Shorten long logs

[lhoestq]

Some datasets may have unexpectedly long features/types (e.g. if the files are not formatted correctly).

In that case we should still be able to log something readable

[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)	0.005191 / 0.011353 (-0.006162)	0.003691 / 0.011008 (-0.007317)	0.063511 / 0.038508 (0.025003)	0.031849 / 0.023109 (0.008740)	0.251691 / 0.275898 (-0.024207)	0.276585 / 0.323480 (-0.046895)	0.004080 / 0.007986 (-0.003906)	0.002751 / 0.004328 (-0.001577)	0.049572 / 0.004250 (0.045322)	0.043010 / 0.037052 (0.005957)	0.267161 / 0.258489 (0.008672)	0.301054 / 0.293841 (0.007213)	0.028068 / 0.128546 (-0.100479)	0.010479 / 0.075646 (-0.065167)	0.208458 / 0.419271 (-0.210814)	0.035688 / 0.043533 (-0.007845)	0.255985 / 0.255139 (0.000846)	0.296016 / 0.283200 (0.012817)	0.017041 / 0.141683 (-0.124642)	1.168626 / 1.452155 (-0.283528)	1.173419 / 1.492716 (-0.319297)

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)	0.092975 / 0.018006 (0.074969)	0.302309 / 0.000490 (0.301820)	0.000219 / 0.000200 (0.000020)	0.000042 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018809 / 0.037411 (-0.018602)	0.062606 / 0.014526 (0.048080)	0.073820 / 0.176557 (-0.102736)	0.119451 / 0.737135 (-0.617684)	0.075086 / 0.296338 (-0.221253)

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)	0.280342 / 0.215209 (0.065133)	2.742477 / 2.077655 (0.664822)	1.409221 / 1.504120 (-0.094899)	1.291679 / 1.541195 (-0.249516)	1.316628 / 1.468490 (-0.151862)	0.554942 / 4.584777 (-4.029835)	2.363301 / 3.745712 (-1.382411)	2.775766 / 5.269862 (-2.494096)	1.729123 / 4.565676 (-2.836554)	0.061254 / 0.424275 (-0.363021)	0.005444 / 0.007607 (-0.002163)	0.330450 / 0.226044 (0.104406)	3.249453 / 2.268929 (0.980524)	1.782415 / 55.444624 (-53.662210)	1.489778 / 6.876477 (-5.386699)	1.521809 / 2.142072 (-0.620263)	0.626622 / 4.805227 (-4.178605)	0.117320 / 6.500664 (-6.383344)	0.043110 / 0.075469 (-0.032359)

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)	0.981954 / 1.841788 (-0.859834)	11.706373 / 8.074308 (3.632064)	9.870815 / 10.191392 (-0.320577)	0.141768 / 0.680424 (-0.538656)	0.014455 / 0.534201 (-0.519746)	0.287451 / 0.579283 (-0.291832)	0.264559 / 0.434364 (-0.169805)	0.326321 / 0.540337 (-0.214017)	0.424084 / 1.386936 (-0.962852)

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)	0.005461 / 0.011353 (-0.005892)	0.003804 / 0.011008 (-0.007204)	0.049872 / 0.038508 (0.011364)	0.029543 / 0.023109 (0.006433)	0.260772 / 0.275898 (-0.015126)	0.291571 / 0.323480 (-0.031909)	0.004305 / 0.007986 (-0.003681)	0.002845 / 0.004328 (-0.001484)	0.049129 / 0.004250 (0.044879)	0.040743 / 0.037052 (0.003690)	0.276497 / 0.258489 (0.018008)	0.303126 / 0.293841 (0.009285)	0.030423 / 0.128546 (-0.098123)	0.010660 / 0.075646 (-0.064986)	0.058857 / 0.419271 (-0.360415)	0.033185 / 0.043533 (-0.010348)	0.260452 / 0.255139 (0.005313)	0.282648 / 0.283200 (-0.000552)	0.018025 / 0.141683 (-0.123658)	1.147432 / 1.452155 (-0.304723)	1.192034 / 1.492716 (-0.300683)

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)	0.093094 / 0.018006 (0.075088)	0.301608 / 0.000490 (0.301119)	0.000209 / 0.000200 (0.000009)	0.000044 / 0.000054 (-0.000010)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.022071 / 0.037411 (-0.015340)	0.075244 / 0.014526 (0.060718)	0.087157 / 0.176557 (-0.089400)	0.127339 / 0.737135 (-0.609797)	0.088527 / 0.296338 (-0.207812)

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)	0.293033 / 0.215209 (0.077824)	2.839842 / 2.077655 (0.762188)	1.544730 / 1.504120 (0.040610)	1.421727 / 1.541195 (-0.119468)	1.446054 / 1.468490 (-0.022436)	0.573285 / 4.584777 (-4.011492)	0.980977 / 3.745712 (-2.764735)	2.829034 / 5.269862 (-2.440828)	1.800747 / 4.565676 (-2.764930)	0.064916 / 0.424275 (-0.359360)	0.005099 / 0.007607 (-0.002508)	0.348054 / 0.226044 (0.122009)	3.449111 / 2.268929 (1.180182)	1.900115 / 55.444624 (-53.544509)	1.620564 / 6.876477 (-5.255913)	1.675474 / 2.142072 (-0.466598)	0.652302 / 4.805227 (-4.152925)	0.118438 / 6.500664 (-6.382226)	0.041779 / 0.075469 (-0.033690)

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)	1.003703 / 1.841788 (-0.838085)	12.466921 / 8.074308 (4.392613)	9.800419 / 10.191392 (-0.390973)	0.131567 / 0.680424 (-0.548856)	0.015684 / 0.534201 (-0.518517)	0.288754 / 0.579283 (-0.290530)	0.126435 / 0.434364 (-0.307929)	0.398608 / 0.540337 (-0.141729)	0.427043 / 1.386936 (-0.959894)