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Add security policy to the project (#2958)
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* Add security policy to the project

* Specify supported versions
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@albertvillanova
albertvillanova authored Oct 21, 2021
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# Security Policy

## Supported Versions
<!--
Use this section to tell people about which versions of your project are
currently being supported with security updates.
| Version | Supported |
| ------- | ------------------ |
| 5.1.x | :white_check_mark: |
| 5.0.x | :x: |
| 4.0.x | :white_check_mark: |
| < 4.0 | :x: |
-->

Each major version is currently being supported with security updates.

| Version | Supported |
| ------- | ------------------ |
| 1.x.x | :white_check_mark: |


## Reporting a Vulnerability
<!--
Use this section to tell people how to report a vulnerability.
Tell them where to go, how often they can expect to get an update on a
reported vulnerability, what to expect if the vulnerability is accepted or
declined, etc.
-->

To report a security vulnerability, please contact: feedback@huggingface.co

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Show benchmarks

PyArrow==3.0.0

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) 0.013102 / 0.011353 (0.001749) 0.004584 / 0.011008 (-0.006424) 0.039996 / 0.038508 (0.001488) 0.039842 / 0.023109 (0.016733) 0.385470 / 0.275898 (0.109572) 0.416643 / 0.323480 (0.093164) 0.009892 / 0.007986 (0.001907) 0.005221 / 0.004328 (0.000892) 0.011705 / 0.004250 (0.007454) 0.049399 / 0.037052 (0.012347) 0.364276 / 0.258489 (0.105787) 0.416680 / 0.293841 (0.122839) 0.040781 / 0.128546 (-0.087765) 0.014702 / 0.075646 (-0.060944) 0.328209 / 0.419271 (-0.091063) 0.065913 / 0.043533 (0.022380) 0.372548 / 0.255139 (0.117409) 0.396993 / 0.283200 (0.113793) 0.089480 / 0.141683 (-0.052202) 2.183695 / 1.452155 (0.731540) 2.213601 / 1.492716 (0.720884)

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) 0.197464 / 0.018006 (0.179458) 0.492772 / 0.000490 (0.492283) 0.017031 / 0.000200 (0.016831) 0.000452 / 0.000054 (0.000397)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.042745 / 0.037411 (0.005333) 0.028119 / 0.014526 (0.013593) 0.044057 / 0.176557 (-0.132500) 0.141209 / 0.737135 (-0.595926) 0.036727 / 0.296338 (-0.259611)

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) 0.640113 / 0.215209 (0.424904) 6.377987 / 2.077655 (4.300332) 2.368172 / 1.504120 (0.864052) 2.022286 / 1.541195 (0.481092) 1.992246 / 1.468490 (0.523756) 0.687257 / 4.584777 (-3.897520) 6.979467 / 3.745712 (3.233755) 1.673609 / 5.269862 (-3.596252) 1.515638 / 4.565676 (-3.050038) 0.073576 / 0.424275 (-0.350699) 0.005740 / 0.007607 (-0.001867) 0.822312 / 0.226044 (0.596267) 8.073808 / 2.268929 (5.804879) 3.139818 / 55.444624 (-52.304806) 2.337951 / 6.876477 (-4.538526) 2.394930 / 2.142072 (0.252858) 0.888376 / 4.805227 (-3.916852) 0.171441 / 6.500664 (-6.329223) 0.064920 / 0.075469 (-0.010549)

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) 1.983773 / 1.841788 (0.141986) 15.870520 / 8.074308 (7.796212) 47.972302 / 10.191392 (37.780910) 1.004124 / 0.680424 (0.323700) 0.698875 / 0.534201 (0.164674) 0.300396 / 0.579283 (-0.278887) 0.722863 / 0.434364 (0.288499) 0.267426 / 0.540337 (-0.272911) 0.265942 / 1.386936 (-1.120994)
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) 0.011559 / 0.011353 (0.000206) 0.004553 / 0.011008 (-0.006455) 0.040764 / 0.038508 (0.002256) 0.043170 / 0.023109 (0.020061) 0.382894 / 0.275898 (0.106996) 0.457082 / 0.323480 (0.133602) 0.009250 / 0.007986 (0.001265) 0.003754 / 0.004328 (-0.000574) 0.011568 / 0.004250 (0.007318) 0.049660 / 0.037052 (0.012608) 0.390032 / 0.258489 (0.131543) 0.466829 / 0.293841 (0.172988) 0.042207 / 0.128546 (-0.086340) 0.013327 / 0.075646 (-0.062320) 0.327148 / 0.419271 (-0.092123) 0.064120 / 0.043533 (0.020587) 0.386304 / 0.255139 (0.131165) 0.443407 / 0.283200 (0.160207) 0.096017 / 0.141683 (-0.045665) 2.100534 / 1.452155 (0.648379) 2.225494 / 1.492716 (0.732777)

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) 0.211285 / 0.018006 (0.193279) 0.499870 / 0.000490 (0.499381) 0.015310 / 0.000200 (0.015110) 0.000480 / 0.000054 (0.000426)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.042908 / 0.037411 (0.005497) 0.027654 / 0.014526 (0.013128) 0.028640 / 0.176557 (-0.147916) 0.144161 / 0.737135 (-0.592974) 0.030867 / 0.296338 (-0.265472)

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) 0.638034 / 0.215209 (0.422825) 6.417477 / 2.077655 (4.339822) 2.447804 / 1.504120 (0.943684) 2.047127 / 1.541195 (0.505933) 2.052506 / 1.468490 (0.584016) 0.681556 / 4.584777 (-3.903221) 6.846010 / 3.745712 (3.100298) 1.642860 / 5.269862 (-3.627001) 1.552557 / 4.565676 (-3.013120) 0.077424 / 0.424275 (-0.346851) 0.006599 / 0.007607 (-0.001008) 0.828051 / 0.226044 (0.602006) 8.167051 / 2.268929 (5.898123) 3.315636 / 55.444624 (-52.128988) 2.506187 / 6.876477 (-4.370290) 2.525642 / 2.142072 (0.383569) 0.904522 / 4.805227 (-3.900705) 0.170245 / 6.500664 (-6.330420) 0.068505 / 0.075469 (-0.006964)

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) 2.019634 / 1.841788 (0.177847) 16.616784 / 8.074308 (8.542476) 46.040070 / 10.191392 (35.848678) 0.946067 / 0.680424 (0.265643) 0.687852 / 0.534201 (0.153651) 0.300036 / 0.579283 (-0.279247) 0.727008 / 0.434364 (0.292644) 0.254763 / 0.540337 (-0.285574) 0.270493 / 1.386936 (-1.116443)

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