that are equal to the default values. This is needed in datasets-server since we parse this information to the viewer
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Temporarily pin fsspec < 2023.9.0
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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) | 0.006494 / 0.011353 (-0.004859) | 0.003896 / 0.011008 (-0.007112) | 0.083940 / 0.038508 (0.045432) | 0.068335 / 0.023109 (0.045225) | 0.365770 / 0.275898 (0.089872) | 0.403702 / 0.323480 (0.080222) | 0.004005 / 0.007986 (-0.003981) | 0.003276 / 0.004328 (-0.001052) | 0.064877 / 0.004250 (0.060626) | 0.053524 / 0.037052 (0.016472) | 0.372951 / 0.258489 (0.114462) | 0.420935 / 0.293841 (0.127094) | 0.030656 / 0.128546 (-0.097890) | 0.009048 / 0.075646 (-0.066599) | 0.287607 / 0.419271 (-0.131665) | 0.052042 / 0.043533 (0.008509) | 0.371446 / 0.255139 (0.116307) | 0.408781 / 0.283200 (0.125581) | 0.024228 / 0.141683 (-0.117455) | 1.483325 / 1.452155 (0.031170) | 1.544321 / 1.492716 (0.051605) |\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) | 0.212355 / 0.018006 (0.194349) | 0.463298 / 0.000490 (0.462808) | 0.005170 / 0.000200 (0.004970) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027824 / 0.037411 (-0.009587) | 0.081880 / 0.014526 (0.067354) | 0.094886 / 0.176557 (-0.081670) | 0.150024 / 0.737135 (-0.587111) | 0.096643 / 0.296338 (-0.199696) |\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) | 0.388521 / 0.215209 (0.173312) | 3.877251 / 2.077655 (1.799596) | 1.931085 / 1.504120 (0.426965) | 1.766525 / 1.541195 (0.225330) | 1.814802 / 1.468490 (0.346312) | 0.489478 / 4.584777 (-4.095299) | 3.570973 / 3.745712 (-0.174739) | 3.190211 / 5.269862 (-2.079651) | 2.015670 / 4.565676 (-2.550006) | 0.057773 / 0.424275 (-0.366503) | 0.007611 / 0.007607 (0.000004) | 0.462162 / 0.226044 (0.236117) | 4.616173 / 2.268929 (2.347244) | 2.360531 / 55.444624 (-53.084094) | 2.053680 / 6.876477 (-4.822797) | 2.228057 / 2.142072 (0.085985) | 0.584921 / 4.805227 (-4.220306) | 0.132470 / 6.500664 (-6.368194) | 0.060482 / 0.075469 (-0.014987) |\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) | 1.263393 / 1.841788 (-0.578394) | 19.416841 / 8.074308 (11.342532) | 14.049032 / 10.191392 (3.857640) | 0.162822 / 0.680424 (-0.517602) | 0.018189 / 0.534201 (-0.516012) | 0.391142 / 0.579283 (-0.188141) | 0.409367 / 0.434364 (-0.024997) | 0.454589 / 0.540337 (-0.085748) | 0.632946 / 1.386936 (-0.753990) |\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) | 0.006568 / 0.011353 (-0.004785) | 0.004026 / 0.011008 (-0.006982) | 0.064522 / 0.038508 (0.026014) | 0.071738 / 0.023109 (0.048629) | 0.395771 / 0.275898 (0.119873) | 0.421553 / 0.323480 (0.098073) | 0.005291 / 0.007986 (-0.002694) | 0.003266 / 0.004328 (-0.001063) | 0.064464 / 0.004250 (0.060214) | 0.054622 / 0.037052 (0.017569) | 0.395010 / 0.258489 (0.136521) | 0.433895 / 0.293841 (0.140054) | 0.031670 / 0.128546 (-0.096876) | 0.008536 / 0.075646 (-0.067111) | 0.071059 / 0.419271 (-0.348212) | 0.047117 / 0.043533 (0.003584) | 0.391210 / 0.255139 (0.136071) | 0.411685 / 0.283200 (0.128486) | 0.022779 / 0.141683 (-0.118904) | 1.479900 / 1.452155 (0.027746) | 1.551853 / 1.492716 (0.059137) |\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) | 0.332814 / 0.018006 (0.314807) | 0.460654 / 0.000490 (0.460164) | 0.062257 / 0.000200 (0.062057) | 0.000374 / 0.000054 (0.000319) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005610) | 0.090730 / 0.014526 (0.076204) | 0.102955 / 0.176557 (-0.073602) | 0.155928 / 0.737135 (-0.581207) | 0.103028 / 0.296338 (-0.193310) |\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) | 0.434460 / 0.215209 (0.219251) | 4.331550 / 2.077655 (2.253895) | 2.335990 / 1.504120 (0.831870) | 2.183985 / 1.541195 (0.642790) | 2.233086 / 1.468490 (0.764595) | 0.488484 / 4.584777 (-4.096293) | 3.603856 / 3.745712 (-0.141856) | 3.229833 / 5.269862 (-2.040029) | 2.007366 / 4.565676 (-2.558311) | 0.057658 / 0.424275 (-0.366617) | 0.007339 / 0.007607 (-0.000268) | 0.512812 / 0.226044 (0.286768) | 5.141497 / 2.268929 (2.872569) | 2.847383 / 55.444624 (-52.597241) | 2.467010 / 6.876477 (-4.409467) | 2.644995 / 2.142072 (0.502923) | 0.581385 / 4.805227 (-4.223842) | 0.130755 / 6.500664 (-6.369909) | 0.058834 / 0.075469 (-0.016635) |\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) | 1.350162 / 1.841788 (-0.491626) | 19.768412 / 8.074308 (11.694104) | 15.079196 / 10.191392 (4.887804) | 0.167083 / 0.680424 (-0.513341) | 0.020372 / 0.534201 (-0.513829) | 0.402685 / 0.579283 (-0.176598) | 0.408338 / 0.434364 (-0.026026) | 0.476788 / 0.540337 (-0.063550) | 0.654765 / 1.386936 (-0.732171) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ff803c7e9f256c5a137c25c090e18d844f9fc6e4 \"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) | 0.008047 / 0.011353 (-0.003305) | 0.004662 / 0.011008 (-0.006346) | 0.102487 / 0.038508 (0.063978) | 0.096832 / 0.023109 (0.073723) | 0.375298 / 0.275898 (0.099400) | 0.420604 / 0.323480 (0.097124) | 0.004655 / 0.007986 (-0.003330) | 0.005699 / 0.004328 (0.001370) | 0.077681 / 0.004250 (0.073430) | 0.065987 / 0.037052 (0.028935) | 0.393146 / 0.258489 (0.134657) | 0.436324 / 0.293841 (0.142483) | 0.036168 / 0.128546 (-0.092378) | 0.010398 / 0.075646 (-0.065248) | 0.347579 / 0.419271 (-0.071693) | 0.061723 / 0.043533 (0.018190) | 0.377439 / 0.255139 (0.122300) | 0.416666 / 0.283200 (0.133467) | 0.031874 / 0.141683 (-0.109809) | 1.818885 / 1.452155 (0.366730) | 1.904749 / 1.492716 (0.412032) |\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) | 0.240497 / 0.018006 (0.222491) | 0.507907 / 0.000490 (0.507417) | 0.004574 / 0.000200 (0.004374) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033504 / 0.037411 (-0.003907) | 0.102919 / 0.014526 (0.088393) | 0.113014 / 0.176557 (-0.063543) | 0.181111 / 0.737135 (-0.556024) | 0.115047 / 0.296338 (-0.181291) |\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) | 0.453640 / 0.215209 (0.238431) | 4.514604 / 2.077655 (2.436949) | 2.219758 / 1.504120 (0.715638) | 2.004735 / 1.541195 (0.463541) | 2.112817 / 1.468490 (0.644327) | 0.579534 / 4.584777 (-4.005243) | 4.095994 / 3.745712 (0.350282) | 3.887204 / 5.269862 (-1.382658) | 2.461755 / 4.565676 (-2.103921) | 0.068930 / 0.424275 (-0.355345) | 0.009102 / 0.007607 (0.001495) | 0.540031 / 0.226044 (0.313987) | 5.394324 / 2.268929 (3.125396) | 2.738906 / 55.444624 (-52.705719) | 2.332041 / 6.876477 (-4.544436) | 2.600764 / 2.142072 (0.458692) | 0.697859 / 4.805227 (-4.107368) | 0.159247 / 6.500664 (-6.341417) | 0.073339 / 0.075469 (-0.002130) |\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) | 1.561082 / 1.841788 (-0.280706) | 23.581031 / 8.074308 (15.506723) | 17.011085 / 10.191392 (6.819693) | 0.196115 / 0.680424 (-0.484308) | 0.022050 / 0.534201 (-0.512151) | 0.470865 / 0.579283 (-0.108418) | 0.480539 / 0.434364 (0.046175) | 0.546458 / 0.540337 (0.006120) | 0.744353 / 1.386936 (-0.642583) |\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) | 0.007884 / 0.011353 (-0.003468) | 0.004723 / 0.011008 (-0.006286) | 0.076431 / 0.038508 (0.037923) | 0.087016 / 0.023109 (0.063907) | 0.501880 / 0.275898 (0.225982) | 0.546286 / 0.323480 (0.222806) | 0.006224 / 0.007986 (-0.001762) | 0.003858 / 0.004328 (-0.000471) | 0.076485 / 0.004250 (0.072234) | 0.066758 / 0.037052 (0.029706) | 0.510090 / 0.258489 (0.251601) | 0.553935 / 0.293841 (0.260094) | 0.037785 / 0.128546 (-0.090761) | 0.009946 / 0.075646 (-0.065700) | 0.084001 / 0.419271 (-0.335270) | 0.056732 / 0.043533 (0.013199) | 0.490724 / 0.255139 (0.235585) | 0.528367 / 0.283200 (0.245168) | 0.026082 / 0.141683 (-0.115601) | 1.769200 / 1.452155 (0.317045) | 1.847559 / 1.492716 (0.354843) |\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) | 0.306752 / 0.018006 (0.288745) | 0.481215 / 0.000490 (0.480725) | 0.048231 / 0.000200 (0.048031) | 0.000249 / 0.000054 (0.000194) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039517 / 0.037411 (0.002106) | 0.112884 / 0.014526 (0.098359) | 0.123858 / 0.176557 (-0.052698) | 0.188260 / 0.737135 (-0.548875) | 0.125819 / 0.296338 (-0.170520) |\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) | 0.515260 / 0.215209 (0.300051) | 5.125038 / 2.077655 (3.047383) | 2.785122 / 1.504120 (1.281003) | 2.590753 / 1.541195 (1.049558) | 2.682084 / 1.468490 (1.213594) | 0.581162 / 4.584777 (-4.003615) | 4.241776 / 3.745712 (0.496063) | 3.860979 / 5.269862 (-1.408883) | 2.434203 / 4.565676 (-2.131473) | 0.068580 / 0.424275 (-0.355695) | 0.008700 / 0.007607 (0.001093) | 0.604712 / 0.226044 (0.378667) | 6.044240 / 2.268929 (3.775311) | 3.379734 / 55.444624 (-52.064890) | 2.968906 / 6.876477 (-3.907571) | 3.195775 / 2.142072 (1.053703) | 0.702431 / 4.805227 (-4.102796) | 0.158752 / 6.500664 (-6.341912) | 0.072795 / 0.075469 (-0.002674) |\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) | 1.616354 / 1.841788 (-0.225434) | 24.258731 / 8.074308 (16.184423) | 17.505483 / 10.191392 (7.314091) | 0.173445 / 0.680424 (-0.506979) | 0.023215 / 0.534201 (-0.510986) | 0.472975 / 0.579283 (-0.106308) | 0.478425 / 0.434364 (0.044061) | 0.566950 / 0.540337 (0.026612) | 0.767648 / 1.386936 (-0.619288) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1d520a5226268f2c6f0303de3e8bfd72198b082 \"CML watermark\")\n" ]
2023-09-04T07:07:07
2023-09-04T07:40:23
2023-09-04T07:30:00
MEMBER
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Temporarily pin fsspec < 2023.9.0 until permanent solution is found. Hot fix #6209.
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6,209
CI is broken with AssertionError: 3 failed, 12 errors
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2023-09-04T06:47:05
2023-09-04T07:30:01
2023-09-04T07:30:01
MEMBER
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Our CI is broken: 3 failed, 12 errors See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041 ``` =========================== short test summary info ============================ FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1) + where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt']) FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10 + where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10 ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3 + where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv']) = 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) = ```
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6,208
Do not filter out .zip extensions from no-script datasets
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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) | 0.006797 / 0.011353 (-0.004556) | 0.003966 / 0.011008 (-0.007042) | 0.085296 / 0.038508 (0.046788) | 0.076873 / 0.023109 (0.053764) | 0.355795 / 0.275898 (0.079897) | 0.397132 / 0.323480 (0.073652) | 0.005325 / 0.007986 (-0.002660) | 0.003343 / 0.004328 (-0.000986) | 0.064966 / 0.004250 (0.060716) | 0.054519 / 0.037052 (0.017467) | 0.357864 / 0.258489 (0.099374) | 0.409238 / 0.293841 (0.115397) | 0.031620 / 0.128546 (-0.096926) | 0.008529 / 0.075646 (-0.067117) | 0.288502 / 0.419271 (-0.130769) | 0.053260 / 0.043533 (0.009728) | 0.355245 / 0.255139 (0.100106) | 0.384139 / 0.283200 (0.100939) | 0.024507 / 0.141683 (-0.117176) | 1.494696 / 1.452155 (0.042541) | 1.579847 / 1.492716 (0.087130) |\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) | 0.204011 / 0.018006 (0.186005) | 0.451729 / 0.000490 (0.451239) | 0.004628 / 0.000200 (0.004428) | 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) | 0.028342 / 0.037411 (-0.009069) | 0.084647 / 0.014526 (0.070121) | 0.096174 / 0.176557 (-0.080383) | 0.151753 / 0.737135 (-0.585382) | 0.096347 / 0.296338 (-0.199991) |\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) | 0.387179 / 0.215209 (0.171970) | 3.861552 / 2.077655 (1.783898) | 1.844033 / 1.504120 (0.339913) | 1.678811 / 1.541195 (0.137616) | 1.793207 / 1.468490 (0.324717) | 0.485836 / 4.584777 (-4.098941) | 3.566274 / 3.745712 (-0.179438) | 3.269888 / 5.269862 (-1.999974) | 2.042850 / 4.565676 (-2.522827) | 0.057088 / 0.424275 (-0.367187) | 0.007627 / 0.007607 (0.000019) | 0.460510 / 0.226044 (0.234465) | 4.602019 / 2.268929 (2.333090) | 2.390984 / 55.444624 (-53.053641) | 1.976150 / 6.876477 (-4.900327) | 2.193394 / 2.142072 (0.051322) | 0.582775 / 4.805227 (-4.222453) | 0.133408 / 6.500664 (-6.367256) | 0.060577 / 0.075469 (-0.014893) |\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) | 1.248505 / 1.841788 (-0.593283) | 19.771301 / 8.074308 (11.696993) | 14.327871 / 10.191392 (4.136479) | 0.155288 / 0.680424 (-0.525136) | 0.018310 / 0.534201 (-0.515891) | 0.393664 / 0.579283 (-0.185619) | 0.410578 / 0.434364 (-0.023786) | 0.459301 / 0.540337 (-0.081037) | 0.631921 / 1.386936 (-0.755015) |\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) | 0.006827 / 0.011353 (-0.004526) | 0.004094 / 0.011008 (-0.006915) | 0.065299 / 0.038508 (0.026791) | 0.079496 / 0.023109 (0.056387) | 0.403661 / 0.275898 (0.127763) | 0.434449 / 0.323480 (0.110969) | 0.005398 / 0.007986 (-0.002588) | 0.003410 / 0.004328 (-0.000919) | 0.064832 / 0.004250 (0.060582) | 0.056303 / 0.037052 (0.019250) | 0.397848 / 0.258489 (0.139359) | 0.438244 / 0.293841 (0.144403) | 0.032637 / 0.128546 (-0.095909) | 0.008584 / 0.075646 (-0.067063) | 0.071406 / 0.419271 (-0.347866) | 0.048265 / 0.043533 (0.004732) | 0.397814 / 0.255139 (0.142675) | 0.421601 / 0.283200 (0.138402) | 0.023815 / 0.141683 (-0.117868) | 1.504814 / 1.452155 (0.052659) | 1.577185 / 1.492716 (0.084469) |\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) | 0.231775 / 0.018006 (0.213769) | 0.445437 / 0.000490 (0.444948) | 0.005252 / 0.000200 (0.005052) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032777 / 0.037411 (-0.004634) | 0.095054 / 0.014526 (0.080528) | 0.106429 / 0.176557 (-0.070127) | 0.160111 / 0.737135 (-0.577024) | 0.108075 / 0.296338 (-0.188263) |\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) | 0.426034 / 0.215209 (0.210825) | 4.244668 / 2.077655 (2.167013) | 2.257938 / 1.504120 (0.753818) | 2.087993 / 1.541195 (0.546798) | 2.170878 / 1.468490 (0.702387) | 0.485228 / 4.584777 (-4.099549) | 3.725912 / 3.745712 (-0.019800) | 3.286925 / 5.269862 (-1.982937) | 2.059929 / 4.565676 (-2.505748) | 0.057813 / 0.424275 (-0.366462) | 0.007518 / 0.007607 (-0.000089) | 0.506632 / 0.226044 (0.280588) | 5.048340 / 2.268929 (2.779411) | 2.744756 / 55.444624 (-52.699869) | 2.406636 / 6.876477 (-4.469841) | 2.617552 / 2.142072 (0.475480) | 0.588476 / 4.805227 (-4.216751) | 0.133518 / 6.500664 (-6.367146) | 0.060778 / 0.075469 (-0.014691) |\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) | 1.356416 / 1.841788 (-0.485372) | 20.467516 / 8.074308 (12.393208) | 15.265443 / 10.191392 (5.074051) | 0.169201 / 0.680424 (-0.511223) | 0.020087 / 0.534201 (-0.514114) | 0.402332 / 0.579283 (-0.176951) | 0.414848 / 0.434364 (-0.019516) | 0.470422 / 0.540337 (-0.069916) | 0.647266 / 1.386936 (-0.739670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb001b4cee7f1d71e393c3ad489a8a5cd8119df5 \"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) | 0.005804 / 0.011353 (-0.005549) | 0.003519 / 0.011008 (-0.007489) | 0.080003 / 0.038508 (0.041495) | 0.055419 / 0.023109 (0.032309) | 0.395254 / 0.275898 (0.119356) | 0.432714 / 0.323480 (0.109234) | 0.004438 / 0.007986 (-0.003548) | 0.002832 / 0.004328 (-0.001496) | 0.062026 / 0.004250 (0.057775) | 0.044334 / 0.037052 (0.007282) | 0.401278 / 0.258489 (0.142789) | 0.451516 / 0.293841 (0.157675) | 0.026791 / 0.128546 (-0.101755) | 0.007946 / 0.075646 (-0.067700) | 0.265166 / 0.419271 (-0.154106) | 0.044119 / 0.043533 (0.000586) | 0.399621 / 0.255139 (0.144482) | 0.422808 / 0.283200 (0.139609) | 0.019998 / 0.141683 (-0.121685) | 1.433559 / 1.452155 (-0.018596) | 1.596902 / 1.492716 (0.104186) |\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) | 0.195662 / 0.018006 (0.177656) | 0.423167 / 0.000490 (0.422677) | 0.003426 / 0.000200 (0.003227) | 0.000066 / 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) | 0.023318 / 0.037411 (-0.014094) | 0.072532 / 0.014526 (0.058006) | 0.082181 / 0.176557 (-0.094375) | 0.142214 / 0.737135 (-0.594921) | 0.083423 / 0.296338 (-0.212915) |\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) | 0.402270 / 0.215209 (0.187061) | 4.027607 / 2.077655 (1.949953) | 2.059803 / 1.504120 (0.555684) | 1.865115 / 1.541195 (0.323920) | 1.934976 / 1.468490 (0.466485) | 0.502145 / 4.584777 (-4.082632) | 2.970865 / 3.745712 (-0.774847) | 2.784155 / 5.269862 (-2.485707) | 1.822003 / 4.565676 (-2.743673) | 0.057699 / 0.424275 (-0.366576) | 0.006668 / 0.007607 (-0.000939) | 0.471164 / 0.226044 (0.245120) | 4.733079 / 2.268929 (2.464150) | 2.445119 / 55.444624 (-52.999505) | 2.132956 / 6.876477 (-4.743521) | 2.335998 / 2.142072 (0.193926) | 0.594881 / 4.805227 (-4.210347) | 0.125801 / 6.500664 (-6.374863) | 0.060780 / 0.075469 (-0.014689) |\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) | 1.233170 / 1.841788 (-0.608618) | 17.942205 / 8.074308 (9.867897) | 13.587020 / 10.191392 (3.395628) | 0.142110 / 0.680424 (-0.538314) | 0.016600 / 0.534201 (-0.517601) | 0.328659 / 0.579283 (-0.250624) | 0.347759 / 0.434364 (-0.086605) | 0.378651 / 0.540337 (-0.161687) | 0.523474 / 1.386936 (-0.863462) |\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) | 0.006028 / 0.011353 (-0.005325) | 0.003552 / 0.011008 (-0.007456) | 0.062175 / 0.038508 (0.023667) | 0.057602 / 0.023109 (0.034493) | 0.444585 / 0.275898 (0.168687) | 0.471238 / 0.323480 (0.147758) | 0.004562 / 0.007986 (-0.003423) | 0.002871 / 0.004328 (-0.001457) | 0.063101 / 0.004250 (0.058851) | 0.046072 / 0.037052 (0.009020) | 0.448253 / 0.258489 (0.189764) | 0.478734 / 0.293841 (0.184893) | 0.028463 / 0.128546 (-0.100084) | 0.008090 / 0.075646 (-0.067557) | 0.068142 / 0.419271 (-0.351130) | 0.040517 / 0.043533 (-0.003016) | 0.447145 / 0.255139 (0.192006) | 0.469472 / 0.283200 (0.186273) | 0.019391 / 0.141683 (-0.122291) | 1.471195 / 1.452155 (0.019040) | 1.532966 / 1.492716 (0.040249) |\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) | 0.259894 / 0.018006 (0.241888) | 0.412987 / 0.000490 (0.412497) | 0.020780 / 0.000200 (0.020580) | 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) | 0.026352 / 0.037411 (-0.011060) | 0.080024 / 0.014526 (0.065498) | 0.088041 / 0.176557 (-0.088516) | 0.142987 / 0.737135 (-0.594148) | 0.090108 / 0.296338 (-0.206231) |\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) | 0.458874 / 0.215209 (0.243665) | 4.573005 / 2.077655 (2.495351) | 2.507885 / 1.504120 (1.003765) | 2.335432 / 1.541195 (0.794238) | 2.379617 / 1.468490 (0.911126) | 0.503331 / 4.584777 (-4.081446) | 3.078284 / 3.745712 (-0.667428) | 2.750580 / 5.269862 (-2.519282) | 1.828100 / 4.565676 (-2.737577) | 0.057572 / 0.424275 (-0.366703) | 0.006553 / 0.007607 (-0.001054) | 0.532283 / 0.226044 (0.306239) | 5.310584 / 2.268929 (3.041656) | 2.943559 / 55.444624 (-52.501065) | 2.587544 / 6.876477 (-4.288932) | 2.718261 / 2.142072 (0.576188) | 0.590267 / 4.805227 (-4.214961) | 0.123229 / 6.500664 (-6.377435) | 0.060219 / 0.075469 (-0.015250) |\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) | 1.340773 / 1.841788 (-0.501014) | 18.420766 / 8.074308 (10.346458) | 14.630550 / 10.191392 (4.439158) | 0.146666 / 0.680424 (-0.533758) | 0.017905 / 0.534201 (-0.516296) | 0.332483 / 0.579283 (-0.246801) | 0.355490 / 0.434364 (-0.078874) | 0.382618 / 0.540337 (-0.157720) | 0.531336 / 1.386936 (-0.855600) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d438617fc577bc0222527714edafea0c52ebf239 \"CML watermark\")\n", "There were CI errors unrelated to this PR.", "<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) | 0.008702 / 0.011353 (-0.002651) | 0.005060 / 0.011008 (-0.005948) | 0.097017 / 0.038508 (0.058509) | 0.073740 / 0.023109 (0.050631) | 0.435138 / 0.275898 (0.159240) | 0.512776 / 0.323480 (0.189296) | 0.006186 / 0.007986 (-0.001800) | 0.003970 / 0.004328 (-0.000358) | 0.089523 / 0.004250 (0.085273) | 0.054441 / 0.037052 (0.017389) | 0.447415 / 0.258489 (0.188926) | 0.464851 / 0.293841 (0.171010) | 0.050264 / 0.128546 (-0.078283) | 0.016643 / 0.075646 (-0.059004) | 0.350565 / 0.419271 (-0.068707) | 0.071220 / 0.043533 (0.027687) | 0.432531 / 0.255139 (0.177392) | 0.472994 / 0.283200 (0.189795) | 0.040229 / 0.141683 (-0.101454) | 1.743431 / 1.452155 (0.291276) | 1.778653 / 1.492716 (0.285936) |\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) | 0.261992 / 0.018006 (0.243986) | 0.571979 / 0.000490 (0.571489) | 0.006270 / 0.000200 (0.006071) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027821 / 0.037411 (-0.009590) | 0.081874 / 0.014526 (0.067348) | 0.103725 / 0.176557 (-0.072831) | 0.170593 / 0.737135 (-0.566542) | 0.108749 / 0.296338 (-0.187590) |\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) | 0.690774 / 0.215209 (0.475565) | 6.770902 / 2.077655 (4.693247) | 2.887218 / 1.504120 (1.383098) | 2.456226 / 1.541195 (0.915032) | 2.509422 / 1.468490 (1.040932) | 0.768451 / 4.584777 (-3.816326) | 4.988933 / 3.745712 (1.243221) | 4.151460 / 5.269862 (-1.118402) | 2.640472 / 4.565676 (-1.925205) | 0.093522 / 0.424275 (-0.330753) | 0.008614 / 0.007607 (0.001007) | 0.696281 / 0.226044 (0.470237) | 6.721077 / 2.268929 (4.452149) | 3.229760 / 55.444624 (-52.214864) | 2.668521 / 6.876477 (-4.207956) | 2.866420 / 2.142072 (0.724347) | 0.945328 / 4.805227 (-3.859899) | 0.197645 / 6.500664 (-6.303019) | 0.074442 / 0.075469 (-0.001027) |\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) | 1.630468 / 1.841788 (-0.211320) | 22.991661 / 8.074308 (14.917353) | 19.816919 / 10.191392 (9.625527) | 0.257410 / 0.680424 (-0.423014) | 0.027228 / 0.534201 (-0.506973) | 0.444515 / 0.579283 (-0.134768) | 0.597067 / 0.434364 (0.162703) | 0.528151 / 0.540337 (-0.012186) | 0.771276 / 1.386936 (-0.615660) |\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) | 0.009154 / 0.011353 (-0.002199) | 0.004648 / 0.011008 (-0.006360) | 0.073054 / 0.038508 (0.034546) | 0.077146 / 0.023109 (0.054037) | 0.481659 / 0.275898 (0.205761) | 0.516985 / 0.323480 (0.193505) | 0.007447 / 0.007986 (-0.000538) | 0.003890 / 0.004328 (-0.000438) | 0.078701 / 0.004250 (0.074450) | 0.059183 / 0.037052 (0.022131) | 0.475350 / 0.258489 (0.216861) | 0.547834 / 0.293841 (0.253993) | 0.058440 / 0.128546 (-0.070106) | 0.013563 / 0.075646 (-0.062083) | 0.084320 / 0.419271 (-0.334951) | 0.065965 / 0.043533 (0.022433) | 0.483541 / 0.255139 (0.228402) | 0.513940 / 0.283200 (0.230740) | 0.042889 / 0.141683 (-0.098794) | 1.676050 / 1.452155 (0.223895) | 1.759206 / 1.492716 (0.266489) |\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) | 0.274848 / 0.018006 (0.256841) | 0.588965 / 0.000490 (0.588475) | 0.006312 / 0.000200 (0.006112) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033871 / 0.037411 (-0.003540) | 0.104013 / 0.014526 (0.089487) | 0.118457 / 0.176557 (-0.058099) | 0.178268 / 0.737135 (-0.558868) | 0.116972 / 0.296338 (-0.179366) |\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) | 0.609952 / 0.215209 (0.394743) | 5.788754 / 2.077655 (3.711100) | 2.812166 / 1.504120 (1.308046) | 2.362861 / 1.541195 (0.821666) | 2.641295 / 1.468490 (1.172804) | 0.767601 / 4.584777 (-3.817176) | 5.027439 / 3.745712 (1.281727) | 4.612511 / 5.269862 (-0.657351) | 2.654364 / 4.565676 (-1.911312) | 0.103100 / 0.424275 (-0.321175) | 0.012233 / 0.007607 (0.004626) | 0.749283 / 0.226044 (0.523238) | 7.511093 / 2.268929 (5.242165) | 3.585867 / 55.444624 (-51.858757) | 3.255110 / 6.876477 (-3.621366) | 3.260174 / 2.142072 (1.118102) | 0.958422 / 4.805227 (-3.846806) | 0.209096 / 6.500664 (-6.291568) | 0.075014 / 0.075469 (-0.000455) |\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) | 1.728283 / 1.841788 (-0.113504) | 25.411147 / 8.074308 (17.336839) | 21.335202 / 10.191392 (11.143810) | 0.199090 / 0.680424 (-0.481334) | 0.031288 / 0.534201 (-0.502913) | 0.449226 / 0.579283 (-0.130057) | 0.555570 / 0.434364 (0.121206) | 0.570297 / 0.540337 (0.029960) | 0.758673 / 1.386936 (-0.628263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa696b4b4f0d11c5b8592eb31cb1d54a707e3d33 \"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) | 0.006862 / 0.011353 (-0.004491) | 0.003959 / 0.011008 (-0.007049) | 0.087219 / 0.038508 (0.048711) | 0.078335 / 0.023109 (0.055226) | 0.319019 / 0.275898 (0.043121) | 0.342871 / 0.323480 (0.019391) | 0.004065 / 0.007986 (-0.003921) | 0.004346 / 0.004328 (0.000017) | 0.065243 / 0.004250 (0.060993) | 0.056698 / 0.037052 (0.019646) | 0.326906 / 0.258489 (0.068417) | 0.354323 / 0.293841 (0.060482) | 0.031252 / 0.128546 (-0.097295) | 0.008587 / 0.075646 (-0.067060) | 0.300323 / 0.419271 (-0.118948) | 0.052810 / 0.043533 (0.009277) | 0.323866 / 0.255139 (0.068727) | 0.346011 / 0.283200 (0.062811) | 0.025584 / 0.141683 (-0.116099) | 1.464475 / 1.452155 (0.012320) | 1.530868 / 1.492716 (0.038152) |\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) | 0.208927 / 0.018006 (0.190921) | 0.454147 / 0.000490 (0.453657) | 0.003945 / 0.000200 (0.003746) | 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) | 0.029901 / 0.037411 (-0.007511) | 0.088889 / 0.014526 (0.074363) | 0.098181 / 0.176557 (-0.078375) | 0.156787 / 0.737135 (-0.580349) | 0.099015 / 0.296338 (-0.197324) |\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) | 0.384981 / 0.215209 (0.169772) | 3.831040 / 2.077655 (1.753386) | 1.858312 / 1.504120 (0.354192) | 1.686846 / 1.541195 (0.145651) | 1.771509 / 1.468490 (0.303019) | 0.485618 / 4.584777 (-4.099159) | 3.430961 / 3.745712 (-0.314751) | 3.264489 / 5.269862 (-2.005372) | 2.040125 / 4.565676 (-2.525551) | 0.057218 / 0.424275 (-0.367057) | 0.007640 / 0.007607 (0.000033) | 0.468072 / 0.226044 (0.242027) | 4.677214 / 2.268929 (2.408286) | 2.348425 / 55.444624 (-53.096199) | 1.994352 / 6.876477 (-4.882125) | 2.217020 / 2.142072 (0.074948) | 0.587467 / 4.805227 (-4.217760) | 0.133550 / 6.500664 (-6.367114) | 0.060571 / 0.075469 (-0.014898) |\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) | 1.271003 / 1.841788 (-0.570785) | 19.986365 / 8.074308 (11.912057) | 14.574046 / 10.191392 (4.382654) | 0.146212 / 0.680424 (-0.534212) | 0.018320 / 0.534201 (-0.515881) | 0.394524 / 0.579283 (-0.184759) | 0.399707 / 0.434364 (-0.034657) | 0.458965 / 0.540337 (-0.081372) | 0.619940 / 1.386936 (-0.766996) |\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) | 0.006982 / 0.011353 (-0.004371) | 0.004061 / 0.011008 (-0.006947) | 0.064520 / 0.038508 (0.026012) | 0.076828 / 0.023109 (0.053719) | 0.402989 / 0.275898 (0.127090) | 0.439697 / 0.323480 (0.116217) | 0.005511 / 0.007986 (-0.002475) | 0.003378 / 0.004328 (-0.000950) | 0.064727 / 0.004250 (0.060477) | 0.058114 / 0.037052 (0.021062) | 0.402054 / 0.258489 (0.143565) | 0.442377 / 0.293841 (0.148536) | 0.032808 / 0.128546 (-0.095738) | 0.008604 / 0.075646 (-0.067043) | 0.070994 / 0.419271 (-0.348278) | 0.048738 / 0.043533 (0.005205) | 0.399786 / 0.255139 (0.144647) | 0.423537 / 0.283200 (0.140338) | 0.022397 / 0.141683 (-0.119286) | 1.504613 / 1.452155 (0.052458) | 1.571064 / 1.492716 (0.078348) |\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) | 0.226876 / 0.018006 (0.208870) | 0.451477 / 0.000490 (0.450987) | 0.004511 / 0.000200 (0.004311) | 0.000095 / 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) | 0.032998 / 0.037411 (-0.004413) | 0.095843 / 0.014526 (0.081317) | 0.105684 / 0.176557 (-0.070873) | 0.158175 / 0.737135 (-0.578960) | 0.107297 / 0.296338 (-0.189041) |\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) | 0.434912 / 0.215209 (0.219703) | 4.326394 / 2.077655 (2.248740) | 2.287310 / 1.504120 (0.783190) | 2.127987 / 1.541195 (0.586793) | 2.202485 / 1.468490 (0.733995) | 0.494305 / 4.584777 (-4.090472) | 3.575176 / 3.745712 (-0.170536) | 3.354358 / 5.269862 (-1.915504) | 2.074293 / 4.565676 (-2.491383) | 0.058967 / 0.424275 (-0.365308) | 0.007712 / 0.007607 (0.000105) | 0.513734 / 0.226044 (0.287690) | 5.107538 / 2.268929 (2.838610) | 2.776190 / 55.444624 (-52.668434) | 2.425051 / 6.876477 (-4.451426) | 2.666715 / 2.142072 (0.524643) | 0.598844 / 4.805227 (-4.206383) | 0.134186 / 6.500664 (-6.366478) | 0.062403 / 0.075469 (-0.013066) |\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) | 1.346730 / 1.841788 (-0.495058) | 20.533190 / 8.074308 (12.458882) | 15.174443 / 10.191392 (4.983051) | 0.167204 / 0.680424 (-0.513219) | 0.020619 / 0.534201 (-0.513582) | 0.399033 / 0.579283 (-0.180250) | 0.394428 / 0.434364 (-0.039936) | 0.468792 / 0.540337 (-0.071545) | 0.640122 / 1.386936 (-0.746814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2c4c2b529e2a262a5006e4caa55fbc003378006a \"CML watermark\")\n" ]
2023-09-04T06:07:12
2023-09-04T09:22:19
2023-09-04T09:13:32
MEMBER
false
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This PR is a hotfix of: - #6207 That PR introduced the filtering out of `.zip` extensions. This PR reverts that. Hot fix #6207. Maybe we should do patch releases: the bug was introduced in 2.13.1. CC: @lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/6208/timeline
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https://github.com/huggingface/datasets/issues/6207
1,879,555,234
I_kwDODunzps5wB7yi
6,207
No-script datasets with ZIP files do not load
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2023-09-04T05:50:27
2023-09-04T09:13:33
2023-09-04T09:13:33
MEMBER
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While investigating an issue on a Hub dataset, I have discovered the no-script datasets containing ZIP files do not load. For example, that no-script dataset containing ZIP files, raises NonMatchingSplitsSizesError: ```python In [2]: ds = load_dataset("sidovic/LearningQ-qg") NonMatchingSplitsSizesError: [ { 'expected': SplitInfo(name='train', num_bytes=0, num_examples=188660, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='validation', num_bytes=0, num_examples=20630, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') }, { 'expected': SplitInfo(name='test', num_bytes=0, num_examples=18227, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, shard_lengths=None, dataset_name='learning_q-qg') } ] ``` As another example, a no-script dataset containing just a (CSV)-ZIP file, raises a DatasetGenerationError: ``` > num_examples, num_bytes = writer.finalize() src/datasets/builder.py:1949: > raise SchemaInferenceError("Please pass `features` or at least one example when writing data") E datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data src/datasets/arrow_writer.py:598: SchemaInferenceError The above exception was the direct cause of the following exception: src/datasets/load.py:2143: in load_dataset builder_instance.download_and_prepare( src/datasets/builder.py:954: in download_and_prepare self._download_and_prepare( src/datasets/builder.py:1049: in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) src/datasets/builder.py:1813: in _prepare_split for job_id, done, content in self._prepare_split_single( > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset src/datasets/builder.py:1958: DatasetGenerationError ``` After investigating, I think this bug was introduced in this PR: - #5972 Related to: - https://huggingface.co/datasets/sidovic/LearningQ-qg/discussions/1 CC: @lhoestq
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1,879,473,745
I_kwDODunzps5wBn5R
6,206
When calling load_dataset, raise error: pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays
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[ "I solved the problem by modifying the \"self DEFAULT_WRITER_BATCH_SIZE\" in \"class MyDataset (datasets. GeneratorBasedBuilder) : __init__\"" ]
2023-09-04T04:14:00
2023-09-04T06:05:50
2023-09-04T06:05:49
NONE
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### Describe the bug When calling load_dataset, raise error ``` Traceback (most recent call last): File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1694, in _pre pare_split_single writer.write(example, key) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 490, in write self.write_examples_on_file() File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 448, in write_examples_on_file self.write_batch(batch_examples=batch_examples) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 559, in write_batch self.write_table(pa_table, writer_batch_size) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/arrow_writer.py", line 571, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 3439, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays The above exception was the direct cause of the following exception: Traceback (most recent call last): dataset = load_dataset( ^^^^^^^^^^^^^ File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py", line 2133, in load_da taset builder_instance.download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 954, in downl oad_and_prepare self._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1717, in _dow nload_and_prepare super()._download_and_prepare( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1049, in _dow nload_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1555, in _pre pare_split for job_id, done, content in self._prepare_split_single( File "/home/aihao/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py", line 1712, in _pre pare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset Setting num_proc from 8 back to 1 for the train split to disable multiprocessing as it only contains one shard. 09/04/2023 12:02:04 - WARNING - datasets.builder - Setting num_proc from 8 back to 1 for the train split to dis able multiprocessing as it only contains one shard. ``` ### Steps to reproduce the bug Call load_dataset with the large image as feature ### Expected behavior no error ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-31-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,877,491,602
I_kwDODunzps5v6D-S
6,203
Support loading from a DVC remote repository
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[ "(cross-posting from the linked DVC issue)\r\n\r\nI think this should already work out of the box with the current `datasets` and `dvc.api` releases by passing the correct `storage_options` into the datasets calls. `storage_options` is essentially just the kwargs dict that gets passed to the fsspec fs constructor.\r\n\r\nThe main thing to note here is that the fsspec DVCFileSystem URL should be `dvc://folder/file.json` (i.e. this should be the DVCFileSystem path that is relative to the DVC repo root). You cannot use a URL like `https://gitlab.com/user/repo/folder/file.json`.\r\n\r\nI think something like this should work for you (in a venv where both DVC and datasets are installed):\r\n```python\r\nimport datasets\r\n\r\n# load a dataset from Git/DVC repository where Git repo is located at https://gitlab.com/user/repo.git\r\n# and path to dataset (relative to git/dvc repo root) is 'folder/file.json'\r\ndatasets.load_from_disk(\r\n \"dvc://folder/file.json\",\r\n storage_options={\"url\": \"https://gitlab.com/user/repo.git\"},\r\n)\r\n```\r\n\r\nbasically the `dvc://` is what tells fsspec to create a `DVCFileSystem` and it will construct it like\r\n```python\r\nfs = DVCFileSystem(**storage_options)\r\n```\r\n\r\nThen the subsequent calls use the rest of the `dvc://...` URL like \r\n```python\r\nfs.exists(\"folder/file.json\")\r\n```", "Hi @pmrowla Thank you for your help, that's very helpful, I was indeed using `fsspec` incorrectly here. There is still an issue with `datasets`:\r\n\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"json\", data_files=\"dvc://folder/file.jsonl\", storage_options={\"url\": \"https://gitlab.com/repo/folder/\"})\r\n```\r\n\r\nresults in the following exception:\r\n\r\n```\r\nTraceback (most recent call last): \r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 217, in info\r\n ret = self.trie.info(key)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/git/objects.py\", line 141, in info\r\n obj = self.trie[key]\r\n ~~~~~~~~~^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 937, in __getitem__\r\n node, _ = self._get_node(key_or_slice)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/pygtrie.py\", line 630, in _get_node\r\n raise KeyError(key)\r\nKeyError: ('dvc:', 'datasets', 'spider', 'train.jsonl')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 2129, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n ^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1815, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 1430, in dataset_module_factory\r\n ).get_module()\r\n ^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/load.py\", line 958, in get_module\r\n data_files = DataFilesDict.from_patterns(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 674, in from_patterns\r\n DataFilesList.from_patterns(\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 589, in from_patterns\r\n origin_metadata = _get_origin_metadata(data_files, download_config=download_config)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 504, in _get_origin_metadata\r\n return thread_map(\r\n ^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield _result_or_cancel(fs.pop())\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 317, in _result_or_cancel\r\n return fut.result(timeout)\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 456, in result\r\n return self.__get_result()\r\n ^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/_base.py\", line 401, in __get_result\r\n raise self._exception\r\n File \"/Users/bilelomrani/.pyenv/versions/3.11.4/lib/python3.11/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/datasets/data_files.py\", line 491, in _get_single_origin_metadata\r\n info = fs.info(data_file)\r\n ^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 357, in info\r\n return self._info(key, path, ignore_subrepos=ignore_subrepos)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc/fs/dvc.py\", line 377, in _info\r\n fs_info = fs.info(fs_path)\r\n ^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/dvc_objects/fs/base.py\", line 501, in info\r\n return self.fs.info(path, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/bilelomrani/Documents/ILLUIN.nosync/instructions-finetuning/.venv/lib/python3.11/site-packages/scmrepo/fs.py\", line 221, in info\r\n raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path)\r\nFileNotFoundError: [Errno 2] No such file or directory: '/dvc:/folder/file.jsonl'\r\n```\r\n\r\nSomehow the URL gets turned into `/dvc:/folder/file.jsonl` inside `datasets`. Otherwise I can confirm that using `fsspec` properly with DVC works as expected.\r\n", "For the record, there was a `dvc.api.DVCFileSystem` bug which is fixed in DVC `main` and will be available in the next DVC release.\r\n\r\nTo use DVC with `datasets` you just need to pass the Git/DVC repo `url` in `storage_options` as discussed above.\r\n\r\n(note that this requires having both `datasets` and `dvc` installed in your python environment)\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\r\n... \"json\",\r\n... data_files=\"dvc://eval/metrics.json\",\r\n... storage_options={\"url\": \"https://github.com/iterative/example-get-started.git\"},\r\n... )\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['avg_prec', 'roc_auc'],\r\n num_rows: 1\r\n })\r\n})\r\n```\r\n\r\nAny additional `DVCFileSystem` args can be passed in the same way, so to get a specific branch/tag/commit from the DVC repo you just need to specify the `rev` in `storage_options` like\r\n```\r\nstorage_options={\"url\": \"https://github.com/iterative/example-get-started.git\", \"rev\": \"main\"}\r\n```\r\n\r\nI think this issue can probably be closed now.", "Thank you for your help, closing." ]
2023-09-01T14:04:52
2023-09-15T15:11:27
2023-09-15T15:11:27
NONE
null
null
### Feature request Adding support for loading a file from a DVC repository, tracked remotely on a SCM. ### Motivation DVC is a popular version control system to version and manage datasets. The files are stored on a remote object storage platform, but they are tracked using Git. Integration with DVC is possible through the `DVCFileSystem`. I have a Gitlab repository where multiple files are tracked using DVC and stored in a GCP bucket. I would like to be able to load these files using `datasets` directly using an URL. My goal is to write a generic code that abstracts the storage layer, such that my users will only have to pass in an `fsspec`-compliant URL and the corresponding files will be loaded. ### Your contribution I managed to instantiate a `DVCFileSystem` pointing to a Gitlab repo from a `fsspec` chained URL in [this pull request](https://github.com/iterative/dvc/pull/9903) to DVC. ```python from fsspec.core import url_to_fs fs, _ = url_to_fs("dvc::https://gitlab.com/repository/group/my-repo") ``` From now I'm not sure how to continue, it seems that `datasets` expects the URL to be fully qualified like so: `dvc::https://gitlab.com/repository/group/my-repo/my-folder/my-file.json` but this fails because `DVCFileSystem` expects the URL to point to the root of an SCM repo. Is there a way to make this work with `datasets`?
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1,876,630,351
I_kwDODunzps5v2xtP
6,202
avoid downgrading jax version
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[ "https://github.com/huggingface/datasets/blob/main/setup.py#L236\r\nCurrently has the highest version at 0.3.25; Not sure if there is any reason for this, other than that was the tested version?" ]
2023-09-01T02:57:57
2023-10-12T16:28:59
2023-10-12T16:28:59
NONE
null
null
### Feature request Whenever I `pip install datasets[jax]` it downgrades jax to version 0.3.25. I seem to be able to install this library first then upgrade jax back to version 0.4.13. ### Motivation It would be nice to not overwrite currently installed version of jax if possible. ### Your contribution I would be willing to beta test. Or maybe write some code if I could get pointed in the right direction, I'm not super familiar with this codebase.
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PR_kwDODunzps5ZOVbV
6,201
Fix to_json ValueError and remove pandas pin
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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) | 0.006852 / 0.011353 (-0.004501) | 0.004195 / 0.011008 (-0.006813) | 0.095008 / 0.038508 (0.056500) | 0.073469 / 0.023109 (0.050360) | 0.350170 / 0.275898 (0.074272) | 0.394309 / 0.323480 (0.070829) | 0.004391 / 0.007986 (-0.003595) | 0.003432 / 0.004328 (-0.000896) | 0.072849 / 0.004250 (0.068599) | 0.058595 / 0.037052 (0.021543) | 0.372335 / 0.258489 (0.113846) | 0.410616 / 0.293841 (0.116775) | 0.034477 / 0.128546 (-0.094069) | 0.009426 / 0.075646 (-0.066220) | 0.329262 / 0.419271 (-0.090009) | 0.057941 / 0.043533 (0.014408) | 0.358624 / 0.255139 (0.103485) | 0.413803 / 0.283200 (0.130604) | 0.025845 / 0.141683 (-0.115837) | 1.684289 / 1.452155 (0.232134) | 1.791567 / 1.492716 (0.298850) |\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) | 0.222731 / 0.018006 (0.204724) | 0.511615 / 0.000490 (0.511126) | 0.004163 / 0.000200 (0.003963) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033260 / 0.037411 (-0.004152) | 0.091685 / 0.014526 (0.077159) | 0.105655 / 0.176557 (-0.070901) | 0.167973 / 0.737135 (-0.569163) | 0.105458 / 0.296338 (-0.190880) |\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) | 0.441789 / 0.215209 (0.226580) | 4.404803 / 2.077655 (2.327148) | 2.163739 / 1.504120 (0.659620) | 1.956828 / 1.541195 (0.415633) | 2.042183 / 1.468490 (0.573693) | 0.552221 / 4.584777 (-4.032556) | 3.951769 / 3.745712 (0.206057) | 3.591983 / 5.269862 (-1.677878) | 2.225058 / 4.565676 (-2.340619) | 0.064528 / 0.424275 (-0.359747) | 0.008403 / 0.007607 (0.000796) | 0.528830 / 0.226044 (0.302786) | 5.233686 / 2.268929 (2.964757) | 2.681156 / 55.444624 (-52.763468) | 2.261188 / 6.876477 (-4.615289) | 2.470037 / 2.142072 (0.327964) | 0.661793 / 4.805227 (-4.143434) | 0.150138 / 6.500664 (-6.350527) | 0.068663 / 0.075469 (-0.006807) |\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) | 1.463086 / 1.841788 (-0.378701) | 21.408232 / 8.074308 (13.333924) | 15.521718 / 10.191392 (5.330326) | 0.164587 / 0.680424 (-0.515837) | 0.021035 / 0.534201 (-0.513166) | 0.445466 / 0.579283 (-0.133817) | 0.462489 / 0.434364 (0.028125) | 0.517733 / 0.540337 (-0.022604) | 0.724242 / 1.386936 (-0.662694) |\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) | 0.007117 / 0.011353 (-0.004236) | 0.004230 / 0.011008 (-0.006778) | 0.072186 / 0.038508 (0.033678) | 0.076758 / 0.023109 (0.053648) | 0.452606 / 0.275898 (0.176708) | 0.491872 / 0.323480 (0.168392) | 0.005989 / 0.007986 (-0.001996) | 0.003611 / 0.004328 (-0.000717) | 0.072642 / 0.004250 (0.068392) | 0.058985 / 0.037052 (0.021933) | 0.463414 / 0.258489 (0.204925) | 0.497538 / 0.293841 (0.203697) | 0.036325 / 0.128546 (-0.092221) | 0.009814 / 0.075646 (-0.065832) | 0.078745 / 0.419271 (-0.340527) | 0.054308 / 0.043533 (0.010775) | 0.468210 / 0.255139 (0.213071) | 0.476434 / 0.283200 (0.193234) | 0.023683 / 0.141683 (-0.118000) | 1.706457 / 1.452155 (0.254302) | 1.775855 / 1.492716 (0.283139) |\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) | 0.241599 / 0.018006 (0.223592) | 0.483859 / 0.000490 (0.483370) | 0.006432 / 0.000200 (0.006233) | 0.000177 / 0.000054 (0.000123) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034723 / 0.037411 (-0.002688) | 0.104420 / 0.014526 (0.089894) | 0.121071 / 0.176557 (-0.055486) | 0.174899 / 0.737135 (-0.562237) | 0.119587 / 0.296338 (-0.176751) |\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) | 0.492731 / 0.215209 (0.277522) | 4.898621 / 2.077655 (2.820967) | 2.710931 / 1.504120 (1.206811) | 2.513889 / 1.541195 (0.972694) | 2.578073 / 1.468490 (1.109583) | 0.548318 / 4.584777 (-4.036459) | 4.048603 / 3.745712 (0.302891) | 3.637654 / 5.269862 (-1.632208) | 2.263682 / 4.565676 (-2.301994) | 0.065786 / 0.424275 (-0.358489) | 0.008119 / 0.007607 (0.000512) | 0.578693 / 0.226044 (0.352649) | 5.780619 / 2.268929 (3.511691) | 3.224625 / 55.444624 (-52.220000) | 2.838750 / 6.876477 (-4.037726) | 2.970276 / 2.142072 (0.828204) | 0.654423 / 4.805227 (-4.150805) | 0.148696 / 6.500664 (-6.351969) | 0.066469 / 0.075469 (-0.009000) |\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) | 1.574772 / 1.841788 (-0.267015) | 21.822356 / 8.074308 (13.748048) | 16.504127 / 10.191392 (6.312735) | 0.183357 / 0.680424 (-0.497067) | 0.022759 / 0.534201 (-0.511442) | 0.453746 / 0.579283 (-0.125537) | 0.447037 / 0.434364 (0.012673) | 0.536562 / 0.540337 (-0.003775) | 0.731063 / 1.386936 (-0.655873) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a9027eb4d9c5b3fa60a18daa7aef121428964d90 \"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) | 0.008542 / 0.011353 (-0.002811) | 0.005481 / 0.011008 (-0.005527) | 0.100122 / 0.038508 (0.061614) | 0.078968 / 0.023109 (0.055858) | 0.403751 / 0.275898 (0.127853) | 0.457559 / 0.323480 (0.134079) | 0.006152 / 0.007986 (-0.001834) | 0.003805 / 0.004328 (-0.000523) | 0.072787 / 0.004250 (0.068536) | 0.054794 / 0.037052 (0.017741) | 0.419815 / 0.258489 (0.161326) | 0.437453 / 0.293841 (0.143612) | 0.044641 / 0.128546 (-0.083905) | 0.013755 / 0.075646 (-0.061892) | 0.374683 / 0.419271 (-0.044589) | 0.071442 / 0.043533 (0.027909) | 0.395814 / 0.255139 (0.140675) | 0.439042 / 0.283200 (0.155842) | 0.034596 / 0.141683 (-0.107087) | 1.655056 / 1.452155 (0.202902) | 1.826410 / 1.492716 (0.333694) |\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) | 0.278667 / 0.018006 (0.260661) | 0.617354 / 0.000490 (0.616864) | 0.004111 / 0.000200 (0.003911) | 0.000138 / 0.000054 (0.000083) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025905 / 0.037411 (-0.011506) | 0.084721 / 0.014526 (0.070195) | 0.099737 / 0.176557 (-0.076819) | 0.163016 / 0.737135 (-0.574119) | 0.095104 / 0.296338 (-0.201234) |\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) | 0.531589 / 0.215209 (0.316380) | 5.455303 / 2.077655 (3.377649) | 2.495112 / 1.504120 (0.990992) | 2.234139 / 1.541195 (0.692944) | 2.295090 / 1.468490 (0.826599) | 0.777627 / 4.584777 (-3.807150) | 5.053069 / 3.745712 (1.307357) | 4.488715 / 5.269862 (-0.781147) | 2.775991 / 4.565676 (-1.789686) | 0.094175 / 0.424275 (-0.330100) | 0.008681 / 0.007607 (0.001074) | 0.668174 / 0.226044 (0.442130) | 6.631876 / 2.268929 (4.362948) | 3.118055 / 55.444624 (-52.326569) | 2.480355 / 6.876477 (-4.396122) | 2.706643 / 2.142072 (0.564571) | 0.927173 / 4.805227 (-3.878054) | 0.217385 / 6.500664 (-6.283279) | 0.067110 / 0.075469 (-0.008359) |\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) | 1.517926 / 1.841788 (-0.323861) | 21.420546 / 8.074308 (13.346238) | 21.108266 / 10.191392 (10.916874) | 0.222449 / 0.680424 (-0.457975) | 0.027969 / 0.534201 (-0.506232) | 0.459484 / 0.579283 (-0.119799) | 0.582629 / 0.434364 (0.148265) | 0.520971 / 0.540337 (-0.019366) | 0.694270 / 1.386936 (-0.692666) |\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) | 0.008257 / 0.011353 (-0.003096) | 0.004511 / 0.011008 (-0.006497) | 0.075031 / 0.038508 (0.036523) | 0.070526 / 0.023109 (0.047416) | 0.445595 / 0.275898 (0.169697) | 0.512312 / 0.323480 (0.188832) | 0.005933 / 0.007986 (-0.002052) | 0.003814 / 0.004328 (-0.000515) | 0.073553 / 0.004250 (0.069302) | 0.058174 / 0.037052 (0.021121) | 0.472307 / 0.258489 (0.213818) | 0.519679 / 0.293841 (0.225838) | 0.046027 / 0.128546 (-0.082520) | 0.011757 / 0.075646 (-0.063889) | 0.084883 / 0.419271 (-0.334388) | 0.056476 / 0.043533 (0.012943) | 0.475608 / 0.255139 (0.220469) | 0.507588 / 0.283200 (0.224388) | 0.031661 / 0.141683 (-0.110022) | 1.673183 / 1.452155 (0.221028) | 1.736836 / 1.492716 (0.244120) |\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) | 0.350887 / 0.018006 (0.332881) | 0.589796 / 0.000490 (0.589306) | 0.023066 / 0.000200 (0.022867) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030764 / 0.037411 (-0.006647) | 0.116967 / 0.014526 (0.102441) | 0.102760 / 0.176557 (-0.073796) | 0.167690 / 0.737135 (-0.569445) | 0.111350 / 0.296338 (-0.184988) |\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) | 0.584565 / 0.215209 (0.369356) | 5.898081 / 2.077655 (3.820426) | 2.770374 / 1.504120 (1.266254) | 2.467519 / 1.541195 (0.926324) | 2.463319 / 1.468490 (0.994829) | 0.794294 / 4.584777 (-3.790483) | 5.272285 / 3.745712 (1.526573) | 4.514830 / 5.269862 (-0.755032) | 2.937259 / 4.565676 (-1.628417) | 0.093702 / 0.424275 (-0.330574) | 0.008012 / 0.007607 (0.000405) | 0.772371 / 0.226044 (0.546327) | 7.574941 / 2.268929 (5.306013) | 3.710965 / 55.444624 (-51.733659) | 2.927964 / 6.876477 (-3.948513) | 3.256036 / 2.142072 (1.113964) | 1.051649 / 4.805227 (-3.753578) | 0.203055 / 6.500664 (-6.297609) | 0.081072 / 0.075469 (0.005603) |\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) | 1.574251 / 1.841788 (-0.267537) | 22.340801 / 8.074308 (14.266493) | 20.497769 / 10.191392 (10.306377) | 0.228725 / 0.680424 (-0.451699) | 0.029095 / 0.534201 (-0.505106) | 0.452460 / 0.579283 (-0.126823) | 0.586419 / 0.434364 (0.152055) | 0.571237 / 0.540337 (0.030900) | 0.745069 / 1.386936 (-0.641867) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b23b028dfc72c297391c5f670342732b9bd9fe \"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) | 0.006529 / 0.011353 (-0.004824) | 0.004062 / 0.011008 (-0.006946) | 0.083712 / 0.038508 (0.045204) | 0.072378 / 0.023109 (0.049269) | 0.358779 / 0.275898 (0.082881) | 0.387216 / 0.323480 (0.063736) | 0.004038 / 0.007986 (-0.003948) | 0.003316 / 0.004328 (-0.001013) | 0.065207 / 0.004250 (0.060956) | 0.054439 / 0.037052 (0.017386) | 0.370689 / 0.258489 (0.112200) | 0.411008 / 0.293841 (0.117167) | 0.031133 / 0.128546 (-0.097413) | 0.008600 / 0.075646 (-0.067047) | 0.287753 / 0.419271 (-0.131518) | 0.051845 / 0.043533 (0.008312) | 0.360327 / 0.255139 (0.105188) | 0.394791 / 0.283200 (0.111591) | 0.025139 / 0.141683 (-0.116544) | 1.488151 / 1.452155 (0.035996) | 1.556776 / 1.492716 (0.064059) |\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) | 0.209462 / 0.018006 (0.191456) | 0.459168 / 0.000490 (0.458678) | 0.006037 / 0.000200 (0.005837) | 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) | 0.028444 / 0.037411 (-0.008967) | 0.082974 / 0.014526 (0.068448) | 0.094919 / 0.176557 (-0.081638) | 0.151875 / 0.737135 (-0.585260) | 0.096143 / 0.296338 (-0.200195) |\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) | 0.402675 / 0.215209 (0.187466) | 4.014787 / 2.077655 (1.937133) | 2.015793 / 1.504120 (0.511673) | 1.838976 / 1.541195 (0.297782) | 1.931733 / 1.468490 (0.463243) | 0.489435 / 4.584777 (-4.095342) | 3.581662 / 3.745712 (-0.164050) | 3.315392 / 5.269862 (-1.954469) | 2.053369 / 4.565676 (-2.512307) | 0.057749 / 0.424275 (-0.366526) | 0.007720 / 0.007607 (0.000113) | 0.483388 / 0.226044 (0.257343) | 4.820798 / 2.268929 (2.551870) | 2.544264 / 55.444624 (-52.900361) | 2.170513 / 6.876477 (-4.705963) | 2.416976 / 2.142072 (0.274903) | 0.588351 / 4.805227 (-4.216876) | 0.136988 / 6.500664 (-6.363676) | 0.062294 / 0.075469 (-0.013175) |\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) | 1.263807 / 1.841788 (-0.577980) | 19.888202 / 8.074308 (11.813894) | 14.352977 / 10.191392 (4.161585) | 0.167200 / 0.680424 (-0.513224) | 0.018449 / 0.534201 (-0.515752) | 0.393262 / 0.579283 (-0.186021) | 0.407854 / 0.434364 (-0.026510) | 0.455852 / 0.540337 (-0.084485) | 0.629024 / 1.386936 (-0.757912) |\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) | 0.006642 / 0.011353 (-0.004710) | 0.004041 / 0.011008 (-0.006967) | 0.065823 / 0.038508 (0.027315) | 0.076810 / 0.023109 (0.053701) | 0.397680 / 0.275898 (0.121782) | 0.430104 / 0.323480 (0.106624) | 0.006035 / 0.007986 (-0.001951) | 0.003389 / 0.004328 (-0.000939) | 0.066056 / 0.004250 (0.061806) | 0.054222 / 0.037052 (0.017170) | 0.397964 / 0.258489 (0.139475) | 0.439277 / 0.293841 (0.145436) | 0.032394 / 0.128546 (-0.096152) | 0.008586 / 0.075646 (-0.067060) | 0.072538 / 0.419271 (-0.346734) | 0.048346 / 0.043533 (0.004813) | 0.399631 / 0.255139 (0.144492) | 0.418684 / 0.283200 (0.135484) | 0.022570 / 0.141683 (-0.119113) | 1.519788 / 1.452155 (0.067633) | 1.581457 / 1.492716 (0.088740) |\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) | 0.243443 / 0.018006 (0.225436) | 0.453095 / 0.000490 (0.452606) | 0.009940 / 0.000200 (0.009740) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032293 / 0.037411 (-0.005118) | 0.091681 / 0.014526 (0.077155) | 0.103729 / 0.176557 (-0.072827) | 0.156361 / 0.737135 (-0.580775) | 0.105034 / 0.296338 (-0.191305) |\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) | 0.427761 / 0.215209 (0.212551) | 4.266044 / 2.077655 (2.188390) | 2.285161 / 1.504120 (0.781041) | 2.118652 / 1.541195 (0.577457) | 2.203469 / 1.468490 (0.734979) | 0.494587 / 4.584777 (-4.090190) | 3.676706 / 3.745712 (-0.069006) | 3.252478 / 5.269862 (-2.017383) | 2.027432 / 4.565676 (-2.538245) | 0.057856 / 0.424275 (-0.366419) | 0.007279 / 0.007607 (-0.000328) | 0.502767 / 0.226044 (0.276723) | 5.031409 / 2.268929 (2.762480) | 2.741767 / 55.444624 (-52.702858) | 2.408480 / 6.876477 (-4.467997) | 2.607193 / 2.142072 (0.465121) | 0.590787 / 4.805227 (-4.214440) | 0.133633 / 6.500664 (-6.367031) | 0.061195 / 0.075469 (-0.014274) |\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) | 1.342824 / 1.841788 (-0.498964) | 20.137195 / 8.074308 (12.062887) | 14.986743 / 10.191392 (4.795351) | 0.168218 / 0.680424 (-0.512206) | 0.020209 / 0.534201 (-0.513992) | 0.397446 / 0.579283 (-0.181837) | 0.427496 / 0.434364 (-0.006868) | 0.475058 / 0.540337 (-0.065279) | 0.648439 / 1.386936 (-0.738497) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b54cbd6c01f52139dedbcf209ff41f0c88b9aa5 \"CML watermark\")\n" ]
2023-08-31T10:38:08
2023-09-05T11:07:07
2023-09-05T10:58:21
MEMBER
false
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This PR fixes the root cause of the issue: - #6197 This PR also removes the temporary pin of `pandas` introduced by: - #6200 Note that for orient in ['records', 'values'], index value is ignored but - in `pandas` < 2.1.0, a ValueError is raised if not index and orient not in ['split', 'table'] - for orient = 'records', we need index = True - default index value is True - in `pandas` = 2.1.0, a ValueError is raised if index is True and orient in ['records', 'values'] - for orient = 'records', we need index = False or None - default index value is None This PR fixes the issue by not passing index and thus using default index value (valid for all pandas versions), unless orient is 'split' or 'table' (where we pass index = False, as it was done before this fix).
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6,200
Temporarily pin pandas < 2.1.0
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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) | 0.008978 / 0.011353 (-0.002375) | 0.005143 / 0.011008 (-0.005865) | 0.104787 / 0.038508 (0.066279) | 0.077069 / 0.023109 (0.053960) | 0.427703 / 0.275898 (0.151805) | 0.469865 / 0.323480 (0.146386) | 0.004618 / 0.007986 (-0.003368) | 0.004074 / 0.004328 (-0.000255) | 0.088656 / 0.004250 (0.084405) | 0.059798 / 0.037052 (0.022746) | 0.465906 / 0.258489 (0.207417) | 0.510281 / 0.293841 (0.216440) | 0.051192 / 0.128546 (-0.077354) | 0.013623 / 0.075646 (-0.062024) | 0.379339 / 0.419271 (-0.039932) | 0.077393 / 0.043533 (0.033860) | 0.445165 / 0.255139 (0.190026) | 0.473577 / 0.283200 (0.190378) | 0.038125 / 0.141683 (-0.103558) | 1.858635 / 1.452155 (0.406480) | 1.869033 / 1.492716 (0.376316) |\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) | 0.209011 / 0.018006 (0.191004) | 0.550978 / 0.000490 (0.550488) | 0.004904 / 0.000200 (0.004704) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031418 / 0.037411 (-0.005993) | 0.089623 / 0.014526 (0.075098) | 0.103491 / 0.176557 (-0.073066) | 0.178158 / 0.737135 (-0.558978) | 0.108515 / 0.296338 (-0.187824) |\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) | 0.648293 / 0.215209 (0.433084) | 6.332361 / 2.077655 (4.254707) | 2.469076 / 1.504120 (0.964956) | 2.286228 / 1.541195 (0.745033) | 2.257408 / 1.468490 (0.788918) | 0.918027 / 4.584777 (-3.666750) | 5.229539 / 3.745712 (1.483827) | 4.676150 / 5.269862 (-0.593712) | 3.220411 / 4.565676 (-1.345266) | 0.095863 / 0.424275 (-0.328413) | 0.008696 / 0.007607 (0.001089) | 0.722356 / 0.226044 (0.496312) | 7.796690 / 2.268929 (5.527762) | 3.715044 / 55.444624 (-51.729581) | 2.852696 / 6.876477 (-4.023780) | 2.891838 / 2.142072 (0.749766) | 1.195536 / 4.805227 (-3.609691) | 0.246908 / 6.500664 (-6.253756) | 0.079454 / 0.075469 (0.003984) |\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) | 1.652740 / 1.841788 (-0.189047) | 23.791791 / 8.074308 (15.717482) | 22.778999 / 10.191392 (12.587607) | 0.253878 / 0.680424 (-0.426546) | 0.031367 / 0.534201 (-0.502834) | 0.509460 / 0.579283 (-0.069823) | 0.603085 / 0.434364 (0.168721) | 0.603890 / 0.540337 (0.063553) | 0.826606 / 1.386936 (-0.560330) |\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) | 0.010407 / 0.011353 (-0.000946) | 0.004751 / 0.011008 (-0.006257) | 0.086761 / 0.038508 (0.048253) | 0.087281 / 0.023109 (0.064172) | 0.498409 / 0.275898 (0.222511) | 0.560727 / 0.323480 (0.237247) | 0.006563 / 0.007986 (-0.001423) | 0.004078 / 0.004328 (-0.000251) | 0.086383 / 0.004250 (0.082133) | 0.065915 / 0.037052 (0.028862) | 0.521871 / 0.258489 (0.263382) | 0.582281 / 0.293841 (0.288440) | 0.057189 / 0.128546 (-0.071357) | 0.015514 / 0.075646 (-0.060133) | 0.102574 / 0.419271 (-0.316697) | 0.069155 / 0.043533 (0.025622) | 0.525000 / 0.255139 (0.269861) | 0.557968 / 0.283200 (0.274769) | 0.036934 / 0.141683 (-0.104749) | 1.919335 / 1.452155 (0.467181) | 1.870948 / 1.492716 (0.378231) |\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) | 0.241932 / 0.018006 (0.223926) | 0.560136 / 0.000490 (0.559646) | 0.006438 / 0.000200 (0.006238) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036192 / 0.037411 (-0.001220) | 0.106829 / 0.014526 (0.092303) | 0.128667 / 0.176557 (-0.047890) | 0.200514 / 0.737135 (-0.536621) | 0.127542 / 0.296338 (-0.168797) |\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) | 0.754556 / 0.215209 (0.539347) | 7.237324 / 2.077655 (5.159670) | 3.267424 / 1.504120 (1.763304) | 2.789601 / 1.541195 (1.248407) | 2.875728 / 1.468490 (1.407238) | 0.894274 / 4.584777 (-3.690503) | 5.394556 / 3.745712 (1.648844) | 4.818523 / 5.269862 (-0.451338) | 2.965827 / 4.565676 (-1.599850) | 0.101967 / 0.424275 (-0.322308) | 0.008506 / 0.007607 (0.000899) | 0.803476 / 0.226044 (0.577432) | 8.614426 / 2.268929 (6.345497) | 4.169113 / 55.444624 (-51.275511) | 3.346346 / 6.876477 (-3.530130) | 3.418206 / 2.142072 (1.276134) | 1.111718 / 4.805227 (-3.693509) | 0.211302 / 6.500664 (-6.289362) | 0.072524 / 0.075469 (-0.002945) |\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) | 1.792705 / 1.841788 (-0.049083) | 24.442484 / 8.074308 (16.368176) | 23.375008 / 10.191392 (13.183616) | 0.227946 / 0.680424 (-0.452478) | 0.034376 / 0.534201 (-0.499825) | 0.489260 / 0.579283 (-0.090023) | 0.563220 / 0.434364 (0.128856) | 0.617405 / 0.540337 (0.077068) | 0.850577 / 1.386936 (-0.536359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4413ea1eaca5023ace1e62ddf1070de2d41b4f6 \"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) | 0.006594 / 0.011353 (-0.004759) | 0.004366 / 0.011008 (-0.006642) | 0.084241 / 0.038508 (0.045733) | 0.071876 / 0.023109 (0.048767) | 0.321604 / 0.275898 (0.045706) | 0.343501 / 0.323480 (0.020021) | 0.004069 / 0.007986 (-0.003917) | 0.003311 / 0.004328 (-0.001017) | 0.065079 / 0.004250 (0.060829) | 0.053754 / 0.037052 (0.016702) | 0.326199 / 0.258489 (0.067710) | 0.356552 / 0.293841 (0.062711) | 0.031568 / 0.128546 (-0.096979) | 0.008581 / 0.075646 (-0.067065) | 0.289170 / 0.419271 (-0.130101) | 0.053097 / 0.043533 (0.009564) | 0.309678 / 0.255139 (0.054539) | 0.345717 / 0.283200 (0.062517) | 0.024144 / 0.141683 (-0.117539) | 1.497351 / 1.452155 (0.045196) | 1.584691 / 1.492716 (0.091975) |\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) | 0.206357 / 0.018006 (0.188351) | 0.459611 / 0.000490 (0.459121) | 0.002586 / 0.000200 (0.002386) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027459 / 0.037411 (-0.009952) | 0.082197 / 0.014526 (0.067671) | 0.095004 / 0.176557 (-0.081553) | 0.151063 / 0.737135 (-0.586072) | 0.095107 / 0.296338 (-0.201231) |\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) | 0.384363 / 0.215209 (0.169154) | 3.836187 / 2.077655 (1.758533) | 1.898312 / 1.504120 (0.394192) | 1.727310 / 1.541195 (0.186115) | 1.803579 / 1.468490 (0.335089) | 0.485946 / 4.584777 (-4.098831) | 3.619134 / 3.745712 (-0.126578) | 3.255274 / 5.269862 (-2.014588) | 2.004603 / 4.565676 (-2.561074) | 0.057107 / 0.424275 (-0.367168) | 0.007601 / 0.007607 (-0.000006) | 0.456545 / 0.226044 (0.230500) | 4.556857 / 2.268929 (2.287929) | 2.379954 / 55.444624 (-53.064671) | 2.045874 / 6.876477 (-4.830603) | 2.203090 / 2.142072 (0.061018) | 0.585400 / 4.805227 (-4.219827) | 0.133018 / 6.500664 (-6.367646) | 0.059457 / 0.075469 (-0.016012) |\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) | 1.292581 / 1.841788 (-0.549207) | 19.360057 / 8.074308 (11.285749) | 14.105359 / 10.191392 (3.913967) | 0.166028 / 0.680424 (-0.514396) | 0.018243 / 0.534201 (-0.515958) | 0.392026 / 0.579283 (-0.187257) | 0.412735 / 0.434364 (-0.021629) | 0.459791 / 0.540337 (-0.080547) | 0.624539 / 1.386936 (-0.762397) |\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) | 0.006677 / 0.011353 (-0.004676) | 0.003897 / 0.011008 (-0.007111) | 0.064139 / 0.038508 (0.025631) | 0.071346 / 0.023109 (0.048237) | 0.431180 / 0.275898 (0.155282) | 0.470870 / 0.323480 (0.147390) | 0.005562 / 0.007986 (-0.002423) | 0.003405 / 0.004328 (-0.000924) | 0.064532 / 0.004250 (0.060282) | 0.055317 / 0.037052 (0.018265) | 0.434667 / 0.258489 (0.176178) | 0.475765 / 0.293841 (0.181924) | 0.032392 / 0.128546 (-0.096154) | 0.008418 / 0.075646 (-0.067228) | 0.071069 / 0.419271 (-0.348203) | 0.047963 / 0.043533 (0.004430) | 0.440225 / 0.255139 (0.185086) | 0.454860 / 0.283200 (0.171661) | 0.022653 / 0.141683 (-0.119029) | 1.489444 / 1.452155 (0.037289) | 1.556913 / 1.492716 (0.064196) |\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) | 0.226733 / 0.018006 (0.208727) | 0.452005 / 0.000490 (0.451516) | 0.004715 / 0.000200 (0.004515) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032042 / 0.037411 (-0.005369) | 0.091226 / 0.014526 (0.076700) | 0.103639 / 0.176557 (-0.072917) | 0.157772 / 0.737135 (-0.579363) | 0.105466 / 0.296338 (-0.190872) |\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) | 0.439751 / 0.215209 (0.224542) | 4.357102 / 2.077655 (2.279448) | 2.362857 / 1.504120 (0.858737) | 2.180559 / 1.541195 (0.639364) | 2.279601 / 1.468490 (0.811111) | 0.495161 / 4.584777 (-4.089616) | 3.729199 / 3.745712 (-0.016513) | 3.334839 / 5.269862 (-1.935023) | 2.099315 / 4.565676 (-2.466362) | 0.058178 / 0.424275 (-0.366097) | 0.007303 / 0.007607 (-0.000304) | 0.506968 / 0.226044 (0.280924) | 5.078600 / 2.268929 (2.809671) | 2.846420 / 55.444624 (-52.598204) | 2.480644 / 6.876477 (-4.395833) | 2.693204 / 2.142072 (0.551132) | 0.590118 / 4.805227 (-4.215109) | 0.132900 / 6.500664 (-6.367764) | 0.060053 / 0.075469 (-0.015416) |\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) | 1.356713 / 1.841788 (-0.485075) | 20.380573 / 8.074308 (12.306265) | 15.066507 / 10.191392 (4.875115) | 0.180655 / 0.680424 (-0.499769) | 0.020954 / 0.534201 (-0.513247) | 0.399638 / 0.579283 (-0.179645) | 0.420694 / 0.434364 (-0.013670) | 0.476124 / 0.540337 (-0.064213) | 0.647192 / 1.386936 (-0.739744) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f8c58002481568eb1aa4f6f86c4509cf476800a \"CML watermark\")\n" ]
2023-08-31T09:45:17
2023-08-31T10:33:24
2023-08-31T10:24:38
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Temporarily pin `pandas` < 2.1.0 until permanent solution is found. Hot fix #6197.
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1,875,165,185
I_kwDODunzps5vxMAB
6,199
Use load_dataset for local json files, but it not works
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[ "Hugging Face's datasets library may prioritize remote configurations. Make sure there are no conflicting configurations causing the library to prefer downloading data\r\nMay be try debugging\r\nraw_datasets = load_dataset('json', data_files=data_files)\r\nprint(raw_datasets)\r\n", "It doesn't download them but writes them to the local HF cache. The logging could indeed be better. Does loading the dataset succeed? If it doesn't, can you share the error stack trace?" ]
2023-08-31T09:42:34
2023-08-31T19:05:07
null
NONE
null
null
### Describe the bug when I use load_dataset to load my local datasets，it always goes to Hugging Face to download the data instead of loading the local dataset. ### Steps to reproduce the bug `raw_datasets = load_dataset( ‘json’, data_files=data_files)` ### Expected behavior ![image](https://github.com/huggingface/datasets/assets/50519434/add3747f-6481-4da7-b374-8f81c5a6472c) ### Environment info python version 3.8.5 datasets version 2.12 os version unbuntu 18.04
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1,875,092,027
PR_kwDODunzps5ZNyBq
6,198
Preserve split order in DataFilesDict
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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) | 0.007621 / 0.011353 (-0.003732) | 0.004534 / 0.011008 (-0.006475) | 0.099834 / 0.038508 (0.061326) | 0.083029 / 0.023109 (0.059920) | 0.387559 / 0.275898 (0.111661) | 0.422453 / 0.323480 (0.098973) | 0.006070 / 0.007986 (-0.001916) | 0.003725 / 0.004328 (-0.000604) | 0.075923 / 0.004250 (0.071672) | 0.060578 / 0.037052 (0.023525) | 0.403569 / 0.258489 (0.145079) | 0.444991 / 0.293841 (0.151150) | 0.035847 / 0.128546 (-0.092699) | 0.009872 / 0.075646 (-0.065774) | 0.335506 / 0.419271 (-0.083766) | 0.060509 / 0.043533 (0.016976) | 0.381034 / 0.255139 (0.125895) | 0.426938 / 0.283200 (0.143738) | 0.027662 / 0.141683 (-0.114021) | 1.729565 / 1.452155 (0.277410) | 1.842082 / 1.492716 (0.349366) |\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) | 0.230371 / 0.018006 (0.212365) | 0.518216 / 0.000490 (0.517726) | 0.003897 / 0.000200 (0.003697) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031942 / 0.037411 (-0.005470) | 0.096609 / 0.014526 (0.082083) | 0.112707 / 0.176557 (-0.063850) | 0.178849 / 0.737135 (-0.558286) | 0.112793 / 0.296338 (-0.183546) |\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) | 0.445896 / 0.215209 (0.230687) | 4.451173 / 2.077655 (2.373519) | 2.183380 / 1.504120 (0.679260) | 1.991583 / 1.541195 (0.450388) | 2.096219 / 1.468490 (0.627729) | 0.566692 / 4.584777 (-4.018085) | 4.078278 / 3.745712 (0.332566) | 3.787950 / 5.269862 (-1.481911) | 2.372651 / 4.565676 (-2.193025) | 0.065500 / 0.424275 (-0.358775) | 0.008918 / 0.007607 (0.001311) | 0.535589 / 0.226044 (0.309545) | 5.364130 / 2.268929 (3.095201) | 2.805381 / 55.444624 (-52.639244) | 2.350769 / 6.876477 (-4.525708) | 2.592887 / 2.142072 (0.450814) | 0.675475 / 4.805227 (-4.129752) | 0.153907 / 6.500664 (-6.346757) | 0.071138 / 0.075469 (-0.004331) |\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) | 1.498236 / 1.841788 (-0.343552) | 22.810460 / 8.074308 (14.736152) | 16.275035 / 10.191392 (6.083643) | 0.200242 / 0.680424 (-0.480182) | 0.021553 / 0.534201 (-0.512648) | 0.469437 / 0.579283 (-0.109846) | 0.477752 / 0.434364 (0.043388) | 0.537411 / 0.540337 (-0.002927) | 0.741730 / 1.386936 (-0.645206) |\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) | 0.008009 / 0.011353 (-0.003344) | 0.004626 / 0.011008 (-0.006382) | 0.074871 / 0.038508 (0.036363) | 0.085214 / 0.023109 (0.062105) | 0.478057 / 0.275898 (0.202159) | 0.522038 / 0.323480 (0.198558) | 0.007055 / 0.007986 (-0.000931) | 0.003813 / 0.004328 (-0.000515) | 0.076238 / 0.004250 (0.071988) | 0.065738 / 0.037052 (0.028686) | 0.484391 / 0.258489 (0.225902) | 0.524425 / 0.293841 (0.230584) | 0.038375 / 0.128546 (-0.090171) | 0.009964 / 0.075646 (-0.065682) | 0.084027 / 0.419271 (-0.335245) | 0.056979 / 0.043533 (0.013447) | 0.486910 / 0.255139 (0.231771) | 0.501185 / 0.283200 (0.217985) | 0.027000 / 0.141683 (-0.114683) | 1.767378 / 1.452155 (0.315224) | 1.870511 / 1.492716 (0.377795) |\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) | 0.267067 / 0.018006 (0.249061) | 0.501714 / 0.000490 (0.501224) | 0.012379 / 0.000200 (0.012179) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036706 / 0.037411 (-0.000706) | 0.110064 / 0.014526 (0.095538) | 0.124896 / 0.176557 (-0.051660) | 0.186730 / 0.737135 (-0.550405) | 0.123501 / 0.296338 (-0.172837) |\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) | 0.510793 / 0.215209 (0.295583) | 5.133056 / 2.077655 (3.055401) | 2.776456 / 1.504120 (1.272336) | 2.595557 / 1.541195 (1.054362) | 2.717922 / 1.468490 (1.249432) | 0.578333 / 4.584777 (-4.006444) | 4.169935 / 3.745712 (0.424223) | 3.800078 / 5.269862 (-1.469784) | 2.385866 / 4.565676 (-2.179810) | 0.068114 / 0.424275 (-0.356161) | 0.008771 / 0.007607 (0.001164) | 0.597894 / 0.226044 (0.371850) | 5.970293 / 2.268929 (3.701364) | 3.352715 / 55.444624 (-52.091909) | 2.972062 / 6.876477 (-3.904415) | 3.179232 / 2.142072 (1.037160) | 0.689838 / 4.805227 (-4.115389) | 0.154890 / 6.500664 (-6.345774) | 0.072321 / 0.075469 (-0.003148) |\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) | 1.613666 / 1.841788 (-0.228121) | 23.441538 / 8.074308 (15.367230) | 17.105417 / 10.191392 (6.914025) | 0.171449 / 0.680424 (-0.508975) | 0.023257 / 0.534201 (-0.510944) | 0.466724 / 0.579283 (-0.112559) | 0.470835 / 0.434364 (0.036471) | 0.561860 / 0.540337 (0.021523) | 0.759048 / 1.386936 (-0.627888) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f3b6eaf69d3352394d3bf3c4d6ed01dd2af5860 \"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) | 0.007557 / 0.011353 (-0.003796) | 0.004211 / 0.011008 (-0.006797) | 0.096243 / 0.038508 (0.057735) | 0.083603 / 0.023109 (0.060493) | 0.367114 / 0.275898 (0.091216) | 0.415182 / 0.323480 (0.091702) | 0.005796 / 0.007986 (-0.002189) | 0.003791 / 0.004328 (-0.000537) | 0.073505 / 0.004250 (0.069254) | 0.060335 / 0.037052 (0.023283) | 0.392182 / 0.258489 (0.133693) | 0.421315 / 0.293841 (0.127474) | 0.036128 / 0.128546 (-0.092419) | 0.009953 / 0.075646 (-0.065693) | 0.338965 / 0.419271 (-0.080307) | 0.061006 / 0.043533 (0.017473) | 0.372317 / 0.255139 (0.117178) | 0.414367 / 0.283200 (0.131167) | 0.026970 / 0.141683 (-0.114713) | 1.730381 / 1.452155 (0.278227) | 1.808340 / 1.492716 (0.315624) |\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) | 0.222622 / 0.018006 (0.204615) | 0.474064 / 0.000490 (0.473574) | 0.004817 / 0.000200 (0.004617) | 0.000089 / 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) | 0.032528 / 0.037411 (-0.004883) | 0.097457 / 0.014526 (0.082931) | 0.112273 / 0.176557 (-0.064283) | 0.177953 / 0.737135 (-0.559182) | 0.112358 / 0.296338 (-0.183981) |\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) | 0.442601 / 0.215209 (0.227392) | 4.442065 / 2.077655 (2.364410) | 2.156813 / 1.504120 (0.652694) | 1.970289 / 1.541195 (0.429094) | 2.052878 / 1.468490 (0.584388) | 0.562661 / 4.584777 (-4.022116) | 4.255529 / 3.745712 (0.509817) | 3.767650 / 5.269862 (-1.502212) | 2.431078 / 4.565676 (-2.134598) | 0.065624 / 0.424275 (-0.358651) | 0.008738 / 0.007607 (0.001131) | 0.546839 / 0.226044 (0.320795) | 5.362863 / 2.268929 (3.093934) | 2.695924 / 55.444624 (-52.748701) | 2.334589 / 6.876477 (-4.541888) | 2.530757 / 2.142072 (0.388685) | 0.675991 / 4.805227 (-4.129236) | 0.153852 / 6.500664 (-6.346813) | 0.069189 / 0.075469 (-0.006280) |\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) | 1.522916 / 1.841788 (-0.318872) | 21.515907 / 8.074308 (13.441599) | 16.411708 / 10.191392 (6.220316) | 0.168245 / 0.680424 (-0.512179) | 0.021165 / 0.534201 (-0.513036) | 0.461838 / 0.579283 (-0.117446) | 0.488867 / 0.434364 (0.054503) | 0.536278 / 0.540337 (-0.004059) | 0.766690 / 1.386936 (-0.620246) |\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) | 0.007683 / 0.011353 (-0.003670) | 0.004401 / 0.011008 (-0.006608) | 0.075463 / 0.038508 (0.036955) | 0.081737 / 0.023109 (0.058628) | 0.466469 / 0.275898 (0.190571) | 0.514909 / 0.323480 (0.191429) | 0.006106 / 0.007986 (-0.001880) | 0.003936 / 0.004328 (-0.000393) | 0.076773 / 0.004250 (0.072523) | 0.061025 / 0.037052 (0.023973) | 0.473348 / 0.258489 (0.214858) | 0.525326 / 0.293841 (0.231485) | 0.038224 / 0.128546 (-0.090322) | 0.009559 / 0.075646 (-0.066087) | 0.080847 / 0.419271 (-0.338424) | 0.056738 / 0.043533 (0.013205) | 0.475116 / 0.255139 (0.219977) | 0.494689 / 0.283200 (0.211490) | 0.029364 / 0.141683 (-0.112319) | 1.796681 / 1.452155 (0.344527) | 1.850600 / 1.492716 (0.357884) |\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) | 0.327126 / 0.018006 (0.309119) | 0.469186 / 0.000490 (0.468696) | 0.050600 / 0.000200 (0.050400) | 0.000439 / 0.000054 (0.000385) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036710 / 0.037411 (-0.000701) | 0.108669 / 0.014526 (0.094143) | 0.119808 / 0.176557 (-0.056748) | 0.181501 / 0.737135 (-0.555634) | 0.121487 / 0.296338 (-0.174852) |\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) | 0.509076 / 0.215209 (0.293867) | 5.056970 / 2.077655 (2.979316) | 2.775958 / 1.504120 (1.271838) | 2.592548 / 1.541195 (1.051353) | 2.654381 / 1.468490 (1.185890) | 0.557407 / 4.584777 (-4.027370) | 4.418232 / 3.745712 (0.672519) | 3.698072 / 5.269862 (-1.571790) | 2.380607 / 4.565676 (-2.185069) | 0.066242 / 0.424275 (-0.358034) | 0.008350 / 0.007607 (0.000743) | 0.572354 / 0.226044 (0.346309) | 5.857637 / 2.268929 (3.588709) | 3.242512 / 55.444624 (-52.202112) | 2.891144 / 6.876477 (-3.985332) | 3.217987 / 2.142072 (1.075915) | 0.676049 / 4.805227 (-4.129178) | 0.155515 / 6.500664 (-6.345149) | 0.068616 / 0.075469 (-0.006853) |\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) | 1.670048 / 1.841788 (-0.171740) | 22.629573 / 8.074308 (14.555265) | 16.887676 / 10.191392 (6.696284) | 0.168571 / 0.680424 (-0.511853) | 0.023361 / 0.534201 (-0.510840) | 0.463358 / 0.579283 (-0.115925) | 0.463278 / 0.434364 (0.028914) | 0.602397 / 0.540337 (0.062060) | 0.793249 / 1.386936 (-0.593687) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eee318573aba6574a43d457aa0347348c1f3e4aa \"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) | 0.006693 / 0.011353 (-0.004660) | 0.004100 / 0.011008 (-0.006908) | 0.084166 / 0.038508 (0.045658) | 0.074469 / 0.023109 (0.051360) | 0.356092 / 0.275898 (0.080194) | 0.392389 / 0.323480 (0.068909) | 0.003996 / 0.007986 (-0.003990) | 0.004020 / 0.004328 (-0.000308) | 0.064997 / 0.004250 (0.060747) | 0.053897 / 0.037052 (0.016845) | 0.362942 / 0.258489 (0.104453) | 0.408694 / 0.293841 (0.114854) | 0.031656 / 0.128546 (-0.096890) | 0.008713 / 0.075646 (-0.066933) | 0.289306 / 0.419271 (-0.129966) | 0.053067 / 0.043533 (0.009534) | 0.358740 / 0.255139 (0.103601) | 0.393347 / 0.283200 (0.110147) | 0.025430 / 0.141683 (-0.116253) | 1.486114 / 1.452155 (0.033959) | 1.572698 / 1.492716 (0.079981) |\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) | 0.215423 / 0.018006 (0.197417) | 0.467694 / 0.000490 (0.467204) | 0.003965 / 0.000200 (0.003765) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027936 / 0.037411 (-0.009475) | 0.084235 / 0.014526 (0.069709) | 0.136275 / 0.176557 (-0.040282) | 0.151154 / 0.737135 (-0.585982) | 0.185592 / 0.296338 (-0.110747) |\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) | 0.393784 / 0.215209 (0.178575) | 3.927878 / 2.077655 (1.850223) | 1.961216 / 1.504120 (0.457096) | 1.802264 / 1.541195 (0.261069) | 1.971186 / 1.468490 (0.502696) | 0.487981 / 4.584777 (-4.096796) | 3.649046 / 3.745712 (-0.096666) | 3.302471 / 5.269862 (-1.967391) | 2.058075 / 4.565676 (-2.507602) | 0.057072 / 0.424275 (-0.367203) | 0.007624 / 0.007607 (0.000017) | 0.470139 / 0.226044 (0.244095) | 4.697711 / 2.268929 (2.428783) | 2.494813 / 55.444624 (-52.949811) | 2.133084 / 6.876477 (-4.743393) | 2.329740 / 2.142072 (0.187667) | 0.585857 / 4.805227 (-4.219371) | 0.134442 / 6.500664 (-6.366223) | 0.060860 / 0.075469 (-0.014609) |\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) | 1.248504 / 1.841788 (-0.593283) | 19.448427 / 8.074308 (11.374119) | 14.446139 / 10.191392 (4.254747) | 0.168081 / 0.680424 (-0.512342) | 0.018028 / 0.534201 (-0.516173) | 0.395061 / 0.579283 (-0.184222) | 0.418777 / 0.434364 (-0.015587) | 0.454509 / 0.540337 (-0.085828) | 0.628488 / 1.386936 (-0.758448) |\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) | 0.006946 / 0.011353 (-0.004406) | 0.004096 / 0.011008 (-0.006912) | 0.065322 / 0.038508 (0.026813) | 0.074336 / 0.023109 (0.051227) | 0.405327 / 0.275898 (0.129429) | 0.436878 / 0.323480 (0.113398) | 0.006083 / 0.007986 (-0.001902) | 0.003345 / 0.004328 (-0.000984) | 0.065725 / 0.004250 (0.061474) | 0.056398 / 0.037052 (0.019345) | 0.406906 / 0.258489 (0.148417) | 0.443330 / 0.293841 (0.149489) | 0.033036 / 0.128546 (-0.095510) | 0.008503 / 0.075646 (-0.067144) | 0.071865 / 0.419271 (-0.347406) | 0.048956 / 0.043533 (0.005423) | 0.404579 / 0.255139 (0.149440) | 0.424904 / 0.283200 (0.141704) | 0.021786 / 0.141683 (-0.119897) | 1.491868 / 1.452155 (0.039713) | 1.565252 / 1.492716 (0.072536) |\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) | 0.231363 / 0.018006 (0.213357) | 0.454962 / 0.000490 (0.454472) | 0.004680 / 0.000200 (0.004480) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032569 / 0.037411 (-0.004843) | 0.094928 / 0.014526 (0.080402) | 0.108096 / 0.176557 (-0.068461) | 0.158727 / 0.737135 (-0.578409) | 0.106951 / 0.296338 (-0.189387) |\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) | 0.431469 / 0.215209 (0.216260) | 4.283929 / 2.077655 (2.206274) | 2.283891 / 1.504120 (0.779771) | 2.118172 / 1.541195 (0.576977) | 2.192628 / 1.468490 (0.724138) | 0.492026 / 4.584777 (-4.092751) | 3.692126 / 3.745712 (-0.053587) | 3.269827 / 5.269862 (-2.000035) | 2.028948 / 4.565676 (-2.536728) | 0.057932 / 0.424275 (-0.366344) | 0.007301 / 0.007607 (-0.000306) | 0.508411 / 0.226044 (0.282367) | 5.072803 / 2.268929 (2.803875) | 2.756532 / 55.444624 (-52.688092) | 2.432192 / 6.876477 (-4.444285) | 2.654864 / 2.142072 (0.512791) | 0.589458 / 4.805227 (-4.215769) | 0.133924 / 6.500664 (-6.366740) | 0.060764 / 0.075469 (-0.014705) |\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) | 1.350737 / 1.841788 (-0.491051) | 20.265217 / 8.074308 (12.190909) | 14.969039 / 10.191392 (4.777647) | 0.164226 / 0.680424 (-0.516198) | 0.020090 / 0.534201 (-0.514111) | 0.397010 / 0.579283 (-0.182273) | 0.412927 / 0.434364 (-0.021437) | 0.473931 / 0.540337 (-0.066406) | 0.653462 / 1.386936 (-0.733474) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00cb5cc57337cdff338d7a54396bf25c5c5abd67 \"CML watermark\")\n" ]
2023-08-31T09:00:26
2023-08-31T13:57:31
2023-08-31T13:48:42
MEMBER
false
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After investigation, I have found that this copy forces the splits to be sorted alphabetically: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/builder.py#L556 This PR removes the alphabetically sort of `DataFilesDict` keys. - Note that for a `dict`, the order of keys is relevant when hashing: ```python hash1 = Hasher.hash({'train': 'train.csv', 'test': 'test.csv'}) hash2 = Hasher.hash({'test': 'test.csv', 'train': 'train.csv'}) assert hash1 != hash2 ``` - The `DataFilesDict` is a subclass of `dict`, thus the order should be relevant as well ```python hash1 = Hasher.hash(DataFilesDict({'train': 'train.csv', 'test': 'test.csv'})) hash2 = Hasher.hash(DataFilesDict({'test': 'test.csv', 'train': 'train.csv'})) assert hash1 != hash2 ``` Fix #6196.
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I_kwDODunzps5vw2wL
6,197
ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'
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[ "Thanks for reporting. We are investigating it.", "This issue is caused by latest `pandas` release 2.1.0 (released yesterday Aug 30).\r\n\r\nSee: https://github.com/huggingface/datasets/actions/runs/6035484010/job/16375932085?pr=6198\r\n", "People using previous releases of `datasets` should pin `pandas` in their local environment:\r\n```\r\npython -m pip install 'pandas<2.1.0'\r\n```" ]
2023-08-31T08:51:50
2023-09-01T10:35:10
2023-08-31T10:24:40
NONE
null
null
### Describe the bug Saving a dataset `.to_json()` fails with a `ValueError` since the latest `pandas` [release](https://pandas.pydata.org/docs/dev/whatsnew/v2.1.0.html) (`2.1.0`) In their latest release we have: > Improved error handling when using [DataFrame.to_json()](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json) with incompatible index and orient arguments ([GH 52143](https://github.com/pandas-dev/pandas/issues/52143)) i.e. an error is now raised for invalid combinations of `index` and `orient`. This means that unfortunately the custom logic at this line might sometimes lead to contradictions: https://github.com/huggingface/datasets/blob/029227a116c14720afca71b9b22e78eb2a1c09a6/src/datasets/io/json.py#L96 e.g. for the default case `orient=records` leads to `index=True`, which now raises a `ValueError` ### Steps to reproduce the bug ```python import datasets if __name__ == '__main__': dataset = datasets.Dataset.from_dict({"A": [1, 2, 3], "B": [4, 5, 6]}) dataset.to_json("dataset.json") ``` ```shell >>> ValueError: 'index=True' is only valid when 'orient' is 'split', 'table', 'index', or 'columns'. ``` ### Expected behavior The dataset is successfully saved as `.json` ### Environment info `python >= 3.9` `pandas >= 2.1.0`
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6,196
Split order is not preserved
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2023-08-31T08:47:16
2023-08-31T13:48:43
2023-08-31T13:48:43
MEMBER
null
null
I have noticed that in some cases the split order is not preserved. For example, consider a no-script dataset with configs: ```yaml configs: - config_name: default data_files: - split: train path: train.csv - split: test path: test.csv ``` - Note the defined split order is [train, test] Once the dataset is loaded, the split order is not preserved: ```python In [16]: ds Out[16]: DatasetDict({ test: Dataset({ features: ['text', 'label'], num_rows: 1 }) train: Dataset({ features: ['text', 'label'], num_rows: 2 }) }) ``` - Note the obtained split order is [test, train]
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6,195
Force to reuse cache at given path
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[ "realized that need to pass the path at `cache_file_name` like\r\n\r\n```python\r\ntokenized_datasets = raw_datasets[\"train\"].map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n remove_columns=[text_column_name],\r\n load_from_cache_file=True,\r\n desc=\"Running tokenizer on dataset line_by_line\",\r\n # cache_file_names= {\"train\": \"cache-1982fea76aa54a13.arrow\"}\r\n cache_file_name=\"/project/huggingface_cache/datasets/..../cache-1982fea76aa54a13.arrow\",\r\n new_fingerprint=\"1982fea76aa54a13\"\r\n )\r\n```", "Thank you so much! I went through a lot of issues before finding similar experiences here. I have to say that the [docs](https://huggingface.co/docs/datasets/v2.11.0/en/package_reference/main_classes#datasets.Dataset.map) of `.map()` is really misleading, probably making people think that just assigning the file name to cache_file_name is enough." ]
2023-08-30T18:44:54
2023-11-03T10:14:21
2023-08-30T19:00:45
NONE
null
null
### Describe the bug I have run the official example of MLM like: ```bash python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name togethercomputer/RedPajama-Data-1T \ --dataset_config_name arxiv \ --per_device_train_batch_size 10 \ --preprocessing_num_workers 20 \ --validation_split_percentage 0 \ --cache_dir /project/huggingface_cache/datasets \ --line_by_line \ --do_train \ --pad_to_max_length \ --output_dir /project/huggingface_cache/test-mlm ``` it successfully runs and at my cache folder has `cache-1982fea76aa54a13_00001_of_00020.arrow`..... `cache-1982fea76aa54a13_00020_of_00020.arrow ` as tokenization cache of `map` method. And the cache works fine every time I run the command above. However, when I switched to jupyter notebook (since I do not want to load datasets every time when I changed other parameters not related to the dataloading). It is not recognizing the cache files and starts to re-run the entire tokenization process. I changed my code to ```python tokenized_datasets = raw_datasets["train"].map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=True, desc="Running tokenizer on dataset line_by_line", # cache_file_names= {"train": "cache-1982fea76aa54a13.arrow"} cache_file_name="cache-1982fea76aa54a13.arrow", new_fingerprint="1982fea76aa54a13" ) ``` it still does not recognize the previously cached files and trying to re-run the tokenization process. ### Steps to reproduce the bug use jupyter notebook for dataset map function. ### Expected behavior the map function accepts the given cache_file_name and new_fingerprint then load the previously cached files. ### Environment info - `datasets` version: 2.14.4.dev0 - Platform: Linux-3.10.0-1160.59.1.el7.x86_64-x86_64-with-glibc2.10 - Python version: 3.8.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,872,598,223
I_kwDODunzps5vnZTP
6,194
Support custom fingerprinting with `Dataset.from_generator`
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[ "The `fingerprint` parameter serves a slightly different purpose - we use it to inject a new fingerprint after transforming a `Dataset` (computed from the previous fingerprint + transform + transform args), e.g., to be able to compute the cache file for a transform. There is no concept of `fingerprint` before a `Dataset` is fully initialized, but we still need to hash the args (e.g., generator func) of the \"dataset creation methods\" (`from_generator`, `from_csv`, etc.) to compute the cache directory (to store the initial version and transformed dataset versions)\r\n\r\nI agree it should be easier to bypass the hashing mechanism in this instance, too. However, we should probably first address https://github.com/huggingface/datasets/issues/5080 before solving this (e.g., maybe exposing `hash` in `load_dataset`/`load_dataset_builder`.", "Adding +1 here:\r\n\r\nIf the generator needs to access some external resources or state, then it's not always straightforward to make it pickle-able. So I'd like to be able to override how the default cache key derivation needs to pickle the generator (and of course, I'd accept responsibility for that part of cache consistency).\r\n\r\nAppears to be a recurrent roadbump: #6118 #5963 #5819 #5750 #4983 ", "Silly hack incoming:\r\n\r\n```python\r\nimport uuid\r\n\r\nclass _DatasetGeneratorPickleHack:\r\n def __init__(self, generator, generator_id=None):\r\n self.generator = generator\r\n self.generator_id = (\r\n generator_id if generator_id is not None else str(uuid.uuid4())\r\n )\r\n\r\n def __call__(self, *args, **kwargs):\r\n return self.generator(*kwargs, **kwargs)\r\n\r\n def __reduce__(self):\r\n return (_DatasetGeneratorPickleHack_raise, (self.generator_id,))\r\n\r\n\r\ndef _DatasetGeneratorPickleHack_raise(*args, **kwargs):\r\n raise AssertionError(\"cannot actually unpickle _DatasetGeneratorPickleHack!\")\r\n```\r\n\r\nNow `Dataset.from_generator(_DatasetGeneratorPickleHack(gen))` works even if `gen` is unpicklable, because Dataset just pickles the shim object that avoids actually traversing `gen`. Then, one can work out how to set `generator_id` meaningfully to allow cache reuse.", "I'd like some way to do this too. I find that sometimes the hash doesn't cover enough, and that the dataset is not regenerated even when underlying data has changed, and by supplying a custom fingerprint I could do a better job of controlling when my dataset is regenerated." ]
2023-08-29T22:43:13
2023-09-30T16:56:51
null
NONE
null
null
### Feature request When using `Dataset.from_generator`, the generator is hashed when building the fingerprint. Similar to `.map`, it would be interesting to let the user bypass this hashing by accepting a `fingerprint` argument to `.from_generator`. ### Motivation Using the `.from_generator` constructor with a non-picklable generator fails. By accepting a `fingerprint` argument to `.from_generator`, the user would have the opportunity to manually fingerprint the dataset and thus bypass the crash. ### Your contribution If validated, I can try to submit a PR for this.
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6,193
Dataset loading script method does not work with .pyc file
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[ "Before dynamically loading `.py` scripts with `importlib.import_module`, we also parse their contents to check imports, which is tricky to implement for binary `.pyc` files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\n@lhoestq What's your opinion on this?", "> Before dynamically loading .py scripts with importlib.import_module, we also parse their contents to check imports, which is tricky to implement for binary .pyc files (requires parsing bytecode), so I don't think this is something we want to support (unless more users request it ofc) as this use case is a bit too specific.\r\n\r\nYes indeed. Though you can use a .py that imports a package that contains your .pyc code and that you previously installed", "Hi @lhoestq ,\r\nCould you share some example code related to the approach that you are suggesting? " ]
2023-08-29T19:35:06
2023-08-31T19:47:29
null
NONE
null
null
### Describe the bug The huggingface dataset library specifically looks for ‘.py’ file while loading the dataset using loading script approach and it does not work with ‘.pyc’ file. While deploying in production, it becomes an issue when we are restricted to use only .pyc files. Is there any work around for this ? ### Steps to reproduce the bug 1. Create a dataset loading script to read the custom data. 2. compile the code to make sure that .pyc file is created 3. Delete the loading script and re-run the code. Usually, python should make use of complied .pyc files. However, in this case, the dataset library errors out with the message that it's unable to find the data loader loading script. ### Expected behavior The code should make use of .pyc file and run without any error. ### Environment info NA
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PR_kwDODunzps5ZDGnI
6,192
Set minimal fsspec version requirement to 2023.1.0
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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) | 0.005972 / 0.011353 (-0.005381) | 0.003636 / 0.011008 (-0.007372) | 0.080254 / 0.038508 (0.041746) | 0.059564 / 0.023109 (0.036455) | 0.310615 / 0.275898 (0.034717) | 0.359307 / 0.323480 (0.035827) | 0.003408 / 0.007986 (-0.004578) | 0.002941 / 0.004328 (-0.001388) | 0.063699 / 0.004250 (0.059449) | 0.046072 / 0.037052 (0.009020) | 0.318670 / 0.258489 (0.060181) | 0.369677 / 0.293841 (0.075836) | 0.026995 / 0.128546 (-0.101552) | 0.007954 / 0.075646 (-0.067693) | 0.261667 / 0.419271 (-0.157604) | 0.045167 / 0.043533 (0.001634) | 0.314276 / 0.255139 (0.059137) | 0.348871 / 0.283200 (0.065672) | 0.021748 / 0.141683 (-0.119935) | 1.438598 / 1.452155 (-0.013557) | 1.530119 / 1.492716 (0.037403) |\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) | 0.196894 / 0.018006 (0.178888) | 0.445757 / 0.000490 (0.445267) | 0.002842 / 0.000200 (0.002642) | 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) | 0.024923 / 0.037411 (-0.012488) | 0.075186 / 0.014526 (0.060661) | 0.087193 / 0.176557 (-0.089364) | 0.147496 / 0.737135 (-0.589639) | 0.087083 / 0.296338 (-0.209255) |\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) | 0.423545 / 0.215209 (0.208336) | 4.187927 / 2.077655 (2.110273) | 2.008656 / 1.504120 (0.504536) | 1.791313 / 1.541195 (0.250119) | 1.849836 / 1.468490 (0.381346) | 0.499458 / 4.584777 (-4.085318) | 2.983206 / 3.745712 (-0.762506) | 2.801005 / 5.269862 (-2.468856) | 1.886207 / 4.565676 (-2.679469) | 0.057343 / 0.424275 (-0.366932) | 0.006666 / 0.007607 (-0.000941) | 0.483948 / 0.226044 (0.257904) | 4.874818 / 2.268929 (2.605890) | 2.439393 / 55.444624 (-53.005231) | 2.049861 / 6.876477 (-4.826616) | 2.217050 / 2.142072 (0.074977) | 0.589760 / 4.805227 (-4.215467) | 0.125298 / 6.500664 (-6.375366) | 0.061123 / 0.075469 (-0.014347) |\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) | 1.234721 / 1.841788 (-0.607067) | 18.193756 / 8.074308 (10.119448) | 13.682835 / 10.191392 (3.491443) | 0.129345 / 0.680424 (-0.551078) | 0.016589 / 0.534201 (-0.517612) | 0.332355 / 0.579283 (-0.246928) | 0.358408 / 0.434364 (-0.075955) | 0.382044 / 0.540337 (-0.158293) | 0.535403 / 1.386936 (-0.851533) |\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) | 0.006193 / 0.011353 (-0.005160) | 0.003674 / 0.011008 (-0.007335) | 0.062481 / 0.038508 (0.023973) | 0.062096 / 0.023109 (0.038987) | 0.449592 / 0.275898 (0.173694) | 0.479245 / 0.323480 (0.155765) | 0.004793 / 0.007986 (-0.003193) | 0.002896 / 0.004328 (-0.001433) | 0.062887 / 0.004250 (0.058636) | 0.050049 / 0.037052 (0.012997) | 0.454940 / 0.258489 (0.196451) | 0.486115 / 0.293841 (0.192274) | 0.028585 / 0.128546 (-0.099961) | 0.007954 / 0.075646 (-0.067692) | 0.067744 / 0.419271 (-0.351528) | 0.040473 / 0.043533 (-0.003060) | 0.448408 / 0.255139 (0.193269) | 0.472423 / 0.283200 (0.189223) | 0.020549 / 0.141683 (-0.121133) | 1.563618 / 1.452155 (0.111463) | 1.520149 / 1.492716 (0.027432) |\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) | 0.226604 / 0.018006 (0.208598) | 0.417615 / 0.000490 (0.417126) | 0.003386 / 0.000200 (0.003186) | 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) | 0.027264 / 0.037411 (-0.010147) | 0.081709 / 0.014526 (0.067184) | 0.091793 / 0.176557 (-0.084763) | 0.145559 / 0.737135 (-0.591576) | 0.091869 / 0.296338 (-0.204469) |\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) | 0.462917 / 0.215209 (0.247708) | 4.629512 / 2.077655 (2.551857) | 2.555715 / 1.504120 (1.051595) | 2.388064 / 1.541195 (0.846870) | 2.458320 / 1.468490 (0.989830) | 0.511615 / 4.584777 (-4.073162) | 3.124566 / 3.745712 (-0.621146) | 2.839190 / 5.269862 (-2.430672) | 1.894551 / 4.565676 (-2.671126) | 0.059565 / 0.424275 (-0.364710) | 0.006481 / 0.007607 (-0.001126) | 0.532023 / 0.226044 (0.305979) | 5.361507 / 2.268929 (3.092579) | 2.982594 / 55.444624 (-52.462031) | 2.644870 / 6.876477 (-4.231606) | 2.831476 / 2.142072 (0.689404) | 0.607381 / 4.805227 (-4.197846) | 0.126067 / 6.500664 (-6.374597) | 0.062130 / 0.075469 (-0.013339) |\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) | 1.350442 / 1.841788 (-0.491345) | 18.829553 / 8.074308 (10.755245) | 14.796701 / 10.191392 (4.605309) | 0.145393 / 0.680424 (-0.535031) | 0.018218 / 0.534201 (-0.515983) | 0.335500 / 0.579283 (-0.243783) | 0.359190 / 0.434364 (-0.075174) | 0.388377 / 0.540337 (-0.151960) | 0.534994 / 1.386936 (-0.851942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ff7629eb72f499d841d64aa03f97e0b1707d1cc7 \"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) | 0.006741 / 0.011353 (-0.004612) | 0.004097 / 0.011008 (-0.006911) | 0.084513 / 0.038508 (0.046005) | 0.074216 / 0.023109 (0.051107) | 0.352481 / 0.275898 (0.076583) | 0.394806 / 0.323480 (0.071326) | 0.005603 / 0.007986 (-0.002383) | 0.003482 / 0.004328 (-0.000847) | 0.065165 / 0.004250 (0.060914) | 0.054065 / 0.037052 (0.017013) | 0.359399 / 0.258489 (0.100910) | 0.409776 / 0.293841 (0.115935) | 0.030997 / 0.128546 (-0.097550) | 0.008717 / 0.075646 (-0.066929) | 0.288692 / 0.419271 (-0.130579) | 0.052372 / 0.043533 (0.008840) | 0.353867 / 0.255139 (0.098728) | 0.391212 / 0.283200 (0.108012) | 0.024033 / 0.141683 (-0.117650) | 1.496552 / 1.452155 (0.044398) | 1.567267 / 1.492716 (0.074550) |\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) | 0.294074 / 0.018006 (0.276067) | 0.595421 / 0.000490 (0.594931) | 0.003826 / 0.000200 (0.003626) | 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) | 0.028676 / 0.037411 (-0.008736) | 0.082064 / 0.014526 (0.067538) | 0.542399 / 0.176557 (0.365842) | 0.217188 / 0.737135 (-0.519947) | 0.099364 / 0.296338 (-0.196975) |\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) | 0.384282 / 0.215209 (0.169073) | 3.832204 / 2.077655 (1.754550) | 1.842500 / 1.504120 (0.338380) | 1.668192 / 1.541195 (0.126997) | 1.745207 / 1.468490 (0.276717) | 0.481881 / 4.584777 (-4.102896) | 3.677819 / 3.745712 (-0.067893) | 3.329062 / 5.269862 (-1.940799) | 2.056882 / 4.565676 (-2.508795) | 0.056898 / 0.424275 (-0.367377) | 0.007624 / 0.007607 (0.000016) | 0.459712 / 0.226044 (0.233667) | 4.611100 / 2.268929 (2.342171) | 2.370244 / 55.444624 (-53.074381) | 2.032756 / 6.876477 (-4.843721) | 2.336056 / 2.142072 (0.193984) | 0.583503 / 4.805227 (-4.221725) | 0.135041 / 6.500664 (-6.365623) | 0.062245 / 0.075469 (-0.013224) |\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) | 1.303894 / 1.841788 (-0.537894) | 20.315185 / 8.074308 (12.240876) | 14.388779 / 10.191392 (4.197387) | 0.169060 / 0.680424 (-0.511364) | 0.018609 / 0.534201 (-0.515592) | 0.395140 / 0.579283 (-0.184143) | 0.418231 / 0.434364 (-0.016133) | 0.461496 / 0.540337 (-0.078842) | 0.630298 / 1.386936 (-0.756638) |\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) | 0.006999 / 0.011353 (-0.004354) | 0.004197 / 0.011008 (-0.006812) | 0.064524 / 0.038508 (0.026016) | 0.078791 / 0.023109 (0.055682) | 0.397563 / 0.275898 (0.121665) | 0.423056 / 0.323480 (0.099576) | 0.005697 / 0.007986 (-0.002288) | 0.003592 / 0.004328 (-0.000736) | 0.066178 / 0.004250 (0.061928) | 0.058114 / 0.037052 (0.021062) | 0.398619 / 0.258489 (0.140130) | 0.435496 / 0.293841 (0.141655) | 0.032758 / 0.128546 (-0.095788) | 0.008677 / 0.075646 (-0.066970) | 0.071359 / 0.419271 (-0.347913) | 0.048636 / 0.043533 (0.005103) | 0.389762 / 0.255139 (0.134623) | 0.412109 / 0.283200 (0.128910) | 0.023511 / 0.141683 (-0.118172) | 1.514768 / 1.452155 (0.062613) | 1.580163 / 1.492716 (0.087446) |\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) | 0.370491 / 0.018006 (0.352485) | 0.529751 / 0.000490 (0.529261) | 0.016959 / 0.000200 (0.016759) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033361 / 0.037411 (-0.004051) | 0.091610 / 0.014526 (0.077084) | 0.106642 / 0.176557 (-0.069915) | 0.160906 / 0.737135 (-0.576229) | 0.106894 / 0.296338 (-0.189444) |\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) | 0.429932 / 0.215209 (0.214723) | 4.276459 / 2.077655 (2.198804) | 2.268518 / 1.504120 (0.764398) | 2.092512 / 1.541195 (0.551317) | 2.182218 / 1.468490 (0.713728) | 0.494464 / 4.584777 (-4.090313) | 3.750731 / 3.745712 (0.005019) | 3.352370 / 5.269862 (-1.917492) | 2.105630 / 4.565676 (-2.460046) | 0.058465 / 0.424275 (-0.365810) | 0.007449 / 0.007607 (-0.000158) | 0.506896 / 0.226044 (0.280851) | 5.070201 / 2.268929 (2.801272) | 2.758128 / 55.444624 (-52.686496) | 2.408378 / 6.876477 (-4.468099) | 2.690633 / 2.142072 (0.548561) | 0.595662 / 4.805227 (-4.209565) | 0.134355 / 6.500664 (-6.366309) | 0.060113 / 0.075469 (-0.015356) |\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) | 1.380413 / 1.841788 (-0.461375) | 20.691210 / 8.074308 (12.616901) | 15.682282 / 10.191392 (5.490890) | 0.165887 / 0.680424 (-0.514536) | 0.020541 / 0.534201 (-0.513660) | 0.397846 / 0.579283 (-0.181437) | 0.425374 / 0.434364 (-0.008990) | 0.476261 / 0.540337 (-0.064076) | 0.648617 / 1.386936 (-0.738319) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88797b8827334674d7f78c39171c00f0a28ceed6 \"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) | 0.008787 / 0.011353 (-0.002566) | 0.007569 / 0.011008 (-0.003439) | 0.103918 / 0.038508 (0.065410) | 0.083347 / 0.023109 (0.060238) | 0.441838 / 0.275898 (0.165940) | 0.420202 / 0.323480 (0.096722) | 0.007295 / 0.007986 (-0.000690) | 0.005366 / 0.004328 (0.001037) | 0.082659 / 0.004250 (0.078409) | 0.059711 / 0.037052 (0.022658) | 0.401821 / 0.258489 (0.143332) | 0.432906 / 0.293841 (0.139065) | 0.048662 / 0.128546 (-0.079885) | 0.014091 / 0.075646 (-0.061555) | 0.352583 / 0.419271 (-0.066689) | 0.064739 / 0.043533 (0.021206) | 0.410890 / 0.255139 (0.155751) | 0.443450 / 0.283200 (0.160251) | 0.035817 / 0.141683 (-0.105866) | 1.754687 / 1.452155 (0.302532) | 1.887338 / 1.492716 (0.394622) |\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) | 0.209440 / 0.018006 (0.191434) | 0.519641 / 0.000490 (0.519152) | 0.005726 / 0.000200 (0.005526) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031027 / 0.037411 (-0.006384) | 0.097503 / 0.014526 (0.082977) | 0.106985 / 0.176557 (-0.069572) | 0.178235 / 0.737135 (-0.558900) | 0.108110 / 0.296338 (-0.188228) |\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) | 0.594325 / 0.215209 (0.379116) | 6.159414 / 2.077655 (4.081759) | 2.664892 / 1.504120 (1.160772) | 2.363355 / 1.541195 (0.822160) | 2.410754 / 1.468490 (0.942264) | 0.842557 / 4.584777 (-3.742220) | 5.112059 / 3.745712 (1.366347) | 4.633152 / 5.269862 (-0.636709) | 2.965891 / 4.565676 (-1.599785) | 0.097922 / 0.424275 (-0.326353) | 0.008602 / 0.007607 (0.000995) | 0.773029 / 0.226044 (0.546985) | 7.462314 / 2.268929 (5.193386) | 3.584776 / 55.444624 (-51.859848) | 2.752375 / 6.876477 (-4.124102) | 2.976345 / 2.142072 (0.834272) | 1.049423 / 4.805227 (-3.755804) | 0.212001 / 6.500664 (-6.288663) | 0.074095 / 0.075469 (-0.001374) |\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) | 1.577905 / 1.841788 (-0.263883) | 23.280931 / 8.074308 (15.206623) | 21.017946 / 10.191392 (10.826554) | 0.228746 / 0.680424 (-0.451678) | 0.027877 / 0.534201 (-0.506324) | 0.469173 / 0.579283 (-0.110110) | 0.567614 / 0.434364 (0.133250) | 0.545041 / 0.540337 (0.004704) | 0.754743 / 1.386936 (-0.632194) |\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) | 0.008958 / 0.011353 (-0.002395) | 0.005077 / 0.011008 (-0.005931) | 0.083990 / 0.038508 (0.045482) | 0.078586 / 0.023109 (0.055476) | 0.482164 / 0.275898 (0.206266) | 0.525575 / 0.323480 (0.202095) | 0.006031 / 0.007986 (-0.001955) | 0.003922 / 0.004328 (-0.000407) | 0.084547 / 0.004250 (0.080296) | 0.064539 / 0.037052 (0.027487) | 0.501256 / 0.258489 (0.242767) | 0.531985 / 0.293841 (0.238144) | 0.050438 / 0.128546 (-0.078109) | 0.014004 / 0.075646 (-0.061642) | 0.091269 / 0.419271 (-0.328003) | 0.060825 / 0.043533 (0.017292) | 0.492573 / 0.255139 (0.237434) | 0.517060 / 0.283200 (0.233861) | 0.033576 / 0.141683 (-0.108107) | 1.775719 / 1.452155 (0.323564) | 1.866865 / 1.492716 (0.374149) |\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) | 0.225026 / 0.018006 (0.207020) | 0.510715 / 0.000490 (0.510225) | 0.005791 / 0.000200 (0.005591) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032795 / 0.037411 (-0.004616) | 0.109206 / 0.014526 (0.094680) | 0.121441 / 0.176557 (-0.055115) | 0.179735 / 0.737135 (-0.557401) | 0.115825 / 0.296338 (-0.180514) |\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) | 0.633259 / 0.215209 (0.418050) | 6.298084 / 2.077655 (4.220430) | 2.892604 / 1.504120 (1.388484) | 2.570858 / 1.541195 (1.029663) | 2.611441 / 1.468490 (1.142951) | 0.897801 / 4.584777 (-3.686976) | 5.185863 / 3.745712 (1.440151) | 4.656897 / 5.269862 (-0.612965) | 3.078575 / 4.565676 (-1.487101) | 0.100563 / 0.424275 (-0.323712) | 0.008368 / 0.007607 (0.000761) | 0.749152 / 0.226044 (0.523108) | 7.687484 / 2.268929 (5.418556) | 3.689238 / 55.444624 (-51.755387) | 2.896779 / 6.876477 (-3.979698) | 3.158688 / 2.142072 (1.016615) | 1.083490 / 4.805227 (-3.721737) | 0.216994 / 6.500664 (-6.283670) | 0.074053 / 0.075469 (-0.001416) |\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) | 1.732812 / 1.841788 (-0.108976) | 23.952127 / 8.074308 (15.877819) | 22.078140 / 10.191392 (11.886748) | 0.229491 / 0.680424 (-0.450933) | 0.032070 / 0.534201 (-0.502131) | 0.503344 / 0.579283 (-0.075939) | 0.588489 / 0.434364 (0.154125) | 0.550199 / 0.540337 (0.009861) | 0.778203 / 1.386936 (-0.608733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e95a508b8d1747b5331bdbbd3e1021e97602c49 \"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) | 0.007569 / 0.011353 (-0.003784) | 0.004447 / 0.011008 (-0.006561) | 0.098573 / 0.038508 (0.060064) | 0.081743 / 0.023109 (0.058634) | 0.379912 / 0.275898 (0.104013) | 0.411203 / 0.323480 (0.087723) | 0.004492 / 0.007986 (-0.003494) | 0.005627 / 0.004328 (0.001298) | 0.075974 / 0.004250 (0.071724) | 0.062512 / 0.037052 (0.025459) | 0.386971 / 0.258489 (0.128482) | 0.433299 / 0.293841 (0.139458) | 0.035935 / 0.128546 (-0.092611) | 0.009845 / 0.075646 (-0.065801) | 0.342940 / 0.419271 (-0.076331) | 0.061343 / 0.043533 (0.017810) | 0.381984 / 0.255139 (0.126845) | 0.417921 / 0.283200 (0.134721) | 0.028469 / 0.141683 (-0.113214) | 1.758472 / 1.452155 (0.306317) | 1.847768 / 1.492716 (0.355051) |\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) | 0.234297 / 0.018006 (0.216291) | 0.520020 / 0.000490 (0.519531) | 0.007375 / 0.000200 (0.007175) | 0.000767 / 0.000054 (0.000713) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032738 / 0.037411 (-0.004673) | 0.097656 / 0.014526 (0.083130) | 0.112476 / 0.176557 (-0.064080) | 0.179222 / 0.737135 (-0.557913) | 0.113638 / 0.296338 (-0.182700) |\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) | 0.453677 / 0.215209 (0.238467) | 4.528143 / 2.077655 (2.450489) | 2.243874 / 1.504120 (0.739754) | 2.051546 / 1.541195 (0.510351) | 2.196050 / 1.468490 (0.727560) | 0.567345 / 4.584777 (-4.017432) | 4.133591 / 3.745712 (0.387879) | 3.855286 / 5.269862 (-1.414576) | 2.393496 / 4.565676 (-2.172180) | 0.066567 / 0.424275 (-0.357708) | 0.009038 / 0.007607 (0.001431) | 0.549166 / 0.226044 (0.323122) | 5.472767 / 2.268929 (3.203839) | 2.788012 / 55.444624 (-52.656612) | 2.426132 / 6.876477 (-4.450345) | 2.684856 / 2.142072 (0.542784) | 0.680198 / 4.805227 (-4.125029) | 0.157782 / 6.500664 (-6.342882) | 0.073000 / 0.075469 (-0.002469) |\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) | 1.622435 / 1.841788 (-0.219352) | 22.965715 / 8.074308 (14.891407) | 16.626903 / 10.191392 (6.435511) | 0.197156 / 0.680424 (-0.483268) | 0.025599 / 0.534201 (-0.508602) | 0.495550 / 0.579283 (-0.083733) | 0.466575 / 0.434364 (0.032211) | 0.565862 / 0.540337 (0.025525) | 0.793835 / 1.386936 (-0.593102) |\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) | 0.007721 / 0.011353 (-0.003632) | 0.004652 / 0.011008 (-0.006356) | 0.076636 / 0.038508 (0.038127) | 0.082183 / 0.023109 (0.059074) | 0.474665 / 0.275898 (0.198767) | 0.511593 / 0.323480 (0.188113) | 0.006240 / 0.007986 (-0.001746) | 0.003750 / 0.004328 (-0.000578) | 0.076939 / 0.004250 (0.072689) | 0.063333 / 0.037052 (0.026281) | 0.476469 / 0.258489 (0.217980) | 0.512514 / 0.293841 (0.218674) | 0.037802 / 0.128546 (-0.090744) | 0.009975 / 0.075646 (-0.065671) | 0.084190 / 0.419271 (-0.335081) | 0.056705 / 0.043533 (0.013172) | 0.475429 / 0.255139 (0.220290) | 0.496414 / 0.283200 (0.213215) | 0.026039 / 0.141683 (-0.115644) | 1.796059 / 1.452155 (0.343905) | 1.867461 / 1.492716 (0.374745) |\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) | 0.285219 / 0.018006 (0.267213) | 0.506311 / 0.000490 (0.505821) | 0.018545 / 0.000200 (0.018345) | 0.000142 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037832 / 0.037411 (0.000420) | 0.110437 / 0.014526 (0.095911) | 0.122953 / 0.176557 (-0.053604) | 0.187049 / 0.737135 (-0.550087) | 0.123539 / 0.296338 (-0.172800) |\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) | 0.508120 / 0.215209 (0.292911) | 5.082836 / 2.077655 (3.005182) | 2.800411 / 1.504120 (1.296291) | 2.579457 / 1.541195 (1.038262) | 2.645945 / 1.468490 (1.177455) | 0.578574 / 4.584777 (-4.006203) | 4.163401 / 3.745712 (0.417689) | 3.858575 / 5.269862 (-1.411286) | 2.389892 / 4.565676 (-2.175785) | 0.068639 / 0.424275 (-0.355636) | 0.008779 / 0.007607 (0.001172) | 0.598925 / 0.226044 (0.372880) | 5.987147 / 2.268929 (3.718219) | 3.361791 / 55.444624 (-52.082833) | 2.910425 / 6.876477 (-3.966051) | 3.156849 / 2.142072 (1.014776) | 0.690945 / 4.805227 (-4.114283) | 0.157441 / 6.500664 (-6.343223) | 0.071596 / 0.075469 (-0.003873) |\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) | 1.672763 / 1.841788 (-0.169025) | 23.599525 / 8.074308 (15.525217) | 17.520087 / 10.191392 (7.328695) | 0.169174 / 0.680424 (-0.511250) | 0.023470 / 0.534201 (-0.510731) | 0.469234 / 0.579283 (-0.110050) | 0.470020 / 0.434364 (0.035656) | 0.579949 / 0.540337 (0.039611) | 0.771353 / 1.386936 (-0.615583) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029227a116c14720afca71b9b22e78eb2a1c09a6 \"CML watermark\")\n" ]
2023-08-29T15:23:41
2023-08-30T14:01:56
2023-08-30T13:51:32
CONTRIBUTOR
false
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Fix https://github.com/huggingface/datasets/issues/6141 Colab installs 2023.6.0, so we should be good 🙂
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PR_kwDODunzps5ZCKmv
6,191
Add missing `revision` argument
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[ "_The documentation is not available anymore as the PR was closed or merged._", "I have found the same issue. Good fix. Should be merged as soon as possible.", "<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) | 0.006258 / 0.011353 (-0.005095) | 0.003717 / 0.011008 (-0.007291) | 0.079444 / 0.038508 (0.040936) | 0.066318 / 0.023109 (0.043209) | 0.310129 / 0.275898 (0.034231) | 0.346948 / 0.323480 (0.023469) | 0.003505 / 0.007986 (-0.004480) | 0.002855 / 0.004328 (-0.001474) | 0.062447 / 0.004250 (0.058197) | 0.050191 / 0.037052 (0.013139) | 0.314550 / 0.258489 (0.056061) | 0.357883 / 0.293841 (0.064042) | 0.027754 / 0.128546 (-0.100792) | 0.008068 / 0.075646 (-0.067578) | 0.262170 / 0.419271 (-0.157102) | 0.045834 / 0.043533 (0.002301) | 0.306938 / 0.255139 (0.051799) | 0.339229 / 0.283200 (0.056030) | 0.021188 / 0.141683 (-0.120495) | 1.430904 / 1.452155 (-0.021251) | 1.542038 / 1.492716 (0.049321) |\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) | 0.201232 / 0.018006 (0.183226) | 0.432848 / 0.000490 (0.432358) | 0.002403 / 0.000200 (0.002203) | 0.000070 / 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) | 0.024068 / 0.037411 (-0.013344) | 0.074077 / 0.014526 (0.059551) | 0.083578 / 0.176557 (-0.092978) | 0.144497 / 0.737135 (-0.592638) | 0.085386 / 0.296338 (-0.210952) |\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) | 0.397912 / 0.215209 (0.182702) | 3.940953 / 2.077655 (1.863299) | 1.935914 / 1.504120 (0.431794) | 1.753688 / 1.541195 (0.212493) | 1.832916 / 1.468490 (0.364426) | 0.503320 / 4.584777 (-4.081457) | 3.068693 / 3.745712 (-0.677019) | 2.867543 / 5.269862 (-2.402318) | 1.876265 / 4.565676 (-2.689412) | 0.057234 / 0.424275 (-0.367041) | 0.006753 / 0.007607 (-0.000854) | 0.468456 / 0.226044 (0.242411) | 4.681671 / 2.268929 (2.412742) | 2.445141 / 55.444624 (-52.999483) | 2.182366 / 6.876477 (-4.694110) | 2.399365 / 2.142072 (0.257293) | 0.591880 / 4.805227 (-4.213347) | 0.126176 / 6.500664 (-6.374488) | 0.061488 / 0.075469 (-0.013982) |\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) | 1.244013 / 1.841788 (-0.597775) | 18.534720 / 8.074308 (10.460412) | 13.853267 / 10.191392 (3.661875) | 0.154167 / 0.680424 (-0.526257) | 0.016685 / 0.534201 (-0.517515) | 0.331044 / 0.579283 (-0.248239) | 0.341399 / 0.434364 (-0.092965) | 0.378878 / 0.540337 (-0.161459) | 0.535707 / 1.386936 (-0.851230) |\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) | 0.006284 / 0.011353 (-0.005069) | 0.003707 / 0.011008 (-0.007301) | 0.062481 / 0.038508 (0.023973) | 0.063342 / 0.023109 (0.040233) | 0.445465 / 0.275898 (0.169567) | 0.482021 / 0.323480 (0.158541) | 0.004909 / 0.007986 (-0.003076) | 0.002908 / 0.004328 (-0.001420) | 0.063111 / 0.004250 (0.058860) | 0.050197 / 0.037052 (0.013145) | 0.453367 / 0.258489 (0.194878) | 0.485249 / 0.293841 (0.191408) | 0.028532 / 0.128546 (-0.100014) | 0.008157 / 0.075646 (-0.067490) | 0.068033 / 0.419271 (-0.351238) | 0.041093 / 0.043533 (-0.002440) | 0.446555 / 0.255139 (0.191416) | 0.469103 / 0.283200 (0.185904) | 0.019529 / 0.141683 (-0.122154) | 1.503135 / 1.452155 (0.050980) | 1.545819 / 1.492716 (0.053103) |\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) | 0.257274 / 0.018006 (0.239268) | 0.418643 / 0.000490 (0.418153) | 0.011604 / 0.000200 (0.011405) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026286 / 0.037411 (-0.011125) | 0.082459 / 0.014526 (0.067933) | 0.090007 / 0.176557 (-0.086550) | 0.144963 / 0.737135 (-0.592173) | 0.093236 / 0.296338 (-0.203102) |\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) | 0.456331 / 0.215209 (0.241122) | 4.559469 / 2.077655 (2.481814) | 2.503452 / 1.504120 (0.999333) | 2.326579 / 1.541195 (0.785384) | 2.387551 / 1.468490 (0.919061) | 0.508683 / 4.584777 (-4.076094) | 3.071293 / 3.745712 (-0.674419) | 2.872820 / 5.269862 (-2.397041) | 1.891674 / 4.565676 (-2.674003) | 0.058951 / 0.424275 (-0.365324) | 0.006493 / 0.007607 (-0.001114) | 0.526747 / 0.226044 (0.300703) | 5.279985 / 2.268929 (3.011057) | 2.986146 / 55.444624 (-52.458478) | 2.603462 / 6.876477 (-4.273015) | 2.766776 / 2.142072 (0.624704) | 0.594685 / 4.805227 (-4.210542) | 0.125174 / 6.500664 (-6.375490) | 0.061430 / 0.075469 (-0.014039) |\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) | 1.350012 / 1.841788 (-0.491776) | 18.991941 / 8.074308 (10.917633) | 14.903483 / 10.191392 (4.712091) | 0.145918 / 0.680424 (-0.534506) | 0.017766 / 0.534201 (-0.516435) | 0.335350 / 0.579283 (-0.243933) | 0.357936 / 0.434364 (-0.076428) | 0.392355 / 0.540337 (-0.147983) | 0.545787 / 1.386936 (-0.841149) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#439e115d34a2d8737af719660c1b586ac32279dc \"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) | 0.005927 / 0.011353 (-0.005426) | 0.003497 / 0.011008 (-0.007512) | 0.079802 / 0.038508 (0.041294) | 0.058994 / 0.023109 (0.035885) | 0.309349 / 0.275898 (0.033451) | 0.344876 / 0.323480 (0.021396) | 0.004631 / 0.007986 (-0.003354) | 0.002814 / 0.004328 (-0.001515) | 0.062228 / 0.004250 (0.057978) | 0.046001 / 0.037052 (0.008949) | 0.312196 / 0.258489 (0.053707) | 0.356283 / 0.293841 (0.062442) | 0.027264 / 0.128546 (-0.101282) | 0.007992 / 0.075646 (-0.067654) | 0.260746 / 0.419271 (-0.158526) | 0.045112 / 0.043533 (0.001579) | 0.310463 / 0.255139 (0.055324) | 0.336456 / 0.283200 (0.053256) | 0.020364 / 0.141683 (-0.121319) | 1.482159 / 1.452155 (0.030005) | 1.541586 / 1.492716 (0.048870) |\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) | 0.185035 / 0.018006 (0.167028) | 0.432104 / 0.000490 (0.431615) | 0.002911 / 0.000200 (0.002711) | 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) | 0.023674 / 0.037411 (-0.013737) | 0.072462 / 0.014526 (0.057936) | 0.080154 / 0.176557 (-0.096402) | 0.143022 / 0.737135 (-0.594114) | 0.082909 / 0.296338 (-0.213430) |\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) | 0.436977 / 0.215209 (0.221768) | 4.359633 / 2.077655 (2.281979) | 2.321479 / 1.504120 (0.817359) | 2.115277 / 1.541195 (0.574082) | 2.172303 / 1.468490 (0.703813) | 0.495735 / 4.584777 (-4.089042) | 3.006773 / 3.745712 (-0.738939) | 2.866560 / 5.269862 (-2.403302) | 1.839339 / 4.565676 (-2.726337) | 0.056925 / 0.424275 (-0.367350) | 0.006777 / 0.007607 (-0.000830) | 0.507217 / 0.226044 (0.281172) | 5.064933 / 2.268929 (2.796004) | 2.737542 / 55.444624 (-52.707082) | 2.386227 / 6.876477 (-4.490250) | 2.566375 / 2.142072 (0.424302) | 0.582965 / 4.805227 (-4.222262) | 0.124715 / 6.500664 (-6.375949) | 0.061560 / 0.075469 (-0.013909) |\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) | 1.295684 / 1.841788 (-0.546103) | 18.178345 / 8.074308 (10.104037) | 13.795886 / 10.191392 (3.604494) | 0.131464 / 0.680424 (-0.548960) | 0.016808 / 0.534201 (-0.517393) | 0.334190 / 0.579283 (-0.245093) | 0.347358 / 0.434364 (-0.087006) | 0.386198 / 0.540337 (-0.154139) | 0.527807 / 1.386936 (-0.859129) |\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) | 0.006118 / 0.011353 (-0.005235) | 0.003634 / 0.011008 (-0.007374) | 0.062117 / 0.038508 (0.023609) | 0.061407 / 0.023109 (0.038298) | 0.448047 / 0.275898 (0.172149) | 0.483382 / 0.323480 (0.159902) | 0.004849 / 0.007986 (-0.003137) | 0.002859 / 0.004328 (-0.001469) | 0.061714 / 0.004250 (0.057463) | 0.047959 / 0.037052 (0.010907) | 0.452038 / 0.258489 (0.193549) | 0.485206 / 0.293841 (0.191365) | 0.028254 / 0.128546 (-0.100292) | 0.008055 / 0.075646 (-0.067591) | 0.067752 / 0.419271 (-0.351519) | 0.040355 / 0.043533 (-0.003178) | 0.446986 / 0.255139 (0.191847) | 0.472554 / 0.283200 (0.189354) | 0.019461 / 0.141683 (-0.122222) | 1.459048 / 1.452155 (0.006893) | 1.497283 / 1.492716 (0.004566) |\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) | 0.241788 / 0.018006 (0.223782) | 0.457352 / 0.000490 (0.456862) | 0.003841 / 0.000200 (0.003641) | 0.000081 / 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) | 0.026429 / 0.037411 (-0.010982) | 0.081604 / 0.014526 (0.067078) | 0.092881 / 0.176557 (-0.083675) | 0.146057 / 0.737135 (-0.591078) | 0.092987 / 0.296338 (-0.203352) |\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) | 0.456641 / 0.215209 (0.241432) | 4.567853 / 2.077655 (2.490198) | 2.491684 / 1.504120 (0.987564) | 2.323647 / 1.541195 (0.782452) | 2.387689 / 1.468490 (0.919198) | 0.505114 / 4.584777 (-4.079663) | 3.071615 / 3.745712 (-0.674098) | 2.912391 / 5.269862 (-2.357471) | 1.922350 / 4.565676 (-2.643326) | 0.057785 / 0.424275 (-0.366490) | 0.006642 / 0.007607 (-0.000965) | 0.532463 / 0.226044 (0.306418) | 5.344084 / 2.268929 (3.075155) | 2.970182 / 55.444624 (-52.474442) | 2.601733 / 6.876477 (-4.274744) | 2.763803 / 2.142072 (0.621731) | 0.596333 / 4.805227 (-4.208894) | 0.127047 / 6.500664 (-6.373617) | 0.062516 / 0.075469 (-0.012953) |\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) | 1.343206 / 1.841788 (-0.498581) | 19.405215 / 8.074308 (11.330907) | 15.406568 / 10.191392 (5.215176) | 0.132328 / 0.680424 (-0.548096) | 0.017882 / 0.534201 (-0.516318) | 0.336393 / 0.579283 (-0.242890) | 0.361989 / 0.434364 (-0.072375) | 0.394336 / 0.540337 (-0.146001) | 0.545166 / 1.386936 (-0.841770) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#439e115d34a2d8737af719660c1b586ac32279dc \"CML watermark\")\n" ]
2023-08-29T13:05:04
2023-09-04T06:38:17
2023-08-31T13:50:00
CONTRIBUTOR
false
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I've noticed that when you're not working on the main branch, there are sometimes errors in the files returned. After some investigation, I realized that the revision was not properly passed everywhere. This PR proposes a fix.
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6,190
`Invalid user token` even when correct user token is passed!
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[ "This is because `download_config.use_auth_token` is deprecated - you should use `download_config.token` instead", "Works! Thanks for the quick fix! <3" ]
2023-08-29T12:37:03
2023-08-29T13:01:10
2023-08-29T13:01:09
MEMBER
null
null
### Describe the bug I'm working on a dataset which comprises other datasets on the hub. URL: https://huggingface.co/datasets/open-asr-leaderboard/datasets-test-only Note: Some of the sub-datasets in this metadataset require explicit access. All the other datasets work fine, except, `common_voice`. ### Steps to reproduce the bug https://github.com/Vaibhavs10/scratchpad/blob/main/cv_datasets_bug_repro.ipynb ### Expected behavior It should work if the provided access token is valid (as it does for all the other datasets) ### Environment info datasets version -> 2.14.4
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https://github.com/huggingface/datasets/pull/6189
1,871,569,855
PR_kwDODunzps5ZB8Z9
6,189
Don't alter input in Features.from_dict
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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) | 0.006166 / 0.011353 (-0.005187) | 0.003643 / 0.011008 (-0.007365) | 0.080966 / 0.038508 (0.042458) | 0.060538 / 0.023109 (0.037429) | 0.309205 / 0.275898 (0.033307) | 0.351007 / 0.323480 (0.027527) | 0.003592 / 0.007986 (-0.004393) | 0.002880 / 0.004328 (-0.001448) | 0.062957 / 0.004250 (0.058707) | 0.049015 / 0.037052 (0.011963) | 0.309436 / 0.258489 (0.050947) | 0.362695 / 0.293841 (0.068854) | 0.027818 / 0.128546 (-0.100728) | 0.008030 / 0.075646 (-0.067616) | 0.262678 / 0.419271 (-0.156594) | 0.046024 / 0.043533 (0.002491) | 0.316246 / 0.255139 (0.061107) | 0.337454 / 0.283200 (0.054254) | 0.022529 / 0.141683 (-0.119154) | 1.432492 / 1.452155 (-0.019662) | 1.499646 / 1.492716 (0.006929) |\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) | 0.190931 / 0.018006 (0.172925) | 0.428053 / 0.000490 (0.427564) | 0.002839 / 0.000200 (0.002639) | 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) | 0.024042 / 0.037411 (-0.013370) | 0.073952 / 0.014526 (0.059426) | 0.905973 / 0.176557 (0.729417) | 0.177767 / 0.737135 (-0.559368) | 0.125779 / 0.296338 (-0.170559) |\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) | 0.398997 / 0.215209 (0.183788) | 3.959575 / 2.077655 (1.881920) | 1.907038 / 1.504120 (0.402918) | 1.732908 / 1.541195 (0.191713) | 1.757038 / 1.468490 (0.288548) | 0.495917 / 4.584777 (-4.088860) | 3.021437 / 3.745712 (-0.724275) | 2.793960 / 5.269862 (-2.475901) | 1.827753 / 4.565676 (-2.737923) | 0.057143 / 0.424275 (-0.367132) | 0.006583 / 0.007607 (-0.001024) | 0.469402 / 0.226044 (0.243357) | 4.685623 / 2.268929 (2.416695) | 2.325200 / 55.444624 (-53.119424) | 1.985559 / 6.876477 (-4.890918) | 2.151208 / 2.142072 (0.009136) | 0.589498 / 4.805227 (-4.215730) | 0.125433 / 6.500664 (-6.375231) | 0.060834 / 0.075469 (-0.014636) |\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) | 1.228217 / 1.841788 (-0.613571) | 18.076089 / 8.074308 (10.001780) | 13.814460 / 10.191392 (3.623068) | 0.144674 / 0.680424 (-0.535750) | 0.016749 / 0.534201 (-0.517452) | 0.332839 / 0.579283 (-0.246444) | 0.357211 / 0.434364 (-0.077153) | 0.380367 / 0.540337 (-0.159971) | 0.531177 / 1.386936 (-0.855759) |\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) | 0.006006 / 0.011353 (-0.005347) | 0.003552 / 0.011008 (-0.007456) | 0.061822 / 0.038508 (0.023313) | 0.057724 / 0.023109 (0.034615) | 0.462326 / 0.275898 (0.186428) | 0.492842 / 0.323480 (0.169362) | 0.004833 / 0.007986 (-0.003152) | 0.002847 / 0.004328 (-0.001481) | 0.062278 / 0.004250 (0.058028) | 0.046754 / 0.037052 (0.009702) | 0.464185 / 0.258489 (0.205696) | 0.496416 / 0.293841 (0.202576) | 0.028949 / 0.128546 (-0.099597) | 0.008038 / 0.075646 (-0.067608) | 0.067572 / 0.419271 (-0.351700) | 0.041176 / 0.043533 (-0.002356) | 0.460047 / 0.255139 (0.204908) | 0.482728 / 0.283200 (0.199528) | 0.020047 / 0.141683 (-0.121635) | 1.455958 / 1.452155 (0.003804) | 1.525730 / 1.492716 (0.033014) |\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) | 0.283643 / 0.018006 (0.265637) | 0.443046 / 0.000490 (0.442556) | 0.041019 / 0.000200 (0.040819) | 0.000340 / 0.000054 (0.000286) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026229 / 0.037411 (-0.011182) | 0.081498 / 0.014526 (0.066972) | 0.091412 / 0.176557 (-0.085145) | 0.146621 / 0.737135 (-0.590514) | 0.092113 / 0.296338 (-0.204225) |\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) | 0.463525 / 0.215209 (0.248315) | 4.629852 / 2.077655 (2.552198) | 2.564831 / 1.504120 (1.060711) | 2.386976 / 1.541195 (0.845781) | 2.457757 / 1.468490 (0.989266) | 0.507317 / 4.584777 (-4.077460) | 3.142418 / 3.745712 (-0.603294) | 2.851642 / 5.269862 (-2.418219) | 1.894444 / 4.565676 (-2.671233) | 0.058495 / 0.424275 (-0.365780) | 0.006453 / 0.007607 (-0.001154) | 0.545363 / 0.226044 (0.319319) | 5.448092 / 2.268929 (3.179164) | 2.996328 / 55.444624 (-52.448296) | 2.664666 / 6.876477 (-4.211811) | 2.832247 / 2.142072 (0.690174) | 0.597631 / 4.805227 (-4.207596) | 0.126101 / 6.500664 (-6.374563) | 0.062573 / 0.075469 (-0.012896) |\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) | 1.366502 / 1.841788 (-0.475286) | 18.872990 / 8.074308 (10.798682) | 14.892114 / 10.191392 (4.700722) | 0.146668 / 0.680424 (-0.533756) | 0.017876 / 0.534201 (-0.516325) | 0.338490 / 0.579283 (-0.240793) | 0.357471 / 0.434364 (-0.076893) | 0.398730 / 0.540337 (-0.141608) | 0.542464 / 1.386936 (-0.844472) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ff3e846d86814fa6962326e9346a4f1f1e8a80 \"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) | 0.009132 / 0.011353 (-0.002221) | 0.005796 / 0.011008 (-0.005212) | 0.119495 / 0.038508 (0.080987) | 0.081708 / 0.023109 (0.058599) | 0.432940 / 0.275898 (0.157042) | 0.466793 / 0.323480 (0.143313) | 0.006464 / 0.007986 (-0.001521) | 0.004308 / 0.004328 (-0.000021) | 0.086344 / 0.004250 (0.082093) | 0.065987 / 0.037052 (0.028935) | 0.445213 / 0.258489 (0.186724) | 0.482405 / 0.293841 (0.188564) | 0.053553 / 0.128546 (-0.074993) | 0.015320 / 0.075646 (-0.060326) | 0.455669 / 0.419271 (0.036397) | 0.071619 / 0.043533 (0.028086) | 0.434843 / 0.255139 (0.179704) | 0.503224 / 0.283200 (0.220025) | 0.038280 / 0.141683 (-0.103403) | 1.901877 / 1.452155 (0.449722) | 2.040406 / 1.492716 (0.547690) |\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) | 0.268275 / 0.018006 (0.250269) | 0.622795 / 0.000490 (0.622305) | 0.004572 / 0.000200 (0.004372) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032514 / 0.037411 (-0.004898) | 0.100619 / 0.014526 (0.086093) | 0.118407 / 0.176557 (-0.058149) | 0.190311 / 0.737135 (-0.546824) | 0.117160 / 0.296338 (-0.179178) |\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) | 0.629836 / 0.215209 (0.414627) | 6.236124 / 2.077655 (4.158470) | 2.750775 / 1.504120 (1.246655) | 2.380111 / 1.541195 (0.838916) | 2.487279 / 1.468490 (1.018789) | 0.849568 / 4.584777 (-3.735209) | 5.571308 / 3.745712 (1.825596) | 4.934114 / 5.269862 (-0.335747) | 3.205478 / 4.565676 (-1.360198) | 0.104804 / 0.424275 (-0.319471) | 0.009856 / 0.007607 (0.002248) | 0.753352 / 0.226044 (0.527308) | 7.523482 / 2.268929 (5.254554) | 3.660088 / 55.444624 (-51.784537) | 2.726493 / 6.876477 (-4.149984) | 3.011344 / 2.142072 (0.869271) | 1.093410 / 4.805227 (-3.711817) | 0.229758 / 6.500664 (-6.270906) | 0.081516 / 0.075469 (0.006047) |\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) | 1.700199 / 1.841788 (-0.141588) | 25.238736 / 8.074308 (17.164428) | 23.188131 / 10.191392 (12.996739) | 0.257862 / 0.680424 (-0.422562) | 0.028885 / 0.534201 (-0.505316) | 0.510693 / 0.579283 (-0.068590) | 0.648474 / 0.434364 (0.214110) | 0.576314 / 0.540337 (0.035976) | 0.800606 / 1.386936 (-0.586330) |\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) | 0.009426 / 0.011353 (-0.001927) | 0.006205 / 0.011008 (-0.004803) | 0.083947 / 0.038508 (0.045438) | 0.089164 / 0.023109 (0.066055) | 0.540500 / 0.275898 (0.264602) | 0.578825 / 0.323480 (0.255345) | 0.006792 / 0.007986 (-0.001194) | 0.005125 / 0.004328 (0.000797) | 0.083284 / 0.004250 (0.079034) | 0.067539 / 0.037052 (0.030487) | 0.544330 / 0.258489 (0.285841) | 0.593836 / 0.293841 (0.299995) | 0.050647 / 0.128546 (-0.077899) | 0.014688 / 0.075646 (-0.060959) | 0.095977 / 0.419271 (-0.323295) | 0.062326 / 0.043533 (0.018793) | 0.536096 / 0.255139 (0.280957) | 0.578691 / 0.283200 (0.295492) | 0.035488 / 0.141683 (-0.106194) | 1.911145 / 1.452155 (0.458990) | 1.977647 / 1.492716 (0.484931) |\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) | 0.368365 / 0.018006 (0.350359) | 0.609836 / 0.000490 (0.609346) | 0.054720 / 0.000200 (0.054520) | 0.000465 / 0.000054 (0.000411) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036057 / 0.037411 (-0.001355) | 0.126434 / 0.014526 (0.111908) | 0.124740 / 0.176557 (-0.051817) | 0.198907 / 0.737135 (-0.538228) | 0.138201 / 0.296338 (-0.158137) |\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) | 0.684814 / 0.215209 (0.469605) | 6.738182 / 2.077655 (4.660527) | 3.231054 / 1.504120 (1.726934) | 2.889550 / 1.541195 (1.348355) | 2.933985 / 1.468490 (1.465495) | 0.867176 / 4.584777 (-3.717601) | 5.465475 / 3.745712 (1.719763) | 4.928370 / 5.269862 (-0.341492) | 3.126382 / 4.565676 (-1.439294) | 0.129673 / 0.424275 (-0.294603) | 0.009755 / 0.007607 (0.002148) | 0.797860 / 0.226044 (0.571816) | 8.003178 / 2.268929 (5.734250) | 4.081658 / 55.444624 (-51.362966) | 3.303837 / 6.876477 (-3.572640) | 3.574577 / 2.142072 (1.432505) | 1.064674 / 4.805227 (-3.740554) | 0.232894 / 6.500664 (-6.267770) | 0.082298 / 0.075469 (0.006829) |\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) | 1.858701 / 1.841788 (0.016913) | 25.839794 / 8.074308 (17.765485) | 24.291425 / 10.191392 (14.100033) | 0.250181 / 0.680424 (-0.430243) | 0.034479 / 0.534201 (-0.499722) | 0.540754 / 0.579283 (-0.038529) | 0.615996 / 0.434364 (0.181632) | 0.631499 / 0.540337 (0.091161) | 0.838719 / 1.386936 (-0.548217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b6bb2f0e7a460d4ed04855eafe1184a7ce7c09c \"CML watermark\")\n" ]
2023-08-29T12:29:47
2023-08-29T13:04:59
2023-08-29T12:52:48
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6,188
[Feature Request] Check the length of batch before writing so that empty batch is allowed
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[ "I think this error means you filter all examples within an (input) batch by deleting its columns. In that case, to avoid the error, you can set the column value to an empty list (`input_batch[\"col\"] = []`) instead." ]
2023-08-29T06:37:34
2023-09-19T21:55:38
2023-09-19T21:55:37
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### Use Case I use `dataset.map(process_fn, batched=True)` to process the dataset, with data **augmentations or filtering**. However, when all examples within a batch is filtered out, i.e. **an empty batch is returned**, the following error will be thrown: ``` ValueError: Schema and number of arrays unequal ``` This is because the empty batch does not comply with the schema of other batches. I think an empty batch should be allowed to facilitate coding (one does not need to assign an empty list manually for all keys.) A simple fix is to check the length of `batch` before writing: ``` if len(batch): writer.write_batch(batch) ``` instead of https://github.com/huggingface/datasets/blob/74d60213dcbd7c99484c62ce1d3dfd90a1df0770/src/datasets/arrow_dataset.py#L3493
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Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory
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[ "Hi! You can load this dataset with:\r\n```python\r\ndata_files = {\r\n \"train\": \"/content/PUBHEALTH/train.tsv\",\r\n \"validation\": \"/content/PUBHEALTH/dev.tsv\",\r\n \"test\": \"/content/PUBHEALTH/test.tsv\",\r\n}\r\n\r\ntsv_datasets_reloaded = load_dataset(\"csv\", data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nTo support your `load_dataset` call, defining aliases for the packaged builders, as suggested in https://github.com/huggingface/datasets/issues/5625, must be implemented. We can consider adding this feature if more people request it.\r\n \r\n(Also answered on the Discord [here](https://discord.com/channels/879548962464493619/1145956791134470224/1146071491260186744))" ]
2023-08-29T05:49:56
2023-08-29T16:21:45
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### Describe the bug ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-48-6a7b3e847019>](https://localhost:8080/#) in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Steps to reproduce the bug ``` data_files = { "train": "/content/PUBHEALTH/train.tsv", "validation": "/content/PUBHEALTH/dev.tsv", "test": "/content/PUBHEALTH/test.tsv", } tsv_datasets_reloaded = load_dataset("tsv", data_files=data_files) tsv_datasets_reloaded ``` ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-48-6a7b3e847019> in <cell line: 7>() 5 } 6 ----> 7 csv_datasets_reloaded = load_dataset("tsv", data_files=data_files) 8 csv_datasets_reloaded 2 frames /usr/local/lib/python3.10/dist-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1489 raise e1 from None 1490 if isinstance(e1, FileNotFoundError): -> 1491 raise FileNotFoundError( 1492 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1493 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Dataset 'tsv' doesn't exist on the Hub ``` ### Expected behavior load the data, push to hub ### Environment info jupyter notebook RTX 3090
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Feature request: add code example of multi-GPU processing
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[ "That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?", "Indeed `if __name__ == \"__main__\"` is important in this case.\r\n\r\nNot sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.device` context manager ?\r\n\r\n> also, should I do it like this or use nn.DataParallel?\r\n\r\nIn this case you wouldn't need a multiprocessed map no ? Since nn.DataParallel would take care of parallelism", "Adding this Tweet for reference: https://twitter.com/jxmnop/status/1716834517909119019.", "I think the issue is that we set `CUDA_VISIBLE_DEVICES` after pytorch is imported ?\r\n\r\nWe should use `torch.cuda.set_device(...)` instead", "@lhoestq \r\n> In this case you wouldn't need a multiprocessed map no ?\r\n\r\nYes. But how to load a model to 2 GPU simultaneously without something like accelerate?", "> @lhoestq\r\n> \r\n> > In this case you wouldn't need a multiprocessed map no ?\r\n> \r\n> Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?\r\n\r\nTake a look at this fix #6550 . Basically, you move the model to each GPU inside of the function to be mapped. \r\n\r\n", "In case someone also runs into this issue, I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete working example by compiling information from several PRs and issues here. Hope it can help. This issue cost me a few hours. I hope my blog post can save you time before the official document gets fixed. ", "Thanks ! I updated the docs in https://github.com/huggingface/datasets/pull/6550" ]
2023-08-28T10:00:59
2024-01-31T13:39:19
2023-11-22T15:42:20
CONTRIBUTOR
null
null
### Feature request Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box. Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel. Here's how I tried to do that: ``` from datasets import load_dataset from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from multiprocess import set_start_method import torch import os dataset = load_dataset("mlfoundations/datacomp_small") tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") # put model on each available GPU # also, should I do it like this or use nn.DataParallel? model.to("cuda:0") model.to("cuda:1") set_start_method("spawn") def translate_captions(batch, rank): os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count()) texts = batch["text"] inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device) translated_tokens = model.generate( **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30 ) translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True) batch["translated_text"] = translated_texts return batch updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256) ``` I've personally tried running this script on a machine with 2 A100 GPUs. ## Error 1 Running the code snippet above from the terminal (python script.py) resulted in the following error: ``` Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main prepare(preparation_data) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path return _run_module_code(code, init_globals, run_name, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code _run_code(code, mod_globals, init_globals, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module> set_start_method("spawn") File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method raise RuntimeError('context has already been set') RuntimeError: context has already been set ``` ## Error 2 Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error: ``` File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp> k: dataset.map( File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool: File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__ self._repopulate_pool() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static w.start() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start self._popen = self._Popen(self) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen return Popen(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__ super().__init__(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__ self._launch(process_obj) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch prep_data = spawn.get_preparation_data(process_obj._name) File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data _check_not_importing_main() File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main raise RuntimeError(''' RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase. This probably means that you are not using fork to start your child processes and you have forgotten to use the proper idiom in the main module: if __name__ == '__main__': freeze_support() ... The "freeze_support()" line can be omitted if the program is not going to be frozen to produce an executable. ``` So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`): ``` Mon Aug 28 12:19:24 2023 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA A100-SXM... On | 00000000:01:00.0 Off | 0 | | N/A 55C P0 76W / 275W | 8747MiB / 81920MiB | 0% Default | | | | Disabled | +-------------------------------+----------------------+----------------------+ | 1 NVIDIA A100-SXM... On | 00000000:47:00.0 Off | 0 | | N/A 67C P0 274W / 275W | 59835MiB / 81920MiB | 100% Default | | | | Disabled | ``` Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch? ### Motivation Would be great to clarify how to do multi-GPU data processing. ### Your contribution If my code snippet can be fixed, I can contribute it to the docs :)
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1,868,077,748
I_kwDODunzps5vWJq0
6,185
Error in saving the PIL image into *.arrow files using datasets.arrow_writer
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[ "You can cast the `input_image` column to the `Image` type to fix the issue:\r\n```python\r\nds.cast_column(\"input_image\", datasets.Image())\r\n```" ]
2023-08-26T12:15:57
2023-08-29T14:49:58
null
NONE
null
null
### Describe the bug I am using the ArrowWriter from datasets.arrow_writer to save a json-style file as arrow files. Within the dictionary, it contains a feature called "image" which is a list of PIL.Image objects. I am saving the json using the following script: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() ``` However, when I attempt to restore the dataset and use the ```Dataset.from_file(path)``` function to load the arrow file, there seems to be an issue with the PIL.Image object in the dataset. The list of PIL.Images appears as follows rather than a normal PIL.Image object: ![1693051705440](https://github.com/huggingface/datasets/assets/14247682/03b204c2-d0fa-4d19-beff-6f4d7b83c848) ### Steps to reproduce the bug 1. Storing the data json into arrow files: ``` def save_to_arrow(path,temp): with ArrowWriter(path=path,writer_batch_size=20) as writer: writer.write_batch(temp) writer.finalize() save_to_arrow( path, json_file ) ``` 2. try to load the arrow file into the Dataset object using the ```Dataset.from_file(path)``` ### Expected behavior Except to saving the contained "image" feature as a list PIL.Image objects as the arrow file. And I can restore the dataset from the file. ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.17 - Python version: 3.8.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.4.4
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I_kwDODunzps5vU9l_
6,184
Map cache does not detect function changes in another module
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[ "This issue is a duplicate of https://github.com/huggingface/datasets/issues/3297. This is a limitation of `dill`, a package we use for caching (non-`__main__` module objects are serialized by reference). You can find more info about it here: https://github.com/uqfoundation/dill/issues/424.\r\n\r\nIn your case, moving \r\n```\r\ndata = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train')\r\ndata = data.map(transform)\r\n``` \r\nto `test.py` and setting `transform.__module__ = None` at the end of `dataset.py` should fix the issue.", "I understand this may be a limitation of an upstream tool, but for a user for datasets this is very annoying, as when you have dozens of different datasets with different preprocessing functions you can't really move them all into the same file. It may be worth seeing if there is a way to specialize the dependency (eg. subclass it) and enforce behaviors that makes sense for your product.\r\n\r\nI was able to work around this for now by setting `__module__ = None`. If such workarounds are required for now it may be better to document it somewhere than a single obscure issue from a long time ago.\r\n\r\nAs this is a duplicate issue I'm closing it.\r\n\r\nI have another issue with the cache https://github.com/huggingface/datasets/issues/6179 can you take a look?" ]
2023-08-25T22:59:14
2023-08-29T20:57:07
2023-08-29T20:56:49
NONE
null
null
```python # dataset.py import os import datasets if not os.path.exists('/tmp/test.json'): with open('/tmp/test.json', 'w') as file: file.write('[{"text": "hello"}]') def transform(example): text = example['text'] # text += ' world' return {'text': text} data = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train') data = data.map(transform) ``` ```python # test.py import dataset print(next(iter(dataset.data))) ``` Initialize cache ``` python3 test.py # {'text': 'hello'} ``` Edit dataset.py and uncomment the commented line, run again ``` python3 test.py # {'text': 'hello'} # expected: {'text': 'hello world'} ``` Clear cache and run again ``` rm -rf ~/.cache/huggingface/datasets/* python3 test.py # {'text': 'hello world'} ``` If instead the two files are combined, then changes to the function are detected correctly. But it's expected when working on any realistic codebase that things will be modularized into separate files.
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1,867,743,276
I_kwDODunzps5vU4As
6,183
Load dataset with non-existent file
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[ "Same problem", "This was fixed in https://github.com/huggingface/datasets/pull/6155, which will be included in the next release (or you can install `datasets` from source to use it immediately)." ]
2023-08-25T22:21:22
2023-08-29T13:26:22
2023-08-29T13:26:22
NONE
null
null
### Describe the bug When load a dataset from datasets and pass a wrong path to json with the data, error message does not contain something abount "wrong path" or "file do not exist" - ```SchemaInferenceError: Please pass `features` or at least one example when writing data``` ### Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('json', data_files='/home/alexey/unreal_file.json') ``` ### Expected behavior Raise os FileNotFound error or custom error with informative message ### Environment info ``` # packages in environment at /home/alexey/.conda/envs/alex_LoRA: # # Name Version Build Channel _libgcc_mutex 0.1 main _openmp_mutex 5.1 1_gnu accelerate 0.21.0 pypi_0 pypi aiohttp 3.8.5 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi antlr4-python3-runtime 4.9.3 pypi_0 pypi appdirs 1.4.4 pypi_0 pypi asttokens 2.0.5 pyhd3eb1b0_0 async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi backcall 0.2.0 pyhd3eb1b0_0 bitsandbytes 0.41.1 pypi_0 pypi bzip2 1.0.8 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config file : /home/alexey/.condarc populated config files : conda version : 23.1.0 conda-build version : 3.22.0 python version : 3.9.13.final.0 virtual packages : __archspec=1=x86_64 __cuda=12.0=0 __glibc=2.35=0 __linux=5.19.0=0 __unix=0=0 base environment : /opt/anaconda/anaconda3 (read only) conda av data dir : /opt/anaconda/anaconda3/etc/conda conda av metadata url : None channel URLs : https://repo.anaconda.com/pkgs/main/linux-64 https://repo.anaconda.com/pkgs/main/noarch https://repo.anaconda.com/pkgs/r/linux-64 https://repo.anaconda.com/pkgs/r/noarch package cache : /opt/anaconda/anaconda3/pkgs /home/alexey/.conda/pkgs envs directories : /home/alexey/.conda/envs /opt/anaconda/anaconda3/envs platform : linux-64 user-agent : conda/23.1.0 requests/2.31.0 CPython/3.9.13 Linux/5.19.0-46-generic ubuntu/22.04.2 glibc/2.35 UID:GID : 1009:1009 netrc file : /home/alexey/.netrc offline mode : False ```
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1,867,203,131
I_kwDODunzps5vS0I7
6,182
Loading Meteor metric in HF evaluate module crashes due to datasets import issue
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[ "Our minimal Python version requirement is 3.8, so we dropped `importlib_metadata`. \r\n\r\nFeel free to open a PR in the `evaluate` repo to replace the problematic import with\r\n```python\r\nif PY_VERSION < version.parse(\"3.8\"):\r\n import importlib_metadata\r\nelse:\r\n import importlib.metadata as importlib_metadata\r\n```", "Any idea when you guys will release the next version which deals with this problem?\r\nI'm still having the same issue with py 3.10 when I install the lib with pip.\r\nI'm assuming that it has not yet been updated since the merge was 3 days ago.", "Yes, this requires a new `evaluate` release (cc @lvwerra for this). \r\n\r\nIn the meantime, you can get the fixed version by installing `evaluate` from `main`: `pip install git+https://github.com/huggingface/evaluate.git`", "I'll aim for a release this week!" ]
2023-08-25T14:54:06
2023-09-04T16:41:11
2023-08-31T14:38:23
NONE
null
null
### Describe the bug When using python3.9 and ```evaluate``` module loading Meteor metric crashes at a non-existent import from ```datasets.config``` in ```datasets v2.14``` ### Steps to reproduce the bug ``` from evaluate import load meteor = load("meteor") ``` produces the following error: ``` from datasets.config import importlib_metadata, version ImportError: cannot import name 'importlib_metadata' from 'datasets.config' (<path_to_project>/venv/lib/python3.9/site-packages/datasets/config.py) ``` ### Expected behavior ```datasets``` of v2.10 has the following workaround in ```config.py```: ``` if PY_VERSION < version.parse("3.8"): import importlib_metadata else: import importlib.metadata as importlib_metadata ``` However, it's absent in v2.14 which might be the cause of the issue. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.5-arm64-arm-64bit - Python version: 3.9.6 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - Evaluate version: 0.4.0
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6,181
Fix import in `image_load` doc
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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) | 0.009072 / 0.011353 (-0.002281) | 0.006088 / 0.011008 (-0.004920) | 0.134520 / 0.038508 (0.096011) | 0.074935 / 0.023109 (0.051826) | 0.480364 / 0.275898 (0.204466) | 0.568943 / 0.323480 (0.245464) | 0.006821 / 0.007986 (-0.001164) | 0.004941 / 0.004328 (0.000612) | 0.083274 / 0.004250 (0.079023) | 0.061080 / 0.037052 (0.024028) | 0.478960 / 0.258489 (0.220471) | 0.542720 / 0.293841 (0.248879) | 0.058023 / 0.128546 (-0.070524) | 0.020120 / 0.075646 (-0.055526) | 0.492680 / 0.419271 (0.073409) | 0.079118 / 0.043533 (0.035585) | 0.425087 / 0.255139 (0.169948) | 0.603228 / 0.283200 (0.320028) | 0.044102 / 0.141683 (-0.097581) | 2.138848 / 1.452155 (0.686693) | 2.454418 / 1.492716 (0.961702) |\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) | 0.255745 / 0.018006 (0.237738) | 0.587559 / 0.000490 (0.587069) | 0.006872 / 0.000200 (0.006672) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038480 / 0.037411 (0.001069) | 0.115479 / 0.014526 (0.100953) | 0.138395 / 0.176557 (-0.038161) | 0.218007 / 0.737135 (-0.519129) | 0.128866 / 0.296338 (-0.167472) |\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) | 0.756089 / 0.215209 (0.540880) | 7.754631 / 2.077655 (5.676976) | 3.615716 / 1.504120 (2.111596) | 2.994327 / 1.541195 (1.453132) | 3.196169 / 1.468490 (1.727679) | 1.066937 / 4.584777 (-3.517840) | 6.079595 / 3.745712 (2.333883) | 5.455523 / 5.269862 (0.185661) | 3.559036 / 4.565676 (-1.006640) | 0.113044 / 0.424275 (-0.311231) | 0.011401 / 0.007607 (0.003794) | 0.961475 / 0.226044 (0.735430) | 8.664226 / 2.268929 (6.395298) | 4.203804 / 55.444624 (-51.240821) | 3.122437 / 6.876477 (-3.754039) | 3.549168 / 2.142072 (1.407095) | 1.213035 / 4.805227 (-3.592193) | 0.274725 / 6.500664 (-6.225939) | 0.094499 / 0.075469 (0.019030) |\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) | 1.770299 / 1.841788 (-0.071489) | 27.644591 / 8.074308 (19.570283) | 23.239529 / 10.191392 (13.048137) | 0.270185 / 0.680424 (-0.410238) | 0.033563 / 0.534201 (-0.500638) | 0.588301 / 0.579283 (0.009018) | 0.658746 / 0.434364 (0.224382) | 0.644476 / 0.540337 (0.104139) | 0.834314 / 1.386936 (-0.552622) |\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) | 0.011021 / 0.011353 (-0.000332) | 0.006719 / 0.011008 (-0.004289) | 0.087669 / 0.038508 (0.049161) | 0.088905 / 0.023109 (0.065796) | 0.594230 / 0.275898 (0.318332) | 0.620929 / 0.323480 (0.297449) | 0.006776 / 0.007986 (-0.001210) | 0.004725 / 0.004328 (0.000396) | 0.082006 / 0.004250 (0.077756) | 0.072164 / 0.037052 (0.035111) | 0.604489 / 0.258489 (0.346000) | 0.598520 / 0.293841 (0.304679) | 0.057534 / 0.128546 (-0.071013) | 0.016799 / 0.075646 (-0.058847) | 0.115029 / 0.419271 (-0.304243) | 0.070013 / 0.043533 (0.026481) | 0.561773 / 0.255139 (0.306634) | 0.624097 / 0.283200 (0.340897) | 0.043518 / 0.141683 (-0.098164) | 2.017089 / 1.452155 (0.564934) | 2.188159 / 1.492716 (0.695443) |\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) | 0.386476 / 0.018006 (0.368469) | 0.633195 / 0.000490 (0.632705) | 0.028469 / 0.000200 (0.028269) | 0.000159 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040020 / 0.037411 (0.002609) | 0.112927 / 0.014526 (0.098402) | 0.143663 / 0.176557 (-0.032894) | 0.205931 / 0.737135 (-0.531204) | 0.177814 / 0.296338 (-0.118524) |\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) | 0.711542 / 0.215209 (0.496333) | 7.518535 / 2.077655 (5.440880) | 3.714930 / 1.504120 (2.210810) | 3.031999 / 1.541195 (1.490804) | 3.328497 / 1.468490 (1.860006) | 0.858912 / 4.584777 (-3.725865) | 6.108384 / 3.745712 (2.362672) | 5.184329 / 5.269862 (-0.085532) | 3.622589 / 4.565676 (-0.943087) | 0.096933 / 0.424275 (-0.327342) | 0.008727 / 0.007607 (0.001120) | 0.830102 / 0.226044 (0.604057) | 8.331959 / 2.268929 (6.063030) | 4.165106 / 55.444624 (-51.279519) | 3.477003 / 6.876477 (-3.399474) | 3.794225 / 2.142072 (1.652153) | 1.237667 / 4.805227 (-3.567561) | 0.233731 / 6.500664 (-6.266933) | 0.076682 / 0.075469 (0.001213) |\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) | 1.944813 / 1.841788 (0.103026) | 27.666997 / 8.074308 (19.592689) | 24.562677 / 10.191392 (14.371285) | 0.279320 / 0.680424 (-0.401104) | 0.037802 / 0.534201 (-0.496399) | 0.553579 / 0.579283 (-0.025704) | 0.718229 / 0.434364 (0.283865) | 0.623456 / 0.540337 (0.083118) | 0.856777 / 1.386936 (-0.530159) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c2a9d31d5e720e85976af8b457d45755a7e6911 \"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) | 0.007716 / 0.011353 (-0.003637) | 0.004624 / 0.011008 (-0.006384) | 0.099987 / 0.038508 (0.061479) | 0.082651 / 0.023109 (0.059542) | 0.376277 / 0.275898 (0.100379) | 0.401210 / 0.323480 (0.077730) | 0.004528 / 0.007986 (-0.003458) | 0.003763 / 0.004328 (-0.000566) | 0.076274 / 0.004250 (0.072024) | 0.062933 / 0.037052 (0.025881) | 0.393881 / 0.258489 (0.135392) | 0.431695 / 0.293841 (0.137854) | 0.036795 / 0.128546 (-0.091752) | 0.009935 / 0.075646 (-0.065712) | 0.343638 / 0.419271 (-0.075634) | 0.061456 / 0.043533 (0.017923) | 0.372235 / 0.255139 (0.117096) | 0.412994 / 0.283200 (0.129794) | 0.027993 / 0.141683 (-0.113690) | 1.798018 / 1.452155 (0.345863) | 1.898502 / 1.492716 (0.405786) |\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) | 0.237330 / 0.018006 (0.219324) | 0.494956 / 0.000490 (0.494467) | 0.003543 / 0.000200 (0.003343) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034084 / 0.037411 (-0.003327) | 0.093407 / 0.014526 (0.078881) | 0.108378 / 0.176557 (-0.068179) | 0.177016 / 0.737135 (-0.560119) | 0.108622 / 0.296338 (-0.187716) |\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) | 0.456449 / 0.215209 (0.241240) | 4.522405 / 2.077655 (2.444750) | 2.206564 / 1.504120 (0.702444) | 1.994185 / 1.541195 (0.452990) | 2.083785 / 1.468490 (0.615295) | 0.563352 / 4.584777 (-4.021425) | 4.207295 / 3.745712 (0.461583) | 3.783061 / 5.269862 (-1.486800) | 2.372874 / 4.565676 (-2.192802) | 0.066907 / 0.424275 (-0.357368) | 0.009013 / 0.007607 (0.001406) | 0.537852 / 0.226044 (0.311808) | 5.349928 / 2.268929 (3.081000) | 2.759409 / 55.444624 (-52.685215) | 2.345972 / 6.876477 (-4.530505) | 2.630559 / 2.142072 (0.488486) | 0.681134 / 4.805227 (-4.124093) | 0.157898 / 6.500664 (-6.342766) | 0.071638 / 0.075469 (-0.003831) |\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) | 1.470730 / 1.841788 (-0.371058) | 22.479252 / 8.074308 (14.404944) | 16.543080 / 10.191392 (6.351688) | 0.191943 / 0.680424 (-0.488481) | 0.021641 / 0.534201 (-0.512560) | 0.467571 / 0.579283 (-0.111712) | 0.486728 / 0.434364 (0.052364) | 0.543359 / 0.540337 (0.003021) | 0.733968 / 1.386936 (-0.652968) |\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) | 0.008135 / 0.011353 (-0.003218) | 0.004662 / 0.011008 (-0.006347) | 0.077218 / 0.038508 (0.038710) | 0.092220 / 0.023109 (0.069111) | 0.481219 / 0.275898 (0.205321) | 0.530373 / 0.323480 (0.206893) | 0.006418 / 0.007986 (-0.001568) | 0.003924 / 0.004328 (-0.000404) | 0.076681 / 0.004250 (0.072431) | 0.068693 / 0.037052 (0.031641) | 0.491938 / 0.258489 (0.233449) | 0.540501 / 0.293841 (0.246660) | 0.038106 / 0.128546 (-0.090441) | 0.010035 / 0.075646 (-0.065611) | 0.084502 / 0.419271 (-0.334769) | 0.057234 / 0.043533 (0.013701) | 0.483239 / 0.255139 (0.228100) | 0.510026 / 0.283200 (0.226826) | 0.028770 / 0.141683 (-0.112913) | 1.854937 / 1.452155 (0.402783) | 1.948268 / 1.492716 (0.455552) |\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) | 0.380192 / 0.018006 (0.362186) | 0.523318 / 0.000490 (0.522828) | 0.051153 / 0.000200 (0.050953) | 0.000691 / 0.000054 (0.000637) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036838 / 0.037411 (-0.000573) | 0.109202 / 0.014526 (0.094676) | 0.124110 / 0.176557 (-0.052446) | 0.186717 / 0.737135 (-0.550419) | 0.124088 / 0.296338 (-0.172250) |\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) | 0.506411 / 0.215209 (0.291202) | 5.045421 / 2.077655 (2.967766) | 2.711911 / 1.504120 (1.207791) | 2.531668 / 1.541195 (0.990474) | 2.635680 / 1.468490 (1.167190) | 0.578395 / 4.584777 (-4.006382) | 4.206891 / 3.745712 (0.461178) | 3.851063 / 5.269862 (-1.418799) | 2.388327 / 4.565676 (-2.177350) | 0.068041 / 0.424275 (-0.356234) | 0.008769 / 0.007607 (0.001162) | 0.594170 / 0.226044 (0.368125) | 5.953138 / 2.268929 (3.684210) | 3.290586 / 55.444624 (-52.154038) | 2.877086 / 6.876477 (-3.999390) | 3.138600 / 2.142072 (0.996528) | 0.686393 / 4.805227 (-4.118834) | 0.156541 / 6.500664 (-6.344123) | 0.071514 / 0.075469 (-0.003955) |\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) | 1.613514 / 1.841788 (-0.228274) | 23.593185 / 8.074308 (15.518877) | 17.146647 / 10.191392 (6.955255) | 0.177230 / 0.680424 (-0.503193) | 0.023661 / 0.534201 (-0.510540) | 0.472367 / 0.579283 (-0.106916) | 0.484614 / 0.434364 (0.050250) | 0.547150 / 0.540337 (0.006813) | 0.843726 / 1.386936 (-0.543210) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dba64cd381bfe384cb64ab9826f6054a0f1df1ff \"CML watermark\")\n" ]
2023-08-25T13:12:19
2023-08-25T16:12:46
2023-08-25T16:02:24
CONTRIBUTOR
false
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Reported on [Discord](https://discord.com/channels/879548962464493619/1144295822209581168/1144295822209581168)
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https://github.com/huggingface/datasets/pull/6180
1,867,032,578
PR_kwDODunzps5Yy1r-
6,180
Use `hf-internal-testing` repos for hosting test dataset repos
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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) | 0.006505 / 0.011353 (-0.004847) | 0.003950 / 0.011008 (-0.007058) | 0.084554 / 0.038508 (0.046046) | 0.074376 / 0.023109 (0.051267) | 0.350184 / 0.275898 (0.074286) | 0.380704 / 0.323480 (0.057224) | 0.004011 / 0.007986 (-0.003975) | 0.003890 / 0.004328 (-0.000438) | 0.065483 / 0.004250 (0.061232) | 0.054912 / 0.037052 (0.017860) | 0.359586 / 0.258489 (0.101097) | 0.403360 / 0.293841 (0.109519) | 0.030614 / 0.128546 (-0.097932) | 0.008530 / 0.075646 (-0.067117) | 0.288220 / 0.419271 (-0.131052) | 0.052270 / 0.043533 (0.008737) | 0.352557 / 0.255139 (0.097418) | 0.380509 / 0.283200 (0.097309) | 0.025513 / 0.141683 (-0.116170) | 1.488469 / 1.452155 (0.036315) | 1.559182 / 1.492716 (0.066466) |\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) | 0.266163 / 0.018006 (0.248157) | 0.596345 / 0.000490 (0.595855) | 0.004368 / 0.000200 (0.004168) | 0.000211 / 0.000054 (0.000156) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027137 / 0.037411 (-0.010274) | 0.082251 / 0.014526 (0.067725) | 0.094745 / 0.176557 (-0.081812) | 0.148756 / 0.737135 (-0.588379) | 0.094580 / 0.296338 (-0.201758) |\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) | 0.383506 / 0.215209 (0.168297) | 3.823147 / 2.077655 (1.745493) | 1.859627 / 1.504120 (0.355507) | 1.687969 / 1.541195 (0.146775) | 1.720786 / 1.468490 (0.252296) | 0.476552 / 4.584777 (-4.108225) | 3.539558 / 3.745712 (-0.206154) | 3.209032 / 5.269862 (-2.060830) | 1.999643 / 4.565676 (-2.566034) | 0.056484 / 0.424275 (-0.367791) | 0.007443 / 0.007607 (-0.000164) | 0.456089 / 0.226044 (0.230044) | 4.562522 / 2.268929 (2.293593) | 2.348286 / 55.444624 (-53.096338) | 1.984323 / 6.876477 (-4.892154) | 2.148988 / 2.142072 (0.006915) | 0.570761 / 4.805227 (-4.234466) | 0.131439 / 6.500664 (-6.369225) | 0.059752 / 0.075469 (-0.015717) |\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) | 1.276803 / 1.841788 (-0.564985) | 19.406812 / 8.074308 (11.332504) | 13.979088 / 10.191392 (3.787696) | 0.157418 / 0.680424 (-0.523006) | 0.018051 / 0.534201 (-0.516150) | 0.392307 / 0.579283 (-0.186976) | 0.406603 / 0.434364 (-0.027760) | 0.458450 / 0.540337 (-0.081888) | 0.622569 / 1.386936 (-0.764367) |\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) | 0.006552 / 0.011353 (-0.004800) | 0.004060 / 0.011008 (-0.006948) | 0.063522 / 0.038508 (0.025014) | 0.072537 / 0.023109 (0.049428) | 0.398452 / 0.275898 (0.122554) | 0.422059 / 0.323480 (0.098579) | 0.005577 / 0.007986 (-0.002409) | 0.003413 / 0.004328 (-0.000916) | 0.064095 / 0.004250 (0.059845) | 0.056883 / 0.037052 (0.019831) | 0.407119 / 0.258489 (0.148630) | 0.435889 / 0.293841 (0.142048) | 0.031549 / 0.128546 (-0.096998) | 0.008418 / 0.075646 (-0.067228) | 0.070315 / 0.419271 (-0.348957) | 0.047828 / 0.043533 (0.004295) | 0.398705 / 0.255139 (0.143566) | 0.416986 / 0.283200 (0.133786) | 0.022304 / 0.141683 (-0.119379) | 1.512597 / 1.452155 (0.060442) | 1.570588 / 1.492716 (0.077871) |\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) | 0.295100 / 0.018006 (0.277094) | 0.541883 / 0.000490 (0.541393) | 0.007375 / 0.000200 (0.007175) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030877 / 0.037411 (-0.006534) | 0.090807 / 0.014526 (0.076281) | 0.106155 / 0.176557 (-0.070402) | 0.155546 / 0.737135 (-0.581589) | 0.103847 / 0.296338 (-0.192492) |\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) | 0.441176 / 0.215209 (0.225967) | 4.401025 / 2.077655 (2.323371) | 2.394764 / 1.504120 (0.890644) | 2.226434 / 1.541195 (0.685239) | 2.247248 / 1.468490 (0.778758) | 0.489149 / 4.584777 (-4.095628) | 3.642468 / 3.745712 (-0.103244) | 3.235597 / 5.269862 (-2.034265) | 1.992660 / 4.565676 (-2.573016) | 0.057457 / 0.424275 (-0.366818) | 0.007192 / 0.007607 (-0.000415) | 0.515978 / 0.226044 (0.289934) | 5.147728 / 2.268929 (2.878800) | 2.837394 / 55.444624 (-52.607230) | 2.505753 / 6.876477 (-4.370723) | 2.653090 / 2.142072 (0.511018) | 0.583274 / 4.805227 (-4.221954) | 0.132116 / 6.500664 (-6.368548) | 0.058794 / 0.075469 (-0.016675) |\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) | 1.331630 / 1.841788 (-0.510158) | 20.056890 / 8.074308 (11.982582) | 14.950561 / 10.191392 (4.759169) | 0.165449 / 0.680424 (-0.514975) | 0.020161 / 0.534201 (-0.514040) | 0.395791 / 0.579283 (-0.183492) | 0.415631 / 0.434364 (-0.018733) | 0.474440 / 0.540337 (-0.065898) | 0.643060 / 1.386936 (-0.743876) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#712185ed5e9cb3ff6d6528b4528882d51935f334 \"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) | 0.007440 / 0.011353 (-0.003913) | 0.004456 / 0.011008 (-0.006552) | 0.099498 / 0.038508 (0.060990) | 0.077579 / 0.023109 (0.054470) | 0.374934 / 0.275898 (0.099036) | 0.409590 / 0.323480 (0.086110) | 0.005876 / 0.007986 (-0.002110) | 0.003642 / 0.004328 (-0.000687) | 0.076781 / 0.004250 (0.072531) | 0.060185 / 0.037052 (0.023133) | 0.374762 / 0.258489 (0.116273) | 0.445608 / 0.293841 (0.151767) | 0.036557 / 0.128546 (-0.091990) | 0.009941 / 0.075646 (-0.065706) | 0.345214 / 0.419271 (-0.074058) | 0.061912 / 0.043533 (0.018379) | 0.378346 / 0.255139 (0.123207) | 0.415275 / 0.283200 (0.132076) | 0.027396 / 0.141683 (-0.114287) | 1.776602 / 1.452155 (0.324447) | 1.827683 / 1.492716 (0.334967) |\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) | 0.235227 / 0.018006 (0.217221) | 0.491846 / 0.000490 (0.491356) | 0.004987 / 0.000200 (0.004787) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032517 / 0.037411 (-0.004894) | 0.099217 / 0.014526 (0.084691) | 0.109749 / 0.176557 (-0.066807) | 0.176190 / 0.737135 (-0.560946) | 0.109868 / 0.296338 (-0.186471) |\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) | 0.455188 / 0.215209 (0.239979) | 4.560143 / 2.077655 (2.482489) | 2.249928 / 1.504120 (0.745809) | 2.032808 / 1.541195 (0.491614) | 2.090096 / 1.468490 (0.621605) | 0.567813 / 4.584777 (-4.016964) | 4.338299 / 3.745712 (0.592587) | 3.701589 / 5.269862 (-1.568273) | 2.404805 / 4.565676 (-2.160871) | 0.067931 / 0.424275 (-0.356344) | 0.009011 / 0.007607 (0.001404) | 0.542565 / 0.226044 (0.316521) | 5.406578 / 2.268929 (3.137650) | 2.773508 / 55.444624 (-52.671116) | 2.402926 / 6.876477 (-4.473550) | 2.679318 / 2.142072 (0.537246) | 0.683781 / 4.805227 (-4.121446) | 0.155970 / 6.500664 (-6.344694) | 0.070108 / 0.075469 (-0.005361) |\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) | 1.541583 / 1.841788 (-0.300205) | 21.592562 / 8.074308 (13.518254) | 16.426868 / 10.191392 (6.235476) | 0.168618 / 0.680424 (-0.511806) | 0.021560 / 0.534201 (-0.512641) | 0.467062 / 0.579283 (-0.112221) | 0.479968 / 0.434364 (0.045604) | 0.540747 / 0.540337 (0.000410) | 0.775502 / 1.386936 (-0.611434) |\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) | 0.008632 / 0.011353 (-0.002721) | 0.004523 / 0.011008 (-0.006485) | 0.075814 / 0.038508 (0.037306) | 0.087096 / 0.023109 (0.063987) | 0.482136 / 0.275898 (0.206238) | 0.529969 / 0.323480 (0.206489) | 0.006882 / 0.007986 (-0.001103) | 0.003720 / 0.004328 (-0.000609) | 0.076232 / 0.004250 (0.071981) | 0.069307 / 0.037052 (0.032254) | 0.491554 / 0.258489 (0.233065) | 0.528989 / 0.293841 (0.235148) | 0.042219 / 0.128546 (-0.086327) | 0.009717 / 0.075646 (-0.065929) | 0.103006 / 0.419271 (-0.316266) | 0.060377 / 0.043533 (0.016844) | 0.484454 / 0.255139 (0.229315) | 0.536072 / 0.283200 (0.252872) | 0.027482 / 0.141683 (-0.114201) | 1.844677 / 1.452155 (0.392522) | 2.001800 / 1.492716 (0.509083) |\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) | 0.252367 / 0.018006 (0.234361) | 0.483601 / 0.000490 (0.483111) | 0.007445 / 0.000200 (0.007245) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036463 / 0.037411 (-0.000948) | 0.108837 / 0.014526 (0.094311) | 0.122256 / 0.176557 (-0.054300) | 0.186455 / 0.737135 (-0.550681) | 0.122270 / 0.296338 (-0.174069) |\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) | 0.506291 / 0.215209 (0.291082) | 5.038044 / 2.077655 (2.960389) | 2.751017 / 1.504120 (1.246897) | 2.553655 / 1.541195 (1.012460) | 2.612724 / 1.468490 (1.144234) | 0.581755 / 4.584777 (-4.003022) | 4.376012 / 3.745712 (0.630300) | 3.749755 / 5.269862 (-1.520107) | 2.394059 / 4.565676 (-2.171618) | 0.068727 / 0.424275 (-0.355548) | 0.008714 / 0.007607 (0.001107) | 0.607371 / 0.226044 (0.381326) | 6.062053 / 2.268929 (3.793125) | 3.278378 / 55.444624 (-52.166247) | 2.866417 / 6.876477 (-4.010060) | 3.056150 / 2.142072 (0.914077) | 0.695090 / 4.805227 (-4.110137) | 0.155274 / 6.500664 (-6.345390) | 0.071106 / 0.075469 (-0.004363) |\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) | 1.584552 / 1.841788 (-0.257236) | 23.092569 / 8.074308 (15.018260) | 17.381905 / 10.191392 (7.190513) | 0.206535 / 0.680424 (-0.473888) | 0.025401 / 0.534201 (-0.508800) | 0.514297 / 0.579283 (-0.064986) | 0.507487 / 0.434364 (0.073123) | 0.566883 / 0.540337 (0.026545) | 0.811074 / 1.386936 (-0.575862) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fb01295bff860f09a4c466e745f3840f851efdc \"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) | 0.008400 / 0.011353 (-0.002953) | 0.004872 / 0.011008 (-0.006136) | 0.104434 / 0.038508 (0.065926) | 0.074411 / 0.023109 (0.051302) | 0.395970 / 0.275898 (0.120072) | 0.431661 / 0.323480 (0.108181) | 0.005365 / 0.007986 (-0.002621) | 0.005495 / 0.004328 (0.001167) | 0.081255 / 0.004250 (0.077004) | 0.057141 / 0.037052 (0.020089) | 0.397242 / 0.258489 (0.138753) | 0.456052 / 0.293841 (0.162211) | 0.048362 / 0.128546 (-0.080184) | 0.014077 / 0.075646 (-0.061569) | 0.351128 / 0.419271 (-0.068143) | 0.067842 / 0.043533 (0.024309) | 0.372820 / 0.255139 (0.117681) | 0.407917 / 0.283200 (0.124717) | 0.037707 / 0.141683 (-0.103976) | 1.677136 / 1.452155 (0.224981) | 1.764614 / 1.492716 (0.271897) |\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) | 0.269850 / 0.018006 (0.251844) | 0.601458 / 0.000490 (0.600969) | 0.006500 / 0.000200 (0.006300) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030340 / 0.037411 (-0.007072) | 0.098041 / 0.014526 (0.083515) | 0.107270 / 0.176557 (-0.069287) | 0.173502 / 0.737135 (-0.563633) | 0.113296 / 0.296338 (-0.183043) |\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) | 0.575788 / 0.215209 (0.360579) | 5.723878 / 2.077655 (3.646223) | 2.326339 / 1.504120 (0.822219) | 2.130667 / 1.541195 (0.589472) | 2.080885 / 1.468490 (0.612395) | 0.800936 / 4.584777 (-3.783841) | 5.227888 / 3.745712 (1.482176) | 4.592647 / 5.269862 (-0.677214) | 2.935765 / 4.565676 (-1.629911) | 0.095909 / 0.424275 (-0.328367) | 0.008763 / 0.007607 (0.001156) | 0.697362 / 0.226044 (0.471318) | 6.968105 / 2.268929 (4.699176) | 3.129070 / 55.444624 (-52.315554) | 2.554818 / 6.876477 (-4.321658) | 2.776005 / 2.142072 (0.633933) | 1.017064 / 4.805227 (-3.788163) | 0.211552 / 6.500664 (-6.289112) | 0.072132 / 0.075469 (-0.003338) |\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) | 1.517072 / 1.841788 (-0.324716) | 23.737742 / 8.074308 (15.663433) | 22.236447 / 10.191392 (12.045055) | 0.235408 / 0.680424 (-0.445016) | 0.031889 / 0.534201 (-0.502312) | 0.458997 / 0.579283 (-0.120286) | 0.610513 / 0.434364 (0.176149) | 0.536508 / 0.540337 (-0.003830) | 0.750137 / 1.386936 (-0.636799) |\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) | 0.008696 / 0.011353 (-0.002657) | 0.005374 / 0.011008 (-0.005634) | 0.077974 / 0.038508 (0.039466) | 0.083471 / 0.023109 (0.060362) | 0.498890 / 0.275898 (0.222992) | 0.517570 / 0.323480 (0.194090) | 0.006523 / 0.007986 (-0.001462) | 0.004315 / 0.004328 (-0.000013) | 0.082262 / 0.004250 (0.078012) | 0.064828 / 0.037052 (0.027776) | 0.473101 / 0.258489 (0.214612) | 0.534172 / 0.293841 (0.240331) | 0.051884 / 0.128546 (-0.076662) | 0.015191 / 0.075646 (-0.060455) | 0.084307 / 0.419271 (-0.334965) | 0.066050 / 0.043533 (0.022517) | 0.518007 / 0.255139 (0.262868) | 0.511145 / 0.283200 (0.227946) | 0.045302 / 0.141683 (-0.096381) | 1.670973 / 1.452155 (0.218818) | 1.829225 / 1.492716 (0.336509) |\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) | 0.436537 / 0.018006 (0.418531) | 0.608380 / 0.000490 (0.607890) | 0.075211 / 0.000200 (0.075011) | 0.000733 / 0.000054 (0.000679) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039117 / 0.037411 (0.001706) | 0.103525 / 0.014526 (0.088999) | 0.124413 / 0.176557 (-0.052144) | 0.192352 / 0.737135 (-0.544783) | 0.120379 / 0.296338 (-0.175959) |\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) | 0.673338 / 0.215209 (0.458129) | 6.799435 / 2.077655 (4.721780) | 3.600913 / 1.504120 (2.096793) | 2.881008 / 1.541195 (1.339814) | 2.667154 / 1.468490 (1.198664) | 0.868775 / 4.584777 (-3.716002) | 5.517063 / 3.745712 (1.771351) | 4.646706 / 5.269862 (-0.623156) | 2.914825 / 4.565676 (-1.650852) | 0.098784 / 0.424275 (-0.325491) | 0.011504 / 0.007607 (0.003897) | 0.724233 / 0.226044 (0.498188) | 7.311045 / 2.268929 (5.042117) | 3.685490 / 55.444624 (-51.759135) | 2.892360 / 6.876477 (-3.984117) | 3.253189 / 2.142072 (1.111117) | 0.983065 / 4.805227 (-3.822162) | 0.201097 / 6.500664 (-6.299567) | 0.068020 / 0.075469 (-0.007450) |\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) | 1.793904 / 1.841788 (-0.047884) | 24.451356 / 8.074308 (16.377048) | 21.697191 / 10.191392 (11.505799) | 0.228545 / 0.680424 (-0.451879) | 0.034600 / 0.534201 (-0.499601) | 0.483253 / 0.579283 (-0.096030) | 0.615103 / 0.434364 (0.180739) | 0.564600 / 0.540337 (0.024262) | 0.799688 / 1.386936 (-0.587248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74d60213dcbd7c99484c62ce1d3dfd90a1df0770 \"CML watermark\")\n" ]
2023-08-25T13:10:26
2023-08-25T16:58:02
2023-08-25T16:46:22
CONTRIBUTOR
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Use `hf-internal-testing` for hosting instead of the maintainers' dataset repos.
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1,867,009,016
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6,179
Map cache with tokenizer
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[ "https://github.com/huggingface/datasets/issues/5147 may be a solution, by passing in the tokenizer in a fn_kwargs and ignoring it in the fingerprint calculations", "I have a similar issue. I was using a Jupyter Notebook and every time I call the map function it performs tokenization from scratch again although the cache files of last run still exists. \r\n\r\nI ran with 20 processes and now in the cache folder there are two groups of cached results of tokenized dataset:\r\n\r\n```\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:46 2023 cache-1982fea76aa54a13_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:02:08 2023 cache-1982fea76aa54a13_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:40 2023 cache-1982fea76aa54a13_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:50:59 2023 cache-1982fea76aa54a13_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:37 2023 cache-1982fea76aa54a13_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:31 2023 cache-1982fea76aa54a13_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:59:47 2023 cache-1982fea76aa54a13_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:59:44 2023 cache-1982fea76aa54a13_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:55:24 2023 cache-1982fea76aa54a13_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:56:21 2023 cache-1982fea76aa54a13_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:24 2023 cache-1982fea76aa54a13_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:00:48 2023 cache-1982fea76aa54a13_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:56 2023 cache-1982fea76aa54a13_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:54 2023 cache-1982fea76aa54a13_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:27 2023 cache-1982fea76aa54a13_00019_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:40 2023 cache-454431f643cdc5e8_00000_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:46 2023 cache-454431f643cdc5e8_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:53 2023 cache-454431f643cdc5e8_00002_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:10 2023 cache-454431f643cdc5e8_00003_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:04 2023 cache-454431f643cdc5e8_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:42 2023 cache-454431f643cdc5e8_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:01:29 2023 cache-454431f643cdc5e8_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:41 2023 cache-454431f643cdc5e8_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:04 2023 cache-454431f643cdc5e8_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:41 2023 cache-454431f643cdc5e8_00009_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:06 2023 cache-454431f643cdc5e8_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:17:16 2023 cache-454431f643cdc5e8_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:15:13 2023 cache-454431f643cdc5e8_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:16:01 2023 cache-454431f643cdc5e8_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:35 2023 cache-454431f643cdc5e8_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:20 2023 cache-454431f643cdc5e8_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:48 2023 cache-454431f643cdc5e8_00016_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 18:59:32 2023 cache-454431f643cdc5e8_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:58 2023 cache-454431f643cdc5e8_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:25 2023 cache-454431f643cdc5e8_00019_of_00020.arrow\r\n```\r\n\r\ncan we specify the cache file for map so that it won't redo everything again?", "@Luosuu [map](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.map) has cache_file_name parameter\r\n\r\nIn my case, I do want the cache to detect when the map function changes, so I can't pass a constant cache file name.", "Implementing a proper hashing function for the (fast) tokenizers is currently impossible for the reasons mentioned in the referenced issues. So the only alternative to the `cache_file_name` (or `new_fingerprint`) parameter is a custom serializer (e.g., that deserializes the tokenizer from a local save path) defined using `copyreg` or a class that wraps the tokenizer object and has `__reduce__`(`__setstate__`/`__getstate__`) " ]
2023-08-25T12:55:18
2023-08-31T15:17:24
null
NONE
null
null
Similar issue to https://github.com/huggingface/datasets/issues/5985, but across different sessions rather than two calls in the same session. Unlike that issue, explicitly calling tokenizer(my_args) before the map() doesn't help, because the tokenizer was created with a different hash to begin with... setup ``` from transformers import AutoTokenizer AutoTokenizer.from_pretrained('bert-base-uncased').save_pretrained("tok") ``` this prints different value each time ``` from transformers import AutoTokenizer from datasets.utils.py_utils import dumps # Huggingface datasets print(hash(dumps(AutoTokenizer.from_pretrained("tok")))) ```
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6,178
'import datasets' throws "invalid syntax error"
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[ "This seems to be related to your environment and not the `datasets` code (e.g., this could happen when exposing the Python 3.9 site packages to a lower Python version (interpreter))" ]
2023-08-25T08:35:14
2023-09-27T17:33:39
2023-09-27T17:33:39
NONE
null
null
### Describe the bug Hi, I have been trying to import the datasets library but I keep gtting this error. `Traceback (most recent call last): File /opt/local/jupyterhub/lib64/python3.9/site-packages/IPython/core/interactiveshell.py:3508 in run_code exec(code_obj, self.user_global_ns, self.user_ns) Cell In[2], line 1 import datasets File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/__init__.py:22 from .arrow_dataset import Dataset File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_dataset.py:67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/arrow_writer.py:27 from .features import Features, Image, Value File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/__init__.py:17 from .audio import Audio File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/features/audio.py:11 from ..download.streaming_download_manager import xopen, xsplitext File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/__init__.py:10 from .streaming_download_manager import StreamingDownloadManager File /opt/local/jupyterhub/lib64/python3.9/site-packages/datasets/download/streaming_download_manager.py:18 from aiohttp.client_exceptions import ClientError File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/__init__.py:7 from .connector import * # noqa File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/connector.py:12 from .client import ClientRequest File /opt/local/jupyterhub/lib64/python3.9/site-packages/aiohttp/client.py:144 yield from asyncio.async(resp.release(), loop=loop) ^ SyntaxError: invalid syntax` I have simply used these commands: `import datasets` and `from datasets import load_dataset` ### Environment info The library has been installed a virtual machine on JupyterHub. Although I have used this library multiple times (on the same VM) before, to train/test an ASR or other ML models, I had never encountered this error.
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6,177
Use object detection images from `huggingface/documentation-images`
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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) | 0.005847 / 0.011353 (-0.005506) | 0.003488 / 0.011008 (-0.007521) | 0.079545 / 0.038508 (0.041037) | 0.055114 / 0.023109 (0.032005) | 0.312694 / 0.275898 (0.036796) | 0.338808 / 0.323480 (0.015329) | 0.004573 / 0.007986 (-0.003413) | 0.002818 / 0.004328 (-0.001510) | 0.062102 / 0.004250 (0.057852) | 0.044072 / 0.037052 (0.007019) | 0.317682 / 0.258489 (0.059192) | 0.354139 / 0.293841 (0.060298) | 0.026905 / 0.128546 (-0.101641) | 0.007990 / 0.075646 (-0.067656) | 0.260071 / 0.419271 (-0.159201) | 0.043658 / 0.043533 (0.000125) | 0.313828 / 0.255139 (0.058689) | 0.339678 / 0.283200 (0.056478) | 0.020076 / 0.141683 (-0.121607) | 1.446321 / 1.452155 (-0.005834) | 1.527046 / 1.492716 (0.034330) |\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) | 0.197801 / 0.018006 (0.179795) | 0.432874 / 0.000490 (0.432385) | 0.004093 / 0.000200 (0.003893) | 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) | 0.023505 / 0.037411 (-0.013906) | 0.072377 / 0.014526 (0.057852) | 0.081058 / 0.176557 (-0.095498) | 0.141628 / 0.737135 (-0.595507) | 0.081622 / 0.296338 (-0.214716) |\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) | 0.395005 / 0.215209 (0.179795) | 3.949006 / 2.077655 (1.871352) | 1.934028 / 1.504120 (0.429908) | 1.756065 / 1.541195 (0.214871) | 1.778719 / 1.468490 (0.310229) | 0.501279 / 4.584777 (-4.083498) | 3.032120 / 3.745712 (-0.713592) | 2.859751 / 5.269862 (-2.410110) | 1.885924 / 4.565676 (-2.679753) | 0.057236 / 0.424275 (-0.367039) | 0.006704 / 0.007607 (-0.000903) | 0.465794 / 0.226044 (0.239750) | 4.648622 / 2.268929 (2.379694) | 2.345649 / 55.444624 (-53.098975) | 1.981122 / 6.876477 (-4.895355) | 2.148235 / 2.142072 (0.006163) | 0.591466 / 4.805227 (-4.213761) | 0.125262 / 6.500664 (-6.375402) | 0.061305 / 0.075469 (-0.014164) |\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) | 1.243932 / 1.841788 (-0.597856) | 17.912110 / 8.074308 (9.837802) | 13.662097 / 10.191392 (3.470705) | 0.148051 / 0.680424 (-0.532373) | 0.016778 / 0.534201 (-0.517423) | 0.340342 / 0.579283 (-0.238941) | 0.351720 / 0.434364 (-0.082644) | 0.377837 / 0.540337 (-0.162501) | 0.521163 / 1.386936 (-0.865774) |\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) | 0.006011 / 0.011353 (-0.005342) | 0.003549 / 0.011008 (-0.007459) | 0.063579 / 0.038508 (0.025071) | 0.056196 / 0.023109 (0.033087) | 0.448879 / 0.275898 (0.172981) | 0.491542 / 0.323480 (0.168062) | 0.004597 / 0.007986 (-0.003389) | 0.002790 / 0.004328 (-0.001539) | 0.063257 / 0.004250 (0.059006) | 0.045653 / 0.037052 (0.008600) | 0.459714 / 0.258489 (0.201225) | 0.491371 / 0.293841 (0.197530) | 0.028124 / 0.128546 (-0.100422) | 0.008016 / 0.075646 (-0.067630) | 0.069418 / 0.419271 (-0.349853) | 0.040393 / 0.043533 (-0.003140) | 0.450978 / 0.255139 (0.195839) | 0.472075 / 0.283200 (0.188875) | 0.020006 / 0.141683 (-0.121677) | 1.451946 / 1.452155 (-0.000209) | 1.513557 / 1.492716 (0.020840) |\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) | 0.225416 / 0.018006 (0.207410) | 0.412287 / 0.000490 (0.411797) | 0.004075 / 0.000200 (0.003875) | 0.000073 / 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) | 0.025949 / 0.037411 (-0.011463) | 0.080633 / 0.014526 (0.066108) | 0.089960 / 0.176557 (-0.086597) | 0.144530 / 0.737135 (-0.592606) | 0.091427 / 0.296338 (-0.204911) |\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) | 0.462311 / 0.215209 (0.247102) | 4.605063 / 2.077655 (2.527408) | 2.541083 / 1.504120 (1.036963) | 2.356341 / 1.541195 (0.815147) | 2.389824 / 1.468490 (0.921334) | 0.507397 / 4.584777 (-4.077380) | 3.079023 / 3.745712 (-0.666689) | 2.792025 / 5.269862 (-2.477837) | 1.846931 / 4.565676 (-2.718746) | 0.058422 / 0.424275 (-0.365853) | 0.006409 / 0.007607 (-0.001199) | 0.530648 / 0.226044 (0.304604) | 5.321030 / 2.268929 (3.052101) | 2.978335 / 55.444624 (-52.466289) | 2.641188 / 6.876477 (-4.235288) | 2.780450 / 2.142072 (0.638378) | 0.593864 / 4.805227 (-4.211363) | 0.125394 / 6.500664 (-6.375270) | 0.061432 / 0.075469 (-0.014037) |\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) | 1.337142 / 1.841788 (-0.504646) | 18.841575 / 8.074308 (10.767267) | 14.678622 / 10.191392 (4.487230) | 0.144491 / 0.680424 (-0.535933) | 0.018145 / 0.534201 (-0.516056) | 0.339376 / 0.579283 (-0.239907) | 0.339129 / 0.434364 (-0.095235) | 0.394842 / 0.540337 (-0.145495) | 0.547924 / 1.386936 (-0.839012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#57af0ab30796df59d28bf933e756ffbe5f34db1e \"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) | 0.006478 / 0.011353 (-0.004875) | 0.003845 / 0.011008 (-0.007163) | 0.084179 / 0.038508 (0.045671) | 0.071327 / 0.023109 (0.048217) | 0.315206 / 0.275898 (0.039308) | 0.353477 / 0.323480 (0.029997) | 0.005267 / 0.007986 (-0.002719) | 0.003282 / 0.004328 (-0.001046) | 0.064062 / 0.004250 (0.059811) | 0.051940 / 0.037052 (0.014888) | 0.332004 / 0.258489 (0.073515) | 0.363199 / 0.293841 (0.069358) | 0.030546 / 0.128546 (-0.098000) | 0.008453 / 0.075646 (-0.067193) | 0.287636 / 0.419271 (-0.131636) | 0.051999 / 0.043533 (0.008466) | 0.325220 / 0.255139 (0.070081) | 0.355324 / 0.283200 (0.072125) | 0.023417 / 0.141683 (-0.118266) | 1.473370 / 1.452155 (0.021215) | 1.596903 / 1.492716 (0.104186) |\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) | 0.212645 / 0.018006 (0.194638) | 0.463766 / 0.000490 (0.463276) | 0.002834 / 0.000200 (0.002634) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028424 / 0.037411 (-0.008987) | 0.082188 / 0.014526 (0.067662) | 0.777186 / 0.176557 (0.600629) | 0.218290 / 0.737135 (-0.518845) | 0.099098 / 0.296338 (-0.197240) |\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) | 0.387138 / 0.215209 (0.171929) | 3.845655 / 2.077655 (1.768000) | 1.929812 / 1.504120 (0.425692) | 1.718263 / 1.541195 (0.177069) | 1.760933 / 1.468490 (0.292443) | 0.475171 / 4.584777 (-4.109606) | 3.523366 / 3.745712 (-0.222346) | 3.167322 / 5.269862 (-2.102540) | 1.975164 / 4.565676 (-2.590513) | 0.056106 / 0.424275 (-0.368169) | 0.007448 / 0.007607 (-0.000159) | 0.459824 / 0.226044 (0.233779) | 4.590566 / 2.268929 (2.321638) | 2.377968 / 55.444624 (-53.066656) | 2.034052 / 6.876477 (-4.842425) | 2.224976 / 2.142072 (0.082904) | 0.575901 / 4.805227 (-4.229326) | 0.131546 / 6.500664 (-6.369118) | 0.059266 / 0.075469 (-0.016203) |\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) | 1.254783 / 1.841788 (-0.587005) | 19.497795 / 8.074308 (11.423487) | 13.937672 / 10.191392 (3.746280) | 0.164092 / 0.680424 (-0.516332) | 0.017915 / 0.534201 (-0.516286) | 0.391430 / 0.579283 (-0.187853) | 0.403681 / 0.434364 (-0.030683) | 0.457711 / 0.540337 (-0.082626) | 0.620395 / 1.386936 (-0.766541) |\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) | 0.006793 / 0.011353 (-0.004560) | 0.004101 / 0.011008 (-0.006907) | 0.064780 / 0.038508 (0.026272) | 0.071087 / 0.023109 (0.047977) | 0.401963 / 0.275898 (0.126065) | 0.433085 / 0.323480 (0.109605) | 0.005348 / 0.007986 (-0.002638) | 0.003289 / 0.004328 (-0.001039) | 0.065209 / 0.004250 (0.060958) | 0.054202 / 0.037052 (0.017150) | 0.405629 / 0.258489 (0.147140) | 0.440326 / 0.293841 (0.146485) | 0.032283 / 0.128546 (-0.096263) | 0.008510 / 0.075646 (-0.067137) | 0.071144 / 0.419271 (-0.348127) | 0.047414 / 0.043533 (0.003881) | 0.402065 / 0.255139 (0.146926) | 0.421217 / 0.283200 (0.138017) | 0.021924 / 0.141683 (-0.119759) | 1.490067 / 1.452155 (0.037913) | 1.539134 / 1.492716 (0.046417) |\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) | 0.280072 / 0.018006 (0.262066) | 0.456130 / 0.000490 (0.455641) | 0.020926 / 0.000200 (0.020726) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032040 / 0.037411 (-0.005371) | 0.092343 / 0.014526 (0.077817) | 0.104866 / 0.176557 (-0.071690) | 0.156631 / 0.737135 (-0.580505) | 0.107203 / 0.296338 (-0.189136) |\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) | 0.426268 / 0.215209 (0.211059) | 4.255539 / 2.077655 (2.177884) | 2.285077 / 1.504120 (0.780957) | 2.114277 / 1.541195 (0.573083) | 2.159242 / 1.468490 (0.690752) | 0.489421 / 4.584777 (-4.095356) | 3.630797 / 3.745712 (-0.114915) | 3.205238 / 5.269862 (-2.064624) | 1.985846 / 4.565676 (-2.579830) | 0.057436 / 0.424275 (-0.366839) | 0.007154 / 0.007607 (-0.000454) | 0.507294 / 0.226044 (0.281250) | 5.050105 / 2.268929 (2.781176) | 2.750474 / 55.444624 (-52.694151) | 2.404116 / 6.876477 (-4.472360) | 2.576483 / 2.142072 (0.434411) | 0.584909 / 4.805227 (-4.220318) | 0.130695 / 6.500664 (-6.369969) | 0.059743 / 0.075469 (-0.015726) |\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) | 1.352702 / 1.841788 (-0.489086) | 19.687944 / 8.074308 (11.613636) | 14.991847 / 10.191392 (4.800455) | 0.185164 / 0.680424 (-0.495260) | 0.020314 / 0.534201 (-0.513887) | 0.395162 / 0.579283 (-0.184121) | 0.408917 / 0.434364 (-0.025447) | 0.467049 / 0.540337 (-0.073288) | 0.649209 / 1.386936 (-0.737727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#885518608ceab83b7ed8ceba7a0b72bc68096026 \"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) | 0.006142 / 0.011353 (-0.005211) | 0.003621 / 0.011008 (-0.007387) | 0.079880 / 0.038508 (0.041372) | 0.059283 / 0.023109 (0.036173) | 0.310971 / 0.275898 (0.035072) | 0.351620 / 0.323480 (0.028140) | 0.003453 / 0.007986 (-0.004532) | 0.003785 / 0.004328 (-0.000543) | 0.062395 / 0.004250 (0.058145) | 0.047614 / 0.037052 (0.010562) | 0.312688 / 0.258489 (0.054199) | 0.363762 / 0.293841 (0.069921) | 0.027051 / 0.128546 (-0.101495) | 0.007920 / 0.075646 (-0.067726) | 0.261080 / 0.419271 (-0.158192) | 0.044476 / 0.043533 (0.000943) | 0.312615 / 0.255139 (0.057476) | 0.343672 / 0.283200 (0.060472) | 0.022723 / 0.141683 (-0.118960) | 1.441449 / 1.452155 (-0.010706) | 1.509253 / 1.492716 (0.016536) |\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) | 0.193171 / 0.018006 (0.175165) | 0.434771 / 0.000490 (0.434281) | 0.003114 / 0.000200 (0.002914) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024209 / 0.037411 (-0.013203) | 0.073891 / 0.014526 (0.059365) | 0.083497 / 0.176557 (-0.093060) | 0.144962 / 0.737135 (-0.592173) | 0.084594 / 0.296338 (-0.211745) |\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) | 0.392512 / 0.215209 (0.177303) | 3.912692 / 2.077655 (1.835037) | 1.914010 / 1.504120 (0.409890) | 1.743827 / 1.541195 (0.202632) | 1.829244 / 1.468490 (0.360753) | 0.497740 / 4.584777 (-4.087037) | 2.979222 / 3.745712 (-0.766490) | 2.849786 / 5.269862 (-2.420076) | 1.874411 / 4.565676 (-2.691265) | 0.057270 / 0.424275 (-0.367005) | 0.006673 / 0.007607 (-0.000934) | 0.460724 / 0.226044 (0.234679) | 4.600617 / 2.268929 (2.331689) | 2.333178 / 55.444624 (-53.111446) | 1.999902 / 6.876477 (-4.876575) | 2.170600 / 2.142072 (0.028528) | 0.587716 / 4.805227 (-4.217511) | 0.126374 / 6.500664 (-6.374290) | 0.061926 / 0.075469 (-0.013543) |\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) | 1.229767 / 1.841788 (-0.612021) | 18.494462 / 8.074308 (10.420154) | 13.799801 / 10.191392 (3.608409) | 0.137952 / 0.680424 (-0.542472) | 0.017037 / 0.534201 (-0.517164) | 0.333252 / 0.579283 (-0.246031) | 0.357276 / 0.434364 (-0.077088) | 0.380069 / 0.540337 (-0.160268) | 0.526968 / 1.386936 (-0.859968) |\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) | 0.006185 / 0.011353 (-0.005168) | 0.003595 / 0.011008 (-0.007413) | 0.063371 / 0.038508 (0.024863) | 0.060461 / 0.023109 (0.037351) | 0.455016 / 0.275898 (0.179118) | 0.490505 / 0.323480 (0.167026) | 0.004738 / 0.007986 (-0.003247) | 0.002852 / 0.004328 (-0.001477) | 0.064161 / 0.004250 (0.059910) | 0.047411 / 0.037052 (0.010359) | 0.453815 / 0.258489 (0.195326) | 0.485354 / 0.293841 (0.191513) | 0.028358 / 0.128546 (-0.100188) | 0.008101 / 0.075646 (-0.067545) | 0.068399 / 0.419271 (-0.350873) | 0.040928 / 0.043533 (-0.002605) | 0.462263 / 0.255139 (0.207124) | 0.478773 / 0.283200 (0.195574) | 0.019787 / 0.141683 (-0.121896) | 1.475798 / 1.452155 (0.023643) | 1.563890 / 1.492716 (0.071174) |\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) | 0.239701 / 0.018006 (0.221695) | 0.417442 / 0.000490 (0.416953) | 0.005895 / 0.000200 (0.005695) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026155 / 0.037411 (-0.011256) | 0.081264 / 0.014526 (0.066738) | 0.089734 / 0.176557 (-0.086822) | 0.143965 / 0.737135 (-0.593171) | 0.092156 / 0.296338 (-0.204182) |\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) | 0.456420 / 0.215209 (0.241211) | 4.545675 / 2.077655 (2.468020) | 2.477141 / 1.504120 (0.973022) | 2.295142 / 1.541195 (0.753947) | 2.349525 / 1.468490 (0.881035) | 0.502485 / 4.584777 (-4.082292) | 3.072347 / 3.745712 (-0.673365) | 2.798565 / 5.269862 (-2.471296) | 1.849030 / 4.565676 (-2.716647) | 0.057789 / 0.424275 (-0.366487) | 0.006436 / 0.007607 (-0.001172) | 0.529648 / 0.226044 (0.303604) | 5.285670 / 2.268929 (3.016741) | 2.954964 / 55.444624 (-52.489660) | 2.593161 / 6.876477 (-4.283316) | 2.735254 / 2.142072 (0.593181) | 0.587635 / 4.805227 (-4.217592) | 0.124732 / 6.500664 (-6.375932) | 0.060999 / 0.075469 (-0.014470) |\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) | 1.354957 / 1.841788 (-0.486831) | 18.803998 / 8.074308 (10.729690) | 14.902712 / 10.191392 (4.711320) | 0.146729 / 0.680424 (-0.533695) | 0.017989 / 0.534201 (-0.516212) | 0.333633 / 0.579283 (-0.245650) | 0.347685 / 0.434364 (-0.086679) | 0.386497 / 0.540337 (-0.153840) | 0.590885 / 1.386936 (-0.796051) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#392d8a46f4da066408785281d9b87760f7273254 \"CML watermark\")\n" ]
2023-08-24T16:16:09
2023-08-25T16:30:00
2023-08-25T16:21:17
CONTRIBUTOR
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how to limit the size of memory mapped file?
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[ "Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.", "The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06:01\r\n host : compute-126.cm.cluster\r\n rank : 0 (local_rank: 0)\r\n exitcode : 1 (pid: 48442)\r\n error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json\r\n traceback : Traceback (most recent call last):\r\n File \"/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 346, in wrapper\r\n return f(*args, **kwargs)\r\n File \"Pretrain.py\", line 214, in main\r\n pair_dataset, c4_dataset = create_dataset('pretrain', config)\r\n File \"/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py\", line 109, in create_dataset\r\n c4_dataset = load_dataset(\"c4\", \"en\", split=\"train\").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py\", line 1810, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 436, in map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\n OSError: Memory mapping file failed: Cannot allocate memory\r\n```", "This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping.\r\n\r\nPS: this is the API to reduce the size of the generated Arrow file:\r\n```python\r\nfrom datasets import load_dataset\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepare(max_shard_size=\"5GB\")\r\ndataset = builder.as_dataset()\r\n```\r\n\r\nIf this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.", "Thanks for the response. The problem seems not resolved. The memory I allocated to the environment is 64G and the following error still occurs\r\n`Python 3.8.16 (default, Jun 12 2023, 18:09:05) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder(\"c4\", \"en\")\r\n>>> builder.download_and_prepare(max_shard_size=\"5GB\")\r\nFound cached dataset c4 (/users/yli7/.cache/huggingface/datasets/c4/en/0.0.0/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01)\r\n>>> dataset = builder.as_dataset()\r\n 0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 444, in map_nested\r\n mapped = [\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 445, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 347, in _single_map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory`", "Have you solved the problem?", "Nope. Streaming works but is slower." ]
2023-08-24T05:33:45
2023-10-11T06:00:10
null
NONE
null
null
### Describe the bug Huggingface datasets use memory-mapped file to map large datasets in memory for fast access. However, it seems like huggingface will occupy all the memory for memory-mapped files, which makes a troublesome situation since we cluster will distribute a small portion of memory to me (once it's over the limit, memory cannot be allocated), however, when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. So is there a way to explicitly limit the size of memory mapped file? ### Steps to reproduce the bug python >>> from datasets import load_dataset >>> dataset = load_dataset("c4", "en", streaming=True) ### Expected behavior In a normal environment, this will not have any problem. However, when the system allocates a portion of the memory to the program and when the dataset checks the total memory, all of the memory will be taken into account which makes huggingface dataset try to allocate more memory than allowed. ### Environment info linux cluster with SGE（Sun Grid Engine）
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https://github.com/huggingface/datasets/pull/6175
1,863,592,678
PR_kwDODunzps5YnKlx
6,175
PyArrow 13 CI fixes
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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) | 0.006095 / 0.011353 (-0.005258) | 0.003580 / 0.011008 (-0.007429) | 0.080146 / 0.038508 (0.041638) | 0.063445 / 0.023109 (0.040336) | 0.321930 / 0.275898 (0.046032) | 0.397933 / 0.323480 (0.074453) | 0.003455 / 0.007986 (-0.004531) | 0.002856 / 0.004328 (-0.001472) | 0.062938 / 0.004250 (0.058687) | 0.048896 / 0.037052 (0.011843) | 0.333070 / 0.258489 (0.074581) | 0.404485 / 0.293841 (0.110644) | 0.027156 / 0.128546 (-0.101390) | 0.007974 / 0.075646 (-0.067672) | 0.261505 / 0.419271 (-0.157766) | 0.045328 / 0.043533 (0.001795) | 0.311203 / 0.255139 (0.056064) | 0.390006 / 0.283200 (0.106806) | 0.023650 / 0.141683 (-0.118033) | 1.468856 / 1.452155 (0.016701) | 1.503867 / 1.492716 (0.011151) |\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) | 0.202110 / 0.018006 (0.184103) | 0.436433 / 0.000490 (0.435944) | 0.002278 / 0.000200 (0.002078) | 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) | 0.024575 / 0.037411 (-0.012836) | 0.073005 / 0.014526 (0.058479) | 0.083609 / 0.176557 (-0.092947) | 0.144881 / 0.737135 (-0.592254) | 0.083495 / 0.296338 (-0.212844) |\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) | 0.398911 / 0.215209 (0.183702) | 3.994035 / 2.077655 (1.916381) | 2.056768 / 1.504120 (0.552649) | 1.913242 / 1.541195 (0.372047) | 1.932934 / 1.468490 (0.464444) | 0.498953 / 4.584777 (-4.085824) | 3.031107 / 3.745712 (-0.714605) | 2.817165 / 5.269862 (-2.452696) | 1.858886 / 4.565676 (-2.706790) | 0.056977 / 0.424275 (-0.367299) | 0.006634 / 0.007607 (-0.000973) | 0.472580 / 0.226044 (0.246536) | 4.738301 / 2.268929 (2.469372) | 2.373938 / 55.444624 (-53.070686) | 2.021057 / 6.876477 (-4.855420) | 2.195419 / 2.142072 (0.053346) | 0.585182 / 4.805227 (-4.220045) | 0.124260 / 6.500664 (-6.376405) | 0.060250 / 0.075469 (-0.015219) |\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) | 1.227350 / 1.841788 (-0.614438) | 18.496525 / 8.074308 (10.422216) | 13.946658 / 10.191392 (3.755266) | 0.140024 / 0.680424 (-0.540399) | 0.017077 / 0.534201 (-0.517124) | 0.334415 / 0.579283 (-0.244868) | 0.351118 / 0.434364 (-0.083246) | 0.379556 / 0.540337 (-0.160782) | 0.525064 / 1.386936 (-0.861872) |\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) | 0.006176 / 0.011353 (-0.005177) | 0.003648 / 0.011008 (-0.007360) | 0.063461 / 0.038508 (0.024953) | 0.062770 / 0.023109 (0.039660) | 0.448786 / 0.275898 (0.172888) | 0.486490 / 0.323480 (0.163010) | 0.005527 / 0.007986 (-0.002458) | 0.002860 / 0.004328 (-0.001469) | 0.063803 / 0.004250 (0.059553) | 0.049657 / 0.037052 (0.012604) | 0.449625 / 0.258489 (0.191136) | 0.489378 / 0.293841 (0.195537) | 0.028406 / 0.128546 (-0.100140) | 0.008062 / 0.075646 (-0.067584) | 0.068417 / 0.419271 (-0.350854) | 0.040854 / 0.043533 (-0.002678) | 0.461670 / 0.255139 (0.206531) | 0.481622 / 0.283200 (0.198423) | 0.021018 / 0.141683 (-0.120665) | 1.450328 / 1.452155 (-0.001826) | 1.501283 / 1.492716 (0.008567) |\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) | 0.269824 / 0.018006 (0.251817) | 0.412296 / 0.000490 (0.411807) | 0.039582 / 0.000200 (0.039382) | 0.000266 / 0.000054 (0.000211) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026436 / 0.037411 (-0.010976) | 0.080633 / 0.014526 (0.066107) | 0.089786 / 0.176557 (-0.086770) | 0.145020 / 0.737135 (-0.592115) | 0.092327 / 0.296338 (-0.204012) |\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) | 0.464349 / 0.215209 (0.249140) | 4.630631 / 2.077655 (2.552976) | 2.560527 / 1.504120 (1.056407) | 2.374195 / 1.541195 (0.833000) | 2.424774 / 1.468490 (0.956284) | 0.510428 / 4.584777 (-4.074349) | 3.099805 / 3.745712 (-0.645907) | 2.781096 / 5.269862 (-2.488765) | 1.854276 / 4.565676 (-2.711400) | 0.058102 / 0.424275 (-0.366173) | 0.006365 / 0.007607 (-0.001242) | 0.534082 / 0.226044 (0.308038) | 5.355003 / 2.268929 (3.086074) | 3.012546 / 55.444624 (-52.432078) | 2.665222 / 6.876477 (-4.211255) | 2.821014 / 2.142072 (0.678942) | 0.597733 / 4.805227 (-4.207494) | 0.125433 / 6.500664 (-6.375231) | 0.060802 / 0.075469 (-0.014667) |\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) | 1.345699 / 1.841788 (-0.496088) | 18.836083 / 8.074308 (10.761774) | 14.895458 / 10.191392 (4.704066) | 0.146843 / 0.680424 (-0.533581) | 0.018082 / 0.534201 (-0.516119) | 0.335729 / 0.579283 (-0.243554) | 0.351013 / 0.434364 (-0.083351) | 0.388435 / 0.540337 (-0.151902) | 0.543826 / 1.386936 (-0.843110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0c7e8c4808a1fb6ee7234b4caa25aa9fcfdc88f \"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) | 0.006593 / 0.011353 (-0.004760) | 0.004089 / 0.011008 (-0.006919) | 0.084753 / 0.038508 (0.046245) | 0.079899 / 0.023109 (0.056790) | 0.311528 / 0.275898 (0.035630) | 0.349722 / 0.323480 (0.026243) | 0.004288 / 0.007986 (-0.003698) | 0.004552 / 0.004328 (0.000224) | 0.065896 / 0.004250 (0.061646) | 0.053813 / 0.037052 (0.016760) | 0.316958 / 0.258489 (0.058469) | 0.367011 / 0.293841 (0.073170) | 0.031082 / 0.128546 (-0.097464) | 0.008684 / 0.075646 (-0.066963) | 0.288003 / 0.419271 (-0.131268) | 0.052560 / 0.043533 (0.009027) | 0.305589 / 0.255139 (0.050450) | 0.349656 / 0.283200 (0.066457) | 0.023857 / 0.141683 (-0.117826) | 1.462360 / 1.452155 (0.010205) | 1.568170 / 1.492716 (0.075454) |\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) | 0.272342 / 0.018006 (0.254336) | 0.585108 / 0.000490 (0.584618) | 0.003427 / 0.000200 (0.003227) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030347 / 0.037411 (-0.007064) | 0.086325 / 0.014526 (0.071799) | 0.100958 / 0.176557 (-0.075598) | 0.156534 / 0.737135 (-0.580601) | 0.102506 / 0.296338 (-0.193832) |\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) | 0.406625 / 0.215209 (0.191416) | 4.065957 / 2.077655 (1.988302) | 2.075867 / 1.504120 (0.571747) | 1.914390 / 1.541195 (0.373196) | 2.013321 / 1.468490 (0.544831) | 0.486832 / 4.584777 (-4.097945) | 3.545940 / 3.745712 (-0.199772) | 3.323226 / 5.269862 (-1.946635) | 2.067742 / 4.565676 (-2.497934) | 0.057884 / 0.424275 (-0.366391) | 0.007751 / 0.007607 (0.000144) | 0.484923 / 0.226044 (0.258878) | 4.844885 / 2.268929 (2.575956) | 2.569828 / 55.444624 (-52.874796) | 2.224058 / 6.876477 (-4.652419) | 2.485587 / 2.142072 (0.343515) | 0.584311 / 4.805227 (-4.220916) | 0.134984 / 6.500664 (-6.365680) | 0.062164 / 0.075469 (-0.013305) |\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) | 1.247182 / 1.841788 (-0.594605) | 20.107500 / 8.074308 (12.033192) | 14.194444 / 10.191392 (4.003052) | 0.147134 / 0.680424 (-0.533290) | 0.018062 / 0.534201 (-0.516138) | 0.392029 / 0.579283 (-0.187254) | 0.402991 / 0.434364 (-0.031373) | 0.457600 / 0.540337 (-0.082737) | 0.632553 / 1.386936 (-0.754383) |\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) | 0.006920 / 0.011353 (-0.004433) | 0.004257 / 0.011008 (-0.006751) | 0.065233 / 0.038508 (0.026725) | 0.078151 / 0.023109 (0.055042) | 0.389141 / 0.275898 (0.113243) | 0.431518 / 0.323480 (0.108038) | 0.005752 / 0.007986 (-0.002234) | 0.003584 / 0.004328 (-0.000745) | 0.065173 / 0.004250 (0.060922) | 0.059113 / 0.037052 (0.022060) | 0.398225 / 0.258489 (0.139736) | 0.430980 / 0.293841 (0.137139) | 0.032802 / 0.128546 (-0.095744) | 0.008702 / 0.075646 (-0.066945) | 0.071345 / 0.419271 (-0.347926) | 0.048269 / 0.043533 (0.004736) | 0.389264 / 0.255139 (0.134125) | 0.416008 / 0.283200 (0.132809) | 0.024845 / 0.141683 (-0.116838) | 1.499100 / 1.452155 (0.046945) | 1.576397 / 1.492716 (0.083681) |\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) | 0.296674 / 0.018006 (0.278668) | 0.540108 / 0.000490 (0.539619) | 0.004293 / 0.000200 (0.004093) | 0.000151 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034108 / 0.037411 (-0.003303) | 0.092747 / 0.014526 (0.078221) | 0.112203 / 0.176557 (-0.064354) | 0.162728 / 0.737135 (-0.574407) | 0.109955 / 0.296338 (-0.186383) |\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) | 0.432006 / 0.215209 (0.216797) | 4.297591 / 2.077655 (2.219937) | 2.379645 / 1.504120 (0.875525) | 2.218680 / 1.541195 (0.677485) | 2.314608 / 1.468490 (0.846117) | 0.495562 / 4.584777 (-4.089215) | 3.589787 / 3.745712 (-0.155925) | 3.349593 / 5.269862 (-1.920268) | 2.119893 / 4.565676 (-2.445783) | 0.057976 / 0.424275 (-0.366299) | 0.007612 / 0.007607 (0.000005) | 0.509422 / 0.226044 (0.283378) | 5.101444 / 2.268929 (2.832515) | 2.794532 / 55.444624 (-52.650092) | 2.459033 / 6.876477 (-4.417444) | 2.714424 / 2.142072 (0.572352) | 0.588444 / 4.805227 (-4.216784) | 0.135763 / 6.500664 (-6.364901) | 0.062593 / 0.075469 (-0.012876) |\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) | 1.361415 / 1.841788 (-0.480372) | 20.940684 / 8.074308 (12.866376) | 15.161364 / 10.191392 (4.969972) | 0.154243 / 0.680424 (-0.526181) | 0.020305 / 0.534201 (-0.513896) | 0.397438 / 0.579283 (-0.181845) | 0.415047 / 0.434364 (-0.019317) | 0.473250 / 0.540337 (-0.067088) | 0.740681 / 1.386936 (-0.646255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e84937af4f24194bf61f09244ebef6528fb7c4c \"CML watermark\")\n" ]
2023-08-23T15:45:53
2023-08-25T13:15:59
2023-08-25T13:06:52
CONTRIBUTOR
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Fixes: * bumps the PyArrow version check in the `cast_array_to_feature` to avoid the offset bug (still not fixed) * aligns the Pandas formatting tests with the Numpy ones (the current test fails due to https://github.com/apache/arrow/pull/35656, which requires `.to_pandas(coerce_temporal_nanoseconds=True)` to always return `datetime [ns]` objects) Fix #6173
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1,863,422,065
I_kwDODunzps5vEZBx
6,173
Fix CI for pyarrow 13.0.0
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2023-08-23T14:11:20
2023-08-25T13:06:53
2023-08-25T13:06:53
MEMBER
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pyarrow 13.0.0 just came out ``` FAILED tests/test_formatting.py::ArrowExtractorTest::test_pandas_extractor - AssertionError: Attributes of Series are different Attribute "dtype" are different [left]: datetime64[us, UTC] [right]: datetime64[ns, UTC] ``` ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ``` e.g. in https://github.com/huggingface/datasets/actions/runs/5952253963/job/16143847230 first error may be related to https://github.com/apache/arrow/issues/33321 second one maybe because `feature.length * len(array) == len(array_values)` is not satisfied anymore somehow ?
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1,863,318,027
I_kwDODunzps5vD_oL
6,172
Make Dataset streaming queries retryable
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[ "Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader\r\n\r\nA minor Hub outage that we experienced yesterday could be the cause.", "I wanted something similar. I have a huge dataset I want to process (laion-2b), but after processing several batches, it sometimes fails with this error: `HTTP 502 Bad Gateway for url`. I had the following code to handle it but this way I believe it restarts processing the data from the first batch? How can I set the attribute values you mention above?\r\n\r\n```\r\niterable_dataset = load_dataset(\"laion/laion2B-multi\", streaming=True, split='train')\r\ndataloader = DataLoader(iterable_dataset, batch_size=131072, collate_fn=custom_collate_fn, num_workers=8)\r\n\r\nMAX_RETRIES = 5\r\nRETRY_WAIT = 10 # wait 10 seconds before retry\r\n\r\n for retry in range(MAX_RETRIES):\r\n try:\r\n for j, batch in enumerate(dataloader):\r\n < process batch>\r\n\r\n except HfHubHTTPError as e:\r\n if \"502\" in str(e) and retry < MAX_RETRIES - 1:\r\n logging.warning(f\"Encountered a 502 error on batch {j}. Waiting for {RETRY_WAIT} seconds before retrying.\")\r\n time.sleep(RETRY_WAIT)\r\n continue\r\n else:\r\n raise", "Hey all! Wondering if there's a way of making Datasets streaming mode somewhat robust to Hub outages? Over the weekend, I got two quite cryptic errors, which I reckon were probably from Hub issues:\r\n\r\n<details>\r\n<summary> Stack Trace 1 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nConnectionError: Caught ConnectionError in DataLoader worker process 8.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1155, in _create_direct_connection\r\n hosts = await asyncio.shield(host_resolved)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 874, in _resolve_host\r\n addrs = await self._resolver.resolve(host, port, family=self._family)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/resolver.py\", line 33, in resolve\r\n infos = await self._loop.getaddrinfo(\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 863, in getaddrinfo\r\n return await self.run_in_executor(\r\n File \"/usr/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/usr/lib/python3.10/socket.py\", line 955, in getaddrinfo\r\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\r\nsocket.gaierror: [Errno -3] Temporary failure in name resolution\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 612, in read\r\n return super().read(length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 439, in _fetch\r\n new = self.fetcher(self.end, bend)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 118, in wrapper\r\n return sync(self.loop, func, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 103, in sync\r\n raise return_result\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 56, in _runner\r\n result[0] = await coro\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 660, in async_fetch_range\r\n r = await self.session.get(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/client.py\", line 562, in _request\r\n conn = await self._connector.connect(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 540, in connect\r\n proto = await self._create_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 901, in _create_connection\r\n _, proto = await self._create_direct_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1169, in _create_direct_connection\r\n raise ClientConnectorError(req.connection_key, exc) from exc\r\naiohttp.client_exceptions.ClientConnectorError: Cannot connect to host huggingface.co:443 ssl:default [Temporary failure in name resolution]\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/.cache/huggingface/modules/datasets_modules/datasets/distil-whisper--switchboard-data/9472ee64cca0e1a7e11909c7033c2354511fa62805f81a2e07616980c765abfe/switchboard-data.py\", line 247, in _generate_tables\r\n for record_batch in pf.iter_batches():\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retries\r\n raise ConnectionError(\"Server Disconnected\") from disconnect_err\r\nConnectionError: Server Disconnected\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n<summary> Stack Trace 2 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nrequests.exceptions.ConnectionError: Caught ConnectionError in DataLoader worker process 13.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nhttp.client.RemoteDisconnected: Remote end closed connection without response\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 486, in send\r\n resp = conn.urlopen(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 845, in urlopen\r\n retries = retries.increment(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/retry.py\", line 470, in increment\r\n raise reraise(type(error), error, _stacktrace)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/util.py\", line 38, in reraise\r\n raise value.with_traceback(tb)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nurllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 410, in _fetch_range\r\n r = http_backoff(\"GET\", url, headers=headers)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 258, in http_backoff\r\n response = session.request(method=method, url=url, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 589, in request\r\n resp = self.send(prep, **send_kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 703, in send\r\n r = adapter.send(request, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 63, in send\r\n return super().send(request, *args, **kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 501, in send\r\n raise ConnectionError(err, request=request)\r\nrequests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 5fce9fc2-e22f-41c2-91af-529f13f1d611)')\r\n```\r\n\r\n</details>\r\n\r\nHaving streaming mode fail when the Hub goes down makes using it problematic for long training runs where large amounts of data is involved. However, this is the precise situation for which streaming mode is so appealing!\r\n\r\nWondering if there were a 'common' set of Hub errors that we could catch in `iterable_datasets` and prevent from crashing the script?\r\n\r\ncc @lhoestq @mariosasko ", "Errors are already caught and requests are already retried.\r\n\r\nWhat you can do is increase the number of retries before an error is raised.\r\n\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.STREAMING_READ_MAX_RETRIES = 20 # default\r\ndatasets.config.STREAMING_READ_RETRY_INTERVAL = 5 # default\r\n```" ]
2023-08-23T13:15:38
2023-11-06T13:54:16
null
NONE
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### Feature request Streaming datasets, as intended, do not load the entire dataset in memory or disk. However, while querying the next data chunk from the remote, sometimes it is possible that the service is down or there might be other issues that may cause the query to fail. In such a scenario, it would be nice to make these queries retryable (perhaps with a backoff strategy). ### Motivation I was working on a model and the model checkpoints after every 1000 steps. At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch `dataloader`. Given the size of my model and data, it took around 2 hours to reach 1800 steps and now it will take about an hour to recover the lost 800. It would be better to get a retryable querying strategy. ### Your contribution It would be better if someone having experience in this area takes this up as this would require some testing.
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PR_kwDODunzps5Yk4AS
6,171
Fix typo in about_mapstyle_vs_iterable.mdx
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6171). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.009315 / 0.011353 (-0.002038) | 0.004931 / 0.011008 (-0.006077) | 0.100534 / 0.038508 (0.062026) | 0.089270 / 0.023109 (0.066161) | 0.394995 / 0.275898 (0.119097) | 0.440244 / 0.323480 (0.116764) | 0.006026 / 0.007986 (-0.001959) | 0.004252 / 0.004328 (-0.000077) | 0.078828 / 0.004250 (0.074577) | 0.066770 / 0.037052 (0.029718) | 0.411152 / 0.258489 (0.152663) | 0.445616 / 0.293841 (0.151775) | 0.048344 / 0.128546 (-0.080203) | 0.013700 / 0.075646 (-0.061946) | 0.361205 / 0.419271 (-0.058066) | 0.072085 / 0.043533 (0.028552) | 0.399173 / 0.255139 (0.144034) | 0.439334 / 0.283200 (0.156134) | 0.035815 / 0.141683 (-0.105868) | 1.779023 / 1.452155 (0.326868) | 1.865099 / 1.492716 (0.372383) |\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) | 0.275978 / 0.018006 (0.257972) | 0.588850 / 0.000490 (0.588360) | 0.004953 / 0.000200 (0.004754) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031329 / 0.037411 (-0.006082) | 0.095435 / 0.014526 (0.080910) | 0.111182 / 0.176557 (-0.065375) | 0.177692 / 0.737135 (-0.559444) | 0.113345 / 0.296338 (-0.182993) |\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) | 0.577882 / 0.215209 (0.362673) | 5.865872 / 2.077655 (3.788217) | 2.664218 / 1.504120 (1.160098) | 2.383354 / 1.541195 (0.842159) | 2.336821 / 1.468490 (0.868331) | 0.834585 / 4.584777 (-3.750192) | 5.418720 / 3.745712 (1.673008) | 4.551790 / 5.269862 (-0.718072) | 2.921874 / 4.565676 (-1.643803) | 0.095738 / 0.424275 (-0.328537) | 0.009625 / 0.007607 (0.002018) | 0.688317 / 0.226044 (0.462273) | 6.831826 / 2.268929 (4.562897) | 3.482607 / 55.444624 (-51.962017) | 2.633482 / 6.876477 (-4.242995) | 2.878786 / 2.142072 (0.736714) | 0.971615 / 4.805227 (-3.833613) | 0.208661 / 6.500664 (-6.292003) | 0.080271 / 0.075469 (0.004802) |\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) | 1.661193 / 1.841788 (-0.180594) | 24.223041 / 8.074308 (16.148733) | 21.621791 / 10.191392 (11.430399) | 0.243809 / 0.680424 (-0.436614) | 0.031630 / 0.534201 (-0.502571) | 0.501408 / 0.579283 (-0.077875) | 0.600002 / 0.434364 (0.165638) | 0.572066 / 0.540337 (0.031728) | 0.791992 / 1.386936 (-0.594944) |\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) | 0.009410 / 0.011353 (-0.001943) | 0.005255 / 0.011008 (-0.005753) | 0.079202 / 0.038508 (0.040693) | 0.078973 / 0.023109 (0.055863) | 0.557416 / 0.275898 (0.281518) | 0.560417 / 0.323480 (0.236937) | 0.007066 / 0.007986 (-0.000920) | 0.004560 / 0.004328 (0.000232) | 0.080359 / 0.004250 (0.076109) | 0.060071 / 0.037052 (0.023019) | 0.538441 / 0.258489 (0.279952) | 0.592486 / 0.293841 (0.298645) | 0.053221 / 0.128546 (-0.075325) | 0.014056 / 0.075646 (-0.061591) | 0.094084 / 0.419271 (-0.325188) | 0.066721 / 0.043533 (0.023188) | 0.521873 / 0.255139 (0.266734) | 0.579637 / 0.283200 (0.296437) | 0.041476 / 0.141683 (-0.100206) | 1.829681 / 1.452155 (0.377527) | 1.948418 / 1.492716 (0.455702) |\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) | 0.347594 / 0.018006 (0.329588) | 0.606906 / 0.000490 (0.606417) | 0.035413 / 0.000200 (0.035213) | 0.000371 / 0.000054 (0.000317) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031987 / 0.037411 (-0.005425) | 0.096985 / 0.014526 (0.082459) | 0.109275 / 0.176557 (-0.067282) | 0.175340 / 0.737135 (-0.561795) | 0.110763 / 0.296338 (-0.185575) |\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) | 0.634823 / 0.215209 (0.419614) | 6.527172 / 2.077655 (4.449517) | 3.135709 / 1.504120 (1.631589) | 2.634357 / 1.541195 (1.093162) | 2.670583 / 1.468490 (1.202093) | 0.888686 / 4.584777 (-3.696091) | 5.382289 / 3.745712 (1.636577) | 4.701189 / 5.269862 (-0.568673) | 3.161290 / 4.565676 (-1.404386) | 0.112414 / 0.424275 (-0.311861) | 0.009443 / 0.007607 (0.001836) | 0.774703 / 0.226044 (0.548658) | 7.905334 / 2.268929 (5.636405) | 3.689548 / 55.444624 (-51.755076) | 3.087263 / 6.876477 (-3.789214) | 3.366568 / 2.142072 (1.224496) | 1.185951 / 4.805227 (-3.619277) | 0.248638 / 6.500664 (-6.252026) | 0.104598 / 0.075469 (0.029129) |\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) | 1.820667 / 1.841788 (-0.021120) | 24.536703 / 8.074308 (16.462395) | 23.083964 / 10.191392 (12.892572) | 0.252897 / 0.680424 (-0.427527) | 0.032954 / 0.534201 (-0.501247) | 0.482467 / 0.579283 (-0.096816) | 0.602247 / 0.434364 (0.167883) | 0.600563 / 0.540337 (0.060225) | 0.824013 / 1.386936 (-0.562923) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07a54ed4d570c5842d7bbe467025805be16ef51 \"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) | 0.009242 / 0.011353 (-0.002111) | 0.005244 / 0.011008 (-0.005764) | 0.112678 / 0.038508 (0.074170) | 0.089176 / 0.023109 (0.066067) | 0.405823 / 0.275898 (0.129925) | 0.465703 / 0.323480 (0.142223) | 0.005227 / 0.007986 (-0.002758) | 0.004296 / 0.004328 (-0.000032) | 0.082961 / 0.004250 (0.078711) | 0.063144 / 0.037052 (0.026092) | 0.422369 / 0.258489 (0.163880) | 0.478185 / 0.293841 (0.184344) | 0.049770 / 0.128546 (-0.078776) | 0.016561 / 0.075646 (-0.059086) | 0.380172 / 0.419271 (-0.039100) | 0.068698 / 0.043533 (0.025165) | 0.397773 / 0.255139 (0.142634) | 0.461284 / 0.283200 (0.178084) | 0.036907 / 0.141683 (-0.104775) | 1.828017 / 1.452155 (0.375862) | 2.028385 / 1.492716 (0.535669) |\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) | 0.291245 / 0.018006 (0.273239) | 0.605519 / 0.000490 (0.605030) | 0.003790 / 0.000200 (0.003590) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029269 / 0.037411 (-0.008142) | 0.087014 / 0.014526 (0.072488) | 0.116984 / 0.176557 (-0.059573) | 0.170644 / 0.737135 (-0.566491) | 0.109011 / 0.296338 (-0.187328) |\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) | 0.603045 / 0.215209 (0.387836) | 6.125308 / 2.077655 (4.047653) | 2.637127 / 1.504120 (1.133007) | 2.468636 / 1.541195 (0.927441) | 2.383773 / 1.468490 (0.915283) | 0.838139 / 4.584777 (-3.746638) | 5.355777 / 3.745712 (1.610065) | 4.753015 / 5.269862 (-0.516846) | 3.097486 / 4.565676 (-1.468191) | 0.094749 / 0.424275 (-0.329526) | 0.009040 / 0.007607 (0.001433) | 0.699987 / 0.226044 (0.473942) | 7.111671 / 2.268929 (4.842742) | 3.297798 / 55.444624 (-52.146827) | 2.614578 / 6.876477 (-4.261898) | 2.927717 / 2.142072 (0.785645) | 1.037292 / 4.805227 (-3.767935) | 0.218025 / 6.500664 (-6.282639) | 0.086306 / 0.075469 (0.010836) |\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) | 1.645146 / 1.841788 (-0.196642) | 24.191875 / 8.074308 (16.117567) | 21.844371 / 10.191392 (11.652979) | 0.245369 / 0.680424 (-0.435055) | 0.031776 / 0.534201 (-0.502425) | 0.465634 / 0.579283 (-0.113649) | 0.565498 / 0.434364 (0.131134) | 0.497409 / 0.540337 (-0.042929) | 0.748048 / 1.386936 (-0.638889) |\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) | 0.009239 / 0.011353 (-0.002114) | 0.005345 / 0.011008 (-0.005663) | 0.072732 / 0.038508 (0.034224) | 0.099880 / 0.023109 (0.076770) | 0.466933 / 0.275898 (0.191035) | 0.471730 / 0.323480 (0.148250) | 0.006164 / 0.007986 (-0.001821) | 0.004486 / 0.004328 (0.000158) | 0.075475 / 0.004250 (0.071224) | 0.068291 / 0.037052 (0.031238) | 0.465925 / 0.258489 (0.207436) | 0.469198 / 0.293841 (0.175357) | 0.047304 / 0.128546 (-0.081242) | 0.013368 / 0.075646 (-0.062278) | 0.083563 / 0.419271 (-0.335708) | 0.063204 / 0.043533 (0.019671) | 0.457422 / 0.255139 (0.202283) | 0.478793 / 0.283200 (0.195593) | 0.036120 / 0.141683 (-0.105563) | 1.841209 / 1.452155 (0.389054) | 1.955984 / 1.492716 (0.463267) |\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) | 0.369160 / 0.018006 (0.351154) | 0.607140 / 0.000490 (0.606650) | 0.047253 / 0.000200 (0.047054) | 0.000475 / 0.000054 (0.000420) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040226 / 0.037411 (0.002815) | 0.107361 / 0.014526 (0.092835) | 0.122424 / 0.176557 (-0.054133) | 0.186447 / 0.737135 (-0.550688) | 0.127060 / 0.296338 (-0.169279) |\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) | 0.706737 / 0.215209 (0.491528) | 6.791287 / 2.077655 (4.713632) | 3.194471 / 1.504120 (1.690352) | 2.928145 / 1.541195 (1.386950) | 2.829078 / 1.468490 (1.360588) | 0.929797 / 4.584777 (-3.654980) | 5.484638 / 3.745712 (1.738926) | 4.841570 / 5.269862 (-0.428292) | 2.995247 / 4.565676 (-1.570430) | 0.104709 / 0.424275 (-0.319566) | 0.009543 / 0.007607 (0.001936) | 0.817605 / 0.226044 (0.591561) | 7.879234 / 2.268929 (5.610305) | 3.838073 / 55.444624 (-51.606551) | 3.189728 / 6.876477 (-3.686749) | 3.483775 / 2.142072 (1.341703) | 1.092823 / 4.805227 (-3.712404) | 0.227660 / 6.500664 (-6.273004) | 0.082452 / 0.075469 (0.006983) |\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) | 1.750413 / 1.841788 (-0.091374) | 27.078082 / 8.074308 (19.003774) | 23.968038 / 10.191392 (13.776646) | 0.248065 / 0.680424 (-0.432359) | 0.029961 / 0.534201 (-0.504240) | 0.508630 / 0.579283 (-0.070653) | 0.608707 / 0.434364 (0.174343) | 0.611062 / 0.540337 (0.070725) | 0.830797 / 1.386936 (-0.556139) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d793220dd8cbaa099a3928c2132c94c9f7453bc \"CML watermark\")\n" ]
2023-08-23T09:21:11
2023-08-23T09:32:59
2023-08-23T09:21:19
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6,170
feat: Return the name of the currently loaded file
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[ "Your change adds a new element in the key used to avoid duplicates when generating the examples of a dataset. I don't think it fixes the issue you're trying to solve." ]
2023-08-23T07:08:17
2023-08-29T12:41:05
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Added an optional parameter return_file_name in the load_dataset function. When it is set to True, the function will include the name of the file corresponding to the current line as a feature in the returned output. I added this here https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/json/json.py#L92. fixes #5806
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6,169
Configurations in yaml not working
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[ "Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.", "Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?", "Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed).\r\n\r\nI've tried uninstalling/reinstnalling to no avail", "hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?" ]
2023-08-23T00:13:22
2023-08-23T15:35:31
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### Dataset configurations cannot be created in YAML/README Hello! I'm trying to follow the docs here in order to create structure in my dataset as added from here (#5331): https://github.com/huggingface/datasets/blob/8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8/docs/source/repository_structure.mdx#L110-L118 I have the exact example in my config file for [my data repo](https://huggingface.co/datasets/tsor13/test): ``` configs: - config_name: main_data data_files: "main_data.csv" - config_name: additional_data data_files: "additional_data.csv" ``` Yet, I'm unable to load different configurations: ``` from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test', use_auth_token=True) ``` returns a single split, `['tsor13--test']` Does anyone have any insights? @polinaeterna thank you for adding this feature, it is super useful. Do you happen to have any ideas? ### Steps to reproduce the bug from datasets import get_dataset_config_names get_dataset_config_names('tsor13/test') ### Expected behavior I would expect there to be two splits, `main_data` and `additional_data`. However, only `['tsor13--test']` test is returned. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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6,168
Fix ArrayXD YAML conversion
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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.009350 / 0.011353 (-0.002003) | 0.005658 / 0.011008 (-0.005350) | 0.123173 / 0.038508 (0.084664) | 0.096354 / 0.023109 (0.073244) | 0.464398 / 0.275898 (0.188500) | 0.544455 / 0.323480 (0.220975) | 0.007337 / 0.007986 (-0.000648) | 0.004424 / 0.004328 (0.000096) | 0.089715 / 0.004250 (0.085465) | 0.072462 / 0.037052 (0.035410) | 0.460601 / 0.258489 (0.202112) | 0.544384 / 0.293841 (0.250543) | 0.052994 / 0.128546 (-0.075552) | 0.014459 / 0.075646 (-0.061187) | 0.464368 / 0.419271 (0.045096) | 0.072889 / 0.043533 (0.029356) | 0.471387 / 0.255139 (0.216248) | 0.560982 / 0.283200 (0.277783) | 0.041398 / 0.141683 (-0.100285) | 1.964688 / 1.452155 (0.512533) | 2.240727 / 1.492716 (0.748011) |\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) | 0.308524 / 0.018006 (0.290518) | 0.669306 / 0.000490 (0.668816) | 0.006644 / 0.000200 (0.006444) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037395 / 0.037411 (-0.000016) | 0.111303 / 0.014526 (0.096777) | 0.158988 / 0.176557 (-0.017569) | 0.236155 / 0.737135 (-0.500980) | 0.134775 / 0.296338 (-0.161564) |\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) | 0.648830 / 0.215209 (0.433621) | 6.614794 / 2.077655 (4.537139) | 2.867526 / 1.504120 (1.363407) | 2.472967 / 1.541195 (0.931772) | 2.488419 / 1.468490 (1.019929) | 0.915785 / 4.584777 (-3.668992) | 6.010754 / 3.745712 (2.265042) | 5.468873 / 5.269862 (0.199011) | 3.446535 / 4.565676 (-1.119141) | 0.118592 / 0.424275 (-0.305684) | 0.012005 / 0.007607 (0.004398) | 0.808467 / 0.226044 (0.582423) | 8.152122 / 2.268929 (5.883193) | 3.751282 / 55.444624 (-51.693342) | 3.009569 / 6.876477 (-3.866908) | 3.282613 / 2.142072 (1.140540) | 1.152727 / 4.805227 (-3.652500) | 0.240224 / 6.500664 (-6.260440) | 0.097871 / 0.075469 (0.022402) |\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) | 1.824944 / 1.841788 (-0.016843) | 27.840842 / 8.074308 (19.766533) | 24.368669 / 10.191392 (14.177277) | 0.260621 / 0.680424 (-0.419803) | 0.033730 / 0.534201 (-0.500471) | 0.552494 / 0.579283 (-0.026789) | 0.666921 / 0.434364 (0.232557) | 0.648812 / 0.540337 (0.108475) | 0.912602 / 1.386936 (-0.474334) |\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) | 0.011688 / 0.011353 (0.000335) | 0.005794 / 0.011008 (-0.005215) | 0.093466 / 0.038508 (0.054958) | 0.102583 / 0.023109 (0.079474) | 0.593572 / 0.275898 (0.317674) | 0.614351 / 0.323480 (0.290871) | 0.007006 / 0.007986 (-0.000980) | 0.005557 / 0.004328 (0.001229) | 0.087779 / 0.004250 (0.083529) | 0.072639 / 0.037052 (0.035586) | 0.577464 / 0.258489 (0.318975) | 0.628240 / 0.293841 (0.334399) | 0.053876 / 0.128546 (-0.074670) | 0.015383 / 0.075646 (-0.060263) | 0.110633 / 0.419271 (-0.308639) | 0.067467 / 0.043533 (0.023934) | 0.613457 / 0.255139 (0.358318) | 0.604939 / 0.283200 (0.321739) | 0.041738 / 0.141683 (-0.099945) | 1.967167 / 1.452155 (0.515012) | 2.121009 / 1.492716 (0.628293) |\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) | 0.449937 / 0.018006 (0.431930) | 0.694410 / 0.000490 (0.693921) | 0.064051 / 0.000200 (0.063851) | 0.000810 / 0.000054 (0.000756) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.045138 / 0.037411 (0.007727) | 0.116831 / 0.014526 (0.102306) | 0.131906 / 0.176557 (-0.044651) | 0.202421 / 0.737135 (-0.534714) | 0.132568 / 0.296338 (-0.163770) |\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) | 0.698046 / 0.215209 (0.482837) | 7.112591 / 2.077655 (5.034936) | 3.332679 / 1.504120 (1.828559) | 2.946384 / 1.541195 (1.405189) | 3.074484 / 1.468490 (1.605994) | 0.970917 / 4.584777 (-3.613859) | 6.143506 / 3.745712 (2.397794) | 5.572496 / 5.269862 (0.302634) | 3.602673 / 4.565676 (-0.963004) | 0.115068 / 0.424275 (-0.309207) | 0.009971 / 0.007607 (0.002364) | 0.891090 / 0.226044 (0.665046) | 8.761788 / 2.268929 (6.492859) | 4.362685 / 55.444624 (-51.081939) | 3.612893 / 6.876477 (-3.263583) | 3.797948 / 2.142072 (1.655876) | 1.202890 / 4.805227 (-3.602337) | 0.238120 / 6.500664 (-6.262544) | 0.095612 / 0.075469 (0.020143) |\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) | 1.958880 / 1.841788 (0.117092) | 28.216454 / 8.074308 (20.142146) | 25.361424 / 10.191392 (15.170032) | 0.308203 / 0.680424 (-0.372221) | 0.032903 / 0.534201 (-0.501298) | 0.539714 / 0.579283 (-0.039569) | 0.688278 / 0.434364 (0.253914) | 0.644818 / 0.540337 (0.104481) | 0.905694 / 1.386936 (-0.481242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5289345e5b23548fee680a0bbc047c0b9a5ee8c \"CML watermark\")\n", "Maybe convert all the tuples by default instead of hardcoding a logic specific to ArrayXD ?", "@mariosasko Have you been able to fix this issue ? we're having quite a rough time updating our dataset lately", "@lhoestq Does it look good now?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005519 / 0.011353 (-0.005834) | 0.003482 / 0.011008 (-0.007527) | 0.064904 / 0.038508 (0.026396) | 0.052399 / 0.023109 (0.029289) | 0.247238 / 0.275898 (-0.028660) | 0.273426 / 0.323480 (-0.050054) | 0.003102 / 0.007986 (-0.004884) | 0.003420 / 0.004328 (-0.000908) | 0.048029 / 0.004250 (0.043779) | 0.039378 / 0.037052 (0.002326) | 0.253809 / 0.258489 (-0.004680) | 0.287483 / 0.293841 (-0.006358) | 0.028096 / 0.128546 (-0.100450) | 0.010806 / 0.075646 (-0.064841) | 0.207799 / 0.419271 (-0.211472) | 0.035861 / 0.043533 (-0.007672) | 0.251912 / 0.255139 (-0.003227) | 0.278877 / 0.283200 (-0.004323) | 0.019498 / 0.141683 (-0.122185) | 1.104916 / 1.452155 (-0.347238) | 1.157376 / 1.492716 (-0.335340) |\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) | 0.093051 / 0.018006 (0.075045) | 0.303331 / 0.000490 (0.302841) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018052 / 0.037411 (-0.019359) | 0.060597 / 0.014526 (0.046071) | 0.074033 / 0.176557 (-0.102524) | 0.120966 / 0.737135 (-0.616169) | 0.075012 / 0.296338 (-0.221326) |\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) | 0.283922 / 0.215209 (0.068713) | 2.805445 / 2.077655 (0.727791) | 1.498292 / 1.504120 (-0.005828) | 1.371686 / 1.541195 (-0.169509) | 1.402074 / 1.468490 (-0.066416) | 0.567231 / 4.584777 (-4.017546) | 2.393291 / 3.745712 (-1.352422) | 2.800329 / 5.269862 (-2.469533) | 1.789197 / 4.565676 (-2.776479) | 0.063620 / 0.424275 (-0.360655) | 0.005008 / 0.007607 (-0.002600) | 0.338929 / 0.226044 (0.112884) | 3.292122 / 2.268929 (1.023193) | 1.813313 / 55.444624 (-53.631311) | 1.557122 / 6.876477 (-5.319354) | 1.576395 / 2.142072 (-0.565677) | 0.666714 / 4.805227 (-4.138513) | 0.118253 / 6.500664 (-6.382411) | 0.042633 / 0.075469 (-0.032836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950678 / 1.841788 (-0.891110) | 11.589806 / 8.074308 (3.515498) | 10.436701 / 10.191392 (0.245309) | 0.141048 / 0.680424 (-0.539376) | 0.014766 / 0.534201 (-0.519435) | 0.298359 / 0.579283 (-0.280924) | 0.268850 / 0.434364 (-0.165514) | 0.340242 / 0.540337 (-0.200095) | 0.451447 / 1.386936 (-0.935489) |\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) | 0.005405 / 0.011353 (-0.005948) | 0.003545 / 0.011008 (-0.007464) | 0.048959 / 0.038508 (0.010451) | 0.056565 / 0.023109 (0.033455) | 0.274289 / 0.275898 (-0.001609) | 0.296565 / 0.323480 (-0.026915) | 0.004790 / 0.007986 (-0.003196) | 0.002772 / 0.004328 (-0.001557) | 0.048605 / 0.004250 (0.044354) | 0.040676 / 0.037052 (0.003624) | 0.279949 / 0.258489 (0.021460) | 0.312816 / 0.293841 (0.018976) | 0.029605 / 0.128546 (-0.098941) | 0.010799 / 0.075646 (-0.064848) | 0.056941 / 0.419271 (-0.362331) | 0.034518 / 0.043533 (-0.009014) | 0.277193 / 0.255139 (0.022054) | 0.292334 / 0.283200 (0.009134) | 0.018836 / 0.141683 (-0.122847) | 1.145228 / 1.452155 (-0.306927) | 1.198958 / 1.492716 (-0.293758) |\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) | 0.093618 / 0.018006 (0.075612) | 0.303687 / 0.000490 (0.303197) | 0.000234 / 0.000200 (0.000034) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021347 / 0.037411 (-0.016065) | 0.067811 / 0.014526 (0.053286) | 0.080631 / 0.176557 (-0.095926) | 0.119289 / 0.737135 (-0.617846) | 0.082085 / 0.296338 (-0.214254) |\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) | 0.293374 / 0.215209 (0.078165) | 2.864516 / 2.077655 (0.786861) | 1.611042 / 1.504120 (0.106922) | 1.466124 / 1.541195 (-0.075071) | 1.480509 / 1.468490 (0.012019) | 0.569463 / 4.584777 (-4.015314) | 2.448181 / 3.745712 (-1.297531) | 2.841732 / 5.269862 (-2.428130) | 1.754458 / 4.565676 (-2.811219) | 0.063771 / 0.424275 (-0.360505) | 0.004976 / 0.007607 (-0.002631) | 0.346094 / 0.226044 (0.120050) | 3.440090 / 2.268929 (1.171162) | 1.961862 / 55.444624 (-53.482763) | 1.675780 / 6.876477 (-5.200697) | 1.679676 / 2.142072 (-0.462396) | 0.641063 / 4.805227 (-4.164164) | 0.116268 / 6.500664 (-6.384396) | 0.041767 / 0.075469 (-0.033702) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980286 / 1.841788 (-0.861502) | 12.055227 / 8.074308 (3.980919) | 10.685417 / 10.191392 (0.494025) | 0.140842 / 0.680424 (-0.539582) | 0.015413 / 0.534201 (-0.518788) | 0.286939 / 0.579283 (-0.292344) | 0.278796 / 0.434364 (-0.155568) | 0.326740 / 0.540337 (-0.213597) | 0.574516 / 1.386936 (-0.812421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53b8c2d9d3714f4a56d74637c0840a90cbfb7aa0 \"CML watermark\")\n" ]
2023-08-22T17:02:54
2023-12-12T15:06:59
2023-12-12T15:00:43
CONTRIBUTOR
false
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Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion. Fix #6112
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6,167
Allow hyphen in split name
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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) | 0.007342 / 0.011353 (-0.004011) | 0.004586 / 0.011008 (-0.006422) | 0.100430 / 0.038508 (0.061922) | 0.081053 / 0.023109 (0.057944) | 0.368130 / 0.275898 (0.092232) | 0.402852 / 0.323480 (0.079372) | 0.004504 / 0.007986 (-0.003482) | 0.003824 / 0.004328 (-0.000505) | 0.075326 / 0.004250 (0.071076) | 0.063329 / 0.037052 (0.026277) | 0.372837 / 0.258489 (0.114348) | 0.437857 / 0.293841 (0.144017) | 0.035512 / 0.128546 (-0.093034) | 0.009756 / 0.075646 (-0.065890) | 0.341035 / 0.419271 (-0.078236) | 0.060503 / 0.043533 (0.016970) | 0.362555 / 0.255139 (0.107416) | 0.409216 / 0.283200 (0.126017) | 0.030093 / 0.141683 (-0.111590) | 1.751550 / 1.452155 (0.299395) | 1.848676 / 1.492716 (0.355959) |\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) | 0.229448 / 0.018006 (0.211442) | 0.500300 / 0.000490 (0.499811) | 0.005195 / 0.000200 (0.004995) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031753 / 0.037411 (-0.005658) | 0.096075 / 0.014526 (0.081549) | 0.111476 / 0.176557 (-0.065081) | 0.179236 / 0.737135 (-0.557899) | 0.113599 / 0.296338 (-0.182739) |\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) | 0.472817 / 0.215209 (0.257608) | 4.715029 / 2.077655 (2.637374) | 2.417934 / 1.504120 (0.913814) | 2.235014 / 1.541195 (0.693819) | 2.323588 / 1.468490 (0.855098) | 0.553751 / 4.584777 (-4.031026) | 4.153467 / 3.745712 (0.407755) | 3.858836 / 5.269862 (-1.411025) | 2.377499 / 4.565676 (-2.188178) | 0.066528 / 0.424275 (-0.357747) | 0.008979 / 0.007607 (0.001372) | 0.561076 / 0.226044 (0.335032) | 5.609817 / 2.268929 (3.340888) | 3.011098 / 55.444624 (-52.433526) | 2.594162 / 6.876477 (-4.282314) | 2.863597 / 2.142072 (0.721525) | 0.681135 / 4.805227 (-4.124092) | 0.158863 / 6.500664 (-6.341801) | 0.072551 / 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) | 1.492230 / 1.841788 (-0.349558) | 23.028828 / 8.074308 (14.954519) | 16.663265 / 10.191392 (6.471873) | 0.173146 / 0.680424 (-0.507278) | 0.021635 / 0.534201 (-0.512566) | 0.478919 / 0.579283 (-0.100364) | 0.472908 / 0.434364 (0.038544) | 0.547248 / 0.540337 (0.006910) | 0.770288 / 1.386936 (-0.616648) |\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) | 0.007728 / 0.011353 (-0.003625) | 0.004477 / 0.011008 (-0.006531) | 0.074858 / 0.038508 (0.036350) | 0.084266 / 0.023109 (0.061157) | 0.420280 / 0.275898 (0.144382) | 0.466835 / 0.323480 (0.143356) | 0.005980 / 0.007986 (-0.002006) | 0.003600 / 0.004328 (-0.000729) | 0.074941 / 0.004250 (0.070691) | 0.066414 / 0.037052 (0.029361) | 0.425949 / 0.258489 (0.167460) | 0.473236 / 0.293841 (0.179395) | 0.037213 / 0.128546 (-0.091333) | 0.009743 / 0.075646 (-0.065903) | 0.083758 / 0.419271 (-0.335513) | 0.057916 / 0.043533 (0.014383) | 0.423031 / 0.255139 (0.167892) | 0.451107 / 0.283200 (0.167907) | 0.028577 / 0.141683 (-0.113106) | 1.810509 / 1.452155 (0.358354) | 1.875579 / 1.492716 (0.382863) |\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) | 0.296052 / 0.018006 (0.278046) | 0.496618 / 0.000490 (0.496128) | 0.028667 / 0.000200 (0.028467) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036694 / 0.037411 (-0.000717) | 0.110873 / 0.014526 (0.096347) | 0.126550 / 0.176557 (-0.050007) | 0.182924 / 0.737135 (-0.554212) | 0.123793 / 0.296338 (-0.172545) |\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) | 0.509881 / 0.215209 (0.294672) | 5.067402 / 2.077655 (2.989747) | 2.696028 / 1.504120 (1.191908) | 2.489861 / 1.541195 (0.948666) | 2.563400 / 1.468490 (1.094910) | 0.571184 / 4.584777 (-4.013593) | 4.154231 / 3.745712 (0.408519) | 3.891004 / 5.269862 (-1.378858) | 2.435290 / 4.565676 (-2.130387) | 0.065825 / 0.424275 (-0.358450) | 0.008460 / 0.007607 (0.000853) | 0.597579 / 0.226044 (0.371534) | 5.914954 / 2.268929 (3.646025) | 3.219305 / 55.444624 (-52.225319) | 2.843548 / 6.876477 (-4.032929) | 3.070300 / 2.142072 (0.928228) | 0.686018 / 4.805227 (-4.119209) | 0.160077 / 6.500664 (-6.340587) | 0.074058 / 0.075469 (-0.001411) |\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) | 1.598748 / 1.841788 (-0.243039) | 23.475685 / 8.074308 (15.401377) | 17.257831 / 10.191392 (7.066439) | 0.176539 / 0.680424 (-0.503885) | 0.021969 / 0.534201 (-0.512232) | 0.473565 / 0.579283 (-0.105718) | 0.465471 / 0.434364 (0.031107) | 0.567107 / 0.540337 (0.026769) | 0.783757 / 1.386936 (-0.603179) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2f6bb450b4a3065a7d5fc50ea67711082749a337 \"CML watermark\")\n", "Note that the https://github.com/huggingface/datasets-server/ explicitly relies on the fact that a split cannot contain a hyphen. cc @lhoestq ", "We can't enable this that easily unfortunately because it could make arrow file names ambiguous in the cache.\r\n\r\ne.g. dataset_name-train-0000-of-0008.arrow", "Oh, this would indeed make the caching for the multi-proc case ambiguous. Implementing this is only worth it if we get more requests, so I'm closing this PR for now." ]
2023-08-22T13:30:59
2024-01-11T06:31:31
2023-08-22T15:38:53
CONTRIBUTOR
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To fix https://discuss.huggingface.co/t/error-when-setting-up-the-dataset-viewer-streamingrowserror/51276.
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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) | 0.009036 / 0.011353 (-0.002317) | 0.004564 / 0.011008 (-0.006444) | 0.114958 / 0.038508 (0.076449) | 0.087329 / 0.023109 (0.064220) | 0.440111 / 0.275898 (0.164213) | 0.486056 / 0.323480 (0.162576) | 0.006580 / 0.007986 (-0.001406) | 0.004257 / 0.004328 (-0.000072) | 0.093458 / 0.004250 (0.089208) | 0.063380 / 0.037052 (0.026328) | 0.469455 / 0.258489 (0.210966) | 0.521630 / 0.293841 (0.227790) | 0.053496 / 0.128546 (-0.075050) | 0.013466 / 0.075646 (-0.062181) | 0.361629 / 0.419271 (-0.057642) | 0.068095 / 0.043533 (0.024562) | 0.472440 / 0.255139 (0.217301) | 0.508682 / 0.283200 (0.225483) | 0.034648 / 0.141683 (-0.107035) | 1.820117 / 1.452155 (0.367962) | 1.933448 / 1.492716 (0.440732) |\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) | 0.276543 / 0.018006 (0.258537) | 0.563380 / 0.000490 (0.562890) | 0.005345 / 0.000200 (0.005146) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029230 / 0.037411 (-0.008181) | 0.095613 / 0.014526 (0.081087) | 0.106178 / 0.176557 (-0.070378) | 0.181095 / 0.737135 (-0.556040) | 0.107789 / 0.296338 (-0.188550) |\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) | 0.612051 / 0.215209 (0.396842) | 6.065008 / 2.077655 (3.987353) | 2.720911 / 1.504120 (1.216791) | 2.495218 / 1.541195 (0.954023) | 2.423351 / 1.468490 (0.954860) | 0.835571 / 4.584777 (-3.749205) | 5.438230 / 3.745712 (1.692518) | 4.550301 / 5.269862 (-0.719561) | 2.919889 / 4.565676 (-1.645788) | 0.097748 / 0.424275 (-0.326527) | 0.009285 / 0.007607 (0.001678) | 0.741968 / 0.226044 (0.515923) | 7.285394 / 2.268929 (5.016466) | 3.433634 / 55.444624 (-52.010991) | 2.680823 / 6.876477 (-4.195654) | 2.931149 / 2.142072 (0.789076) | 1.012852 / 4.805227 (-3.792375) | 0.224899 / 6.500664 (-6.275765) | 0.089411 / 0.075469 (0.013942) |\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) | 1.622759 / 1.841788 (-0.219029) | 23.690030 / 8.074308 (15.615721) | 21.034451 / 10.191392 (10.843059) | 0.241504 / 0.680424 (-0.438920) | 0.030109 / 0.534201 (-0.504092) | 0.472536 / 0.579283 (-0.106747) | 0.631396 / 0.434364 (0.197032) | 0.598997 / 0.540337 (0.058659) | 0.798680 / 1.386936 (-0.588256) |\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) | 0.008696 / 0.011353 (-0.002657) | 0.005032 / 0.011008 (-0.005977) | 0.087369 / 0.038508 (0.048861) | 0.078105 / 0.023109 (0.054996) | 0.464861 / 0.275898 (0.188963) | 0.509620 / 0.323480 (0.186140) | 0.006399 / 0.007986 (-0.001587) | 0.004276 / 0.004328 (-0.000052) | 0.081643 / 0.004250 (0.077393) | 0.062560 / 0.037052 (0.025508) | 0.495377 / 0.258489 (0.236888) | 0.484885 / 0.293841 (0.191044) | 0.054354 / 0.128546 (-0.074193) | 0.013851 / 0.075646 (-0.061795) | 0.089531 / 0.419271 (-0.329740) | 0.068732 / 0.043533 (0.025199) | 0.455842 / 0.255139 (0.200703) | 0.528775 / 0.283200 (0.245575) | 0.039646 / 0.141683 (-0.102037) | 1.733600 / 1.452155 (0.281445) | 1.879074 / 1.492716 (0.386358) |\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) | 0.369616 / 0.018006 (0.351610) | 0.607426 / 0.000490 (0.606936) | 0.055540 / 0.000200 (0.055341) | 0.000543 / 0.000054 (0.000488) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036026 / 0.037411 (-0.001385) | 0.103968 / 0.014526 (0.089442) | 0.114852 / 0.176557 (-0.061705) | 0.187313 / 0.737135 (-0.549822) | 0.116839 / 0.296338 (-0.179500) |\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) | 0.614018 / 0.215209 (0.398809) | 6.139914 / 2.077655 (4.062259) | 2.826246 / 1.504120 (1.322126) | 2.524133 / 1.541195 (0.982938) | 2.606981 / 1.468490 (1.138491) | 0.844604 / 4.584777 (-3.740173) | 5.537178 / 3.745712 (1.791465) | 4.594624 / 5.269862 (-0.675237) | 3.032145 / 4.565676 (-1.533532) | 0.094771 / 0.424275 (-0.329504) | 0.008132 / 0.007607 (0.000525) | 0.714287 / 0.226044 (0.488242) | 7.296733 / 2.268929 (5.027804) | 3.698066 / 55.444624 (-51.746558) | 2.862781 / 6.876477 (-4.013696) | 3.114502 / 2.142072 (0.972429) | 0.986612 / 4.805227 (-3.818616) | 0.214438 / 6.500664 (-6.286226) | 0.076201 / 0.075469 (0.000732) |\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) | 1.747728 / 1.841788 (-0.094060) | 24.159845 / 8.074308 (16.085537) | 23.553485 / 10.191392 (13.362093) | 0.248387 / 0.680424 (-0.432037) | 0.029850 / 0.534201 (-0.504351) | 0.526416 / 0.579283 (-0.052867) | 0.625681 / 0.434364 (0.191317) | 0.619690 / 0.540337 (0.079352) | 0.827485 / 1.386936 (-0.559451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#75639f9064dab9549add79fd5ee7de2a4429992c \"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) | 0.006728 / 0.011353 (-0.004625) | 0.003960 / 0.011008 (-0.007048) | 0.085569 / 0.038508 (0.047061) | 0.077463 / 0.023109 (0.054354) | 0.343112 / 0.275898 (0.067214) | 0.379128 / 0.323480 (0.055648) | 0.004087 / 0.007986 (-0.003899) | 0.003357 / 0.004328 (-0.000972) | 0.065570 / 0.004250 (0.061320) | 0.056259 / 0.037052 (0.019207) | 0.368595 / 0.258489 (0.110106) | 0.402672 / 0.293841 (0.108831) | 0.030946 / 0.128546 (-0.097600) | 0.008509 / 0.075646 (-0.067137) | 0.288552 / 0.419271 (-0.130719) | 0.052134 / 0.043533 (0.008601) | 0.344653 / 0.255139 (0.089514) | 0.374199 / 0.283200 (0.090999) | 0.026251 / 0.141683 (-0.115432) | 1.488258 / 1.452155 (0.036103) | 1.567119 / 1.492716 (0.074402) |\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) | 0.218740 / 0.018006 (0.200734) | 0.465483 / 0.000490 (0.464994) | 0.003959 / 0.000200 (0.003759) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029860 / 0.037411 (-0.007551) | 0.087968 / 0.014526 (0.073442) | 0.098257 / 0.176557 (-0.078299) | 0.155478 / 0.737135 (-0.581657) | 0.100696 / 0.296338 (-0.195642) |\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) | 0.384642 / 0.215209 (0.169432) | 3.821692 / 2.077655 (1.744038) | 1.838012 / 1.504120 (0.333892) | 1.677554 / 1.541195 (0.136360) | 1.764284 / 1.468490 (0.295794) | 0.487512 / 4.584777 (-4.097265) | 3.614572 / 3.745712 (-0.131141) | 3.300740 / 5.269862 (-1.969122) | 2.079044 / 4.565676 (-2.486632) | 0.057392 / 0.424275 (-0.366883) | 0.007642 / 0.007607 (0.000035) | 0.456161 / 0.226044 (0.230117) | 4.554124 / 2.268929 (2.285196) | 2.319288 / 55.444624 (-53.125336) | 1.972024 / 6.876477 (-4.904452) | 2.210598 / 2.142072 (0.068526) | 0.588442 / 4.805227 (-4.216785) | 0.134474 / 6.500664 (-6.366191) | 0.062682 / 0.075469 (-0.012787) |\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) | 1.243548 / 1.841788 (-0.598239) | 20.267230 / 8.074308 (12.192922) | 14.872096 / 10.191392 (4.680704) | 0.165164 / 0.680424 (-0.515260) | 0.018985 / 0.534201 (-0.515216) | 0.394526 / 0.579283 (-0.184757) | 0.413918 / 0.434364 (-0.020446) | 0.467130 / 0.540337 (-0.073208) | 0.627055 / 1.386936 (-0.759881) |\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) | 0.006940 / 0.011353 (-0.004412) | 0.004203 / 0.011008 (-0.006805) | 0.065828 / 0.038508 (0.027320) | 0.076604 / 0.023109 (0.053495) | 0.401781 / 0.275898 (0.125883) | 0.434838 / 0.323480 (0.111358) | 0.005626 / 0.007986 (-0.002359) | 0.003409 / 0.004328 (-0.000920) | 0.064702 / 0.004250 (0.060452) | 0.057525 / 0.037052 (0.020473) | 0.405032 / 0.258489 (0.146543) | 0.440906 / 0.293841 (0.147065) | 0.032713 / 0.128546 (-0.095833) | 0.008723 / 0.075646 (-0.066923) | 0.071448 / 0.419271 (-0.347823) | 0.048186 / 0.043533 (0.004653) | 0.403950 / 0.255139 (0.148811) | 0.419506 / 0.283200 (0.136307) | 0.023532 / 0.141683 (-0.118150) | 1.496435 / 1.452155 (0.044280) | 1.567236 / 1.492716 (0.074519) |\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) | 0.229194 / 0.018006 (0.211188) | 0.451363 / 0.000490 (0.450873) | 0.003651 / 0.000200 (0.003451) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033674 / 0.037411 (-0.003737) | 0.097521 / 0.014526 (0.082995) | 0.108806 / 0.176557 (-0.067751) | 0.161002 / 0.737135 (-0.576133) | 0.108594 / 0.296338 (-0.187745) |\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) | 0.436638 / 0.215209 (0.221429) | 4.348844 / 2.077655 (2.271189) | 2.341737 / 1.504120 (0.837617) | 2.195850 / 1.541195 (0.654656) | 2.332147 / 1.468490 (0.863657) | 0.496180 / 4.584777 (-4.088597) | 3.680987 / 3.745712 (-0.064725) | 3.332203 / 5.269862 (-1.937659) | 2.099541 / 4.565676 (-2.466136) | 0.058629 / 0.424275 (-0.365646) | 0.007363 / 0.007607 (-0.000245) | 0.517658 / 0.226044 (0.291614) | 5.175321 / 2.268929 (2.906392) | 2.858660 / 55.444624 (-52.585964) | 2.540557 / 6.876477 (-4.335920) | 2.755360 / 2.142072 (0.613288) | 0.595488 / 4.805227 (-4.209739) | 0.134265 / 6.500664 (-6.366399) | 0.062033 / 0.075469 (-0.013436) |\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) | 1.389950 / 1.841788 (-0.451838) | 20.800274 / 8.074308 (12.725966) | 15.314531 / 10.191392 (5.123139) | 0.166822 / 0.680424 (-0.513602) | 0.021099 / 0.534201 (-0.513102) | 0.400388 / 0.579283 (-0.178895) | 0.419981 / 0.434364 (-0.014383) | 0.474259 / 0.540337 (-0.066078) | 0.731678 / 1.386936 (-0.655258) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4566827557acbeba0d4cb66449bb70367e341b05 \"CML watermark\")\n" ]
2023-08-22T11:27:41
2023-08-23T14:01:25
2023-08-23T13:52:36
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Related to https://github.com/huggingface/datasets/issues/6130
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6,165
Fix multiprocessing with spawn in iterable datasets
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[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq \r\nA test is failing, but I don't think it is due to my changes", "Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements", "@lhoestq \r\nI added the test and fixed one last method", "<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) | 0.006537 / 0.011353 (-0.004816) | 0.003960 / 0.011008 (-0.007048) | 0.085135 / 0.038508 (0.046627) | 0.079271 / 0.023109 (0.056162) | 0.383743 / 0.275898 (0.107845) | 0.414622 / 0.323480 (0.091143) | 0.004202 / 0.007986 (-0.003784) | 0.003537 / 0.004328 (-0.000791) | 0.065758 / 0.004250 (0.061508) | 0.054225 / 0.037052 (0.017173) | 0.395715 / 0.258489 (0.137226) | 0.438985 / 0.293841 (0.145144) | 0.030590 / 0.128546 (-0.097956) | 0.008754 / 0.075646 (-0.066892) | 0.288415 / 0.419271 (-0.130857) | 0.051863 / 0.043533 (0.008330) | 0.382501 / 0.255139 (0.127363) | 0.414428 / 0.283200 (0.131228) | 0.024084 / 0.141683 (-0.117599) | 1.478726 / 1.452155 (0.026572) | 1.544763 / 1.492716 (0.052047) |\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) | 0.285143 / 0.018006 (0.267136) | 0.603859 / 0.000490 (0.603369) | 0.004330 / 0.000200 (0.004131) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027856 / 0.037411 (-0.009555) | 0.081963 / 0.014526 (0.067437) | 0.104106 / 0.176557 (-0.072451) | 0.151378 / 0.737135 (-0.585757) | 0.096476 / 0.296338 (-0.199862) |\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) | 0.402938 / 0.215209 (0.187729) | 4.042312 / 2.077655 (1.964657) | 2.068421 / 1.504120 (0.564301) | 1.877870 / 1.541195 (0.336675) | 1.947643 / 1.468490 (0.479153) | 0.482031 / 4.584777 (-4.102746) | 3.554747 / 3.745712 (-0.190965) | 3.307811 / 5.269862 (-1.962050) | 2.082886 / 4.565676 (-2.482791) | 0.056853 / 0.424275 (-0.367422) | 0.007535 / 0.007607 (-0.000072) | 0.483694 / 0.226044 (0.257649) | 4.827906 / 2.268929 (2.558978) | 2.567572 / 55.444624 (-52.877052) | 2.167206 / 6.876477 (-4.709271) | 2.414442 / 2.142072 (0.272369) | 0.579472 / 4.805227 (-4.225755) | 0.132976 / 6.500664 (-6.367688) | 0.059315 / 0.075469 (-0.016154) |\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) | 1.260086 / 1.841788 (-0.581702) | 19.438297 / 8.074308 (11.363989) | 14.188161 / 10.191392 (3.996769) | 0.168534 / 0.680424 (-0.511890) | 0.018070 / 0.534201 (-0.516131) | 0.394241 / 0.579283 (-0.185043) | 0.411057 / 0.434364 (-0.023307) | 0.461123 / 0.540337 (-0.079215) | 0.626844 / 1.386936 (-0.760092) |\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) | 0.006896 / 0.011353 (-0.004457) | 0.004207 / 0.011008 (-0.006801) | 0.064981 / 0.038508 (0.026473) | 0.080261 / 0.023109 (0.057152) | 0.399403 / 0.275898 (0.123505) | 0.433099 / 0.323480 (0.109619) | 0.005697 / 0.007986 (-0.002288) | 0.003601 / 0.004328 (-0.000728) | 0.065924 / 0.004250 (0.061673) | 0.058868 / 0.037052 (0.021815) | 0.403705 / 0.258489 (0.145216) | 0.439218 / 0.293841 (0.145377) | 0.032789 / 0.128546 (-0.095757) | 0.008675 / 0.075646 (-0.066971) | 0.071217 / 0.419271 (-0.348055) | 0.048487 / 0.043533 (0.004954) | 0.399878 / 0.255139 (0.144739) | 0.412816 / 0.283200 (0.129616) | 0.023905 / 0.141683 (-0.117778) | 1.541402 / 1.452155 (0.089247) | 1.588080 / 1.492716 (0.095364) |\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) | 0.322863 / 0.018006 (0.304856) | 0.530291 / 0.000490 (0.529802) | 0.004862 / 0.000200 (0.004662) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032697 / 0.037411 (-0.004715) | 0.092416 / 0.014526 (0.077891) | 0.107355 / 0.176557 (-0.069201) | 0.160217 / 0.737135 (-0.576918) | 0.109286 / 0.296338 (-0.187052) |\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) | 0.437375 / 0.215209 (0.222166) | 4.362644 / 2.077655 (2.284990) | 2.335404 / 1.504120 (0.831284) | 2.173215 / 1.541195 (0.632020) | 2.254061 / 1.468490 (0.785571) | 0.493906 / 4.584777 (-4.090871) | 3.609025 / 3.745712 (-0.136687) | 3.352380 / 5.269862 (-1.917481) | 2.074185 / 4.565676 (-2.491492) | 0.057863 / 0.424275 (-0.366412) | 0.007297 / 0.007607 (-0.000310) | 0.512464 / 0.226044 (0.286420) | 5.135921 / 2.268929 (2.866993) | 2.788889 / 55.444624 (-52.655736) | 2.479097 / 6.876477 (-4.397379) | 2.717848 / 2.142072 (0.575776) | 0.590442 / 4.805227 (-4.214785) | 0.133721 / 6.500664 (-6.366943) | 0.061491 / 0.075469 (-0.013978) |\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) | 1.429564 / 1.841788 (-0.412224) | 20.628733 / 8.074308 (12.554425) | 15.299571 / 10.191392 (5.108179) | 0.171032 / 0.680424 (-0.509392) | 0.019995 / 0.534201 (-0.514206) | 0.401283 / 0.579283 (-0.178000) | 0.416504 / 0.434364 (-0.017860) | 0.471219 / 0.540337 (-0.069118) | 0.641299 / 1.386936 (-0.745637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5503e7beb5a31926aec03c6c9b24813f9f83cd7b \"CML watermark\")\n" ]
2023-08-22T10:07:23
2023-08-29T13:27:14
2023-08-29T13:18:11
CONTRIBUTOR
false
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The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems. This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers. I fixed the issue by replacing lambda and local methods which are not pickle-able. See the example below: ```python from datasets import load_dataset from torch.utils.data import DataLoader if __name__ == "__main__": dataset = load_dataset("lhoestq/demo1", split="train") dataset = dataset.to_iterable_dataset(num_shards=3) dataset = dataset.remove_columns(["package_name"]) dataset = dataset.rename_columns({ "review": "review1" }) dataset = dataset.rename_column("date", "date1") for sample in DataLoader(dataset, batch_size=None, num_workers=3): print(sample) ``` To notice the fix on a linux system, adding these lines should do the trick: ```python import multiprocessing multiprocessing.set_start_method('spawn') ``` I also removed what looks like code duplication between rename_colums and rename_column
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6,164
Fix: Missing a MetadataConfigs init when the repo has a `datasets_info.json` but no README
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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) | 0.006874 / 0.011353 (-0.004479) | 0.004276 / 0.011008 (-0.006732) | 0.085198 / 0.038508 (0.046690) | 0.084281 / 0.023109 (0.061171) | 0.344767 / 0.275898 (0.068869) | 0.377798 / 0.323480 (0.054318) | 0.005656 / 0.007986 (-0.002330) | 0.003601 / 0.004328 (-0.000727) | 0.065486 / 0.004250 (0.061235) | 0.056191 / 0.037052 (0.019139) | 0.351412 / 0.258489 (0.092923) | 0.398591 / 0.293841 (0.104750) | 0.031662 / 0.128546 (-0.096884) | 0.008901 / 0.075646 (-0.066745) | 0.290423 / 0.419271 (-0.128849) | 0.053793 / 0.043533 (0.010260) | 0.347968 / 0.255139 (0.092829) | 0.376978 / 0.283200 (0.093778) | 0.026745 / 0.141683 (-0.114938) | 1.514119 / 1.452155 (0.061964) | 1.580920 / 1.492716 (0.088203) |\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) | 0.273648 / 0.018006 (0.255642) | 0.575176 / 0.000490 (0.574686) | 0.003557 / 0.000200 (0.003357) | 0.000093 / 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) | 0.031714 / 0.037411 (-0.005697) | 0.089166 / 0.014526 (0.074640) | 0.101525 / 0.176557 (-0.075032) | 0.161855 / 0.737135 (-0.575281) | 0.101391 / 0.296338 (-0.194947) |\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) | 0.380947 / 0.215209 (0.165738) | 3.800527 / 2.077655 (1.722873) | 1.820789 / 1.504120 (0.316669) | 1.657327 / 1.541195 (0.116132) | 1.776242 / 1.468490 (0.307752) | 0.486954 / 4.584777 (-4.097823) | 3.688340 / 3.745712 (-0.057372) | 3.354453 / 5.269862 (-1.915409) | 2.119995 / 4.565676 (-2.445682) | 0.057446 / 0.424275 (-0.366829) | 0.007752 / 0.007607 (0.000145) | 0.461907 / 0.226044 (0.235862) | 4.617870 / 2.268929 (2.348942) | 2.337025 / 55.444624 (-53.107599) | 1.964770 / 6.876477 (-4.911707) | 2.252066 / 2.142072 (0.109993) | 0.591585 / 4.805227 (-4.213642) | 0.134655 / 6.500664 (-6.366009) | 0.060646 / 0.075469 (-0.014823) |\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) | 1.263271 / 1.841788 (-0.578517) | 20.822286 / 8.074308 (12.747978) | 14.710256 / 10.191392 (4.518864) | 0.167285 / 0.680424 (-0.513139) | 0.018302 / 0.534201 (-0.515899) | 0.401023 / 0.579283 (-0.178260) | 0.428956 / 0.434364 (-0.005407) | 0.466120 / 0.540337 (-0.074218) | 0.637868 / 1.386936 (-0.749069) |\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) | 0.007174 / 0.011353 (-0.004179) | 0.004418 / 0.011008 (-0.006590) | 0.065731 / 0.038508 (0.027223) | 0.090457 / 0.023109 (0.067348) | 0.387306 / 0.275898 (0.111408) | 0.427178 / 0.323480 (0.103698) | 0.005699 / 0.007986 (-0.002286) | 0.003662 / 0.004328 (-0.000666) | 0.066190 / 0.004250 (0.061940) | 0.062860 / 0.037052 (0.025808) | 0.388855 / 0.258489 (0.130366) | 0.427853 / 0.293841 (0.134012) | 0.032770 / 0.128546 (-0.095776) | 0.008780 / 0.075646 (-0.066866) | 0.071156 / 0.419271 (-0.348116) | 0.050174 / 0.043533 (0.006641) | 0.385254 / 0.255139 (0.130115) | 0.405069 / 0.283200 (0.121869) | 0.025561 / 0.141683 (-0.116122) | 1.506907 / 1.452155 (0.054752) | 1.543270 / 1.492716 (0.050554) |\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) | 0.304651 / 0.018006 (0.286645) | 0.577269 / 0.000490 (0.576780) | 0.004479 / 0.000200 (0.004279) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034070 / 0.037411 (-0.003341) | 0.097664 / 0.014526 (0.083138) | 0.106969 / 0.176557 (-0.069588) | 0.163093 / 0.737135 (-0.574043) | 0.109384 / 0.296338 (-0.186955) |\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) | 0.414823 / 0.215209 (0.199614) | 4.148390 / 2.077655 (2.070735) | 2.114038 / 1.504120 (0.609918) | 1.959316 / 1.541195 (0.418121) | 2.098138 / 1.468490 (0.629648) | 0.486338 / 4.584777 (-4.098439) | 3.642850 / 3.745712 (-0.102863) | 3.458311 / 5.269862 (-1.811551) | 2.185662 / 4.565676 (-2.380014) | 0.057555 / 0.424275 (-0.366720) | 0.007522 / 0.007607 (-0.000085) | 0.497975 / 0.226044 (0.271931) | 4.971528 / 2.268929 (2.702600) | 2.614087 / 55.444624 (-52.830537) | 2.288406 / 6.876477 (-4.588070) | 2.564067 / 2.142072 (0.421995) | 0.582248 / 4.805227 (-4.222979) | 0.134931 / 6.500664 (-6.365733) | 0.062689 / 0.075469 (-0.012780) |\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) | 1.343331 / 1.841788 (-0.498457) | 21.398950 / 8.074308 (13.324642) | 14.620971 / 10.191392 (4.429579) | 0.169779 / 0.680424 (-0.510644) | 0.018683 / 0.534201 (-0.515518) | 0.396152 / 0.579283 (-0.183131) | 0.409596 / 0.434364 (-0.024768) | 0.482875 / 0.540337 (-0.057463) | 0.659977 / 1.386936 (-0.726959) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fd2234b8c802d47db5a5aa939148f98c9c49350 \"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) | 0.006662 / 0.011353 (-0.004691) | 0.003959 / 0.011008 (-0.007049) | 0.084447 / 0.038508 (0.045939) | 0.070267 / 0.023109 (0.047158) | 0.310301 / 0.275898 (0.034403) | 0.339866 / 0.323480 (0.016386) | 0.004008 / 0.007986 (-0.003977) | 0.003270 / 0.004328 (-0.001058) | 0.064997 / 0.004250 (0.060746) | 0.053151 / 0.037052 (0.016099) | 0.327867 / 0.258489 (0.069378) | 0.368560 / 0.293841 (0.074719) | 0.031436 / 0.128546 (-0.097111) | 0.008547 / 0.075646 (-0.067099) | 0.288513 / 0.419271 (-0.130758) | 0.051833 / 0.043533 (0.008300) | 0.312660 / 0.255139 (0.057521) | 0.347180 / 0.283200 (0.063980) | 0.024982 / 0.141683 (-0.116701) | 1.472487 / 1.452155 (0.020333) | 1.550138 / 1.492716 (0.057422) |\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) | 0.208443 / 0.018006 (0.190437) | 0.451927 / 0.000490 (0.451437) | 0.004452 / 0.000200 (0.004252) | 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) | 0.029164 / 0.037411 (-0.008247) | 0.085801 / 0.014526 (0.071275) | 0.096229 / 0.176557 (-0.080327) | 0.153063 / 0.737135 (-0.584072) | 0.097712 / 0.296338 (-0.198626) |\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) | 0.383969 / 0.215209 (0.168760) | 3.829216 / 2.077655 (1.751561) | 1.854466 / 1.504120 (0.350346) | 1.684149 / 1.541195 (0.142954) | 1.759422 / 1.468490 (0.290932) | 0.480229 / 4.584777 (-4.104548) | 3.653363 / 3.745712 (-0.092349) | 3.264456 / 5.269862 (-2.005406) | 2.020579 / 4.565676 (-2.545097) | 0.056920 / 0.424275 (-0.367355) | 0.007625 / 0.007607 (0.000018) | 0.458559 / 0.226044 (0.232515) | 4.580288 / 2.268929 (2.311359) | 2.353783 / 55.444624 (-53.090841) | 1.967223 / 6.876477 (-4.909253) | 2.182707 / 2.142072 (0.040634) | 0.631341 / 4.805227 (-4.173886) | 0.141656 / 6.500664 (-6.359008) | 0.059918 / 0.075469 (-0.015551) |\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) | 1.279635 / 1.841788 (-0.562153) | 19.725763 / 8.074308 (11.651455) | 14.477946 / 10.191392 (4.286554) | 0.164360 / 0.680424 (-0.516064) | 0.018286 / 0.534201 (-0.515915) | 0.394935 / 0.579283 (-0.184348) | 0.419638 / 0.434364 (-0.014726) | 0.460366 / 0.540337 (-0.079972) | 0.636876 / 1.386936 (-0.750060) |\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) | 0.006568 / 0.011353 (-0.004785) | 0.004270 / 0.011008 (-0.006738) | 0.065522 / 0.038508 (0.027014) | 0.071597 / 0.023109 (0.048487) | 0.394929 / 0.275898 (0.119031) | 0.427548 / 0.323480 (0.104068) | 0.005320 / 0.007986 (-0.002665) | 0.003366 / 0.004328 (-0.000962) | 0.065780 / 0.004250 (0.061530) | 0.055390 / 0.037052 (0.018338) | 0.397950 / 0.258489 (0.139461) | 0.435800 / 0.293841 (0.141959) | 0.031816 / 0.128546 (-0.096730) | 0.008555 / 0.075646 (-0.067091) | 0.072110 / 0.419271 (-0.347161) | 0.049077 / 0.043533 (0.005544) | 0.390065 / 0.255139 (0.134926) | 0.410294 / 0.283200 (0.127094) | 0.023389 / 0.141683 (-0.118294) | 1.491491 / 1.452155 (0.039336) | 1.551057 / 1.492716 (0.058341) |\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) | 0.243869 / 0.018006 (0.225862) | 0.451961 / 0.000490 (0.451471) | 0.019834 / 0.000200 (0.019634) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031031 / 0.037411 (-0.006380) | 0.088189 / 0.014526 (0.073663) | 0.101743 / 0.176557 (-0.074814) | 0.155236 / 0.737135 (-0.581899) | 0.101245 / 0.296338 (-0.195094) |\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) | 0.422178 / 0.215209 (0.206969) | 4.199989 / 2.077655 (2.122334) | 2.228816 / 1.504120 (0.724696) | 2.057172 / 1.541195 (0.515978) | 2.162651 / 1.468490 (0.694161) | 0.491186 / 4.584777 (-4.093591) | 3.666221 / 3.745712 (-0.079491) | 3.289531 / 5.269862 (-1.980331) | 2.050027 / 4.565676 (-2.515650) | 0.057464 / 0.424275 (-0.366811) | 0.007379 / 0.007607 (-0.000228) | 0.506532 / 0.226044 (0.280487) | 5.066385 / 2.268929 (2.797456) | 2.694405 / 55.444624 (-52.750219) | 2.372200 / 6.876477 (-4.504277) | 2.562724 / 2.142072 (0.420652) | 0.615474 / 4.805227 (-4.189753) | 0.148284 / 6.500664 (-6.352380) | 0.061380 / 0.075469 (-0.014089) |\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) | 1.332649 / 1.841788 (-0.509139) | 20.591063 / 8.074308 (12.516755) | 14.105253 / 10.191392 (3.913861) | 0.151886 / 0.680424 (-0.528537) | 0.018200 / 0.534201 (-0.516001) | 0.395278 / 0.579283 (-0.184005) | 0.407113 / 0.434364 (-0.027251) | 0.473168 / 0.540337 (-0.067170) | 0.660766 / 1.386936 (-0.726170) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8 \"CML watermark\")\n" ]
2023-08-21T14:57:54
2023-08-21T16:27:05
2023-08-21T16:18:26
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When I try to push to an arrow repo (can provide the link on Slack), it uploads the files but fails to update the metadata, with ``` File "app.py", line 123, in add_new_eval eval_results[level].push_to_hub(my_repo, token=TOKEN, split=SPLIT) File "blabla_my_env_path/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5501, in push_to_hub if not metadata_configs: UnboundLocalError: local variable 'metadata_configs' referenced before assignment ``` This fixes it.
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Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32
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[ "Answered on the forum [here](https://discuss.huggingface.co/t/error-type-arrowinvalid-details-failed-to-parse-string-254-254-as-a-scalar-of-type-int32/51323)." ]
2023-08-19T11:34:40
2023-08-21T13:28:16
null
NONE
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### Describe the bug I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas. Can anyone please help me out? FYI : I am using Chrome browser. Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32 ![Screenshot 2023-08-19 165827](https://github.com/huggingface/datasets/assets/90616801/95fad96e-7dce-4bb5-9f83-9f1659a32891) ### Steps to reproduce the bug Kindly let me know how to fix this? ### Expected behavior Kindly let me know how to fix this? ### Environment info Kindly let me know how to fix this?
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load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields
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[ "Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields", "@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ", "> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example", "@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used " ]
2023-08-18T07:19:39
2023-08-18T17:00:35
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NONE
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### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called **symlink_target: string>**. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, **symlink_target: string>** to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the **symlink_target: string>** do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,161
Fix protocol prefix for Beam
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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) | 0.006736 / 0.011353 (-0.004617) | 0.004099 / 0.011008 (-0.006909) | 0.084339 / 0.038508 (0.045831) | 0.073715 / 0.023109 (0.050605) | 0.311962 / 0.275898 (0.036064) | 0.356108 / 0.323480 (0.032628) | 0.005321 / 0.007986 (-0.002665) | 0.003390 / 0.004328 (-0.000939) | 0.064622 / 0.004250 (0.060372) | 0.053978 / 0.037052 (0.016926) | 0.328967 / 0.258489 (0.070478) | 0.370506 / 0.293841 (0.076665) | 0.031123 / 0.128546 (-0.097423) | 0.008465 / 0.075646 (-0.067181) | 0.288136 / 0.419271 (-0.131136) | 0.052909 / 0.043533 (0.009376) | 0.325189 / 0.255139 (0.070050) | 0.360112 / 0.283200 (0.076912) | 0.023389 / 0.141683 (-0.118294) | 1.492899 / 1.452155 (0.040744) | 1.586449 / 1.492716 (0.093733) |\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) | 0.219708 / 0.018006 (0.201702) | 0.469550 / 0.000490 (0.469060) | 0.002776 / 0.000200 (0.002576) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028985 / 0.037411 (-0.008427) | 0.083487 / 0.014526 (0.068961) | 0.096938 / 0.176557 (-0.079619) | 0.152886 / 0.737135 (-0.584249) | 0.096242 / 0.296338 (-0.200096) |\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) | 0.381959 / 0.215209 (0.166750) | 3.800033 / 2.077655 (1.722378) | 1.831903 / 1.504120 (0.327783) | 1.663207 / 1.541195 (0.122012) | 1.747282 / 1.468490 (0.278792) | 0.481671 / 4.584777 (-4.103106) | 3.653725 / 3.745712 (-0.091987) | 3.253058 / 5.269862 (-2.016804) | 2.022014 / 4.565676 (-2.543663) | 0.056651 / 0.424275 (-0.367624) | 0.007640 / 0.007607 (0.000033) | 0.461795 / 0.226044 (0.235750) | 4.625535 / 2.268929 (2.356606) | 2.356341 / 55.444624 (-53.088283) | 1.977437 / 6.876477 (-4.899040) | 2.179672 / 2.142072 (0.037599) | 0.582875 / 4.805227 (-4.222353) | 0.132964 / 6.500664 (-6.367700) | 0.060398 / 0.075469 (-0.015071) |\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) | 1.309567 / 1.841788 (-0.532220) | 19.856306 / 8.074308 (11.781997) | 14.074350 / 10.191392 (3.882958) | 0.149615 / 0.680424 (-0.530809) | 0.018487 / 0.534201 (-0.515714) | 0.393995 / 0.579283 (-0.185288) | 0.409057 / 0.434364 (-0.025307) | 0.459551 / 0.540337 (-0.080787) | 0.644594 / 1.386936 (-0.742342) |\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) | 0.006824 / 0.011353 (-0.004529) | 0.004099 / 0.011008 (-0.006909) | 0.064415 / 0.038508 (0.025907) | 0.077983 / 0.023109 (0.054874) | 0.359351 / 0.275898 (0.083453) | 0.395168 / 0.323480 (0.071688) | 0.005384 / 0.007986 (-0.002602) | 0.003298 / 0.004328 (-0.001030) | 0.065041 / 0.004250 (0.060791) | 0.056717 / 0.037052 (0.019664) | 0.366882 / 0.258489 (0.108393) | 0.401337 / 0.293841 (0.107496) | 0.032273 / 0.128546 (-0.096273) | 0.008666 / 0.075646 (-0.066981) | 0.071442 / 0.419271 (-0.347829) | 0.049999 / 0.043533 (0.006466) | 0.365001 / 0.255139 (0.109862) | 0.379579 / 0.283200 (0.096379) | 0.023357 / 0.141683 (-0.118326) | 1.476839 / 1.452155 (0.024684) | 1.541703 / 1.492716 (0.048987) |\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) | 0.239014 / 0.018006 (0.221008) | 0.460678 / 0.000490 (0.460188) | 0.003368 / 0.000200 (0.003168) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030981 / 0.037411 (-0.006430) | 0.088287 / 0.014526 (0.073761) | 0.102459 / 0.176557 (-0.074098) | 0.154695 / 0.737135 (-0.582441) | 0.103479 / 0.296338 (-0.192860) |\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) | 0.416084 / 0.215209 (0.200874) | 4.128365 / 2.077655 (2.050710) | 2.113053 / 1.504120 (0.608934) | 1.948993 / 1.541195 (0.407798) | 2.035609 / 1.468490 (0.567119) | 0.481705 / 4.584777 (-4.103072) | 3.630366 / 3.745712 (-0.115346) | 3.340837 / 5.269862 (-1.929024) | 2.052573 / 4.565676 (-2.513104) | 0.056805 / 0.424275 (-0.367470) | 0.007294 / 0.007607 (-0.000313) | 0.489597 / 0.226044 (0.263553) | 4.892728 / 2.268929 (2.623799) | 2.564692 / 55.444624 (-52.879932) | 2.251964 / 6.876477 (-4.624513) | 2.457912 / 2.142072 (0.315839) | 0.588433 / 4.805227 (-4.216794) | 0.133588 / 6.500664 (-6.367076) | 0.062298 / 0.075469 (-0.013171) |\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) | 1.328566 / 1.841788 (-0.513222) | 20.145568 / 8.074308 (12.071260) | 14.231306 / 10.191392 (4.039914) | 0.168356 / 0.680424 (-0.512067) | 0.018333 / 0.534201 (-0.515868) | 0.390901 / 0.579283 (-0.188382) | 0.415005 / 0.434364 (-0.019359) | 0.477282 / 0.540337 (-0.063055) | 0.652085 / 1.386936 (-0.734851) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#341a41880a70b29f030caa0d36f1e297535ba5f9 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6161). All of your documentation changes will be reflected on that endpoint.", "<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) | 0.006388 / 0.011353 (-0.004965) | 0.003917 / 0.011008 (-0.007092) | 0.087397 / 0.038508 (0.048889) | 0.068522 / 0.023109 (0.045412) | 0.313299 / 0.275898 (0.037401) | 0.342884 / 0.323480 (0.019405) | 0.005216 / 0.007986 (-0.002770) | 0.003293 / 0.004328 (-0.001035) | 0.067474 / 0.004250 (0.063224) | 0.051122 / 0.037052 (0.014070) | 0.326443 / 0.258489 (0.067954) | 0.355744 / 0.293841 (0.061903) | 0.031130 / 0.128546 (-0.097416) | 0.008617 / 0.075646 (-0.067029) | 0.291201 / 0.419271 (-0.128070) | 0.052050 / 0.043533 (0.008517) | 0.312135 / 0.255139 (0.056996) | 0.347233 / 0.283200 (0.064034) | 0.023775 / 0.141683 (-0.117907) | 1.478807 / 1.452155 (0.026652) | 1.581239 / 1.492716 (0.088522) |\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) | 0.208252 / 0.018006 (0.190246) | 0.466314 / 0.000490 (0.465824) | 0.004439 / 0.000200 (0.004239) | 0.000104 / 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) | 0.027918 / 0.037411 (-0.009494) | 0.082410 / 0.014526 (0.067884) | 0.094231 / 0.176557 (-0.082326) | 0.150189 / 0.737135 (-0.586946) | 0.095404 / 0.296338 (-0.200935) |\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) | 0.382026 / 0.215209 (0.166817) | 3.822213 / 2.077655 (1.744559) | 1.833716 / 1.504120 (0.329596) | 1.666250 / 1.541195 (0.125055) | 1.703350 / 1.468490 (0.234860) | 0.477918 / 4.584777 (-4.106859) | 3.629304 / 3.745712 (-0.116408) | 3.199672 / 5.269862 (-2.070190) | 1.977855 / 4.565676 (-2.587821) | 0.056275 / 0.424275 (-0.368000) | 0.007538 / 0.007607 (-0.000070) | 0.455995 / 0.226044 (0.229950) | 4.559234 / 2.268929 (2.290305) | 2.333819 / 55.444624 (-53.110805) | 2.006851 / 6.876477 (-4.869625) | 2.150683 / 2.142072 (0.008611) | 0.576786 / 4.805227 (-4.228441) | 0.132352 / 6.500664 (-6.368312) | 0.059359 / 0.075469 (-0.016110) |\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) | 1.261525 / 1.841788 (-0.580262) | 19.174957 / 8.074308 (11.100649) | 14.286796 / 10.191392 (4.095404) | 0.144610 / 0.680424 (-0.535813) | 0.018213 / 0.534201 (-0.515988) | 0.390404 / 0.579283 (-0.188879) | 0.404678 / 0.434364 (-0.029686) | 0.455636 / 0.540337 (-0.084701) | 0.620801 / 1.386936 (-0.766135) |\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) | 0.006383 / 0.011353 (-0.004970) | 0.003852 / 0.011008 (-0.007156) | 0.064116 / 0.038508 (0.025607) | 0.068920 / 0.023109 (0.045810) | 0.359439 / 0.275898 (0.083541) | 0.388904 / 0.323480 (0.065425) | 0.005192 / 0.007986 (-0.002794) | 0.003233 / 0.004328 (-0.001095) | 0.064589 / 0.004250 (0.060339) | 0.054496 / 0.037052 (0.017444) | 0.368699 / 0.258489 (0.110210) | 0.400420 / 0.293841 (0.106579) | 0.030869 / 0.128546 (-0.097677) | 0.008424 / 0.075646 (-0.067222) | 0.071015 / 0.419271 (-0.348257) | 0.048333 / 0.043533 (0.004801) | 0.360652 / 0.255139 (0.105513) | 0.393534 / 0.283200 (0.110334) | 0.022685 / 0.141683 (-0.118998) | 1.495565 / 1.452155 (0.043410) | 1.537947 / 1.492716 (0.045230) |\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) | 0.232911 / 0.018006 (0.214905) | 0.454191 / 0.000490 (0.453702) | 0.005711 / 0.000200 (0.005511) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029486 / 0.037411 (-0.007925) | 0.087249 / 0.014526 (0.072724) | 0.100104 / 0.176557 (-0.076453) | 0.151556 / 0.737135 (-0.585580) | 0.100853 / 0.296338 (-0.195485) |\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) | 0.415134 / 0.215209 (0.199925) | 4.139068 / 2.077655 (2.061413) | 2.121079 / 1.504120 (0.616959) | 1.945616 / 1.541195 (0.404421) | 1.988188 / 1.468490 (0.519698) | 0.483994 / 4.584777 (-4.100783) | 3.640366 / 3.745712 (-0.105347) | 3.218896 / 5.269862 (-2.050966) | 2.015527 / 4.565676 (-2.550149) | 0.056946 / 0.424275 (-0.367329) | 0.007262 / 0.007607 (-0.000345) | 0.486075 / 0.226044 (0.260031) | 4.864191 / 2.268929 (2.595262) | 2.590853 / 55.444624 (-52.853772) | 2.315359 / 6.876477 (-4.561118) | 2.418733 / 2.142072 (0.276661) | 0.582378 / 4.805227 (-4.222849) | 0.134097 / 6.500664 (-6.366568) | 0.060797 / 0.075469 (-0.014672) |\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) | 1.337021 / 1.841788 (-0.504766) | 19.468907 / 8.074308 (11.394599) | 14.348874 / 10.191392 (4.157482) | 0.170408 / 0.680424 (-0.510016) | 0.018414 / 0.534201 (-0.515787) | 0.394551 / 0.579283 (-0.184732) | 0.404750 / 0.434364 (-0.029613) | 0.471972 / 0.540337 (-0.068365) | 0.650607 / 1.386936 (-0.736329) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab4d978e2d5c246dc91e2fed041b06a38190be3b \"CML watermark\")\n", "The CI errors are unrelated to the changes" ]
2023-08-17T22:40:37
2023-08-18T13:47:59
null
CONTRIBUTOR
false
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Fix #6147
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https://github.com/huggingface/datasets/pull/6160
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6,160
Fix Parquet loading with `columns`
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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) | 0.008368 / 0.011353 (-0.002985) | 0.004754 / 0.011008 (-0.006254) | 0.096646 / 0.038508 (0.058138) | 0.088980 / 0.023109 (0.065871) | 0.374532 / 0.275898 (0.098633) | 0.404840 / 0.323480 (0.081360) | 0.006026 / 0.007986 (-0.001960) | 0.005716 / 0.004328 (0.001387) | 0.076297 / 0.004250 (0.072047) | 0.072335 / 0.037052 (0.035283) | 0.379435 / 0.258489 (0.120946) | 0.423449 / 0.293841 (0.129608) | 0.041344 / 0.128546 (-0.087202) | 0.009758 / 0.075646 (-0.065889) | 0.341550 / 0.419271 (-0.077721) | 0.068559 / 0.043533 (0.025026) | 0.368313 / 0.255139 (0.113174) | 0.415147 / 0.283200 (0.131947) | 0.028692 / 0.141683 (-0.112990) | 1.816198 / 1.452155 (0.364044) | 1.983351 / 1.492716 (0.490635) |\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) | 0.222712 / 0.018006 (0.204706) | 0.517850 / 0.000490 (0.517360) | 0.004436 / 0.000200 (0.004236) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033168 / 0.037411 (-0.004243) | 0.101353 / 0.014526 (0.086827) | 0.113235 / 0.176557 (-0.063322) | 0.180308 / 0.737135 (-0.556827) | 0.114604 / 0.296338 (-0.181734) |\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) | 0.454415 / 0.215209 (0.239206) | 4.500355 / 2.077655 (2.422701) | 2.188223 / 1.504120 (0.684103) | 1.974256 / 1.541195 (0.433061) | 2.067331 / 1.468490 (0.598841) | 0.572982 / 4.584777 (-4.011795) | 4.239160 / 3.745712 (0.493448) | 3.836812 / 5.269862 (-1.433049) | 2.367022 / 4.565676 (-2.198655) | 0.066886 / 0.424275 (-0.357389) | 0.009111 / 0.007607 (0.001504) | 0.539881 / 0.226044 (0.313837) | 5.362247 / 2.268929 (3.093319) | 2.784044 / 55.444624 (-52.660580) | 2.320975 / 6.876477 (-4.555502) | 2.543108 / 2.142072 (0.401036) | 0.685751 / 4.805227 (-4.119477) | 0.156840 / 6.500664 (-6.343824) | 0.071764 / 0.075469 (-0.003705) |\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) | 1.549830 / 1.841788 (-0.291958) | 22.799622 / 8.074308 (14.725314) | 16.750692 / 10.191392 (6.559300) | 0.196192 / 0.680424 (-0.484232) | 0.024518 / 0.534201 (-0.509683) | 0.479302 / 0.579283 (-0.099981) | 0.522256 / 0.434364 (0.087892) | 0.545809 / 0.540337 (0.005471) | 0.748437 / 1.386936 (-0.638499) |\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) | 0.007789 / 0.011353 (-0.003564) | 0.004563 / 0.011008 (-0.006445) | 0.074631 / 0.038508 (0.036123) | 0.086892 / 0.023109 (0.063783) | 0.427014 / 0.275898 (0.151116) | 0.463257 / 0.323480 (0.139777) | 0.005987 / 0.007986 (-0.001999) | 0.003803 / 0.004328 (-0.000526) | 0.074799 / 0.004250 (0.070549) | 0.063473 / 0.037052 (0.026420) | 0.429905 / 0.258489 (0.171416) | 0.468967 / 0.293841 (0.175127) | 0.036768 / 0.128546 (-0.091778) | 0.009675 / 0.075646 (-0.065971) | 0.082546 / 0.419271 (-0.336725) | 0.058027 / 0.043533 (0.014494) | 0.429813 / 0.255139 (0.174674) | 0.449200 / 0.283200 (0.166001) | 0.026713 / 0.141683 (-0.114969) | 1.812022 / 1.452155 (0.359867) | 1.847305 / 1.492716 (0.354589) |\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) | 0.320383 / 0.018006 (0.302377) | 0.485995 / 0.000490 (0.485505) | 0.024365 / 0.000200 (0.024165) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036341 / 0.037411 (-0.001071) | 0.104635 / 0.014526 (0.090110) | 0.119456 / 0.176557 (-0.057101) | 0.182042 / 0.737135 (-0.555093) | 0.118944 / 0.296338 (-0.177395) |\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) | 0.506410 / 0.215209 (0.291201) | 5.061119 / 2.077655 (2.983465) | 2.756557 / 1.504120 (1.252437) | 2.546504 / 1.541195 (1.005309) | 2.585509 / 1.468490 (1.117019) | 0.564291 / 4.584777 (-4.020486) | 4.281219 / 3.745712 (0.535507) | 3.919439 / 5.269862 (-1.350423) | 2.588788 / 4.565676 (-1.976889) | 0.066900 / 0.424275 (-0.357375) | 0.008680 / 0.007607 (0.001073) | 0.598435 / 0.226044 (0.372390) | 5.976054 / 2.268929 (3.707125) | 3.260211 / 55.444624 (-52.184414) | 2.874597 / 6.876477 (-4.001880) | 3.105769 / 2.142072 (0.963697) | 0.692938 / 4.805227 (-4.112289) | 0.157777 / 6.500664 (-6.342887) | 0.073128 / 0.075469 (-0.002341) |\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) | 1.559380 / 1.841788 (-0.282408) | 22.986540 / 8.074308 (14.912232) | 16.305564 / 10.191392 (6.114172) | 0.174939 / 0.680424 (-0.505485) | 0.021932 / 0.534201 (-0.512269) | 0.468162 / 0.579283 (-0.111121) | 0.472610 / 0.434364 (0.038246) | 0.574574 / 0.540337 (0.034237) | 0.783505 / 1.386936 (-0.603431) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#550923b5d6ae64eb20b8f66da843395e9fa404ac \"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) | 0.012553 / 0.011353 (0.001201) | 0.005358 / 0.011008 (-0.005650) | 0.108338 / 0.038508 (0.069830) | 0.101105 / 0.023109 (0.077995) | 0.416808 / 0.275898 (0.140910) | 0.454599 / 0.323480 (0.131119) | 0.006665 / 0.007986 (-0.001321) | 0.004186 / 0.004328 (-0.000143) | 0.084900 / 0.004250 (0.080649) | 0.062881 / 0.037052 (0.025829) | 0.424423 / 0.258489 (0.165934) | 0.482651 / 0.293841 (0.188810) | 0.055740 / 0.128546 (-0.072807) | 0.014469 / 0.075646 (-0.061177) | 0.383267 / 0.419271 (-0.036005) | 0.067487 / 0.043533 (0.023955) | 0.414983 / 0.255139 (0.159844) | 0.459437 / 0.283200 (0.176237) | 0.038679 / 0.141683 (-0.103004) | 1.828002 / 1.452155 (0.375847) | 1.951946 / 1.492716 (0.459230) |\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) | 0.288033 / 0.018006 (0.270027) | 0.603536 / 0.000490 (0.603046) | 0.004874 / 0.000200 (0.004674) | 0.000138 / 0.000054 (0.000084) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031988 / 0.037411 (-0.005423) | 0.095807 / 0.014526 (0.081281) | 0.113459 / 0.176557 (-0.063098) | 0.182012 / 0.737135 (-0.555123) | 0.113121 / 0.296338 (-0.183217) |\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) | 0.620709 / 0.215209 (0.405500) | 6.096569 / 2.077655 (4.018915) | 2.754612 / 1.504120 (1.250492) | 2.449786 / 1.541195 (0.908591) | 2.470694 / 1.468490 (1.002204) | 0.837016 / 4.584777 (-3.747761) | 5.237290 / 3.745712 (1.491578) | 4.713220 / 5.269862 (-0.556642) | 3.020934 / 4.565676 (-1.544743) | 0.096892 / 0.424275 (-0.327383) | 0.009423 / 0.007607 (0.001816) | 0.720313 / 0.226044 (0.494269) | 7.369673 / 2.268929 (5.100744) | 3.550384 / 55.444624 (-51.894241) | 2.868868 / 6.876477 (-4.007609) | 3.081469 / 2.142072 (0.939397) | 1.042968 / 4.805227 (-3.762259) | 0.232530 / 6.500664 (-6.268134) | 0.080805 / 0.075469 (0.005336) |\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) | 1.645777 / 1.841788 (-0.196011) | 24.590862 / 8.074308 (16.516554) | 21.315496 / 10.191392 (11.124104) | 0.228796 / 0.680424 (-0.451628) | 0.028479 / 0.534201 (-0.505722) | 0.494413 / 0.579283 (-0.084870) | 0.582773 / 0.434364 (0.148409) | 0.552575 / 0.540337 (0.012238) | 0.787217 / 1.386936 (-0.599719) |\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) | 0.008743 / 0.011353 (-0.002609) | 0.005253 / 0.011008 (-0.005755) | 0.083766 / 0.038508 (0.045257) | 0.086305 / 0.023109 (0.063195) | 0.520171 / 0.275898 (0.244273) | 0.565812 / 0.323480 (0.242332) | 0.006465 / 0.007986 (-0.001520) | 0.004585 / 0.004328 (0.000257) | 0.085344 / 0.004250 (0.081094) | 0.063418 / 0.037052 (0.026366) | 0.519759 / 0.258489 (0.261270) | 0.552770 / 0.293841 (0.258929) | 0.049439 / 0.128546 (-0.079107) | 0.017564 / 0.075646 (-0.058082) | 0.092713 / 0.419271 (-0.326559) | 0.065837 / 0.043533 (0.022305) | 0.516133 / 0.255139 (0.260994) | 0.539813 / 0.283200 (0.256613) | 0.036531 / 0.141683 (-0.105152) | 1.919275 / 1.452155 (0.467121) | 2.039987 / 1.492716 (0.547271) |\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) | 0.297978 / 0.018006 (0.279972) | 0.608243 / 0.000490 (0.607753) | 0.006611 / 0.000200 (0.006411) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033909 / 0.037411 (-0.003503) | 0.106370 / 0.014526 (0.091844) | 0.119032 / 0.176557 (-0.057524) | 0.180319 / 0.737135 (-0.556816) | 0.122826 / 0.296338 (-0.173513) |\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) | 0.639265 / 0.215209 (0.424056) | 6.248430 / 2.077655 (4.170775) | 2.944760 / 1.504120 (1.440640) | 2.654005 / 1.541195 (1.112811) | 2.733625 / 1.468490 (1.265134) | 0.837172 / 4.584777 (-3.747605) | 5.245084 / 3.745712 (1.499372) | 4.722614 / 5.269862 (-0.547248) | 3.008286 / 4.565676 (-1.557391) | 0.102340 / 0.424275 (-0.321935) | 0.009433 / 0.007607 (0.001826) | 0.762991 / 0.226044 (0.536946) | 7.385020 / 2.268929 (5.116092) | 3.787648 / 55.444624 (-51.656977) | 3.234345 / 6.876477 (-3.642132) | 3.394444 / 2.142072 (1.252371) | 1.023472 / 4.805227 (-3.781756) | 0.208199 / 6.500664 (-6.292465) | 0.081513 / 0.075469 (0.006043) |\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) | 1.795864 / 1.841788 (-0.045923) | 25.270852 / 8.074308 (17.196544) | 23.356413 / 10.191392 (13.165021) | 0.228002 / 0.680424 (-0.452422) | 0.031851 / 0.534201 (-0.502350) | 0.499424 / 0.579283 (-0.079859) | 0.588027 / 0.434364 (0.153664) | 0.581746 / 0.540337 (0.041408) | 0.814183 / 1.386936 (-0.572753) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ee536876a667403ee44574bd685073261c4903 \"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) | 0.006477 / 0.011353 (-0.004876) | 0.003878 / 0.011008 (-0.007130) | 0.084085 / 0.038508 (0.045577) | 0.071297 / 0.023109 (0.048188) | 0.309176 / 0.275898 (0.033278) | 0.342830 / 0.323480 (0.019350) | 0.005189 / 0.007986 (-0.002796) | 0.003263 / 0.004328 (-0.001065) | 0.063920 / 0.004250 (0.059670) | 0.052233 / 0.037052 (0.015180) | 0.324830 / 0.258489 (0.066341) | 0.357956 / 0.293841 (0.064115) | 0.030459 / 0.128546 (-0.098087) | 0.008350 / 0.075646 (-0.067297) | 0.287330 / 0.419271 (-0.131942) | 0.051005 / 0.043533 (0.007473) | 0.309227 / 0.255139 (0.054088) | 0.346184 / 0.283200 (0.062984) | 0.023961 / 0.141683 (-0.117722) | 1.463983 / 1.452155 (0.011829) | 1.573036 / 1.492716 (0.080319) |\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) | 0.205653 / 0.018006 (0.187647) | 0.457336 / 0.000490 (0.456846) | 0.005347 / 0.000200 (0.005147) | 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) | 0.028080 / 0.037411 (-0.009332) | 0.081755 / 0.014526 (0.067229) | 0.095716 / 0.176557 (-0.080841) | 0.151340 / 0.737135 (-0.585795) | 0.097174 / 0.296338 (-0.199164) |\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) | 0.390725 / 0.215209 (0.175516) | 3.899114 / 2.077655 (1.821459) | 1.895352 / 1.504120 (0.391232) | 1.716072 / 1.541195 (0.174877) | 1.784952 / 1.468490 (0.316462) | 0.477247 / 4.584777 (-4.107530) | 3.606641 / 3.745712 (-0.139071) | 3.203337 / 5.269862 (-2.066524) | 2.017003 / 4.565676 (-2.548674) | 0.056182 / 0.424275 (-0.368094) | 0.007508 / 0.007607 (-0.000099) | 0.461965 / 0.226044 (0.235921) | 4.605926 / 2.268929 (2.336997) | 2.466695 / 55.444624 (-52.977929) | 2.136376 / 6.876477 (-4.740100) | 2.277334 / 2.142072 (0.135261) | 0.576119 / 4.805227 (-4.229109) | 0.131497 / 6.500664 (-6.369167) | 0.060068 / 0.075469 (-0.015401) |\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) | 1.262681 / 1.841788 (-0.579107) | 19.411572 / 8.074308 (11.337264) | 14.383421 / 10.191392 (4.192029) | 0.166115 / 0.680424 (-0.514308) | 0.018366 / 0.534201 (-0.515835) | 0.393903 / 0.579283 (-0.185380) | 0.408788 / 0.434364 (-0.025576) | 0.461796 / 0.540337 (-0.078541) | 0.628460 / 1.386936 (-0.758476) |\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) | 0.006501 / 0.011353 (-0.004852) | 0.003915 / 0.011008 (-0.007093) | 0.065245 / 0.038508 (0.026737) | 0.073146 / 0.023109 (0.050037) | 0.363537 / 0.275898 (0.087639) | 0.391571 / 0.323480 (0.068092) | 0.005181 / 0.007986 (-0.002805) | 0.003272 / 0.004328 (-0.001056) | 0.065060 / 0.004250 (0.060810) | 0.054302 / 0.037052 (0.017249) | 0.361571 / 0.258489 (0.103082) | 0.400221 / 0.293841 (0.106380) | 0.030762 / 0.128546 (-0.097784) | 0.008449 / 0.075646 (-0.067197) | 0.071148 / 0.419271 (-0.348123) | 0.048111 / 0.043533 (0.004578) | 0.360327 / 0.255139 (0.105188) | 0.379073 / 0.283200 (0.095874) | 0.024367 / 0.141683 (-0.117316) | 1.451080 / 1.452155 (-0.001074) | 1.510818 / 1.492716 (0.018102) |\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) | 0.267078 / 0.018006 (0.249072) | 0.454074 / 0.000490 (0.453584) | 0.015055 / 0.000200 (0.014855) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030916 / 0.037411 (-0.006496) | 0.089212 / 0.014526 (0.074686) | 0.100005 / 0.176557 (-0.076552) | 0.155100 / 0.737135 (-0.582035) | 0.101759 / 0.296338 (-0.194580) |\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) | 0.412826 / 0.215209 (0.197616) | 4.122520 / 2.077655 (2.044865) | 2.107870 / 1.504120 (0.603750) | 1.911936 / 1.541195 (0.370741) | 1.984936 / 1.468490 (0.516446) | 0.483835 / 4.584777 (-4.100942) | 3.641860 / 3.745712 (-0.103852) | 3.220540 / 5.269862 (-2.049322) | 2.015521 / 4.565676 (-2.550155) | 0.056913 / 0.424275 (-0.367362) | 0.007285 / 0.007607 (-0.000322) | 0.484886 / 0.226044 (0.258842) | 4.854734 / 2.268929 (2.585805) | 2.593550 / 55.444624 (-52.851074) | 2.233904 / 6.876477 (-4.642572) | 2.438858 / 2.142072 (0.296785) | 0.580880 / 4.805227 (-4.224347) | 0.133891 / 6.500664 (-6.366773) | 0.061678 / 0.075469 (-0.013791) |\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) | 1.336843 / 1.841788 (-0.504944) | 19.731571 / 8.074308 (11.657263) | 14.290228 / 10.191392 (4.098836) | 0.167635 / 0.680424 (-0.512789) | 0.018767 / 0.534201 (-0.515434) | 0.394953 / 0.579283 (-0.184330) | 0.407711 / 0.434364 (-0.026653) | 0.472371 / 0.540337 (-0.067966) | 0.655278 / 1.386936 (-0.731658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#528b15f775a4724836bdefdc38d932c06484d702 \"CML watermark\")\n" ]
2023-08-17T21:58:24
2023-08-17T22:44:59
2023-08-17T22:36:04
CONTRIBUTOR
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Fix #6149
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6,159
Add `BoundingBox` feature
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2023-08-17T20:49:51
2023-08-17T20:49:51
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CONTRIBUTOR
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... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them). cc @NielsRogge @severo
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[docs] Complete `to_iterable_dataset`
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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) | 0.008219 / 0.011353 (-0.003134) | 0.005201 / 0.011008 (-0.005807) | 0.108542 / 0.038508 (0.070034) | 0.076427 / 0.023109 (0.053318) | 0.441257 / 0.275898 (0.165358) | 0.436477 / 0.323480 (0.112997) | 0.006915 / 0.007986 (-0.001071) | 0.004215 / 0.004328 (-0.000113) | 0.072517 / 0.004250 (0.068267) | 0.066906 / 0.037052 (0.029853) | 0.431153 / 0.258489 (0.172664) | 0.413359 / 0.293841 (0.119518) | 0.051112 / 0.128546 (-0.077435) | 0.014664 / 0.075646 (-0.060982) | 0.358385 / 0.419271 (-0.060887) | 0.069682 / 0.043533 (0.026149) | 0.434810 / 0.255139 (0.179671) | 0.484372 / 0.283200 (0.201172) | 0.035731 / 0.141683 (-0.105952) | 1.827648 / 1.452155 (0.375494) | 2.039761 / 1.492716 (0.547045) |\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) | 0.277386 / 0.018006 (0.259379) | 0.599771 / 0.000490 (0.599282) | 0.005033 / 0.000200 (0.004833) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030652 / 0.037411 (-0.006759) | 0.103435 / 0.014526 (0.088909) | 0.120072 / 0.176557 (-0.056485) | 0.177886 / 0.737135 (-0.559249) | 0.140636 / 0.296338 (-0.155702) |\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) | 0.603729 / 0.215209 (0.388520) | 6.144213 / 2.077655 (4.066558) | 2.785080 / 1.504120 (1.280960) | 2.368958 / 1.541195 (0.827763) | 2.409806 / 1.468490 (0.941316) | 0.836531 / 4.584777 (-3.748246) | 5.154035 / 3.745712 (1.408323) | 4.620224 / 5.269862 (-0.649638) | 2.879441 / 4.565676 (-1.686235) | 0.087322 / 0.424275 (-0.336953) | 0.007698 / 0.007607 (0.000090) | 0.678443 / 0.226044 (0.452399) | 7.431798 / 2.268929 (5.162869) | 3.589905 / 55.444624 (-51.854719) | 2.679349 / 6.876477 (-4.197127) | 3.100569 / 2.142072 (0.958496) | 1.021501 / 4.805227 (-3.783726) | 0.203150 / 6.500664 (-6.297514) | 0.073545 / 0.075469 (-0.001924) |\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) | 1.669981 / 1.841788 (-0.171806) | 23.379274 / 8.074308 (15.304966) | 19.811451 / 10.191392 (9.620059) | 0.197705 / 0.680424 (-0.482719) | 0.030112 / 0.534201 (-0.504089) | 0.501720 / 0.579283 (-0.077563) | 0.582413 / 0.434364 (0.148049) | 0.513261 / 0.540337 (-0.027076) | 0.729710 / 1.386936 (-0.657226) |\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) | 0.011493 / 0.011353 (0.000140) | 0.005478 / 0.011008 (-0.005530) | 0.070955 / 0.038508 (0.032447) | 0.073877 / 0.023109 (0.050768) | 0.425765 / 0.275898 (0.149867) | 0.440869 / 0.323480 (0.117389) | 0.008322 / 0.007986 (0.000337) | 0.004004 / 0.004328 (-0.000325) | 0.071968 / 0.004250 (0.067718) | 0.060576 / 0.037052 (0.023524) | 0.448731 / 0.258489 (0.190242) | 0.517038 / 0.293841 (0.223197) | 0.051542 / 0.128546 (-0.077005) | 0.013219 / 0.075646 (-0.062427) | 0.077933 / 0.419271 (-0.341339) | 0.072879 / 0.043533 (0.029346) | 0.436553 / 0.255139 (0.181414) | 0.510050 / 0.283200 (0.226850) | 0.037136 / 0.141683 (-0.104547) | 1.535706 / 1.452155 (0.083552) | 1.611909 / 1.492716 (0.119192) |\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) | 0.335648 / 0.018006 (0.317642) | 0.612787 / 0.000490 (0.612297) | 0.021934 / 0.000200 (0.021734) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028164 / 0.037411 (-0.009247) | 0.097686 / 0.014526 (0.083160) | 0.093343 / 0.176557 (-0.083214) | 0.156871 / 0.737135 (-0.580264) | 0.102694 / 0.296338 (-0.193645) |\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) | 0.609348 / 0.215209 (0.394139) | 5.835798 / 2.077655 (3.758144) | 2.792700 / 1.504120 (1.288580) | 2.539597 / 1.541195 (0.998403) | 2.413003 / 1.468490 (0.944513) | 0.882404 / 4.584777 (-3.702372) | 5.170564 / 3.745712 (1.424852) | 4.621663 / 5.269862 (-0.648199) | 3.029683 / 4.565676 (-1.535993) | 0.097061 / 0.424275 (-0.327214) | 0.008940 / 0.007607 (0.001333) | 0.723052 / 0.226044 (0.497007) | 7.484947 / 2.268929 (5.216018) | 3.833049 / 55.444624 (-51.611575) | 3.019606 / 6.876477 (-3.856871) | 3.270503 / 2.142072 (1.128430) | 0.977870 / 4.805227 (-3.827357) | 0.210090 / 6.500664 (-6.290574) | 0.094723 / 0.075469 (0.019254) |\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) | 1.585278 / 1.841788 (-0.256510) | 22.769727 / 8.074308 (14.695419) | 19.503640 / 10.191392 (9.312248) | 0.231996 / 0.680424 (-0.448428) | 0.032641 / 0.534201 (-0.501560) | 0.429833 / 0.579283 (-0.149451) | 0.549606 / 0.434364 (0.115242) | 0.527405 / 0.540337 (-0.012933) | 0.713302 / 1.386936 (-0.673634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#546c7bb5cbeff0f8673cf60c4432ea167283cc42 \"CML watermark\")\n" ]
2023-08-17T17:02:11
2023-08-17T19:24:20
2023-08-17T19:13:15
MEMBER
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Finishes the `to_iterable_dataset` documentation by adding it to the relevant sections in the tutorial and guide.
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1,855,265,663
I_kwDODunzps5ulRt_
6,157
DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'
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[ "Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.", "@mariosasko Ok. What exactly does it mean to provide a reproducer", "To provide a code that reproduces the issue :)", "@mariosasko I complete the above code, is it enough?", "@mariosasko That's all the code, I'm using locally stored data", "Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?", "@mariosasko I didn't add any parameters for catch. Nor did any cache configuration change.", "@mariosasko And I changed the data file, but executing load_dataset is always the previous result. I had to change something in images.py to use the new results. Using 'cleanup_cache_files' is invalid! Help me.", "@mariosasko I added a comprehensive error message. Check that _column_requires_decoding is being passed where it shouldn't be. DatasetInfo.__init__() Whether this parameter is required", "I can see the issue now... \r\n\r\nYou can fix it by returning a `DatasetInfo` object in the `_info` method as follows:\r\n```python\r\n def _info(self):\r\n if self.config.name == \"similar_pairs\":\r\n features = datasets.Features(\r\n {\r\n \"image1\": datasets.features.Image(),\r\n \"prompt1\": datasets.Value(\"string\"),\r\n \"image2\": datasets.features.Image(),\r\n \"prompt2\": datasets.Value(\"string\"),\r\n \"similarity\": datasets.Value(\"float32\"),\r\n }\r\n )\r\n elif self.config.name == \"image_prompt_pairs\":\r\n features = datasets.Features(\r\n {\"image\": datasets.features.Image(), \"prompt\": datasets.Value(\"string\")}\r\n )\r\n return datasets.DatasetInfo(features=features)\r\n```", "@mariosasko Oh, that's the problem. Thank you very much. Returned the wrong object and it actually works? I've been training with it for a long time", "@mariosasko The original code can still see progress. emmm, I can't see how many examples is generated so far, so I don't know if we should wait", "The original issue has been addressed, so I'm closing it. \r\n\r\nPlease open a new issue if you encounter more errors." ]
2023-08-17T15:48:11
2023-09-27T17:36:14
2023-09-27T17:36:14
NONE
null
null
### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset( 2 "/home/aihao/workspace/DeepLearningContent/datasets/manga", 3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga", 4 split="train", 5 ) File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2142 # Build dataset for splits 2143 keep_in_memory = ( 2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2145 ) -> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2147 # Rename and cast features to match task schema 2148 if task is not None: 2149 # To avoid issuing the same warning twice File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1189 # Create a dataset for each of the given splits -> 1190 datasets = map_nested( 1191 partial( 1192 self._build_single_dataset, ... File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self) 378 def copy(self) -> "DatasetInfo": --> 379 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ``` ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ImagesConfig, self).__init__(**kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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1,854,768,618
I_kwDODunzps5ujYXq
6,156
Why not use self._epoch as seed to shuffle in distributed training with IterableDataset
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[ "@lhoestq ", "`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`", "Thanks for the reply" ]
2023-08-17T10:58:20
2023-08-17T14:33:15
2023-08-17T14:33:14
CONTRIBUTOR
null
null
### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related
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6,155
Raise FileNotFoundError when passing data_files that don't exist
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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) | 0.009288 / 0.011353 (-0.002065) | 0.005950 / 0.011008 (-0.005058) | 0.122376 / 0.038508 (0.083868) | 0.093177 / 0.023109 (0.070068) | 0.448517 / 0.275898 (0.172619) | 0.474999 / 0.323480 (0.151520) | 0.005133 / 0.007986 (-0.002853) | 0.005123 / 0.004328 (0.000795) | 0.085479 / 0.004250 (0.081229) | 0.065613 / 0.037052 (0.028561) | 0.451179 / 0.258489 (0.192690) | 0.516876 / 0.293841 (0.223036) | 0.047536 / 0.128546 (-0.081010) | 0.013894 / 0.075646 (-0.061752) | 0.382149 / 0.419271 (-0.037122) | 0.067380 / 0.043533 (0.023848) | 0.419282 / 0.255139 (0.164143) | 0.482042 / 0.283200 (0.198842) | 0.041230 / 0.141683 (-0.100452) | 1.818127 / 1.452155 (0.365972) | 1.938123 / 1.492716 (0.445406) |\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) | 0.271824 / 0.018006 (0.253817) | 0.604933 / 0.000490 (0.604443) | 0.004953 / 0.000200 (0.004753) | 0.000173 / 0.000054 (0.000119) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036682 / 0.037411 (-0.000729) | 0.095604 / 0.014526 (0.081078) | 0.116862 / 0.176557 (-0.059695) | 0.191335 / 0.737135 (-0.545800) | 0.116620 / 0.296338 (-0.179718) |\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) | 0.620735 / 0.215209 (0.405526) | 6.157119 / 2.077655 (4.079465) | 2.848548 / 1.504120 (1.344428) | 2.493731 / 1.541195 (0.952536) | 2.505801 / 1.468490 (1.037311) | 0.837315 / 4.584777 (-3.747462) | 5.360653 / 3.745712 (1.614941) | 4.908863 / 5.269862 (-0.360999) | 3.184672 / 4.565676 (-1.381004) | 0.105687 / 0.424275 (-0.318588) | 0.011350 / 0.007607 (0.003743) | 0.745729 / 0.226044 (0.519684) | 7.431584 / 2.268929 (5.162655) | 3.644670 / 55.444624 (-51.799954) | 2.910159 / 6.876477 (-3.966317) | 3.257137 / 2.142072 (1.115065) | 1.041377 / 4.805227 (-3.763851) | 0.213289 / 6.500664 (-6.287375) | 0.089208 / 0.075469 (0.013739) |\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) | 1.727274 / 1.841788 (-0.114513) | 25.448436 / 8.074308 (17.374128) | 23.016108 / 10.191392 (12.824716) | 0.219454 / 0.680424 (-0.460970) | 0.028531 / 0.534201 (-0.505670) | 0.500231 / 0.579283 (-0.079052) | 0.614631 / 0.434364 (0.180267) | 0.557926 / 0.540337 (0.017588) | 0.786261 / 1.386936 (-0.600675) |\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) | 0.008608 / 0.011353 (-0.002745) | 0.006185 / 0.011008 (-0.004823) | 0.089258 / 0.038508 (0.050750) | 0.090109 / 0.023109 (0.067000) | 0.522200 / 0.275898 (0.246302) | 0.559218 / 0.323480 (0.235738) | 0.008983 / 0.007986 (0.000997) | 0.004488 / 0.004328 (0.000159) | 0.083658 / 0.004250 (0.079408) | 0.064962 / 0.037052 (0.027909) | 0.519477 / 0.258489 (0.260988) | 0.573842 / 0.293841 (0.280001) | 0.053984 / 0.128546 (-0.074562) | 0.014665 / 0.075646 (-0.060982) | 0.089438 / 0.419271 (-0.329834) | 0.065756 / 0.043533 (0.022223) | 0.525131 / 0.255139 (0.269992) | 0.568934 / 0.283200 (0.285734) | 0.037308 / 0.141683 (-0.104375) | 1.928790 / 1.452155 (0.476635) | 2.027926 / 1.492716 (0.535209) |\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) | 0.309595 / 0.018006 (0.291588) | 0.615675 / 0.000490 (0.615186) | 0.004869 / 0.000200 (0.004669) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033306 / 0.037411 (-0.004105) | 0.104429 / 0.014526 (0.089904) | 0.116989 / 0.176557 (-0.059568) | 0.183638 / 0.737135 (-0.553497) | 0.132624 / 0.296338 (-0.163714) |\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) | 0.644511 / 0.215209 (0.429302) | 6.425544 / 2.077655 (4.347889) | 3.079071 / 1.504120 (1.574951) | 2.720963 / 1.541195 (1.179769) | 2.835607 / 1.468490 (1.367117) | 0.863561 / 4.584777 (-3.721216) | 5.333462 / 3.745712 (1.587750) | 4.843183 / 5.269862 (-0.426678) | 3.106858 / 4.565676 (-1.458819) | 0.106790 / 0.424275 (-0.317485) | 0.008829 / 0.007607 (0.001222) | 0.759003 / 0.226044 (0.532958) | 7.771247 / 2.268929 (5.502318) | 3.896844 / 55.444624 (-51.547780) | 3.246671 / 6.876477 (-3.629806) | 3.486167 / 2.142072 (1.344094) | 1.071290 / 4.805227 (-3.733937) | 0.217972 / 6.500664 (-6.282692) | 0.089848 / 0.075469 (0.014379) |\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) | 1.816048 / 1.841788 (-0.025739) | 25.625084 / 8.074308 (17.550776) | 24.490882 / 10.191392 (14.299490) | 0.242356 / 0.680424 (-0.438067) | 0.027886 / 0.534201 (-0.506315) | 0.496997 / 0.579283 (-0.082286) | 0.613815 / 0.434364 (0.179451) | 0.607132 / 0.540337 (0.066795) | 0.833051 / 1.386936 (-0.553885) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0adfa9ada14c38fce5973b5e3f196a2c46dc9170 \"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) | 0.011580 / 0.011353 (0.000227) | 0.004199 / 0.011008 (-0.006809) | 0.084055 / 0.038508 (0.045547) | 0.096824 / 0.023109 (0.073715) | 0.308755 / 0.275898 (0.032857) | 0.341717 / 0.323480 (0.018237) | 0.006018 / 0.007986 (-0.001968) | 0.003597 / 0.004328 (-0.000731) | 0.064953 / 0.004250 (0.060702) | 0.059577 / 0.037052 (0.022525) | 0.316292 / 0.258489 (0.057803) | 0.358991 / 0.293841 (0.065150) | 0.033925 / 0.128546 (-0.094621) | 0.008828 / 0.075646 (-0.066818) | 0.288673 / 0.419271 (-0.130599) | 0.055494 / 0.043533 (0.011961) | 0.311181 / 0.255139 (0.056042) | 0.345220 / 0.283200 (0.062021) | 0.024033 / 0.141683 (-0.117649) | 1.504709 / 1.452155 (0.052554) | 1.587920 / 1.492716 (0.095204) |\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) | 0.301099 / 0.018006 (0.283093) | 0.594497 / 0.000490 (0.594007) | 0.006244 / 0.000200 (0.006044) | 0.000228 / 0.000054 (0.000174) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027663 / 0.037411 (-0.009748) | 0.081767 / 0.014526 (0.067241) | 0.097342 / 0.176557 (-0.079215) | 0.153200 / 0.737135 (-0.583935) | 0.097474 / 0.296338 (-0.198864) |\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) | 0.405929 / 0.215209 (0.190719) | 4.045398 / 2.077655 (1.967743) | 2.044669 / 1.504120 (0.540549) | 1.872889 / 1.541195 (0.331694) | 1.911901 / 1.468490 (0.443411) | 0.480939 / 4.584777 (-4.103838) | 3.652833 / 3.745712 (-0.092879) | 3.281659 / 5.269862 (-1.988202) | 2.038023 / 4.565676 (-2.527654) | 0.056501 / 0.424275 (-0.367775) | 0.007571 / 0.007607 (-0.000036) | 0.481053 / 0.226044 (0.255009) | 4.802048 / 2.268929 (2.533119) | 2.560479 / 55.444624 (-52.884145) | 2.164852 / 6.876477 (-4.711625) | 2.374595 / 2.142072 (0.232523) | 0.576309 / 4.805227 (-4.228918) | 0.134831 / 6.500664 (-6.365833) | 0.060649 / 0.075469 (-0.014820) |\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) | 1.254210 / 1.841788 (-0.587578) | 19.826143 / 8.074308 (11.751835) | 14.446391 / 10.191392 (4.254999) | 0.165707 / 0.680424 (-0.514717) | 0.018221 / 0.534201 (-0.515980) | 0.395996 / 0.579283 (-0.183287) | 0.424567 / 0.434364 (-0.009796) | 0.459836 / 0.540337 (-0.080501) | 0.635969 / 1.386936 (-0.750967) |\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) | 0.006696 / 0.011353 (-0.004657) | 0.004131 / 0.011008 (-0.006877) | 0.064587 / 0.038508 (0.026079) | 0.079189 / 0.023109 (0.056080) | 0.359977 / 0.275898 (0.084079) | 0.389331 / 0.323480 (0.065851) | 0.005502 / 0.007986 (-0.002483) | 0.003492 / 0.004328 (-0.000837) | 0.064967 / 0.004250 (0.060716) | 0.055953 / 0.037052 (0.018901) | 0.363997 / 0.258489 (0.105508) | 0.398405 / 0.293841 (0.104564) | 0.031292 / 0.128546 (-0.097254) | 0.008693 / 0.075646 (-0.066953) | 0.070451 / 0.419271 (-0.348820) | 0.048965 / 0.043533 (0.005432) | 0.358288 / 0.255139 (0.103149) | 0.379136 / 0.283200 (0.095936) | 0.024364 / 0.141683 (-0.117319) | 1.478998 / 1.452155 (0.026843) | 1.547282 / 1.492716 (0.054566) |\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) | 0.328188 / 0.018006 (0.310182) | 0.525968 / 0.000490 (0.525478) | 0.003782 / 0.000200 (0.003582) | 0.000089 / 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) | 0.032528 / 0.037411 (-0.004883) | 0.087685 / 0.014526 (0.073159) | 0.100684 / 0.176557 (-0.075872) | 0.155944 / 0.737135 (-0.581192) | 0.101949 / 0.296338 (-0.194389) |\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) | 0.418591 / 0.215209 (0.203382) | 4.199235 / 2.077655 (2.121580) | 2.183880 / 1.504120 (0.679760) | 2.024502 / 1.541195 (0.483307) | 2.017435 / 1.468490 (0.548945) | 0.488881 / 4.584777 (-4.095896) | 3.635002 / 3.745712 (-0.110710) | 3.359992 / 5.269862 (-1.909870) | 2.089686 / 4.565676 (-2.475991) | 0.057813 / 0.424275 (-0.366462) | 0.007349 / 0.007607 (-0.000258) | 0.490719 / 0.226044 (0.264674) | 4.859950 / 2.268929 (2.591022) | 2.616711 / 55.444624 (-52.827914) | 2.238671 / 6.876477 (-4.637806) | 2.442262 / 2.142072 (0.300190) | 0.598368 / 4.805227 (-4.206859) | 0.135281 / 6.500664 (-6.365383) | 0.063072 / 0.075469 (-0.012397) |\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) | 1.356396 / 1.841788 (-0.485392) | 20.075123 / 8.074308 (12.000815) | 14.191317 / 10.191392 (3.999925) | 0.167691 / 0.680424 (-0.512732) | 0.018290 / 0.534201 (-0.515911) | 0.392881 / 0.579283 (-0.186402) | 0.413665 / 0.434364 (-0.020699) | 0.480766 / 0.540337 (-0.059571) | 0.655625 / 1.386936 (-0.731311) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ca9cc138754629be261522301e725c7d14152 \"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) | 0.007834 / 0.011353 (-0.003519) | 0.004744 / 0.011008 (-0.006264) | 0.102061 / 0.038508 (0.063553) | 0.089246 / 0.023109 (0.066137) | 0.399936 / 0.275898 (0.124038) | 0.436974 / 0.323480 (0.113494) | 0.004791 / 0.007986 (-0.003195) | 0.005976 / 0.004328 (0.001647) | 0.079336 / 0.004250 (0.075086) | 0.065947 / 0.037052 (0.028894) | 0.403747 / 0.258489 (0.145258) | 0.460249 / 0.293841 (0.166408) | 0.038065 / 0.128546 (-0.090482) | 0.010179 / 0.075646 (-0.065467) | 0.403620 / 0.419271 (-0.015652) | 0.066439 / 0.043533 (0.022906) | 0.412123 / 0.255139 (0.156984) | 0.452121 / 0.283200 (0.168921) | 0.033533 / 0.141683 (-0.108150) | 1.858650 / 1.452155 (0.406495) | 1.916248 / 1.492716 (0.423532) |\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) | 0.237180 / 0.018006 (0.219174) | 0.526844 / 0.000490 (0.526354) | 0.004220 / 0.000200 (0.004020) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033860 / 0.037411 (-0.003552) | 0.105054 / 0.014526 (0.090528) | 0.116494 / 0.176557 (-0.060063) | 0.185990 / 0.737135 (-0.551145) | 0.119072 / 0.296338 (-0.177266) |\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) | 0.488549 / 0.215209 (0.273340) | 4.884950 / 2.077655 (2.807295) | 2.521819 / 1.504120 (1.017699) | 2.329382 / 1.541195 (0.788188) | 2.413710 / 1.468490 (0.945220) | 0.568325 / 4.584777 (-4.016452) | 4.243505 / 3.745712 (0.497793) | 3.785983 / 5.269862 (-1.483879) | 2.387146 / 4.565676 (-2.178531) | 0.067176 / 0.424275 (-0.357099) | 0.009145 / 0.007607 (0.001538) | 0.571482 / 0.226044 (0.345437) | 5.688822 / 2.268929 (3.419894) | 3.067346 / 55.444624 (-52.377278) | 2.688723 / 6.876477 (-4.187754) | 2.883785 / 2.142072 (0.741713) | 0.679326 / 4.805227 (-4.125901) | 0.156018 / 6.500664 (-6.344646) | 0.070947 / 0.075469 (-0.004522) |\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) | 1.556611 / 1.841788 (-0.285177) | 23.545074 / 8.074308 (15.470766) | 17.125108 / 10.191392 (6.933716) | 0.180180 / 0.680424 (-0.500244) | 0.021420 / 0.534201 (-0.512781) | 0.466888 / 0.579283 (-0.112395) | 0.485746 / 0.434364 (0.051383) | 0.606181 / 0.540337 (0.065843) | 0.776691 / 1.386936 (-0.610245) |\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) | 0.007820 / 0.011353 (-0.003533) | 0.004531 / 0.011008 (-0.006478) | 0.076142 / 0.038508 (0.037634) | 0.086367 / 0.023109 (0.063258) | 0.456150 / 0.275898 (0.180252) | 0.499712 / 0.323480 (0.176232) | 0.006545 / 0.007986 (-0.001441) | 0.003760 / 0.004328 (-0.000568) | 0.076400 / 0.004250 (0.072150) | 0.069689 / 0.037052 (0.032637) | 0.459732 / 0.258489 (0.201243) | 0.504217 / 0.293841 (0.210376) | 0.037838 / 0.128546 (-0.090709) | 0.009804 / 0.075646 (-0.065843) | 0.084654 / 0.419271 (-0.334617) | 0.060301 / 0.043533 (0.016768) | 0.452984 / 0.255139 (0.197845) | 0.479956 / 0.283200 (0.196757) | 0.029674 / 0.141683 (-0.112009) | 1.814059 / 1.452155 (0.361904) | 1.878886 / 1.492716 (0.386170) |\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) | 0.326174 / 0.018006 (0.308168) | 0.539722 / 0.000490 (0.539232) | 0.025637 / 0.000200 (0.025437) | 0.000209 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036328 / 0.037411 (-0.001084) | 0.106369 / 0.014526 (0.091843) | 0.118598 / 0.176557 (-0.057958) | 0.182760 / 0.737135 (-0.554376) | 0.120013 / 0.296338 (-0.176326) |\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) | 0.507328 / 0.215209 (0.292119) | 5.092689 / 2.077655 (3.015034) | 2.962334 / 1.504120 (1.458214) | 2.507699 / 1.541195 (0.966504) | 2.612245 / 1.468490 (1.143755) | 0.568625 / 4.584777 (-4.016152) | 4.296484 / 3.745712 (0.550772) | 4.037788 / 5.269862 (-1.232073) | 2.579826 / 4.565676 (-1.985850) | 0.068558 / 0.424275 (-0.355717) | 0.008916 / 0.007607 (0.001309) | 0.601054 / 0.226044 (0.375010) | 6.016061 / 2.268929 (3.747133) | 3.311880 / 55.444624 (-52.132744) | 2.912926 / 6.876477 (-3.963551) | 3.101465 / 2.142072 (0.959393) | 0.686848 / 4.805227 (-4.118380) | 0.160243 / 6.500664 (-6.340421) | 0.074084 / 0.075469 (-0.001385) |\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) | 1.754343 / 1.841788 (-0.087444) | 24.215302 / 8.074308 (16.140994) | 17.211007 / 10.191392 (7.019615) | 0.188370 / 0.680424 (-0.492054) | 0.028157 / 0.534201 (-0.506044) | 0.490879 / 0.579283 (-0.088404) | 0.501508 / 0.434364 (0.067144) | 0.599719 / 0.540337 (0.059381) | 0.852438 / 1.386936 (-0.534498) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d84cd1d6f51ca75ec5f5c3db3f372f093758cac9 \"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) | 0.009736 / 0.011353 (-0.001617) | 0.004761 / 0.011008 (-0.006247) | 0.100069 / 0.038508 (0.061561) | 0.077944 / 0.023109 (0.054835) | 0.419944 / 0.275898 (0.144046) | 0.459803 / 0.323480 (0.136323) | 0.006296 / 0.007986 (-0.001689) | 0.005375 / 0.004328 (0.001047) | 0.089457 / 0.004250 (0.085207) | 0.060585 / 0.037052 (0.023532) | 0.437988 / 0.258489 (0.179499) | 0.482676 / 0.293841 (0.188835) | 0.049126 / 0.128546 (-0.079420) | 0.015043 / 0.075646 (-0.060603) | 0.342500 / 0.419271 (-0.076771) | 0.067088 / 0.043533 (0.023555) | 0.418364 / 0.255139 (0.163225) | 0.458259 / 0.283200 (0.175059) | 0.034091 / 0.141683 (-0.107592) | 1.721589 / 1.452155 (0.269434) | 1.823142 / 1.492716 (0.330426) |\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) | 0.212110 / 0.018006 (0.194103) | 0.530957 / 0.000490 (0.530467) | 0.003581 / 0.000200 (0.003382) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030202 / 0.037411 (-0.007210) | 0.100552 / 0.014526 (0.086026) | 0.108150 / 0.176557 (-0.068407) | 0.173203 / 0.737135 (-0.563932) | 0.108624 / 0.296338 (-0.187715) |\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) | 0.577340 / 0.215209 (0.362131) | 5.794197 / 2.077655 (3.716543) | 2.396285 / 1.504120 (0.892165) | 2.151972 / 1.541195 (0.610777) | 2.109485 / 1.468490 (0.640995) | 0.873906 / 4.584777 (-3.710871) | 5.083302 / 3.745712 (1.337589) | 4.600756 / 5.269862 (-0.669105) | 2.891731 / 4.565676 (-1.673945) | 0.096293 / 0.424275 (-0.327982) | 0.008651 / 0.007607 (0.001044) | 0.719095 / 0.226044 (0.493051) | 7.193225 / 2.268929 (4.924297) | 3.220145 / 55.444624 (-52.224479) | 2.496715 / 6.876477 (-4.379762) | 2.672972 / 2.142072 (0.530900) | 1.031656 / 4.805227 (-3.773571) | 0.207854 / 6.500664 (-6.292810) | 0.074507 / 0.075469 (-0.000962) |\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) | 1.552821 / 1.841788 (-0.288967) | 22.573015 / 8.074308 (14.498707) | 21.074321 / 10.191392 (10.882929) | 0.231911 / 0.680424 (-0.448513) | 0.027761 / 0.534201 (-0.506440) | 0.474644 / 0.579283 (-0.104639) | 0.563780 / 0.434364 (0.129416) | 0.527593 / 0.540337 (-0.012745) | 0.732299 / 1.386936 (-0.654637) |\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) | 0.008675 / 0.011353 (-0.002678) | 0.005268 / 0.011008 (-0.005741) | 0.079078 / 0.038508 (0.040570) | 0.073505 / 0.023109 (0.050395) | 0.453982 / 0.275898 (0.178083) | 0.487839 / 0.323480 (0.164359) | 0.005950 / 0.007986 (-0.002035) | 0.003848 / 0.004328 (-0.000481) | 0.076004 / 0.004250 (0.071754) | 0.058410 / 0.037052 (0.021358) | 0.460099 / 0.258489 (0.201610) | 0.514860 / 0.293841 (0.221019) | 0.048843 / 0.128546 (-0.079703) | 0.014275 / 0.075646 (-0.061371) | 0.090243 / 0.419271 (-0.329029) | 0.060092 / 0.043533 (0.016559) | 0.455669 / 0.255139 (0.200530) | 0.484738 / 0.283200 (0.201538) | 0.033012 / 0.141683 (-0.108671) | 1.738854 / 1.452155 (0.286699) | 1.852552 / 1.492716 (0.359835) |\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) | 0.245453 / 0.018006 (0.227447) | 0.519929 / 0.000490 (0.519439) | 0.007262 / 0.000200 (0.007062) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031446 / 0.037411 (-0.005965) | 0.094236 / 0.014526 (0.079710) | 0.114457 / 0.176557 (-0.062100) | 0.167448 / 0.737135 (-0.569687) | 0.108791 / 0.296338 (-0.187548) |\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) | 0.603331 / 0.215209 (0.388122) | 6.051556 / 2.077655 (3.973902) | 2.797110 / 1.504120 (1.292990) | 2.500517 / 1.541195 (0.959322) | 2.531421 / 1.468490 (1.062931) | 0.852075 / 4.584777 (-3.732702) | 5.034140 / 3.745712 (1.288427) | 4.576573 / 5.269862 (-0.693289) | 2.973541 / 4.565676 (-1.592135) | 0.101303 / 0.424275 (-0.322972) | 0.008467 / 0.007607 (0.000860) | 0.707143 / 0.226044 (0.481098) | 7.262803 / 2.268929 (4.993874) | 3.548841 / 55.444624 (-51.895783) | 2.895975 / 6.876477 (-3.980502) | 3.063521 / 2.142072 (0.921449) | 1.014961 / 4.805227 (-3.790266) | 0.208527 / 6.500664 (-6.292137) | 0.074939 / 0.075469 (-0.000530) |\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) | 1.670708 / 1.841788 (-0.171080) | 22.685227 / 8.074308 (14.610919) | 20.393017 / 10.191392 (10.201625) | 0.239303 / 0.680424 (-0.441121) | 0.027742 / 0.534201 (-0.506459) | 0.467230 / 0.579283 (-0.112053) | 0.564169 / 0.434364 (0.129805) | 0.554859 / 0.540337 (0.014522) | 0.767471 / 1.386936 (-0.619465) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72a57356a46ded67f4d7a02741141a96061246a8 \"CML watermark\")\n" ]
2023-08-17T09:49:48
2023-08-18T13:45:58
2023-08-18T13:35:13
MEMBER
false
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e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")`
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6,154
Use yaml instead of get data patterns when possible
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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) | 0.006829 / 0.011353 (-0.004524) | 0.004535 / 0.011008 (-0.006473) | 0.085255 / 0.038508 (0.046747) | 0.080861 / 0.023109 (0.057752) | 0.366023 / 0.275898 (0.090125) | 0.403095 / 0.323480 (0.079615) | 0.005615 / 0.007986 (-0.002370) | 0.003830 / 0.004328 (-0.000498) | 0.064502 / 0.004250 (0.060251) | 0.053916 / 0.037052 (0.016863) | 0.366010 / 0.258489 (0.107521) | 0.414565 / 0.293841 (0.120724) | 0.031500 / 0.128546 (-0.097046) | 0.009252 / 0.075646 (-0.066394) | 0.289584 / 0.419271 (-0.129688) | 0.052984 / 0.043533 (0.009451) | 0.352626 / 0.255139 (0.097487) | 0.390964 / 0.283200 (0.107764) | 0.025118 / 0.141683 (-0.116565) | 1.462316 / 1.452155 (0.010161) | 1.565682 / 1.492716 (0.072966) |\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) | 0.294432 / 0.018006 (0.276426) | 0.618366 / 0.000490 (0.617876) | 0.003270 / 0.000200 (0.003071) | 0.000081 / 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) | 0.031194 / 0.037411 (-0.006217) | 0.088892 / 0.014526 (0.074366) | 0.102580 / 0.176557 (-0.073977) | 0.159449 / 0.737135 (-0.577686) | 0.104434 / 0.296338 (-0.191905) |\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) | 0.385690 / 0.215209 (0.170481) | 3.832782 / 2.077655 (1.755128) | 1.862521 / 1.504120 (0.358401) | 1.685674 / 1.541195 (0.144479) | 1.724984 / 1.468490 (0.256494) | 0.483700 / 4.584777 (-4.101077) | 3.664154 / 3.745712 (-0.081558) | 3.323023 / 5.269862 (-1.946839) | 2.055958 / 4.565676 (-2.509718) | 0.056990 / 0.424275 (-0.367285) | 0.007674 / 0.007607 (0.000067) | 0.460642 / 0.226044 (0.234598) | 4.609964 / 2.268929 (2.341036) | 2.434868 / 55.444624 (-53.009756) | 2.003347 / 6.876477 (-4.873130) | 2.209520 / 2.142072 (0.067448) | 0.629363 / 4.805227 (-4.175864) | 0.135434 / 6.500664 (-6.365230) | 0.060498 / 0.075469 (-0.014971) |\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) | 1.253917 / 1.841788 (-0.587870) | 19.988953 / 8.074308 (11.914645) | 14.353739 / 10.191392 (4.162347) | 0.165987 / 0.680424 (-0.514437) | 0.018299 / 0.534201 (-0.515902) | 0.395532 / 0.579283 (-0.183751) | 0.418708 / 0.434364 (-0.015656) | 0.460865 / 0.540337 (-0.079472) | 0.633925 / 1.386936 (-0.753011) |\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) | 0.006631 / 0.011353 (-0.004722) | 0.004109 / 0.011008 (-0.006899) | 0.065003 / 0.038508 (0.026495) | 0.080407 / 0.023109 (0.057297) | 0.362966 / 0.275898 (0.087068) | 0.389727 / 0.323480 (0.066247) | 0.005588 / 0.007986 (-0.002397) | 0.003517 / 0.004328 (-0.000812) | 0.065821 / 0.004250 (0.061570) | 0.057614 / 0.037052 (0.020561) | 0.367422 / 0.258489 (0.108932) | 0.400706 / 0.293841 (0.106865) | 0.031560 / 0.128546 (-0.096986) | 0.008659 / 0.075646 (-0.066987) | 0.070756 / 0.419271 (-0.348516) | 0.049821 / 0.043533 (0.006288) | 0.360836 / 0.255139 (0.105697) | 0.383981 / 0.283200 (0.100781) | 0.023719 / 0.141683 (-0.117963) | 1.485197 / 1.452155 (0.033043) | 1.544899 / 1.492716 (0.052182) |\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) | 0.336480 / 0.018006 (0.318474) | 0.532839 / 0.000490 (0.532349) | 0.003767 / 0.000200 (0.003567) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034132 / 0.037411 (-0.003280) | 0.090131 / 0.014526 (0.075605) | 0.104086 / 0.176557 (-0.072471) | 0.158385 / 0.737135 (-0.578751) | 0.106417 / 0.296338 (-0.189922) |\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) | 0.416462 / 0.215209 (0.201253) | 4.160409 / 2.077655 (2.082755) | 2.195355 / 1.504120 (0.691235) | 2.051234 / 1.541195 (0.510040) | 2.012116 / 1.468490 (0.543626) | 0.477414 / 4.584777 (-4.107363) | 3.590326 / 3.745712 (-0.155386) | 3.318490 / 5.269862 (-1.951371) | 2.064124 / 4.565676 (-2.501553) | 0.057040 / 0.424275 (-0.367235) | 0.007283 / 0.007607 (-0.000324) | 0.480490 / 0.226044 (0.254445) | 4.804013 / 2.268929 (2.535084) | 2.625940 / 55.444624 (-52.818685) | 2.231537 / 6.876477 (-4.644939) | 2.441649 / 2.142072 (0.299576) | 0.573207 / 4.805227 (-4.232020) | 0.131685 / 6.500664 (-6.368979) | 0.060112 / 0.075469 (-0.015357) |\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) | 1.358587 / 1.841788 (-0.483200) | 20.457562 / 8.074308 (12.383254) | 14.236304 / 10.191392 (4.044912) | 0.152860 / 0.680424 (-0.527563) | 0.018466 / 0.534201 (-0.515735) | 0.401391 / 0.579283 (-0.177893) | 0.410252 / 0.434364 (-0.024111) | 0.484335 / 0.540337 (-0.056002) | 0.663818 / 1.386936 (-0.723118) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#acac88873abcb585892dc361eb9f6a70a1fd9a59 \"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) | 0.007725 / 0.011353 (-0.003628) | 0.004448 / 0.011008 (-0.006560) | 0.098689 / 0.038508 (0.060180) | 0.082919 / 0.023109 (0.059809) | 0.380707 / 0.275898 (0.104809) | 0.452977 / 0.323480 (0.129497) | 0.004430 / 0.007986 (-0.003555) | 0.003712 / 0.004328 (-0.000616) | 0.076675 / 0.004250 (0.072425) | 0.062281 / 0.037052 (0.025228) | 0.403370 / 0.258489 (0.144881) | 0.464557 / 0.293841 (0.170716) | 0.035646 / 0.128546 (-0.092900) | 0.009776 / 0.075646 (-0.065870) | 0.341955 / 0.419271 (-0.077316) | 0.059515 / 0.043533 (0.015983) | 0.388421 / 0.255139 (0.133282) | 0.439496 / 0.283200 (0.156296) | 0.029090 / 0.141683 (-0.112593) | 1.727473 / 1.452155 (0.275319) | 1.810448 / 1.492716 (0.317732) |\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) | 0.221215 / 0.018006 (0.203208) | 0.486660 / 0.000490 (0.486171) | 0.005467 / 0.000200 (0.005267) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032491 / 0.037411 (-0.004920) | 0.094446 / 0.014526 (0.079920) | 0.110339 / 0.176557 (-0.066217) | 0.175004 / 0.737135 (-0.562131) | 0.109209 / 0.296338 (-0.187129) |\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) | 0.453966 / 0.215209 (0.238757) | 4.515842 / 2.077655 (2.438187) | 2.240512 / 1.504120 (0.736392) | 2.059911 / 1.541195 (0.518717) | 2.150635 / 1.468490 (0.682145) | 0.564509 / 4.584777 (-4.020268) | 4.055208 / 3.745712 (0.309496) | 3.614084 / 5.269862 (-1.655778) | 2.295760 / 4.565676 (-2.269917) | 0.066507 / 0.424275 (-0.357768) | 0.008909 / 0.007607 (0.001302) | 0.542604 / 0.226044 (0.316560) | 5.412162 / 2.268929 (3.143233) | 2.758757 / 55.444624 (-52.685867) | 2.430693 / 6.876477 (-4.445784) | 2.669866 / 2.142072 (0.527793) | 0.681756 / 4.805227 (-4.123471) | 0.156524 / 6.500664 (-6.344140) | 0.069499 / 0.075469 (-0.005970) |\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) | 1.571591 / 1.841788 (-0.270197) | 22.543437 / 8.074308 (14.469129) | 16.068426 / 10.191392 (5.877034) | 0.169860 / 0.680424 (-0.510564) | 0.021216 / 0.534201 (-0.512985) | 0.468745 / 0.579283 (-0.110538) | 0.475924 / 0.434364 (0.041560) | 0.535574 / 0.540337 (-0.004763) | 0.733823 / 1.386936 (-0.653113) |\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) | 0.008038 / 0.011353 (-0.003315) | 0.004565 / 0.011008 (-0.006443) | 0.076892 / 0.038508 (0.038384) | 0.089559 / 0.023109 (0.066450) | 0.456752 / 0.275898 (0.180854) | 0.497282 / 0.323480 (0.173802) | 0.005991 / 0.007986 (-0.001995) | 0.003784 / 0.004328 (-0.000545) | 0.076339 / 0.004250 (0.072089) | 0.066050 / 0.037052 (0.028998) | 0.462708 / 0.258489 (0.204219) | 0.503711 / 0.293841 (0.209870) | 0.037098 / 0.128546 (-0.091448) | 0.009869 / 0.075646 (-0.065777) | 0.083678 / 0.419271 (-0.335594) | 0.058166 / 0.043533 (0.014633) | 0.461839 / 0.255139 (0.206700) | 0.481546 / 0.283200 (0.198347) | 0.027755 / 0.141683 (-0.113928) | 1.738490 / 1.452155 (0.286335) | 1.832276 / 1.492716 (0.339560) |\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) | 0.329935 / 0.018006 (0.311929) | 0.497438 / 0.000490 (0.496949) | 0.034644 / 0.000200 (0.034444) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035427 / 0.037411 (-0.001984) | 0.105689 / 0.014526 (0.091163) | 0.117706 / 0.176557 (-0.058850) | 0.177862 / 0.737135 (-0.559273) | 0.116791 / 0.296338 (-0.179547) |\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) | 0.484851 / 0.215209 (0.269642) | 4.804346 / 2.077655 (2.726691) | 2.494801 / 1.504120 (0.990681) | 2.320185 / 1.541195 (0.778990) | 2.374090 / 1.468490 (0.905600) | 0.567397 / 4.584777 (-4.017380) | 4.087402 / 3.745712 (0.341690) | 3.794245 / 5.269862 (-1.475616) | 2.378481 / 4.565676 (-2.187195) | 0.068228 / 0.424275 (-0.356047) | 0.008740 / 0.007607 (0.001133) | 0.574876 / 0.226044 (0.348832) | 5.742644 / 2.268929 (3.473716) | 3.047661 / 55.444624 (-52.396963) | 2.729742 / 6.876477 (-4.146735) | 2.852510 / 2.142072 (0.710438) | 0.679450 / 4.805227 (-4.125777) | 0.156162 / 6.500664 (-6.344502) | 0.074051 / 0.075469 (-0.001418) |\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) | 1.576182 / 1.841788 (-0.265605) | 23.298147 / 8.074308 (15.223839) | 16.344621 / 10.191392 (6.153229) | 0.167571 / 0.680424 (-0.512852) | 0.021423 / 0.534201 (-0.512778) | 0.464511 / 0.579283 (-0.114772) | 0.453257 / 0.434364 (0.018893) | 0.563439 / 0.540337 (0.023102) | 0.764759 / 1.386936 (-0.622177) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8dc4b32b0d91bdb0971f8203ee37e6588c7770e \"CML watermark\")\n", "This should also fix https://github.com/huggingface/datasets/issues/6140, so please link it with this PR before merging.", "Done !", "<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) | 0.006719 / 0.011353 (-0.004634) | 0.004299 / 0.011008 (-0.006709) | 0.085296 / 0.038508 (0.046788) | 0.085144 / 0.023109 (0.062035) | 0.361703 / 0.275898 (0.085805) | 0.397721 / 0.323480 (0.074241) | 0.005920 / 0.007986 (-0.002065) | 0.003853 / 0.004328 (-0.000476) | 0.065633 / 0.004250 (0.061383) | 0.057000 / 0.037052 (0.019947) | 0.379981 / 0.258489 (0.121492) | 0.419041 / 0.293841 (0.125200) | 0.031225 / 0.128546 (-0.097322) | 0.008868 / 0.075646 (-0.066779) | 0.288808 / 0.419271 (-0.130463) | 0.052391 / 0.043533 (0.008859) | 0.362349 / 0.255139 (0.107210) | 0.399858 / 0.283200 (0.116658) | 0.025843 / 0.141683 (-0.115840) | 1.498988 / 1.452155 (0.046834) | 1.547290 / 1.492716 (0.054574) |\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) | 0.278091 / 0.018006 (0.260085) | 0.621794 / 0.000490 (0.621305) | 0.003770 / 0.000200 (0.003570) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029128 / 0.037411 (-0.008283) | 0.082061 / 0.014526 (0.067536) | 0.101758 / 0.176557 (-0.074799) | 0.155724 / 0.737135 (-0.581411) | 0.102173 / 0.296338 (-0.194165) |\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) | 0.387145 / 0.215209 (0.171935) | 3.868262 / 2.077655 (1.790607) | 1.886440 / 1.504120 (0.382320) | 1.723305 / 1.541195 (0.182111) | 1.805411 / 1.468490 (0.336921) | 0.485024 / 4.584777 (-4.099753) | 3.637859 / 3.745712 (-0.107853) | 3.319593 / 5.269862 (-1.950269) | 2.087860 / 4.565676 (-2.477817) | 0.056992 / 0.424275 (-0.367283) | 0.007623 / 0.007607 (0.000016) | 0.468182 / 0.226044 (0.242138) | 4.681112 / 2.268929 (2.412183) | 2.407010 / 55.444624 (-53.037614) | 2.026604 / 6.876477 (-4.849872) | 2.298158 / 2.142072 (0.156086) | 0.581839 / 4.805227 (-4.223388) | 0.132101 / 6.500664 (-6.368563) | 0.060472 / 0.075469 (-0.014997) |\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) | 1.236422 / 1.841788 (-0.605365) | 20.505168 / 8.074308 (12.430860) | 14.356081 / 10.191392 (4.164689) | 0.148808 / 0.680424 (-0.531616) | 0.018433 / 0.534201 (-0.515768) | 0.391323 / 0.579283 (-0.187960) | 0.413142 / 0.434364 (-0.021222) | 0.453484 / 0.540337 (-0.086853) | 0.620771 / 1.386936 (-0.766165) |\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) | 0.007030 / 0.011353 (-0.004323) | 0.004430 / 0.011008 (-0.006578) | 0.065578 / 0.038508 (0.027070) | 0.090751 / 0.023109 (0.067642) | 0.389121 / 0.275898 (0.113223) | 0.424657 / 0.323480 (0.101177) | 0.006575 / 0.007986 (-0.001410) | 0.003855 / 0.004328 (-0.000473) | 0.066175 / 0.004250 (0.061925) | 0.063255 / 0.037052 (0.026202) | 0.397161 / 0.258489 (0.138672) | 0.435291 / 0.293841 (0.141450) | 0.031622 / 0.128546 (-0.096925) | 0.008900 / 0.075646 (-0.066747) | 0.071694 / 0.419271 (-0.347577) | 0.049161 / 0.043533 (0.005628) | 0.386214 / 0.255139 (0.131075) | 0.404571 / 0.283200 (0.121372) | 0.024821 / 0.141683 (-0.116862) | 1.489514 / 1.452155 (0.037359) | 1.576139 / 1.492716 (0.083423) |\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) | 0.289884 / 0.018006 (0.271878) | 0.629342 / 0.000490 (0.628852) | 0.004799 / 0.000200 (0.004599) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032081 / 0.037411 (-0.005331) | 0.088152 / 0.014526 (0.073626) | 0.107289 / 0.176557 (-0.069267) | 0.164598 / 0.737135 (-0.572537) | 0.108395 / 0.296338 (-0.187944) |\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) | 0.426723 / 0.215209 (0.211514) | 4.267719 / 2.077655 (2.190064) | 2.289657 / 1.504120 (0.785537) | 2.117435 / 1.541195 (0.576240) | 2.187292 / 1.468490 (0.718802) | 0.478387 / 4.584777 (-4.106390) | 3.625096 / 3.745712 (-0.120616) | 3.408036 / 5.269862 (-1.861826) | 2.124117 / 4.565676 (-2.441559) | 0.056537 / 0.424275 (-0.367738) | 0.007489 / 0.007607 (-0.000118) | 0.502434 / 0.226044 (0.276389) | 5.025357 / 2.268929 (2.756428) | 2.740554 / 55.444624 (-52.704070) | 2.418841 / 6.876477 (-4.457635) | 2.730764 / 2.142072 (0.588691) | 0.600013 / 4.805227 (-4.205214) | 0.133039 / 6.500664 (-6.367625) | 0.061466 / 0.075469 (-0.014003) |\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) | 1.330211 / 1.841788 (-0.511577) | 21.092100 / 8.074308 (13.017792) | 14.463054 / 10.191392 (4.271662) | 0.154149 / 0.680424 (-0.526274) | 0.018891 / 0.534201 (-0.515310) | 0.393078 / 0.579283 (-0.186205) | 0.415279 / 0.434364 (-0.019085) | 0.479469 / 0.540337 (-0.060868) | 0.659953 / 1.386936 (-0.726983) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ca2ba050340829b4dd44791afc15db0d82a3276 \"CML watermark\")\n" ]
2023-08-17T09:17:05
2023-08-17T20:46:25
2023-08-17T20:37:19
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This would make the data files resolution faster: no need to list all the data files to infer the dataset builder to use. fix https://github.com/huggingface/datasets/issues/6140
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1,852,494,646
I_kwDODunzps5uatM2
6,152
FolderBase Dataset automatically resolves under current directory when data_dir is not specified
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[ "@lhoestq ", "Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)", "I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.", "This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed", "@mariosasko can you please assign this issue to me,I want to work on this", "#self-assign", "@mariosasko is this issue still open? i would love to kickstart my journey to open source with this issue!\r\nRegards\r\nzutarich", "@zutarich It is unless @debrupf2946 is working on it.", "#self-assign", "I am working and will open a pull request soon @Etelis \r\n" ]
2023-08-16T04:38:09
2024-01-22T15:04:51
null
CONTRIBUTOR
null
null
### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3
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I_kwDODunzps5uW51a
6,151
Faster sorting for single key items
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[ "`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```", "Ok interesting, I'll continue debugging to see what is going wrong on my end." ]
2023-08-15T14:02:31
2023-08-21T14:38:26
2023-08-21T14:38:25
NONE
null
null
### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. **Code snippet:** ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```
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6,150
Allow dataset implement .take
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[ "```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n", "hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770", "so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac", "Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`." ]
2023-08-15T00:17:51
2023-08-17T13:49:37
null
NONE
null
null
### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)
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I_kwDODunzps5uT3NQ
6,149
Dataset.from_parquet cannot load subset of columns
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[ "Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)" ]
2023-08-14T23:28:22
2023-08-17T22:36:05
2023-08-17T22:36:05
CONTRIBUTOR
null
null
### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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Ignore parallel warning in map_nested
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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) | 0.006818 / 0.011353 (-0.004534) | 0.004166 / 0.011008 (-0.006842) | 0.086756 / 0.038508 (0.048248) | 0.084444 / 0.023109 (0.061335) | 0.319249 / 0.275898 (0.043351) | 0.358689 / 0.323480 (0.035209) | 0.004344 / 0.007986 (-0.003641) | 0.003564 / 0.004328 (-0.000765) | 0.065021 / 0.004250 (0.060771) | 0.055991 / 0.037052 (0.018939) | 0.319573 / 0.258489 (0.061084) | 0.373239 / 0.293841 (0.079398) | 0.031431 / 0.128546 (-0.097115) | 0.008671 / 0.075646 (-0.066975) | 0.288484 / 0.419271 (-0.130788) | 0.053501 / 0.043533 (0.009968) | 0.316934 / 0.255139 (0.061795) | 0.354233 / 0.283200 (0.071034) | 0.028088 / 0.141683 (-0.113595) | 1.510905 / 1.452155 (0.058750) | 1.568614 / 1.492716 (0.075898) |\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) | 0.292343 / 0.018006 (0.274337) | 0.592309 / 0.000490 (0.591819) | 0.003850 / 0.000200 (0.003650) | 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) | 0.033510 / 0.037411 (-0.003901) | 0.089546 / 0.014526 (0.075020) | 0.104909 / 0.176557 (-0.071648) | 0.162219 / 0.737135 (-0.574916) | 0.104137 / 0.296338 (-0.192202) |\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) | 0.407993 / 0.215209 (0.192784) | 4.063423 / 2.077655 (1.985768) | 2.050237 / 1.504120 (0.546117) | 1.888939 / 1.541195 (0.347744) | 2.015195 / 1.468490 (0.546704) | 0.492617 / 4.584777 (-4.092160) | 3.595871 / 3.745712 (-0.149841) | 3.320467 / 5.269862 (-1.949395) | 2.099987 / 4.565676 (-2.465690) | 0.058513 / 0.424275 (-0.365762) | 0.007709 / 0.007607 (0.000102) | 0.479277 / 0.226044 (0.253233) | 4.790712 / 2.268929 (2.521783) | 2.517292 / 55.444624 (-52.927332) | 2.167461 / 6.876477 (-4.709016) | 2.432011 / 2.142072 (0.289939) | 0.600537 / 4.805227 (-4.204690) | 0.133538 / 6.500664 (-6.367126) | 0.059621 / 0.075469 (-0.015848) |\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) | 1.280375 / 1.841788 (-0.561413) | 20.777971 / 8.074308 (12.703663) | 14.869539 / 10.191392 (4.678147) | 0.159372 / 0.680424 (-0.521052) | 0.018096 / 0.534201 (-0.516105) | 0.393945 / 0.579283 (-0.185338) | 0.409598 / 0.434364 (-0.024766) | 0.459202 / 0.540337 (-0.081136) | 0.632298 / 1.386936 (-0.754638) |\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) | 0.006694 / 0.011353 (-0.004659) | 0.004299 / 0.011008 (-0.006709) | 0.064880 / 0.038508 (0.026372) | 0.083233 / 0.023109 (0.060124) | 0.366488 / 0.275898 (0.090590) | 0.405049 / 0.323480 (0.081569) | 0.005602 / 0.007986 (-0.002384) | 0.003623 / 0.004328 (-0.000705) | 0.064410 / 0.004250 (0.060160) | 0.057962 / 0.037052 (0.020910) | 0.365318 / 0.258489 (0.106829) | 0.403151 / 0.293841 (0.109310) | 0.031285 / 0.128546 (-0.097261) | 0.008867 / 0.075646 (-0.066780) | 0.071137 / 0.419271 (-0.348135) | 0.048398 / 0.043533 (0.004865) | 0.360187 / 0.255139 (0.105048) | 0.383872 / 0.283200 (0.100673) | 0.023232 / 0.141683 (-0.118451) | 1.526980 / 1.452155 (0.074826) | 1.587265 / 1.492716 (0.094549) |\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) | 0.362603 / 0.018006 (0.344596) | 0.557034 / 0.000490 (0.556544) | 0.025303 / 0.000200 (0.025103) | 0.000562 / 0.000054 (0.000508) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030636 / 0.037411 (-0.006775) | 0.088085 / 0.014526 (0.073559) | 0.103238 / 0.176557 (-0.073318) | 0.155208 / 0.737135 (-0.581928) | 0.106661 / 0.296338 (-0.189678) |\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) | 0.413660 / 0.215209 (0.198451) | 4.122717 / 2.077655 (2.045063) | 2.097656 / 1.504120 (0.593536) | 1.931995 / 1.541195 (0.390801) | 2.071497 / 1.468490 (0.603007) | 0.490257 / 4.584777 (-4.094520) | 3.588076 / 3.745712 (-0.157636) | 3.423087 / 5.269862 (-1.846774) | 2.147974 / 4.565676 (-2.417703) | 0.058783 / 0.424275 (-0.365492) | 0.007456 / 0.007607 (-0.000151) | 0.492350 / 0.226044 (0.266305) | 4.935935 / 2.268929 (2.667006) | 2.604217 / 55.444624 (-52.840407) | 2.333723 / 6.876477 (-4.542754) | 2.585293 / 2.142072 (0.443220) | 0.608800 / 4.805227 (-4.196427) | 0.135806 / 6.500664 (-6.364858) | 0.062716 / 0.075469 (-0.012753) |\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) | 1.347359 / 1.841788 (-0.494429) | 21.420505 / 8.074308 (13.346197) | 14.325914 / 10.191392 (4.134522) | 0.159617 / 0.680424 (-0.520806) | 0.018769 / 0.534201 (-0.515432) | 0.399677 / 0.579283 (-0.179606) | 0.402992 / 0.434364 (-0.031372) | 0.484629 / 0.540337 (-0.055709) | 0.656007 / 1.386936 (-0.730929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac94bb10d5c00ce8fdaf461eb1ff4b8572cfe956 \"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) | 0.007291 / 0.011353 (-0.004062) | 0.004501 / 0.011008 (-0.006508) | 0.097529 / 0.038508 (0.059021) | 0.079257 / 0.023109 (0.056147) | 0.356390 / 0.275898 (0.080492) | 0.390065 / 0.323480 (0.066585) | 0.006071 / 0.007986 (-0.001914) | 0.003783 / 0.004328 (-0.000546) | 0.074598 / 0.004250 (0.070348) | 0.059626 / 0.037052 (0.022574) | 0.395344 / 0.258489 (0.136855) | 0.418564 / 0.293841 (0.124723) | 0.041843 / 0.128546 (-0.086704) | 0.009293 / 0.075646 (-0.066354) | 0.332668 / 0.419271 (-0.086604) | 0.065753 / 0.043533 (0.022220) | 0.357285 / 0.255139 (0.102146) | 0.402974 / 0.283200 (0.119775) | 0.028714 / 0.141683 (-0.112968) | 1.733913 / 1.452155 (0.281759) | 1.802574 / 1.492716 (0.309858) |\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) | 0.253114 / 0.018006 (0.235108) | 0.606338 / 0.000490 (0.605848) | 0.006871 / 0.000200 (0.006671) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031850 / 0.037411 (-0.005562) | 0.095148 / 0.014526 (0.080622) | 0.111499 / 0.176557 (-0.065057) | 0.174653 / 0.737135 (-0.562483) | 0.109396 / 0.296338 (-0.186943) |\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) | 0.440442 / 0.215209 (0.225233) | 4.408792 / 2.077655 (2.331137) | 2.149778 / 1.504120 (0.645658) | 1.922430 / 1.541195 (0.381235) | 2.029281 / 1.468490 (0.560791) | 0.611586 / 4.584777 (-3.973191) | 4.204571 / 3.745712 (0.458859) | 3.638194 / 5.269862 (-1.631668) | 2.336146 / 4.565676 (-2.229531) | 0.065383 / 0.424275 (-0.358892) | 0.008441 / 0.007607 (0.000834) | 0.527357 / 0.226044 (0.301313) | 5.247892 / 2.268929 (2.978963) | 2.654005 / 55.444624 (-52.790620) | 2.256596 / 6.876477 (-4.619881) | 2.432191 / 2.142072 (0.290119) | 0.672759 / 4.805227 (-4.132469) | 0.148494 / 6.500664 (-6.352170) | 0.068248 / 0.075469 (-0.007221) |\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) | 1.544250 / 1.841788 (-0.297538) | 21.882016 / 8.074308 (13.807708) | 16.470182 / 10.191392 (6.278790) | 0.166107 / 0.680424 (-0.514317) | 0.021305 / 0.534201 (-0.512896) | 0.445069 / 0.579283 (-0.134214) | 0.500631 / 0.434364 (0.066267) | 0.525801 / 0.540337 (-0.014536) | 0.806534 / 1.386936 (-0.580402) |\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) | 0.007322 / 0.011353 (-0.004030) | 0.004206 / 0.011008 (-0.006802) | 0.074827 / 0.038508 (0.036319) | 0.084759 / 0.023109 (0.061650) | 0.421204 / 0.275898 (0.145306) | 0.464442 / 0.323480 (0.140962) | 0.006523 / 0.007986 (-0.001463) | 0.003613 / 0.004328 (-0.000716) | 0.073796 / 0.004250 (0.069545) | 0.066609 / 0.037052 (0.029557) | 0.430108 / 0.258489 (0.171619) | 0.463165 / 0.293841 (0.169324) | 0.036015 / 0.128546 (-0.092532) | 0.009696 / 0.075646 (-0.065951) | 0.083326 / 0.419271 (-0.335946) | 0.056804 / 0.043533 (0.013271) | 0.423333 / 0.255139 (0.168194) | 0.450538 / 0.283200 (0.167338) | 0.027067 / 0.141683 (-0.114616) | 1.700563 / 1.452155 (0.248408) | 1.748738 / 1.492716 (0.256021) |\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) | 0.395682 / 0.018006 (0.377675) | 0.540192 / 0.000490 (0.539702) | 0.140049 / 0.000200 (0.139849) | 0.000694 / 0.000054 (0.000639) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036643 / 0.037411 (-0.000769) | 0.104422 / 0.014526 (0.089896) | 0.113072 / 0.176557 (-0.063484) | 0.179561 / 0.737135 (-0.557575) | 0.118620 / 0.296338 (-0.177718) |\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) | 0.476547 / 0.215209 (0.261338) | 4.716009 / 2.077655 (2.638354) | 2.412111 / 1.504120 (0.907991) | 2.246389 / 1.541195 (0.705194) | 2.307058 / 1.468490 (0.838568) | 0.552759 / 4.584777 (-4.032018) | 4.172484 / 3.745712 (0.426771) | 3.848419 / 5.269862 (-1.421443) | 2.310338 / 4.565676 (-2.255339) | 0.071757 / 0.424275 (-0.352518) | 0.011206 / 0.007607 (0.003599) | 0.609526 / 0.226044 (0.383482) | 5.583065 / 2.268929 (3.314136) | 3.081227 / 55.444624 (-52.363397) | 2.637782 / 6.876477 (-4.238695) | 2.887561 / 2.142072 (0.745489) | 0.667227 / 4.805227 (-4.138000) | 0.154421 / 6.500664 (-6.346243) | 0.070772 / 0.075469 (-0.004697) |\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) | 1.605500 / 1.841788 (-0.236288) | 22.872717 / 8.074308 (14.798409) | 15.865333 / 10.191392 (5.673941) | 0.170353 / 0.680424 (-0.510071) | 0.021854 / 0.534201 (-0.512347) | 0.461467 / 0.579283 (-0.117816) | 0.477743 / 0.434364 (0.043379) | 0.597234 / 0.540337 (0.056896) | 0.800416 / 1.386936 (-0.586520) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a \"CML watermark\")\n" ]
2023-08-14T10:43:41
2023-08-17T08:54:06
2023-08-17T08:43:58
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This warning message was shown every time you pass num_proc to `load_dataset` because of `map_nested` ``` parallel_map is experimental and might be subject to breaking changes in the future ``` This PR removes it for `map_nested`. If someone uses another parallel backend they're already warned when `parallel_backend` is called anyway
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1,848,914,830
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6,147
ValueError when running BeamBasedBuilder with GCS path in cache_dir
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[ "The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```" ]
2023-08-14T03:11:34
2023-08-14T03:19:43
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### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline | beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3
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1,848,417,366
I_kwDODunzps5uLJxW
6,146
DatasetGenerationError when load glue benchmark datasets from `load_dataset`
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[ "I've tried clear the .cache file, doesn't work.", "This issue happens on AWS sagemaker", "This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?", "> This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: #5228). Is this the case?\r\n\r\nThats correct!\r\nSorry for my late response." ]
2023-08-13T05:17:56
2023-08-26T22:09:09
2023-08-26T22:09:09
NONE
null
null
### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10
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1,852,630,074
I_kwDODunzps5ubOQ6
6,153
custom load dataset to hub
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[ "This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).", "> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).\r\n\r\nThanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them ", "You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.", "> You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n\r\nhow about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot ! ", "> > You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n> \r\n> how about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot !\r\n\r\nI solved it by upgrading `Datasets` version to 2.15.0" ]
2023-08-13T04:42:22
2023-11-21T11:50:28
2023-10-08T17:04:16
NONE
null
null
### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub
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