Improve error message for gated datasets on load

[lewtun]

Internal Slack discussion: https://huggingface.slack.com/archives/C02V51Q3800/p1708424971135029

[lhoestq]

Thank you ! Should we also add the link to the dataset page ?

[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.

[lewtun]

Thank you ! Should we also add the link to the dataset page ?

Good idea! Done in 4ab5521

[lhoestq]

Looks like a test is failing: test_load_dataset_cached_local_script .

Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal

[lewtun]

Ah let me take a look!

[lewtun]

Looks like a test is failing: test_load_dataset_cached_local_script .

Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal

Fixed by reverting the error message root + added a small clarifying part

[lhoestq]

Cool thanks !

[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)	0.005634 / 0.011353 (-0.005719)	0.003786 / 0.011008 (-0.007222)	0.064245 / 0.038508 (0.025737)	0.031228 / 0.023109 (0.008119)	0.248162 / 0.275898 (-0.027736)	0.273454 / 0.323480 (-0.050026)	0.003176 / 0.007986 (-0.004809)	0.002814 / 0.004328 (-0.001515)	0.049234 / 0.004250 (0.044984)	0.046075 / 0.037052 (0.009023)	0.262410 / 0.258489 (0.003921)	0.290597 / 0.293841 (-0.003244)	0.028545 / 0.128546 (-0.100001)	0.010881 / 0.075646 (-0.064766)	0.212098 / 0.419271 (-0.207173)	0.036406 / 0.043533 (-0.007127)	0.244571 / 0.255139 (-0.010568)	0.269537 / 0.283200 (-0.013663)	0.019574 / 0.141683 (-0.122109)	1.120369 / 1.452155 (-0.331785)	1.170188 / 1.492716 (-0.322529)

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)	0.108088 / 0.018006 (0.090082)	0.299836 / 0.000490 (0.299346)	0.000204 / 0.000200 (0.000004)	0.000043 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.020881 / 0.037411 (-0.016531)	0.065290 / 0.014526 (0.050764)	0.074283 / 0.176557 (-0.102274)	0.122189 / 0.737135 (-0.614947)	0.077772 / 0.296338 (-0.218566)

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)	0.278329 / 0.215209 (0.063120)	2.709885 / 2.077655 (0.632230)	1.428824 / 1.504120 (-0.075296)	1.314338 / 1.541195 (-0.226857)	1.349445 / 1.468490 (-0.119045)	0.571863 / 4.584777 (-4.012914)	2.358306 / 3.745712 (-1.387407)	2.873498 / 5.269862 (-2.396364)	1.779897 / 4.565676 (-2.785779)	0.062828 / 0.424275 (-0.361447)	0.005416 / 0.007607 (-0.002191)	0.337645 / 0.226044 (0.111601)	3.328868 / 2.268929 (1.059940)	1.793387 / 55.444624 (-53.651238)	1.539201 / 6.876477 (-5.337276)	1.589552 / 2.142072 (-0.552520)	0.645454 / 4.805227 (-4.159773)	0.116966 / 6.500664 (-6.383698)	0.043339 / 0.075469 (-0.032130)

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)	0.995743 / 1.841788 (-0.846045)	12.096551 / 8.074308 (4.022243)	10.214299 / 10.191392 (0.022907)	0.133025 / 0.680424 (-0.547399)	0.014393 / 0.534201 (-0.519808)	0.289018 / 0.579283 (-0.290266)	0.267879 / 0.434364 (-0.166485)	0.324362 / 0.540337 (-0.215976)	0.425596 / 1.386936 (-0.961340)

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)	0.005739 / 0.011353 (-0.005614)	0.003992 / 0.011008 (-0.007017)	0.051362 / 0.038508 (0.012854)	0.031707 / 0.023109 (0.008598)	0.274807 / 0.275898 (-0.001091)	0.298897 / 0.323480 (-0.024583)	0.004363 / 0.007986 (-0.003622)	0.002862 / 0.004328 (-0.001466)	0.050462 / 0.004250 (0.046212)	0.048158 / 0.037052 (0.011106)	0.282759 / 0.258489 (0.024270)	0.317766 / 0.293841 (0.023926)	0.060245 / 0.128546 (-0.068301)	0.011279 / 0.075646 (-0.064367)	0.061175 / 0.419271 (-0.358097)	0.035876 / 0.043533 (-0.007656)	0.273963 / 0.255139 (0.018824)	0.288788 / 0.283200 (0.005589)	0.019690 / 0.141683 (-0.121992)	1.167074 / 1.452155 (-0.285080)	1.206344 / 1.492716 (-0.286372)

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)	0.091211 / 0.018006 (0.073205)	0.299295 / 0.000490 (0.298805)	0.000216 / 0.000200 (0.000016)	0.000043 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.022718 / 0.037411 (-0.014693)	0.079483 / 0.014526 (0.064957)	0.087437 / 0.176557 (-0.089120)	0.126977 / 0.737135 (-0.610159)	0.089678 / 0.296338 (-0.206660)

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)	0.294719 / 0.215209 (0.079510)	2.864505 / 2.077655 (0.786851)	1.583993 / 1.504120 (0.079873)	1.455079 / 1.541195 (-0.086115)	1.504080 / 1.468490 (0.035590)	0.569040 / 4.584777 (-4.015737)	2.423472 / 3.745712 (-1.322240)	2.742848 / 5.269862 (-2.527014)	1.785244 / 4.565676 (-2.780432)	0.062655 / 0.424275 (-0.361620)	0.005027 / 0.007607 (-0.002580)	0.343863 / 0.226044 (0.117818)	3.376286 / 2.268929 (1.107358)	1.933846 / 55.444624 (-53.510779)	1.667316 / 6.876477 (-5.209161)	1.815621 / 2.142072 (-0.326451)	0.639378 / 4.805227 (-4.165850)	0.116514 / 6.500664 (-6.384150)	0.042191 / 0.075469 (-0.033279)

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)	1.007103 / 1.841788 (-0.834685)	12.791193 / 8.074308 (4.716885)	10.870575 / 10.191392 (0.679183)	0.131040 / 0.680424 (-0.549384)	0.016510 / 0.534201 (-0.517691)	0.288372 / 0.579283 (-0.290911)	0.275574 / 0.434364 (-0.158790)	0.327801 / 0.540337 (-0.212536)	0.415942 / 1.386936 (-0.970994)