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Fix dataset name for community Hub script-datasets #6855

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merged 8 commits into from
May 3, 2024
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Fix dataset name for community Hub script-datasets by passing explicit dataset_name to HubDatasetModuleFactoryWithScript.

Fix #6854.

CC: @Wauplin

@HuggingFaceDocBuilderDev

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

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albertvillanova commented May 2, 2024

The CI errors were unrelated. I am merging main once they were fixed:

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The new CI tests failing are also unrelated to this PR.

They are caused the the release of huggingface_hub-0.23.0, which now raises a FutureWarning for resume_download. See:

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I have merged main once the CI was fixed:

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This PR is ready for review @huggingface/datasets.

@albertvillanova albertvillanova merged commit 1bf8a46 into main May 3, 2024
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@albertvillanova albertvillanova deleted the fix-6854 branch May 3, 2024 15:51
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github-actions bot commented May 3, 2024

Show benchmarks

PyArrow==8.0.0

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) 0.005015 / 0.011353 (-0.006338) 0.003576 / 0.011008 (-0.007432) 0.063797 / 0.038508 (0.025289) 0.030198 / 0.023109 (0.007089) 0.237408 / 0.275898 (-0.038490) 0.266534 / 0.323480 (-0.056946) 0.003133 / 0.007986 (-0.004852) 0.002639 / 0.004328 (-0.001689) 0.049051 / 0.004250 (0.044801) 0.044650 / 0.037052 (0.007597) 0.253239 / 0.258489 (-0.005250) 0.288301 / 0.293841 (-0.005540) 0.027459 / 0.128546 (-0.101087) 0.010457 / 0.075646 (-0.065189) 0.207209 / 0.419271 (-0.212063) 0.035537 / 0.043533 (-0.007996) 0.240914 / 0.255139 (-0.014225) 0.266817 / 0.283200 (-0.016383) 0.019133 / 0.141683 (-0.122550) 1.113268 / 1.452155 (-0.338887) 1.183576 / 1.492716 (-0.309140)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.091218 / 0.018006 (0.073212) 0.301690 / 0.000490 (0.301200) 0.000234 / 0.000200 (0.000034) 0.000043 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.018489 / 0.037411 (-0.018922) 0.061379 / 0.014526 (0.046853) 0.072854 / 0.176557 (-0.103703) 0.120470 / 0.737135 (-0.616665) 0.074206 / 0.296338 (-0.222133)

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) 0.281725 / 0.215209 (0.066516) 2.805469 / 2.077655 (0.727814) 1.478755 / 1.504120 (-0.025365) 1.361718 / 1.541195 (-0.179477) 1.381460 / 1.468490 (-0.087030) 0.570758 / 4.584777 (-4.014019) 2.434707 / 3.745712 (-1.311005) 2.853322 / 5.269862 (-2.416539) 1.785684 / 4.565676 (-2.779992) 0.063551 / 0.424275 (-0.360724) 0.005322 / 0.007607 (-0.002285) 0.330938 / 0.226044 (0.104894) 3.247414 / 2.268929 (0.978486) 1.821401 / 55.444624 (-53.623223) 1.554258 / 6.876477 (-5.322219) 1.589263 / 2.142072 (-0.552809) 0.651232 / 4.805227 (-4.153995) 0.117903 / 6.500664 (-6.382761) 0.041948 / 0.075469 (-0.033522)

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) 1.000386 / 1.841788 (-0.841402) 11.645406 / 8.074308 (3.571098) 9.567803 / 10.191392 (-0.623589) 0.142869 / 0.680424 (-0.537555) 0.014250 / 0.534201 (-0.519951) 0.287054 / 0.579283 (-0.292229) 0.268849 / 0.434364 (-0.165515) 0.323307 / 0.540337 (-0.217031) 0.418965 / 1.386936 (-0.967971)
PyArrow==latest
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) 0.005216 / 0.011353 (-0.006137) 0.003714 / 0.011008 (-0.007294) 0.049544 / 0.038508 (0.011036) 0.030897 / 0.023109 (0.007788) 0.262478 / 0.275898 (-0.013420) 0.289693 / 0.323480 (-0.033787) 0.004226 / 0.007986 (-0.003760) 0.002811 / 0.004328 (-0.001518) 0.048256 / 0.004250 (0.044006) 0.040974 / 0.037052 (0.003922) 0.279431 / 0.258489 (0.020942) 0.306538 / 0.293841 (0.012697) 0.029493 / 0.128546 (-0.099054) 0.010550 / 0.075646 (-0.065097) 0.057826 / 0.419271 (-0.361445) 0.033045 / 0.043533 (-0.010488) 0.264820 / 0.255139 (0.009681) 0.282362 / 0.283200 (-0.000838) 0.018387 / 0.141683 (-0.123296) 1.167956 / 1.452155 (-0.284199) 1.247261 / 1.492716 (-0.245455)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.091962 / 0.018006 (0.073956) 0.300725 / 0.000490 (0.300236) 0.000209 / 0.000200 (0.000009) 0.000044 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.021835 / 0.037411 (-0.015576) 0.076954 / 0.014526 (0.062428) 0.087224 / 0.176557 (-0.089332) 0.127529 / 0.737135 (-0.609606) 0.089651 / 0.296338 (-0.206688)

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) 0.290878 / 0.215209 (0.075669) 2.845647 / 2.077655 (0.767992) 1.550515 / 1.504120 (0.046395) 1.422251 / 1.541195 (-0.118944) 1.425366 / 1.468490 (-0.043124) 0.559228 / 4.584777 (-4.025549) 0.970661 / 3.745712 (-2.775051) 2.755494 / 5.269862 (-2.514367) 1.724285 / 4.565676 (-2.841391) 0.062981 / 0.424275 (-0.361294) 0.006644 / 0.007607 (-0.000963) 0.344315 / 0.226044 (0.118270) 3.383452 / 2.268929 (1.114524) 1.914809 / 55.444624 (-53.529815) 1.626189 / 6.876477 (-5.250288) 1.614631 / 2.142072 (-0.527441) 0.636415 / 4.805227 (-4.168812) 0.115318 / 6.500664 (-6.385346) 0.040337 / 0.075469 (-0.035132)

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) 1.006257 / 1.841788 (-0.835531) 12.152942 / 8.074308 (4.078634) 9.744413 / 10.191392 (-0.446979) 0.139431 / 0.680424 (-0.540993) 0.015601 / 0.534201 (-0.518600) 0.287069 / 0.579283 (-0.292214) 0.125020 / 0.434364 (-0.309344) 0.380366 / 0.540337 (-0.159971) 0.423486 / 1.386936 (-0.963450)

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Wrong example of usage when config name is missing for community script-datasets
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