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@lhoestq
lhoestq committed Feb 15, 2021
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2 changes: 1 addition & 1 deletion docs/source/loading_datasets.rst
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Expand Up @@ -25,7 +25,7 @@ All the datasets currently available on the `Hub <https://huggingface.co/dataset
>>> from datasets import list_datasets
>>> datasets_list = list_datasets()
>>> len(datasets_list)
136
656
>>> print(', '.join(dataset for dataset in datasets_list))
aeslc, ag_news, ai2_arc, allocine, anli, arcd, art, billsum, blended_skill_talk, blimp, blog_authorship_corpus, bookcorpus, boolq, break_data,
c4, cfq, civil_comments, cmrc2018, cnn_dailymail, coarse_discourse, com_qa, commonsense_qa, compguesswhat, coqa, cornell_movie_dialog, cos_e,
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Show benchmarks

PyArrow==0.17.1

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.020629 / 0.011353 (0.009276) 0.016859 / 0.011008 (0.005850) 0.049078 / 0.038508 (0.010570) 0.035666 / 0.023109 (0.012557) 0.229410 / 0.275898 (-0.046488) 0.264479 / 0.323480 (-0.059000) 0.009586 / 0.007986 (0.001600) 0.005772 / 0.004328 (0.001444) 0.007584 / 0.004250 (0.003333) 0.050951 / 0.037052 (0.013899) 0.233315 / 0.258489 (-0.025174) 0.258543 / 0.293841 (-0.035297) 0.176965 / 0.128546 (0.048419) 0.140126 / 0.075646 (0.064480) 0.481196 / 0.419271 (0.061924) 0.452474 / 0.043533 (0.408942) 0.232986 / 0.255139 (-0.022153) 0.248795 / 0.283200 (-0.034405) 1.852808 / 0.141683 (1.711125) 1.999793 / 1.452155 (0.547639) 2.080390 / 1.492716 (0.587674)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.044611 / 0.037411 (0.007200) 0.021068 / 0.014526 (0.006542) 0.032551 / 0.176557 (-0.144005) 0.050465 / 0.737135 (-0.686671) 0.049139 / 0.296338 (-0.247199)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.289416 / 0.215209 (0.074207) 2.915345 / 2.077655 (0.837691) 1.434845 / 1.504120 (-0.069275) 1.261823 / 1.541195 (-0.279372) 1.315509 / 1.468490 (-0.152982) 7.926689 / 4.584777 (3.341912) 7.001592 / 3.745712 (3.255879) 9.739219 / 5.269862 (4.469358) 8.590292 / 4.565676 (4.024615) 0.777239 / 0.424275 (0.352964) 0.011722 / 0.007607 (0.004115) 0.337907 / 0.226044 (0.111863) 3.564836 / 2.268929 (1.295908) 2.021227 / 55.444624 (-53.423398) 1.685720 / 6.876477 (-5.190757) 1.697481 / 2.142072 (-0.444591) 7.954109 / 4.805227 (3.148882) 6.391537 / 6.500664 (-0.109127) 5.869268 / 0.075469 (5.793799)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 12.318009 / 1.841788 (10.476222) 15.650642 / 8.074308 (7.576333) 23.872408 / 10.191392 (13.681016) 0.534489 / 0.680424 (-0.145935) 0.339103 / 0.534201 (-0.195098) 0.950505 / 0.579283 (0.371222) 0.715661 / 0.434364 (0.281297) 0.791970 / 0.540337 (0.251633) 1.727729 / 1.386936 (0.340793)
PyArrow==1.0
Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.021326 / 0.011353 (0.009973) 0.018000 / 0.011008 (0.006991) 0.060675 / 0.038508 (0.022167) 0.038064 / 0.023109 (0.014955) 0.381540 / 0.275898 (0.105642) 0.439018 / 0.323480 (0.115539) 0.007009 / 0.007986 (-0.000977) 0.005131 / 0.004328 (0.000802) 0.007424 / 0.004250 (0.003174) 0.053579 / 0.037052 (0.016526) 0.452429 / 0.258489 (0.193940) 0.490622 / 0.293841 (0.196781) 0.176542 / 0.128546 (0.047996) 0.151349 / 0.075646 (0.075703) 0.489034 / 0.419271 (0.069762) 0.475741 / 0.043533 (0.432208) 0.376555 / 0.255139 (0.121416) 0.412914 / 0.283200 (0.129714) 2.070222 / 0.141683 (1.928539) 2.102812 / 1.452155 (0.650658) 2.191654 / 1.492716 (0.698938)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.047239 / 0.037411 (0.009828) 0.023429 / 0.014526 (0.008903) 0.044685 / 0.176557 (-0.131871) 0.054196 / 0.737135 (-0.682940) 0.050091 / 0.296338 (-0.246247)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.387571 / 0.215209 (0.172362) 3.834517 / 2.077655 (1.756862) 2.452741 / 1.504120 (0.948621) 2.262388 / 1.541195 (0.721193) 2.350735 / 1.468490 (0.882245) 7.830033 / 4.584777 (3.245257) 6.883510 / 3.745712 (3.137798) 9.662861 / 5.269862 (4.392999) 8.428749 / 4.565676 (3.863073) 0.781671 / 0.424275 (0.357396) 0.012168 / 0.007607 (0.004561) 0.424533 / 0.226044 (0.198488) 4.182692 / 2.268929 (1.913763) 2.743574 / 55.444624 (-52.701051) 2.436926 / 6.876477 (-4.439551) 2.414752 / 2.142072 (0.272679) 7.737301 / 4.805227 (2.932074) 5.735969 / 6.500664 (-0.764695) 7.340925 / 0.075469 (7.265456)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 13.928679 / 1.841788 (12.086892) 16.493771 / 8.074308 (8.419463) 24.724669 / 10.191392 (14.533277) 1.049155 / 0.680424 (0.368731) 0.658568 / 0.534201 (0.124367) 0.868326 / 0.579283 (0.289043) 0.695856 / 0.434364 (0.261492) 0.822319 / 0.540337 (0.281982) 1.734542 / 1.386936 (0.347606)

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