Support push_to_hub without org/user to default to logged-in user

[albertvillanova]

This behavior is aligned with:

• the behavior of datasets before merging Support push_to_hub canonical datasets #6519
  • the behavior described in the corresponding docstring
• the behavior of huggingface_hub.create_repo

Revert "Support push_to_hub canonical datasets (#6519)"

• This reverts commit a887ee7.

Fix #6597.

[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.

[albertvillanova]

@huggingface/datasets, feel free to review this PR so that it can be included in the next release.

[lhoestq]

Great ! And people (i.e. us) that want to push to canonical datasets can just use versions of datasets before this PR's change

[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)	0.005222 / 0.011353 (-0.006131)	0.003621 / 0.011008 (-0.007387)	0.063091 / 0.038508 (0.024583)	0.029395 / 0.023109 (0.006285)	0.231445 / 0.275898 (-0.044453)	0.256716 / 0.323480 (-0.066764)	0.004905 / 0.007986 (-0.003081)	0.002703 / 0.004328 (-0.001625)	0.048526 / 0.004250 (0.044276)	0.041382 / 0.037052 (0.004330)	0.247468 / 0.258489 (-0.011021)	0.270670 / 0.293841 (-0.023171)	0.028088 / 0.128546 (-0.100458)	0.010661 / 0.075646 (-0.064985)	0.205812 / 0.419271 (-0.213459)	0.035880 / 0.043533 (-0.007653)	0.237310 / 0.255139 (-0.017829)	0.255440 / 0.283200 (-0.027760)	0.018334 / 0.141683 (-0.123349)	1.128815 / 1.452155 (-0.323340)	1.204771 / 1.492716 (-0.287945)

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)	0.089175 / 0.018006 (0.071169)	0.298584 / 0.000490 (0.298095)	0.000206 / 0.000200 (0.000006)	0.000043 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018532 / 0.037411 (-0.018880)	0.061158 / 0.014526 (0.046632)	0.074177 / 0.176557 (-0.102380)	0.119408 / 0.737135 (-0.617728)	0.073821 / 0.296338 (-0.222518)

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)	0.277630 / 0.215209 (0.062420)	2.735038 / 2.077655 (0.657383)	1.437251 / 1.504120 (-0.066868)	1.304596 / 1.541195 (-0.236598)	1.316830 / 1.468490 (-0.151661)	0.551057 / 4.584777 (-4.033720)	2.337247 / 3.745712 (-1.408465)	2.761501 / 5.269862 (-2.508361)	1.729000 / 4.565676 (-2.836677)	0.069398 / 0.424275 (-0.354877)	0.005059 / 0.007607 (-0.002548)	0.359594 / 0.226044 (0.133550)	3.283325 / 2.268929 (1.014397)	1.777410 / 55.444624 (-53.667214)	1.518522 / 6.876477 (-5.357954)	1.546712 / 2.142072 (-0.595361)	0.627047 / 4.805227 (-4.178180)	0.117058 / 6.500664 (-6.383606)	0.043437 / 0.075469 (-0.032032)

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)	1.056303 / 1.841788 (-0.785484)	11.552295 / 8.074308 (3.477987)	10.184582 / 10.191392 (-0.006810)	0.129061 / 0.680424 (-0.551363)	0.014093 / 0.534201 (-0.520108)	0.292268 / 0.579283 (-0.287015)	0.264750 / 0.434364 (-0.169614)	0.334770 / 0.540337 (-0.205567)	0.436749 / 1.386936 (-0.950187)

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)	0.005408 / 0.011353 (-0.005945)	0.003650 / 0.011008 (-0.007358)	0.054263 / 0.038508 (0.015755)	0.031112 / 0.023109 (0.008003)	0.270582 / 0.275898 (-0.005316)	0.303506 / 0.323480 (-0.019974)	0.004351 / 0.007986 (-0.003635)	0.002654 / 0.004328 (-0.001674)	0.049631 / 0.004250 (0.045381)	0.045209 / 0.037052 (0.008156)	0.284992 / 0.258489 (0.026503)	0.316653 / 0.293841 (0.022812)	0.049526 / 0.128546 (-0.079020)	0.010696 / 0.075646 (-0.064951)	0.057859 / 0.419271 (-0.361413)	0.034227 / 0.043533 (-0.009306)	0.269656 / 0.255139 (0.014517)	0.288766 / 0.283200 (0.005567)	0.017892 / 0.141683 (-0.123791)	1.167492 / 1.452155 (-0.284662)	1.217263 / 1.492716 (-0.275454)

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)	0.089306 / 0.018006 (0.071299)	0.300774 / 0.000490 (0.300284)	0.000198 / 0.000200 (-0.000002)	0.000042 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.022050 / 0.037411 (-0.015361)	0.076781 / 0.014526 (0.062255)	0.086597 / 0.176557 (-0.089959)	0.125094 / 0.737135 (-0.612042)	0.089412 / 0.296338 (-0.206927)

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)	0.287444 / 0.215209 (0.072235)	2.830047 / 2.077655 (0.752392)	1.567492 / 1.504120 (0.063372)	1.439875 / 1.541195 (-0.101320)	1.461699 / 1.468490 (-0.006791)	0.569595 / 4.584777 (-4.015182)	2.454391 / 3.745712 (-1.291322)	2.655829 / 5.269862 (-2.614032)	1.756122 / 4.565676 (-2.809554)	0.063333 / 0.424275 (-0.360942)	0.005086 / 0.007607 (-0.002521)	0.351210 / 0.226044 (0.125166)	3.375545 / 2.268929 (1.106617)	1.945367 / 55.444624 (-53.499258)	1.662635 / 6.876477 (-5.213841)	1.762859 / 2.142072 (-0.379213)	0.651889 / 4.805227 (-4.153339)	0.118341 / 6.500664 (-6.382323)	0.040897 / 0.075469 (-0.034572)

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)	1.005270 / 1.841788 (-0.836518)	12.247847 / 8.074308 (4.173539)	10.828131 / 10.191392 (0.636739)	0.129741 / 0.680424 (-0.550683)	0.015184 / 0.534201 (-0.519017)	0.295440 / 0.579283 (-0.283843)	0.276759 / 0.434364 (-0.157605)	0.329046 / 0.540337 (-0.211291)	0.421750 / 1.386936 (-0.965186)