Fix args of feature docstrings

[albertvillanova]

Fix Args section of feature docstrings.

Currently, some args do not appear in the docs because they are not properly parsed due to the lack of their type (between parentheses).

[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.

[github-actions]

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)	0.005255 / 0.011353 (-0.006098)	0.003344 / 0.011008 (-0.007664)	0.062062 / 0.038508 (0.023554)	0.030154 / 0.023109 (0.007045)	0.233728 / 0.275898 (-0.042170)	0.258799 / 0.323480 (-0.064681)	0.004105 / 0.007986 (-0.003880)	0.002708 / 0.004328 (-0.001621)	0.048689 / 0.004250 (0.044439)	0.041864 / 0.037052 (0.004812)	0.247221 / 0.258489 (-0.011268)	0.274067 / 0.293841 (-0.019774)	0.029108 / 0.128546 (-0.099439)	0.011867 / 0.075646 (-0.063779)	0.203181 / 0.419271 (-0.216090)	0.035162 / 0.043533 (-0.008371)	0.239723 / 0.255139 (-0.015416)	0.256679 / 0.283200 (-0.026521)	0.018362 / 0.141683 (-0.123321)	1.139974 / 1.452155 (-0.312181)	1.193946 / 1.492716 (-0.298770)

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)	0.135477 / 0.018006 (0.117471)	0.298500 / 0.000490 (0.298011)	0.000225 / 0.000200 (0.000025)	0.000042 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018743 / 0.037411 (-0.018668)	0.062999 / 0.014526 (0.048474)	0.073466 / 0.176557 (-0.103090)	0.119227 / 0.737135 (-0.617908)	0.074338 / 0.296338 (-0.222000)

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)	0.280747 / 0.215209 (0.065538)	2.750660 / 2.077655 (0.673006)	1.461004 / 1.504120 (-0.043116)	1.348439 / 1.541195 (-0.192756)	1.365209 / 1.468490 (-0.103281)	0.718416 / 4.584777 (-3.866361)	2.333568 / 3.745712 (-1.412144)	2.854639 / 5.269862 (-2.415223)	1.821144 / 4.565676 (-2.744532)	0.077234 / 0.424275 (-0.347041)	0.005111 / 0.007607 (-0.002497)	0.330749 / 0.226044 (0.104705)	3.277189 / 2.268929 (1.008260)	1.825886 / 55.444624 (-53.618739)	1.515078 / 6.876477 (-5.361399)	1.527288 / 2.142072 (-0.614785)	0.786922 / 4.805227 (-4.018305)	0.131539 / 6.500664 (-6.369125)	0.042365 / 0.075469 (-0.033104)

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)	0.961809 / 1.841788 (-0.879979)	11.184540 / 8.074308 (3.110232)	9.473338 / 10.191392 (-0.718054)	0.138460 / 0.680424 (-0.541964)	0.014588 / 0.534201 (-0.519613)	0.301503 / 0.579283 (-0.277780)	0.261092 / 0.434364 (-0.173271)	0.336480 / 0.540337 (-0.203857)	0.427665 / 1.386936 (-0.959271)

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)	0.005517 / 0.011353 (-0.005836)	0.003417 / 0.011008 (-0.007591)	0.049338 / 0.038508 (0.010830)	0.033411 / 0.023109 (0.010302)	0.264328 / 0.275898 (-0.011570)	0.286750 / 0.323480 (-0.036730)	0.004299 / 0.007986 (-0.003686)	0.002506 / 0.004328 (-0.001823)	0.049511 / 0.004250 (0.045260)	0.041471 / 0.037052 (0.004418)	0.276732 / 0.258489 (0.018243)	0.311908 / 0.293841 (0.018067)	0.031683 / 0.128546 (-0.096863)	0.011700 / 0.075646 (-0.063946)	0.060084 / 0.419271 (-0.359188)	0.037757 / 0.043533 (-0.005776)	0.265342 / 0.255139 (0.010203)	0.287782 / 0.283200 (0.004583)	0.018692 / 0.141683 (-0.122990)	1.163462 / 1.452155 (-0.288692)	1.219236 / 1.492716 (-0.273481)

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)	0.094102 / 0.018006 (0.076096)	0.303976 / 0.000490 (0.303487)	0.000208 / 0.000200 (0.000008)	0.000042 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023252 / 0.037411 (-0.014160)	0.076986 / 0.014526 (0.062461)	0.088831 / 0.176557 (-0.087726)	0.128661 / 0.737135 (-0.608475)	0.089082 / 0.296338 (-0.207256)

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)	0.297428 / 0.215209 (0.082218)	2.951568 / 2.077655 (0.873913)	1.597627 / 1.504120 (0.093508)	1.466556 / 1.541195 (-0.074639)	1.455522 / 1.468490 (-0.012968)	0.723576 / 4.584777 (-3.861201)	0.951113 / 3.745712 (-2.794599)	2.889671 / 5.269862 (-2.380190)	1.877330 / 4.565676 (-2.688347)	0.079124 / 0.424275 (-0.345151)	0.005146 / 0.007607 (-0.002461)	0.344063 / 0.226044 (0.118018)	3.432190 / 2.268929 (1.163261)	1.927049 / 55.444624 (-53.517576)	1.638552 / 6.876477 (-5.237924)	1.647791 / 2.142072 (-0.494282)	0.800526 / 4.805227 (-4.004701)	0.131858 / 6.500664 (-6.368806)	0.040852 / 0.075469 (-0.034618)

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)	1.025536 / 1.841788 (-0.816252)	11.798302 / 8.074308 (3.723994)	10.012051 / 10.191392 (-0.179341)	0.137701 / 0.680424 (-0.542723)	0.015151 / 0.534201 (-0.519050)	0.298972 / 0.579283 (-0.280311)	0.123816 / 0.434364 (-0.310548)	0.337292 / 0.540337 (-0.203046)	0.432729 / 1.386936 (-0.954207)