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fix docs
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@lhoestq
lhoestq committed Feb 12, 2021
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8 changes: 4 additions & 4 deletions docs/source/share_dataset.rst
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Expand Up @@ -112,7 +112,7 @@ In this page, we will show you how to share a dataset with the community on the
Optionally, you can join an existing organization or create a new one.

Prepare your dataset for uploading
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

We have seen in the :doc:`dataset script tutorial <add_dataset>`: how to write a dataset loading script. Let's see how you can share it on the
`datasets hub <https://huggingface.co/datasets>`__.
Expand Down Expand Up @@ -203,7 +203,7 @@ Additionally, if you want to change multiple repos at once, the `change_config.p


Check the directory before pushing to the datasets hub.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Make sure there are no garbage files in the directory you'll upload. It should only have:

Expand Down Expand Up @@ -240,11 +240,11 @@ And pushed to the remote:
git push
This will upload the folder containing the dataset script and dataset infos we have just prepared.
This will upload the folder containing the dataset script and dataset infos that we have just prepared.


Using your dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Your dataset now has a page on huggingface.co/datasets 🔥

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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) 0.018537 / 0.011353 (0.007185) 0.015328 / 0.011008 (0.004320) 0.047474 / 0.038508 (0.008966) 0.039919 / 0.023109 (0.016810) 0.223834 / 0.275898 (-0.052064) 0.248975 / 0.323480 (-0.074504) 0.009377 / 0.007986 (0.001392) 0.004858 / 0.004328 (0.000529) 0.007842 / 0.004250 (0.003591) 0.055177 / 0.037052 (0.018125) 0.220159 / 0.258489 (-0.038330) 0.252770 / 0.293841 (-0.041070) 0.159719 / 0.128546 (0.031172) 0.119957 / 0.075646 (0.044311) 0.498123 / 0.419271 (0.078852) 0.432090 / 0.043533 (0.388557) 0.213852 / 0.255139 (-0.041287) 0.240977 / 0.283200 (-0.042223) 1.782952 / 0.141683 (1.641269) 1.886732 / 1.452155 (0.434577) 1.921747 / 1.492716 (0.429031)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.043933 / 0.037411 (0.006522) 0.021153 / 0.014526 (0.006627) 0.032517 / 0.176557 (-0.144039) 0.058131 / 0.737135 (-0.679004) 0.051358 / 0.296338 (-0.244980)

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) 0.255436 / 0.215209 (0.040227) 2.420226 / 2.077655 (0.342572) 1.283169 / 1.504120 (-0.220951) 1.179306 / 1.541195 (-0.361889) 1.250077 / 1.468490 (-0.218413) 6.485783 / 4.584777 (1.901006) 5.804975 / 3.745712 (2.059263) 8.448916 / 5.269862 (3.179055) 7.245710 / 4.565676 (2.680034) 0.644775 / 0.424275 (0.220500) 0.011058 / 0.007607 (0.003451) 0.265361 / 0.226044 (0.039317) 2.781958 / 2.268929 (0.513029) 1.812002 / 55.444624 (-53.632623) 1.662693 / 6.876477 (-5.213784) 1.650041 / 2.142072 (-0.492032) 6.491149 / 4.805227 (1.685922) 6.137152 / 6.500664 (-0.363512) 6.883020 / 0.075469 (6.807551)

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) 10.583278 / 1.841788 (8.741490) 16.361562 / 8.074308 (8.287254) 17.439754 / 10.191392 (7.248362) 0.820703 / 0.680424 (0.140280) 0.320010 / 0.534201 (-0.214191) 0.814758 / 0.579283 (0.235475) 0.654097 / 0.434364 (0.219734) 0.693229 / 0.540337 (0.152891) 1.522373 / 1.386936 (0.135437)
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) 0.018611 / 0.011353 (0.007258) 0.015157 / 0.011008 (0.004149) 0.067575 / 0.038508 (0.029067) 0.040045 / 0.023109 (0.016936) 0.394651 / 0.275898 (0.118753) 0.468092 / 0.323480 (0.144612) 0.010050 / 0.007986 (0.002064) 0.004663 / 0.004328 (0.000334) 0.006854 / 0.004250 (0.002603) 0.054258 / 0.037052 (0.017206) 0.398192 / 0.258489 (0.139703) 0.454040 / 0.293841 (0.160199) 0.143416 / 0.128546 (0.014869) 0.116046 / 0.075646 (0.040399) 0.495647 / 0.419271 (0.076375) 0.435443 / 0.043533 (0.391910) 0.389182 / 0.255139 (0.134043) 0.419272 / 0.283200 (0.136072) 1.759788 / 0.141683 (1.618105) 1.887263 / 1.452155 (0.435109) 2.038490 / 1.492716 (0.545774)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.044690 / 0.037411 (0.007279) 0.022100 / 0.014526 (0.007574) 0.032328 / 0.176557 (-0.144228) 0.052277 / 0.737135 (-0.684858) 0.033030 / 0.296338 (-0.263308)

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) 0.303804 / 0.215209 (0.088595) 3.071687 / 2.077655 (0.994032) 2.008926 / 1.504120 (0.504806) 1.933328 / 1.541195 (0.392133) 2.069212 / 1.468490 (0.600722) 6.864054 / 4.584777 (2.279277) 5.913427 / 3.745712 (2.167715) 7.975629 / 5.269862 (2.705768) 7.036918 / 4.565676 (2.471242) 0.680225 / 0.424275 (0.255950) 0.012682 / 0.007607 (0.005075) 0.367370 / 0.226044 (0.141326) 3.427821 / 2.268929 (1.158893) 2.443561 / 55.444624 (-53.001064) 2.251233 / 6.876477 (-4.625244) 2.358842 / 2.142072 (0.216770) 6.820242 / 4.805227 (2.015015) 4.859902 / 6.500664 (-1.640762) 8.190022 / 0.075469 (8.114553)

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) 10.945223 / 1.841788 (9.103435) 17.282562 / 8.074308 (9.208254) 18.947905 / 10.191392 (8.756513) 0.876842 / 0.680424 (0.196418) 0.599842 / 0.534201 (0.065641) 0.745591 / 0.579283 (0.166308) 0.586044 / 0.434364 (0.151680) 0.646779 / 0.540337 (0.106441) 1.653592 / 1.386936 (0.266656)

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