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https://api.github.com/repos/huggingface/datasets/issues/6237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6237/comments | https://api.github.com/repos/huggingface/datasets/issues/6237/events | https://github.com/huggingface/datasets/issues/6237 | 1,893,822,321 | I_kwDODunzps5w4W9x | 6,237 | Tokenization with multiple workers is too slow | {
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Code snippet:
```
raw_datasets.map(
encode_function,
batched=False,
num_proc=args.preprocessing_num_workers,
load_from_cache_file=not args.overwrite_cache,
remove_columns=[name for name in raw_datasets["train"].column_names if name not in ["input_ids", "labels", "attention_mask"]],
desc="Tokenizing data",
)
```
Details:
```
transformers==4.28.0.dev0
datasets==4.28.0.dev0
preprocessing_num_workers==48
```
tokenizer == decapoda-research/llama-7b-hf
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] | open | false | null | [] | [] | 2023-09-13T03:19:44 | 2023-09-13T03:19:44 | null | NONE | ### Feature request
I'm using to_tf_dataset to convert a large dataset to tf.data.Dataset and use Keras fit to train model.
Currently, to_tf_dataset only supports full size shuffle, which can be very slow on large dataset.
tf.data.Dataset support buffer shuffle by default.
shuffle(
buffer_size, seed=None, reshuffle_each_iteration=None, name=None
)
### Motivation
I'm very frustrated to find the loading with shuffling large dataset is very slow. It seems impossible to shuffle before training Keras with big dataset.
### Your contribution
NA | {
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] | open | false | null | [] | [] | 2023-09-12T21:51:08 | 2023-09-12T21:51:08 | null | NONE | ### Feature request
Current multiprocessing for download/extract is not done nestedly. For example, when processing SlimPajama, there is only 3 processes (for train/test/val), while there are many files inside these 3 folders
```
Downloading data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #1: 0%| | 0/1 [00:00<?, ?obj/s]
Downloading data files #2: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #0: 0%| | 0/1 [00:00<?, ?obj/s]
Extracting data files #1: 0%| | 0/1 [00:00<?, ?obj/s][A
Extracting data files #2: 0%| | 0/1 [00:00<?, ?obj/s][A[A
```
### Motivation
speedup dataset loading
### Your contribution
I can help test the feature | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6233). All of your documentation changes will be reflected on that endpoint."
] | 2023-09-12T06:53:06 | 2023-09-12T16:07:40 | null | NONE | fixed a typo | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6232). All of your documentation changes will be reflected on that endpoint."
] | 2023-09-11T19:11:58 | 2023-09-12T05:55:50 | null | NONE | The error message in the fingerprint module was missing the f-string 'f' symbol, so the error message returned by fingerprint.py, line 469 was literally "function {func} is missing parameters {fingerprint_names} in signature."
This has been fixed. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6231). All of your documentation changes will be reflected on that endpoint."
] | 2023-09-11T16:27:09 | 2023-09-12T15:23:15 | null | CONTRIBUTOR | Currently if we push data as default config with `.push_to_hub` to a repo that has a legacy `dataset_infos.json` file containing a legacy default config name like `{username}--{dataset_name}`, new key `"default"` is added to `dataset_infos.json` along with the legacy one. I think the legacy one should be dropped in this case.
Also, in `load.py` I suggest to check if a legacy config name is indeed a legacy config name because after this fix it might not be the case (this check was first introduced in https://github.com/huggingface/datasets/pull/6218) | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005894 / 0.011353 (-0.005459) | 0.003621 / 0.011008 (-0.007387) | 0.080446 / 0.038508 (0.041938) | 0.056800 / 0.023109 (0.033691) | 0.326485 / 0.275898 (0.050587) | 0.376207 / 0.323480 (0.052727) | 0.004640 / 0.007986 (-0.003346) | 0.002795 / 0.004328 (-0.001533) | 0.062815 / 0.004250 (0.058565) | 0.045761 / 0.037052 (0.008709) | 0.341417 / 0.258489 (0.082928) | 0.373129 / 0.293841 (0.079288) | 0.027226 / 0.128546 (-0.101321) | 0.007873 / 0.075646 (-0.067774) | 0.261737 / 0.419271 (-0.157535) | 0.044648 / 0.043533 (0.001115) | 0.320195 / 0.255139 (0.065056) | 0.381892 / 0.283200 (0.098692) | 0.020431 / 0.141683 (-0.121252) | 1.405332 / 1.452155 (-0.046823) | 1.455592 / 1.492716 (-0.037125) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191539 / 0.018006 (0.173533) | 0.423655 / 0.000490 (0.423165) | 0.002741 / 0.000200 (0.002541) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023952 / 0.037411 (-0.013459) | 0.073387 / 0.014526 (0.058861) | 0.083746 / 0.176557 (-0.092810) | 0.144977 / 0.737135 (-0.592159) | 0.083808 / 0.296338 (-0.212530) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436228 / 0.215209 (0.221019) | 4.370510 / 2.077655 (2.292855) | 2.340426 / 1.504120 (0.836306) | 2.202215 / 1.541195 (0.661021) | 2.258528 / 1.468490 (0.790037) | 0.503455 / 4.584777 (-4.081322) | 3.043695 / 3.745712 (-0.702017) | 2.784033 / 5.269862 (-2.485829) | 1.847956 / 4.565676 (-2.717721) | 0.057702 / 0.424275 (-0.366573) | 0.006703 / 0.007607 (-0.000904) | 0.510628 / 0.226044 (0.284583) | 5.101890 / 2.268929 (2.832961) | 2.816469 / 55.444624 (-52.628155) | 2.474220 / 6.876477 (-4.402257) | 2.617851 / 2.142072 (0.475779) | 0.593585 / 4.805227 (-4.211642) | 0.125895 / 6.500664 (-6.374769) | 0.062170 / 0.075469 (-0.013299) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238792 / 1.841788 (-0.602996) | 18.096417 / 8.074308 (10.022108) | 13.548778 / 10.191392 (3.357386) | 0.144878 / 0.680424 (-0.535546) | 0.016644 / 0.534201 (-0.517557) | 0.334556 / 0.579283 (-0.244728) | 0.343680 / 0.434364 (-0.090684) | 0.383093 / 0.540337 (-0.157244) | 0.525075 / 1.386936 (-0.861861) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006125 / 0.011353 (-0.005228) | 0.003668 / 0.011008 (-0.007340) | 0.062650 / 0.038508 (0.024142) | 0.058882 / 0.023109 (0.035772) | 0.454643 / 0.275898 (0.178745) | 0.486659 / 0.323480 (0.163179) | 0.005558 / 0.007986 (-0.002427) | 0.002858 / 0.004328 (-0.001471) | 0.062603 / 0.004250 (0.058353) | 0.049701 / 0.037052 (0.012649) | 0.455903 / 0.258489 (0.197413) | 0.491544 / 0.293841 (0.197703) | 0.028581 / 0.128546 (-0.099965) | 0.008040 / 0.075646 (-0.067607) | 0.068314 / 0.419271 (-0.350957) | 0.040637 / 0.043533 (-0.002896) | 0.450288 / 0.255139 (0.195149) | 0.476330 / 0.283200 (0.193131) | 0.018989 / 0.141683 (-0.122693) | 1.455122 / 1.452155 (0.002967) | 1.496941 / 1.492716 (0.004225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227382 / 0.018006 (0.209376) | 0.432637 / 0.000490 (0.432147) | 0.002727 / 0.000200 (0.002527) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026125 / 0.037411 (-0.011286) | 0.081342 / 0.014526 (0.066817) | 0.091227 / 0.176557 (-0.085329) | 0.145175 / 0.737135 (-0.591960) | 0.091988 / 0.296338 (-0.204351) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454293 / 0.215209 (0.239083) | 4.537912 / 2.077655 (2.460257) | 2.489146 / 1.504120 (0.985026) | 2.307166 / 1.541195 (0.765971) | 2.380866 / 1.468490 (0.912376) | 0.509015 / 4.584777 (-4.075762) | 3.111069 / 3.745712 (-0.634644) | 2.839181 / 5.269862 (-2.430681) | 1.874630 / 4.565676 (-2.691047) | 0.058540 / 0.424275 (-0.365735) | 0.006693 / 0.007607 (-0.000914) | 0.528408 / 0.226044 (0.302363) | 5.285802 / 2.268929 (3.016874) | 2.952090 / 55.444624 (-52.492534) | 2.591496 / 6.876477 (-4.284980) | 2.741080 / 2.142072 (0.599007) | 0.595610 / 4.805227 (-4.209617) | 0.124387 / 6.500664 (-6.376277) | 0.061032 / 0.075469 (-0.014437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365816 / 1.841788 (-0.475972) | 18.684534 / 8.074308 (10.610226) | 14.540438 / 10.191392 (4.349046) | 0.146793 / 0.680424 (-0.533631) | 0.018165 / 0.534201 (-0.516036) | 0.333794 / 0.579283 (-0.245489) | 0.345533 / 0.434364 (-0.088830) | 0.384453 / 0.540337 (-0.155885) | 0.529104 / 1.386936 (-0.857832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6c884967dd5f4e8aa3d1f3c2e3a414ae53afe261 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6230). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006121 / 0.011353 (-0.005232) | 0.003683 / 0.011008 (-0.007325) | 0.083329 / 0.038508 (0.044821) | 0.063350 / 0.023109 (0.040241) | 0.329959 / 0.275898 (0.054061) | 0.396111 / 0.323480 (0.072631) | 0.003554 / 0.007986 (-0.004432) | 0.002907 / 0.004328 (-0.001421) | 0.064152 / 0.004250 (0.059902) | 0.049182 / 0.037052 (0.012130) | 0.343862 / 0.258489 (0.085373) | 0.414568 / 0.293841 (0.120727) | 0.027157 / 0.128546 (-0.101389) | 0.007957 / 0.075646 (-0.067689) | 0.261868 / 0.419271 (-0.157404) | 0.044938 / 0.043533 (0.001405) | 0.318470 / 0.255139 (0.063331) | 0.393319 / 0.283200 (0.110119) | 0.022848 / 0.141683 (-0.118835) | 1.419916 / 1.452155 (-0.032238) | 1.508783 / 1.492716 (0.016067) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200530 / 0.018006 (0.182523) | 0.433586 / 0.000490 (0.433097) | 0.002063 / 0.000200 (0.001863) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024803 / 0.037411 (-0.012609) | 0.075894 / 0.014526 (0.061368) | 0.086488 / 0.176557 (-0.090069) | 0.149058 / 0.737135 (-0.588077) | 0.087046 / 0.296338 (-0.209292) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390771 / 0.215209 (0.175562) | 3.886178 / 2.077655 (1.808523) | 1.868626 / 1.504120 (0.364506) | 1.708532 / 1.541195 (0.167338) | 1.788491 / 1.468490 (0.320001) | 0.505706 / 4.584777 (-4.079071) | 3.062094 / 3.745712 (-0.683618) | 2.898559 / 5.269862 (-2.371302) | 1.901225 / 4.565676 (-2.664452) | 0.058366 / 0.424275 (-0.365909) | 0.006851 / 0.007607 (-0.000756) | 0.465382 / 0.226044 (0.239337) | 4.650187 / 2.268929 (2.381258) | 2.316152 / 55.444624 (-53.128472) | 1.989597 / 6.876477 (-4.886879) | 2.169266 / 2.142072 (0.027194) | 0.593257 / 4.805227 (-4.211970) | 0.126440 / 6.500664 (-6.374224) | 0.062227 / 0.075469 (-0.013242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283591 / 1.841788 (-0.558197) | 18.384667 / 8.074308 (10.310358) | 14.079611 / 10.191392 (3.888219) | 0.150453 / 0.680424 (-0.529971) | 0.017100 / 0.534201 (-0.517101) | 0.330503 / 0.579283 (-0.248780) | 0.348134 / 0.434364 (-0.086230) | 0.385726 / 0.540337 (-0.154612) | 0.529147 / 1.386936 (-0.857789) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006168 / 0.011353 (-0.005185) | 0.003801 / 0.011008 (-0.007208) | 0.063168 / 0.038508 (0.024660) | 0.062331 / 0.023109 (0.039221) | 0.448321 / 0.275898 (0.172423) | 0.484416 / 0.323480 (0.160937) | 0.004827 / 0.007986 (-0.003159) | 0.002848 / 0.004328 (-0.001480) | 0.062736 / 0.004250 (0.058486) | 0.049128 / 0.037052 (0.012075) | 0.449276 / 0.258489 (0.190787) | 0.499035 / 0.293841 (0.205194) | 0.028577 / 0.128546 (-0.099969) | 0.008114 / 0.075646 (-0.067532) | 0.068297 / 0.419271 (-0.350974) | 0.040835 / 0.043533 (-0.002698) | 0.453556 / 0.255139 (0.198417) | 0.475420 / 0.283200 (0.192220) | 0.020292 / 0.141683 (-0.121390) | 1.472226 / 1.452155 (0.020071) | 1.523809 / 1.492716 (0.031093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230662 / 0.018006 (0.212655) | 0.439697 / 0.000490 (0.439207) | 0.009899 / 0.000200 (0.009699) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026418 / 0.037411 (-0.010993) | 0.082188 / 0.014526 (0.067662) | 0.091039 / 0.176557 (-0.085518) | 0.146646 / 0.737135 (-0.590489) | 0.091693 / 0.296338 (-0.204645) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462086 / 0.215209 (0.246877) | 4.620925 / 2.077655 (2.543271) | 2.539234 / 1.504120 (1.035114) | 2.371178 / 1.541195 (0.829983) | 2.440538 / 1.468490 (0.972048) | 0.511047 / 4.584777 (-4.073730) | 3.082088 / 3.745712 (-0.663624) | 2.918162 / 5.269862 (-2.351700) | 1.899651 / 4.565676 (-2.666025) | 0.059003 / 0.424275 (-0.365272) | 0.006746 / 0.007607 (-0.000861) | 0.537863 / 0.226044 (0.311819) | 5.382355 / 2.268929 (3.113426) | 3.060091 / 55.444624 (-52.384534) | 2.754969 / 6.876477 (-4.121507) | 2.863156 / 2.142072 (0.721084) | 0.606888 / 4.805227 (-4.198339) | 0.127448 / 6.500664 (-6.373216) | 0.062975 / 0.075469 (-0.012494) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336065 / 1.841788 (-0.505722) | 19.019902 / 8.074308 (10.945594) | 15.057979 / 10.191392 (4.866587) | 0.160646 / 0.680424 (-0.519778) | 0.018340 / 0.534201 (-0.515861) | 0.341664 / 0.579283 (-0.237619) | 0.356536 / 0.434364 (-0.077828) | 0.393974 / 0.540337 (-0.146363) | 0.546036 / 1.386936 (-0.840900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd04e445bd36d7eb4af4d5a6b8519ab8e306ecf5 \"CML watermark\")\n"
] | 2023-09-11T13:29:19 | 2023-09-11T17:03:52 | null | CONTRIBUTOR | Required for `load_dataset(<format>, data_files=["path/to/.hidden_file"])` to work as expected | {
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https://api.github.com/repos/huggingface/datasets/issues/6229 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6229/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6229/comments | https://api.github.com/repos/huggingface/datasets/issues/6229/events | https://github.com/huggingface/datasets/issues/6229 | 1,889,050,954 | I_kwDODunzps5wmKFK | 6,229 | Apply inference on all images in the dataset | {
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"From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object). ",
"> From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object).\r\n\r\nThanks @mariosasko for your reply...\r\ni tried :\r\n```\r\n# Define a function to apply the code to each image in the dataset\r\ndef process_image(image_path):\r\n print(\"Processing image:\", image_path)\r\n result = inferencer(image_path)['predictions']\r\n mask = np.where(result == 12, 255, 0).astype('uint8')\r\n return Image.fromarray(mask)\r\n\r\n# Process and save masks for each image in the dataset\r\nfor idx, example in enumerate(dataset['train']):\r\n image_path = np.array(example['image'])\r\n mask_image = process_image(image_path)\r\n mask_image.save(f\"mask_{idx}.png\")\r\n```\r\nand got\r\n```\r\nProcessing image: [[[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [243 205 122]\r\n [244 202 120]\r\n [242 200 118]]\r\n\r\n [[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [244 206 123]\r\n [245 203 121]\r\n [243 201 119]]\r\n\r\n [[203 164 87]\r\n [204 165 88]\r\n [207 168 91]\r\n ...\r\n [245 207 126]\r\n [246 204 122]\r\n [245 203 121]]\r\n\r\n ...\r\n\r\n [[154 123 56]\r\n [155 124 57]\r\n [158 125 56]\r\n ...\r\n [ 3 3 1]\r\n [ 3 3 1]\r\n [ 3 3 1]]\r\n\r\n [[154 123 56]\r\n [154 123 56]\r\n [155 124 57]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]\r\n\r\n [[152 121 54]\r\n [152 121 54]\r\n [153 122 55]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 39 44 40]\r\n [ 39 44 40]\r\n [ 39 43 44]\r\n ...\r\n [187 185 164]\r\n [208 204 175]\r\n [203 198 166]]\r\n\r\n [[ 42 47 43]\r\n [ 40 45 41]\r\n [ 40 44 45]\r\n ...\r\n [188 186 165]\r\n [202 198 169]\r\n [201 196 164]]\r\n\r\n [[ 41 46 42]\r\n [ 39 44 40]\r\n [ 40 44 45]\r\n ...\r\n [187 184 165]\r\n [197 193 166]\r\n [201 196 166]]\r\n\r\n ...\r\n\r\n [[ 29 27 30]\r\n [ 28 26 29]\r\n [ 25 23 26]\r\n ...\r\n [ 48 33 28]\r\n [ 44 31 25]\r\n [ 39 26 20]]\r\n\r\n [[ 34 29 33]\r\n [ 32 27 31]\r\n [ 29 24 28]\r\n ...\r\n [ 30 17 11]\r\n [ 36 23 15]\r\n [ 41 28 20]]\r\n\r\n [[ 35 30 34]\r\n [ 33 28 32]\r\n [ 28 23 27]\r\n ...\r\n [ 28 15 9]\r\n [ 41 28 20]\r\n [ 46 33 25]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 65 53 55]\r\n [ 65 53 55]\r\n [ 51 39 41]\r\n ...\r\n [133 127 111]\r\n [150 141 124]\r\n [133 124 107]]\r\n\r\n [[ 58 45 52]\r\n [ 61 48 55]\r\n [ 51 38 45]\r\n ...\r\n [148 141 123]\r\n [178 169 152]\r\n [144 135 118]]\r\n\r\n [[ 79 66 83]\r\n [ 73 60 77]\r\n [ 65 51 66]\r\n ...\r\n [140 131 114]\r\n [142 133 116]\r\n [147 136 118]]\r\n\r\n ...\r\n\r\n [[132 122 133]\r\n [ 95 85 94]\r\n [ 61 51 60]\r\n ...\r\n [ 39 28 42]\r\n [ 46 36 45]\r\n [ 25 16 21]]\r\n\r\n [[150 143 151]\r\n [114 107 115]\r\n [ 64 54 63]\r\n ...\r\n [ 47 35 47]\r\n [ 38 27 35]\r\n [140 129 133]]\r\n\r\n [[145 138 146]\r\n [115 108 116]\r\n [ 69 59 67]\r\n ...\r\n [ 31 19 31]\r\n [128 117 123]\r\n [196 185 189]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[159 151 140]\r\n [171 163 152]\r\n [161 148 142]\r\n ...\r\n [198 184 171]\r\n [189 175 162]\r\n [183 169 156]]\r\n\r\n [[128 118 106]\r\n [138 128 116]\r\n [138 125 116]\r\n ...\r\n [200 186 173]\r\n [190 176 163]\r\n [187 173 160]]\r\n\r\n [[165 153 137]\r\n [170 158 142]\r\n [174 162 148]\r\n ...\r\n [200 187 171]\r\n [188 175 159]\r\n [182 169 153]]\r\n```\r\nHowever , when trying to add to:\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('Andyrasika/cat_kingdom')\r\ndataset\r\n```\r\ni did \r\n```\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\nnew_column\r\ndataset = dataset.add_column(\"/workspace/data\", new_column)\r\n\r\nprint(dataset)\r\n```\r\ngot error:\r\n```\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[11], line 3\r\n 1 new_column = [\"mask\"] * len(dataset[\"train\"])\r\n 2 new_column\r\n----> 3 dataset = dataset.add_column(\"/workspace/data\", new_column)\r\n 5 print(dataset)\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'add_column'\r\n```"
] | 2023-09-10T08:36:12 | 2023-09-13T06:05:20 | null | NONE | ### Describe the bug
```
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[14], line 11
9 for idx, example in enumerate(dataset['train']):
10 image_path = example['image']
---> 11 mask_image = process_image(image_path)
12 mask_image.save(f"mask_{idx}.png")
Cell In[14], line 4, in process_image(image_path)
2 def process_image(image_path):
3 print("Processing image:", image_path)
----> 4 result = inferencer(image_path)['predictions']
5 mask = np.where(result == 12, 255, 0).astype('uint8')
6 return Image.fromarray(mask)
File /usr/local/lib/python3.10/dist-packages/mmseg/apis/mmseg_inferencer.py:183, in MMSegInferencer.__call__(self, inputs, return_datasamples, batch_size, show, wait_time, out_dir, img_out_dir, pred_out_dir, **kwargs)
180 pred_out_dir = ''
181 img_out_dir = ''
--> 183 return super().__call__(
184 inputs=inputs,
185 return_datasamples=return_datasamples,
186 batch_size=batch_size,
187 show=show,
188 wait_time=wait_time,
189 img_out_dir=img_out_dir,
190 pred_out_dir=pred_out_dir,
191 **kwargs)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:221, in BaseInferencer.__call__(self, inputs, return_datasamples, batch_size, **kwargs)
218 inputs = self.preprocess(
219 ori_inputs, batch_size=batch_size, **preprocess_kwargs)
220 preds = []
--> 221 for data in (track(inputs, description='Inference')
222 if self.show_progress else inputs):
223 preds.extend(self.forward(data, **forward_kwargs))
224 visualization = self.visualize(
225 ori_inputs, preds,
226 **visualize_kwargs) # type: ignore # noqa: E501
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:168, in track(sequence, description, total, auto_refresh, console, transient, get_time, refresh_per_second, style, complete_style, finished_style, pulse_style, update_period, disable, show_speed)
157 progress = Progress(
158 *columns,
159 auto_refresh=auto_refresh,
(...)
164 disable=disable,
165 )
167 with progress:
--> 168 yield from progress.track(
169 sequence, total=total, description=description, update_period=update_period
170 )
File /usr/local/lib/python3.10/dist-packages/rich/progress.py:1210, in Progress.track(self, sequence, total, task_id, description, update_period)
1208 if self.live.auto_refresh:
1209 with _TrackThread(self, task_id, update_period) as track_thread:
-> 1210 for value in sequence:
1211 yield value
1212 track_thread.completed += 1
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:291, in BaseInferencer.preprocess(self, inputs, batch_size, **kwargs)
266 """Process the inputs into a model-feedable format.
267
268 Customize your preprocess by overriding this method. Preprocess should
(...)
287 Any: Data processed by the ``pipeline`` and ``collate_fn``.
288 """
289 chunked_data = self._get_chunk_data(
290 map(self.pipeline, inputs), batch_size)
--> 291 yield from map(self.collate_fn, chunked_data)
File /usr/local/lib/python3.10/dist-packages/mmengine/infer/infer.py:588, in BaseInferencer._get_chunk_data(self, inputs, chunk_size)
586 chunk_data = []
587 for _ in range(chunk_size):
--> 588 processed_data = next(inputs_iter)
589 chunk_data.append(processed_data)
590 yield chunk_data
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/wrappers.py:88, in Compose.transform(self, results)
79 """Call function to apply transforms sequentially.
80
81 Args:
(...)
85 dict or None: Transformed results.
86 """
87 for t in self.transforms:
---> 88 results = t(results) # type: ignore
89 if results is None:
90 return None
File /usr/local/lib/python3.10/dist-packages/mmcv/transforms/base.py:12, in BaseTransform.__call__(self, results)
9 def __call__(self,
10 results: Dict) -> Optional[Union[Dict, Tuple[List, List]]]:
---> 12 return self.transform(results)
File /usr/local/lib/python3.10/dist-packages/mmseg/datasets/transforms/loading.py:496, in InferencerLoader.transform(self, single_input)
494 inputs = single_input
495 else:
--> 496 raise NotImplementedError
498 if 'img' in inputs:
499 return self.from_ndarray(inputs)
NotImplementedError:
````
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset('Andyrasika/cat_kingdom')
dataset
from mmseg.apis import MMSegInferencer
checkpoint_name = 'segformer_mit-b5_8xb2-160k_ade20k-640x640'
inferencer = MMSegInferencer(model=checkpoint_name)
# Define a function to apply the code to each image in the dataset
def process_image(image_path):
print("Processing image:", image_path)
result = inferencer(image_path)['predictions']
mask = np.where(result == 12, 255, 0).astype('uint8')
return Image.fromarray(mask)
# Process and save masks for each image in the dataset
for idx, example in enumerate(dataset['train']):
image_path = example['image']
mask_image = process_image(image_path)
mask_image.save(f"mask_{idx}.png")
```
### Expected behavior
create a separate column with masks in the dataset and further shows as a separate column in hub
### Environment info
jupyter notebook RTX 3090 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009443 / 0.011353 (-0.001910) | 0.005274 / 0.011008 (-0.005734) | 0.105950 / 0.038508 (0.067441) | 0.079947 / 0.023109 (0.056837) | 0.414248 / 0.275898 (0.138350) | 0.440611 / 0.323480 (0.117131) | 0.006779 / 0.007986 (-0.001206) | 0.004301 / 0.004328 (-0.000028) | 0.080616 / 0.004250 (0.076366) | 0.061425 / 0.037052 (0.024372) | 0.418460 / 0.258489 (0.159971) | 0.468108 / 0.293841 (0.174267) | 0.051090 / 0.128546 (-0.077456) | 0.014133 / 0.075646 (-0.061513) | 0.376121 / 0.419271 (-0.043151) | 0.070715 / 0.043533 (0.027182) | 0.415435 / 0.255139 (0.160296) | 0.457925 / 0.283200 (0.174725) | 0.053653 / 0.141683 (-0.088030) | 1.872681 / 1.452155 (0.420527) | 1.961187 / 1.492716 (0.468470) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255829 / 0.018006 (0.237823) | 0.574224 / 0.000490 (0.573735) | 0.007597 / 0.000200 (0.007397) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032562 / 0.037411 (-0.004849) | 0.097528 / 0.014526 (0.083003) | 0.113487 / 0.176557 (-0.063070) | 0.185670 / 0.737135 (-0.551465) | 0.118909 / 0.296338 (-0.177430) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.611441 / 0.215209 (0.396232) | 5.908576 / 2.077655 (3.830921) | 2.586758 / 1.504120 (1.082638) | 2.310199 / 1.541195 (0.769004) | 2.333396 / 1.468490 (0.864906) | 0.900884 / 4.584777 (-3.683893) | 5.438304 / 3.745712 (1.692591) | 4.806611 / 5.269862 (-0.463250) | 2.970631 / 4.565676 (-1.595046) | 0.097861 / 0.424275 (-0.326414) | 0.009873 / 0.007607 (0.002266) | 0.739553 / 0.226044 (0.513509) | 7.104953 / 2.268929 (4.836024) | 3.150128 / 55.444624 (-52.294497) | 2.469552 / 6.876477 (-4.406924) | 2.709206 / 2.142072 (0.567133) | 0.983081 / 4.805227 (-3.822147) | 0.205150 / 6.500664 (-6.295514) | 0.075947 / 0.075469 (0.000478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.631255 / 1.841788 (-0.210532) | 24.213679 / 8.074308 (16.139370) | 21.514481 / 10.191392 (11.323089) | 0.220360 / 0.680424 (-0.460063) | 0.031663 / 0.534201 (-0.502538) | 0.516029 / 0.579283 (-0.063254) | 0.591461 / 0.434364 (0.157097) | 0.612398 / 0.540337 (0.072061) | 0.807609 / 1.386936 (-0.579328) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009443 / 0.011353 (-0.001910) | 0.005510 / 0.011008 (-0.005498) | 0.085722 / 0.038508 (0.047214) | 0.076256 / 0.023109 (0.053146) | 0.604248 / 0.275898 (0.328349) | 0.596222 / 0.323480 (0.272742) | 0.006786 / 0.007986 (-0.001200) | 0.004135 / 0.004328 (-0.000193) | 0.085934 / 0.004250 (0.081683) | 0.065890 / 0.037052 (0.028838) | 0.592080 / 0.258489 (0.333591) | 0.624560 / 0.293841 (0.330719) | 0.048200 / 0.128546 (-0.080346) | 0.015477 / 0.075646 (-0.060169) | 0.097042 / 0.419271 (-0.322230) | 0.060513 / 0.043533 (0.016981) | 0.557171 / 0.255139 (0.302032) | 0.582057 / 0.283200 (0.298858) | 0.035678 / 0.141683 (-0.106005) | 1.894947 / 1.452155 (0.442792) | 1.956652 / 1.492716 (0.463936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268927 / 0.018006 (0.250921) | 0.566086 / 0.000490 (0.565597) | 0.007190 / 0.000200 (0.006990) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042090 / 0.037411 (0.004679) | 0.109618 / 0.014526 (0.095092) | 0.126588 / 0.176557 (-0.049968) | 0.200426 / 0.737135 (-0.536709) | 0.127032 / 0.296338 (-0.169306) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669773 / 0.215209 (0.454564) | 6.453417 / 2.077655 (4.375763) | 3.119147 / 1.504120 (1.615027) | 2.818632 / 1.541195 (1.277437) | 2.930880 / 1.468490 (1.462390) | 0.922164 / 4.584777 (-3.662612) | 5.769564 / 3.745712 (2.023852) | 4.885108 / 5.269862 (-0.384754) | 3.041640 / 4.565676 (-1.524037) | 0.100186 / 0.424275 (-0.324090) | 0.009417 / 0.007607 (0.001810) | 0.783138 / 0.226044 (0.557094) | 8.113361 / 2.268929 (5.844432) | 4.018630 / 55.444624 (-51.425995) | 3.246772 / 6.876477 (-3.629704) | 3.520690 / 2.142072 (1.378618) | 1.063686 / 4.805227 (-3.741541) | 0.218667 / 6.500664 (-6.281997) | 0.084169 / 0.075469 (0.008700) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.791949 / 1.841788 (-0.049839) | 23.148341 / 8.074308 (15.074033) | 23.321125 / 10.191392 (13.129733) | 0.245391 / 0.680424 (-0.435032) | 0.031911 / 0.534201 (-0.502290) | 0.470707 / 0.579283 (-0.108576) | 0.608195 / 0.434364 (0.173832) | 0.559590 / 0.540337 (0.019253) | 0.786007 / 1.386936 (-0.600929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8e071f565cc0801f73f7f34fba92dc30a43946a9 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008428 / 0.011353 (-0.002925) | 0.004064 / 0.011008 (-0.006944) | 0.088421 / 0.038508 (0.049913) | 0.078042 / 0.023109 (0.054933) | 0.306356 / 0.275898 (0.030458) | 0.349766 / 0.323480 (0.026286) | 0.004086 / 0.007986 (-0.003900) | 0.003900 / 0.004328 (-0.000428) | 0.068379 / 0.004250 (0.064129) | 0.056214 / 0.037052 (0.019161) | 0.310211 / 0.258489 (0.051722) | 0.363692 / 0.293841 (0.069851) | 0.050421 / 0.128546 (-0.078125) | 0.011661 / 0.075646 (-0.063985) | 0.298400 / 0.419271 (-0.120871) | 0.063503 / 0.043533 (0.019970) | 0.339799 / 0.255139 (0.084660) | 0.359479 / 0.283200 (0.076279) | 0.039265 / 0.141683 (-0.102418) | 1.390578 / 1.452155 (-0.061576) | 1.573333 / 1.492716 (0.080617) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260442 / 0.018006 (0.242436) | 0.560390 / 0.000490 (0.559900) | 0.003926 / 0.000200 (0.003726) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025809 / 0.037411 (-0.011602) | 0.081902 / 0.014526 (0.067376) | 0.093655 / 0.176557 (-0.082901) | 0.149432 / 0.737135 (-0.587703) | 0.099059 / 0.296338 (-0.197279) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.505644 / 0.215209 (0.290435) | 5.108292 / 2.077655 (3.030638) | 2.121689 / 1.504120 (0.617569) | 1.846576 / 1.541195 (0.305381) | 1.836587 / 1.468490 (0.368097) | 0.708088 / 4.584777 (-3.876689) | 4.562630 / 3.745712 (0.816918) | 3.934747 / 5.269862 (-1.335115) | 2.453409 / 4.565676 (-2.112267) | 0.081908 / 0.424275 (-0.342367) | 0.012996 / 0.007607 (0.005389) | 0.636588 / 0.226044 (0.410544) | 6.361086 / 2.268929 (4.092157) | 2.911681 / 55.444624 (-52.532943) | 2.271809 / 6.876477 (-4.604667) | 2.670327 / 2.142072 (0.528254) | 0.943688 / 4.805227 (-3.861539) | 0.191677 / 6.500664 (-6.308988) | 0.066008 / 0.075469 (-0.009461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.400139 / 1.841788 (-0.441648) | 21.896198 / 8.074308 (13.821890) | 17.853604 / 10.191392 (7.662212) | 0.226603 / 0.680424 (-0.453821) | 0.026682 / 0.534201 (-0.507518) | 0.460131 / 0.579283 (-0.119152) | 0.536790 / 0.434364 (0.102427) | 0.492913 / 0.540337 (-0.047424) | 0.724290 / 1.386936 (-0.662646) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007795 / 0.011353 (-0.003557) | 0.009045 / 0.011008 (-0.001963) | 0.085480 / 0.038508 (0.046972) | 0.071881 / 0.023109 (0.048772) | 0.514520 / 0.275898 (0.238622) | 0.569762 / 0.323480 (0.246282) | 0.006126 / 0.007986 (-0.001859) | 0.004153 / 0.004328 (-0.000175) | 0.072150 / 0.004250 (0.067900) | 0.056511 / 0.037052 (0.019458) | 0.484097 / 0.258489 (0.225607) | 0.532673 / 0.293841 (0.238832) | 0.040974 / 0.128546 (-0.087572) | 0.012071 / 0.075646 (-0.063575) | 0.102608 / 0.419271 (-0.316663) | 0.052893 / 0.043533 (0.009360) | 0.485832 / 0.255139 (0.230693) | 0.530479 / 0.283200 (0.247280) | 0.031556 / 0.141683 (-0.110127) | 1.737508 / 1.452155 (0.285354) | 1.834637 / 1.492716 (0.341921) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.423314 / 0.018006 (0.405308) | 0.614163 / 0.000490 (0.613673) | 0.052784 / 0.000200 (0.052584) | 0.000206 / 0.000054 (0.000151) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031728 / 0.037411 (-0.005684) | 0.088048 / 0.014526 (0.073522) | 0.105759 / 0.176557 (-0.070798) | 0.181433 / 0.737135 (-0.555703) | 0.103133 / 0.296338 (-0.193205) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.659710 / 0.215209 (0.444501) | 5.876378 / 2.077655 (3.798723) | 2.899444 / 1.504120 (1.395324) | 2.871592 / 1.541195 (1.330397) | 2.861205 / 1.468490 (1.392715) | 0.879452 / 4.584777 (-3.705325) | 5.395988 / 3.745712 (1.650275) | 4.548359 / 5.269862 (-0.721502) | 2.946601 / 4.565676 (-1.619076) | 0.099832 / 0.424275 (-0.324443) | 0.008958 / 0.007607 (0.001351) | 0.778480 / 0.226044 (0.552435) | 7.672282 / 2.268929 (5.403354) | 3.963701 / 55.444624 (-51.480923) | 3.154950 / 6.876477 (-3.721527) | 3.351070 / 2.142072 (1.208997) | 1.059459 / 4.805227 (-3.745768) | 0.212035 / 6.500664 (-6.288629) | 0.076941 / 0.075469 (0.001472) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.639813 / 1.841788 (-0.201975) | 24.807517 / 8.074308 (16.733208) | 20.662500 / 10.191392 (10.471108) | 0.244486 / 0.680424 (-0.435937) | 0.032335 / 0.534201 (-0.501866) | 0.470896 / 0.579283 (-0.108387) | 0.581561 / 0.434364 (0.147197) | 0.495158 / 0.540337 (-0.045179) | 0.788350 / 1.386936 (-0.598586) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#99641ced2e08a28cb876f483babcdd43f7dd76d2 \"CML watermark\")\n"
] | 2023-09-08T16:09:13 | 2023-09-08T18:02:49 | 2023-09-08T17:52:16 | CONTRIBUTOR | Fix #6225 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005920 / 0.011353 (-0.005433) | 0.003623 / 0.011008 (-0.007385) | 0.079283 / 0.038508 (0.040775) | 0.058325 / 0.023109 (0.035216) | 0.313733 / 0.275898 (0.037835) | 0.360790 / 0.323480 (0.037310) | 0.004653 / 0.007986 (-0.003332) | 0.002876 / 0.004328 (-0.001452) | 0.062137 / 0.004250 (0.057886) | 0.045084 / 0.037052 (0.008031) | 0.328569 / 0.258489 (0.070079) | 0.368965 / 0.293841 (0.075124) | 0.027085 / 0.128546 (-0.101461) | 0.008051 / 0.075646 (-0.067595) | 0.260222 / 0.419271 (-0.159050) | 0.045477 / 0.043533 (0.001944) | 0.315344 / 0.255139 (0.060205) | 0.348215 / 0.283200 (0.065015) | 0.021352 / 0.141683 (-0.120331) | 1.432200 / 1.452155 (-0.019955) | 1.509217 / 1.492716 (0.016501) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199843 / 0.018006 (0.181837) | 0.427925 / 0.000490 (0.427435) | 0.002903 / 0.000200 (0.002703) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023121 / 0.037411 (-0.014291) | 0.072451 / 0.014526 (0.057925) | 0.083260 / 0.176557 (-0.093296) | 0.142879 / 0.737135 (-0.594257) | 0.084053 / 0.296338 (-0.212286) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394922 / 0.215209 (0.179713) | 3.956111 / 2.077655 (1.878456) | 1.926411 / 1.504120 (0.422291) | 1.743840 / 1.541195 (0.202646) | 1.776957 / 1.468490 (0.308467) | 0.502134 / 4.584777 (-4.082643) | 3.001721 / 3.745712 (-0.743991) | 2.852496 / 5.269862 (-2.417365) | 1.862794 / 4.565676 (-2.702883) | 0.057544 / 0.424275 (-0.366731) | 0.006751 / 0.007607 (-0.000856) | 0.470619 / 0.226044 (0.244575) | 4.696674 / 2.268929 (2.427746) | 2.326545 / 55.444624 (-53.118080) | 1.980888 / 6.876477 (-4.895589) | 2.139172 / 2.142072 (-0.002901) | 0.590256 / 4.805227 (-4.214971) | 0.125815 / 6.500664 (-6.374849) | 0.061000 / 0.075469 (-0.014469) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261948 / 1.841788 (-0.579839) | 18.317473 / 8.074308 (10.243165) | 13.810883 / 10.191392 (3.619491) | 0.146180 / 0.680424 (-0.534244) | 0.016701 / 0.534201 (-0.517500) | 0.330731 / 0.579283 (-0.248552) | 0.345103 / 0.434364 (-0.089261) | 0.374449 / 0.540337 (-0.165889) | 0.522463 / 1.386936 (-0.864473) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006217 / 0.011353 (-0.005136) | 0.003678 / 0.011008 (-0.007331) | 0.062321 / 0.038508 (0.023813) | 0.059256 / 0.023109 (0.036147) | 0.444501 / 0.275898 (0.168603) | 0.475881 / 0.323480 (0.152401) | 0.004863 / 0.007986 (-0.003123) | 0.002916 / 0.004328 (-0.001412) | 0.062197 / 0.004250 (0.057946) | 0.048449 / 0.037052 (0.011396) | 0.443680 / 0.258489 (0.185191) | 0.484570 / 0.293841 (0.190729) | 0.028694 / 0.128546 (-0.099852) | 0.008096 / 0.075646 (-0.067550) | 0.068347 / 0.419271 (-0.350924) | 0.041031 / 0.043533 (-0.002502) | 0.443907 / 0.255139 (0.188768) | 0.469888 / 0.283200 (0.186689) | 0.020237 / 0.141683 (-0.121445) | 1.438484 / 1.452155 (-0.013671) | 1.512652 / 1.492716 (0.019936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243118 / 0.018006 (0.225111) | 0.416797 / 0.000490 (0.416308) | 0.010421 / 0.000200 (0.010221) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026191 / 0.037411 (-0.011220) | 0.080881 / 0.014526 (0.066355) | 0.093207 / 0.176557 (-0.083349) | 0.146708 / 0.737135 (-0.590428) | 0.091676 / 0.296338 (-0.204663) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461475 / 0.215209 (0.246266) | 4.617351 / 2.077655 (2.539696) | 2.564369 / 1.504120 (1.060249) | 2.393263 / 1.541195 (0.852068) | 2.447343 / 1.468490 (0.978853) | 0.508764 / 4.584777 (-4.076013) | 3.075460 / 3.745712 (-0.670252) | 2.884683 / 5.269862 (-2.385179) | 1.866432 / 4.565676 (-2.699244) | 0.058759 / 0.424275 (-0.365516) | 0.006591 / 0.007607 (-0.001016) | 0.537718 / 0.226044 (0.311674) | 5.378709 / 2.268929 (3.109781) | 3.006751 / 55.444624 (-52.437873) | 2.666653 / 6.876477 (-4.209824) | 2.847559 / 2.142072 (0.705486) | 0.596878 / 4.805227 (-4.208350) | 0.125073 / 6.500664 (-6.375591) | 0.061345 / 0.075469 (-0.014124) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.349066 / 1.841788 (-0.492721) | 18.684735 / 8.074308 (10.610427) | 15.128142 / 10.191392 (4.936750) | 0.149254 / 0.680424 (-0.531170) | 0.017911 / 0.534201 (-0.516290) | 0.344057 / 0.579283 (-0.235226) | 0.363474 / 0.434364 (-0.070890) | 0.399425 / 0.540337 (-0.140912) | 0.549329 / 1.386936 (-0.837607) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e675a2396efb5204a4553721001f3b46aa4cc334 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005843 / 0.011353 (-0.005510) | 0.003549 / 0.011008 (-0.007460) | 0.082318 / 0.038508 (0.043810) | 0.056835 / 0.023109 (0.033726) | 0.312968 / 0.275898 (0.037070) | 0.345918 / 0.323480 (0.022438) | 0.003239 / 0.007986 (-0.004747) | 0.002762 / 0.004328 (-0.001567) | 0.062362 / 0.004250 (0.058111) | 0.045934 / 0.037052 (0.008882) | 0.317035 / 0.258489 (0.058546) | 0.358473 / 0.293841 (0.064632) | 0.027311 / 0.128546 (-0.101235) | 0.007994 / 0.075646 (-0.067652) | 0.261565 / 0.419271 (-0.157706) | 0.044942 / 0.043533 (0.001410) | 0.313092 / 0.255139 (0.057953) | 0.339021 / 0.283200 (0.055821) | 0.021555 / 0.141683 (-0.120127) | 1.421232 / 1.452155 (-0.030923) | 1.487597 / 1.492716 (-0.005119) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206432 / 0.018006 (0.188425) | 0.421932 / 0.000490 (0.421442) | 0.002825 / 0.000200 (0.002625) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022795 / 0.037411 (-0.014616) | 0.072666 / 0.014526 (0.058140) | 0.082779 / 0.176557 (-0.093778) | 0.142320 / 0.737135 (-0.594815) | 0.083343 / 0.296338 (-0.212995) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394227 / 0.215209 (0.179018) | 3.931858 / 2.077655 (1.854203) | 1.909953 / 1.504120 (0.405833) | 1.711298 / 1.541195 (0.170104) | 1.745816 / 1.468490 (0.277326) | 0.503670 / 4.584777 (-4.081107) | 3.053677 / 3.745712 (-0.692035) | 2.802597 / 5.269862 (-2.467264) | 1.825315 / 4.565676 (-2.740362) | 0.057741 / 0.424275 (-0.366534) | 0.006581 / 0.007607 (-0.001027) | 0.463597 / 0.226044 (0.237552) | 4.638821 / 2.268929 (2.369893) | 2.301266 / 55.444624 (-53.143358) | 1.967111 / 6.876477 (-4.909365) | 2.097756 / 2.142072 (-0.044317) | 0.589840 / 4.805227 (-4.215387) | 0.125538 / 6.500664 (-6.375126) | 0.061203 / 0.075469 (-0.014266) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291815 / 1.841788 (-0.549973) | 17.997040 / 8.074308 (9.922732) | 13.616252 / 10.191392 (3.424860) | 0.137349 / 0.680424 (-0.543075) | 0.016626 / 0.534201 (-0.517575) | 0.329611 / 0.579283 (-0.249672) | 0.346592 / 0.434364 (-0.087772) | 0.379521 / 0.540337 (-0.160817) | 0.528058 / 1.386936 (-0.858878) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006073 / 0.011353 (-0.005280) | 0.003594 / 0.011008 (-0.007414) | 0.062537 / 0.038508 (0.024029) | 0.057503 / 0.023109 (0.034394) | 0.449427 / 0.275898 (0.173529) | 0.482729 / 0.323480 (0.159249) | 0.004690 / 0.007986 (-0.003295) | 0.002901 / 0.004328 (-0.001428) | 0.062421 / 0.004250 (0.058171) | 0.046405 / 0.037052 (0.009353) | 0.456578 / 0.258489 (0.198089) | 0.492268 / 0.293841 (0.198427) | 0.028283 / 0.128546 (-0.100263) | 0.008028 / 0.075646 (-0.067618) | 0.067885 / 0.419271 (-0.351387) | 0.041273 / 0.043533 (-0.002260) | 0.449870 / 0.255139 (0.194731) | 0.472305 / 0.283200 (0.189106) | 0.018556 / 0.141683 (-0.123127) | 1.449016 / 1.452155 (-0.003138) | 1.490839 / 1.492716 (-0.001877) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226569 / 0.018006 (0.208563) | 0.417106 / 0.000490 (0.416616) | 0.002784 / 0.000200 (0.002584) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025803 / 0.037411 (-0.011608) | 0.081084 / 0.014526 (0.066559) | 0.091851 / 0.176557 (-0.084706) | 0.143982 / 0.737135 (-0.593153) | 0.090511 / 0.296338 (-0.205827) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463664 / 0.215209 (0.248454) | 4.634528 / 2.077655 (2.556874) | 2.574739 / 1.504120 (1.070619) | 2.412857 / 1.541195 (0.871662) | 2.442858 / 1.468490 (0.974368) | 0.511990 / 4.584777 (-4.072787) | 3.070345 / 3.745712 (-0.675367) | 2.842290 / 5.269862 (-2.427571) | 1.846727 / 4.565676 (-2.718950) | 0.058852 / 0.424275 (-0.365424) | 0.006624 / 0.007607 (-0.000983) | 0.539616 / 0.226044 (0.313571) | 5.410784 / 2.268929 (3.141856) | 3.065593 / 55.444624 (-52.379031) | 2.677930 / 6.876477 (-4.198547) | 2.817548 / 2.142072 (0.675476) | 0.602672 / 4.805227 (-4.202555) | 0.125689 / 6.500664 (-6.374975) | 0.062007 / 0.075469 (-0.013462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.335336 / 1.841788 (-0.506452) | 18.310099 / 8.074308 (10.235791) | 14.818452 / 10.191392 (4.627060) | 0.154001 / 0.680424 (-0.526423) | 0.017892 / 0.534201 (-0.516309) | 0.345989 / 0.579283 (-0.233294) | 0.352108 / 0.434364 (-0.082256) | 0.394333 / 0.540337 (-0.146004) | 0.547680 / 1.386936 (-0.839256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d058d6e9b849acb5bc61d7df597a94253b487eb6 \"CML watermark\")\n"
] | 2023-09-08T11:08:55 | 2023-09-08T12:29:21 | 2023-09-08T12:20:51 | MEMBER | null | {
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https://api.github.com/repos/huggingface/datasets/issues/6225 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6225/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6225/comments | https://api.github.com/repos/huggingface/datasets/issues/6225/events | https://github.com/huggingface/datasets/issues/6225 | 1,887,054,320 | I_kwDODunzps5weinw | 6,225 | Conversion from RGB to BGR in Object Detection tutorial | {
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"Good catch!"
] | 2023-09-08T06:49:19 | 2023-09-08T17:52:18 | 2023-09-08T17:52:17 | NONE | The [tutorial](https://huggingface.co/docs/datasets/main/en/object_detection) mentions the necessity of conversion the input image from BGR to RGB
> albumentations expects the image to be in BGR format, not RGB, so you’ll have to convert the image before applying the transform.
[Link to tutorial](https://github.com/huggingface/datasets/blob/0a068dbf3b446417ffd89d32857608394ec699e6/docs/source/object_detection.mdx#L77)
However, relevant albumentations' tutorials [on channels conversion](https://albumentations.ai/docs/examples/example/#read-the-image-from-the-disk-and-convert-it-from-the-bgr-color-space-to-the-rgb-color-space) and [on boxes](https://albumentations.ai/docs/examples/example_bboxes/) imply that it's not really true no more.
I suggest removing this outdated conversion from the tutorial. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009450 / 0.011353 (-0.001903) | 0.007339 / 0.011008 (-0.003669) | 0.110150 / 0.038508 (0.071641) | 0.087794 / 0.023109 (0.064685) | 0.472099 / 0.275898 (0.196201) | 0.476622 / 0.323480 (0.153142) | 0.005057 / 0.007986 (-0.002929) | 0.005262 / 0.004328 (0.000933) | 0.103059 / 0.004250 (0.098808) | 0.069815 / 0.037052 (0.032763) | 0.489377 / 0.258489 (0.230888) | 0.547087 / 0.293841 (0.253247) | 0.048883 / 0.128546 (-0.079663) | 0.019192 / 0.075646 (-0.056454) | 0.410865 / 0.419271 (-0.008407) | 0.076215 / 0.043533 (0.032682) | 0.484825 / 0.255139 (0.229686) | 0.519035 / 0.283200 (0.235835) | 0.042030 / 0.141683 (-0.099653) | 1.909630 / 1.452155 (0.457475) | 2.120869 / 1.492716 (0.628153) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267600 / 0.018006 (0.249594) | 0.619135 / 0.000490 (0.618645) | 0.005897 / 0.000200 (0.005697) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033265 / 0.037411 (-0.004146) | 0.104476 / 0.014526 (0.089950) | 0.129199 / 0.176557 (-0.047358) | 0.196898 / 0.737135 (-0.540238) | 0.118852 / 0.296338 (-0.177487) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598908 / 0.215209 (0.383699) | 6.263096 / 2.077655 (4.185441) | 2.672134 / 1.504120 (1.168014) | 2.428706 / 1.541195 (0.887511) | 2.431651 / 1.468490 (0.963161) | 0.918465 / 4.584777 (-3.666312) | 5.667857 / 3.745712 (1.922145) | 5.113696 / 5.269862 (-0.156166) | 3.276805 / 4.565676 (-1.288872) | 0.101829 / 0.424275 (-0.322446) | 0.010224 / 0.007607 (0.002617) | 0.741547 / 0.226044 (0.515502) | 7.517002 / 2.268929 (5.248073) | 3.546353 / 55.444624 (-51.898272) | 2.845956 / 6.876477 (-4.030521) | 3.172777 / 2.142072 (1.030705) | 1.153485 / 4.805227 (-3.651742) | 0.225758 / 6.500664 (-6.274906) | 0.084333 / 0.075469 (0.008864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.704645 / 1.841788 (-0.137143) | 27.044110 / 8.074308 (18.969801) | 24.653837 / 10.191392 (14.462445) | 0.235452 / 0.680424 (-0.444971) | 0.029285 / 0.534201 (-0.504916) | 0.576122 / 0.579283 (-0.003161) | 0.626263 / 0.434364 (0.191899) | 0.600201 / 0.540337 (0.059864) | 0.838406 / 1.386936 (-0.548530) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.013754 / 0.011353 (0.002401) | 0.005954 / 0.011008 (-0.005054) | 0.089766 / 0.038508 (0.051258) | 0.096126 / 0.023109 (0.073017) | 0.556455 / 0.275898 (0.280557) | 0.579302 / 0.323480 (0.255822) | 0.009222 / 0.007986 (0.001236) | 0.006128 / 0.004328 (0.001800) | 0.099725 / 0.004250 (0.095475) | 0.075642 / 0.037052 (0.038589) | 0.556645 / 0.258489 (0.298156) | 0.615898 / 0.293841 (0.322057) | 0.057728 / 0.128546 (-0.070818) | 0.016746 / 0.075646 (-0.058900) | 0.098053 / 0.419271 (-0.321219) | 0.066676 / 0.043533 (0.023143) | 0.534156 / 0.255139 (0.279017) | 0.590020 / 0.283200 (0.306820) | 0.038782 / 0.141683 (-0.102901) | 1.952301 / 1.452155 (0.500146) | 2.104255 / 1.492716 (0.611539) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305945 / 0.018006 (0.287939) | 0.643915 / 0.000490 (0.643426) | 0.006268 / 0.000200 (0.006068) | 0.000156 / 0.000054 (0.000102) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039891 / 0.037411 (0.002479) | 0.117888 / 0.014526 (0.103363) | 0.134230 / 0.176557 (-0.042326) | 0.212544 / 0.737135 (-0.524591) | 0.128858 / 0.296338 (-0.167480) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.718165 / 0.215209 (0.502955) | 7.023867 / 2.077655 (4.946212) | 3.391344 / 1.504120 (1.887224) | 3.021248 / 1.541195 (1.480053) | 3.010217 / 1.468490 (1.541727) | 0.932608 / 4.584777 (-3.652169) | 5.787536 / 3.745712 (2.041824) | 5.221305 / 5.269862 (-0.048557) | 3.282552 / 4.565676 (-1.283125) | 0.105486 / 0.424275 (-0.318789) | 0.009800 / 0.007607 (0.002193) | 0.839358 / 0.226044 (0.613314) | 8.279712 / 2.268929 (6.010784) | 4.118466 / 55.444624 (-51.326158) | 3.407738 / 6.876477 (-3.468739) | 3.632538 / 2.142072 (1.490466) | 1.109673 / 4.805227 (-3.695555) | 0.216541 / 6.500664 (-6.284123) | 0.094031 / 0.075469 (0.018562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.983979 / 1.841788 (0.142191) | 27.125882 / 8.074308 (19.051573) | 24.714002 / 10.191392 (14.522610) | 0.264417 / 0.680424 (-0.416007) | 0.034783 / 0.534201 (-0.499418) | 0.533304 / 0.579283 (-0.045979) | 0.647798 / 0.434364 (0.213434) | 0.588680 / 0.540337 (0.048343) | 0.854250 / 1.386936 (-0.532686) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#491604b46b1fd8d6fd1b7531f7917ccd657665a6 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006664 / 0.011353 (-0.004689) | 0.004164 / 0.011008 (-0.006844) | 0.085192 / 0.038508 (0.046684) | 0.073578 / 0.023109 (0.050469) | 0.356379 / 0.275898 (0.080481) | 0.389381 / 0.323480 (0.065902) | 0.005527 / 0.007986 (-0.002459) | 0.003488 / 0.004328 (-0.000840) | 0.065640 / 0.004250 (0.061390) | 0.055013 / 0.037052 (0.017960) | 0.358002 / 0.258489 (0.099513) | 0.400663 / 0.293841 (0.106822) | 0.030937 / 0.128546 (-0.097609) | 0.008838 / 0.075646 (-0.066808) | 0.287488 / 0.419271 (-0.131784) | 0.051503 / 0.043533 (0.007971) | 0.353945 / 0.255139 (0.098806) | 0.388778 / 0.283200 (0.105579) | 0.023346 / 0.141683 (-0.118337) | 1.479621 / 1.452155 (0.027466) | 1.559164 / 1.492716 (0.066448) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245160 / 0.018006 (0.227154) | 0.561890 / 0.000490 (0.561400) | 0.004339 / 0.000200 (0.004139) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028460 / 0.037411 (-0.008952) | 0.082046 / 0.014526 (0.067520) | 0.098005 / 0.176557 (-0.078552) | 0.154171 / 0.737135 (-0.582965) | 0.097632 / 0.296338 (-0.198707) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389993 / 0.215209 (0.174784) | 3.893287 / 2.077655 (1.815632) | 1.885668 / 1.504120 (0.381549) | 1.715055 / 1.541195 (0.173860) | 1.778008 / 1.468490 (0.309518) | 0.482818 / 4.584777 (-4.101959) | 3.572153 / 3.745712 (-0.173559) | 3.267666 / 5.269862 (-2.002196) | 2.088394 / 4.565676 (-2.477282) | 0.056961 / 0.424275 (-0.367314) | 0.007784 / 0.007607 (0.000177) | 0.466586 / 0.226044 (0.240542) | 4.652505 / 2.268929 (2.383576) | 2.491392 / 55.444624 (-52.953233) | 2.127600 / 6.876477 (-4.748877) | 2.296778 / 2.142072 (0.154705) | 0.582332 / 4.805227 (-4.222895) | 0.134372 / 6.500664 (-6.366292) | 0.061737 / 0.075469 (-0.013732) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253647 / 1.841788 (-0.588140) | 19.802353 / 8.074308 (11.728045) | 14.262815 / 10.191392 (4.071423) | 0.169489 / 0.680424 (-0.510935) | 0.018108 / 0.534201 (-0.516093) | 0.391711 / 0.579283 (-0.187572) | 0.406169 / 0.434364 (-0.028195) | 0.456728 / 0.540337 (-0.083609) | 0.633538 / 1.386936 (-0.753398) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006661 / 0.011353 (-0.004692) | 0.004181 / 0.011008 (-0.006827) | 0.064945 / 0.038508 (0.026437) | 0.073965 / 0.023109 (0.050856) | 0.406549 / 0.275898 (0.130651) | 0.441568 / 0.323480 (0.118089) | 0.005579 / 0.007986 (-0.002407) | 0.003523 / 0.004328 (-0.000805) | 0.065270 / 0.004250 (0.061019) | 0.055596 / 0.037052 (0.018544) | 0.407701 / 0.258489 (0.149212) | 0.444609 / 0.293841 (0.150768) | 0.031749 / 0.128546 (-0.096797) | 0.008680 / 0.075646 (-0.066966) | 0.071154 / 0.419271 (-0.348117) | 0.047376 / 0.043533 (0.003843) | 0.406409 / 0.255139 (0.151270) | 0.420477 / 0.283200 (0.137278) | 0.023707 / 0.141683 (-0.117976) | 1.484516 / 1.452155 (0.032361) | 1.568493 / 1.492716 (0.075777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266534 / 0.018006 (0.248528) | 0.573806 / 0.000490 (0.573316) | 0.006247 / 0.000200 (0.006048) | 0.000165 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033436 / 0.037411 (-0.003976) | 0.091947 / 0.014526 (0.077421) | 0.105556 / 0.176557 (-0.071000) | 0.162094 / 0.737135 (-0.575041) | 0.107879 / 0.296338 (-0.188459) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429126 / 0.215209 (0.213917) | 4.281329 / 2.077655 (2.203675) | 2.295406 / 1.504120 (0.791286) | 2.123336 / 1.541195 (0.582141) | 2.190804 / 1.468490 (0.722314) | 0.492972 / 4.584777 (-4.091805) | 3.638485 / 3.745712 (-0.107227) | 3.304576 / 5.269862 (-1.965285) | 2.063694 / 4.565676 (-2.501983) | 0.058549 / 0.424275 (-0.365726) | 0.007591 / 0.007607 (-0.000016) | 0.504268 / 0.226044 (0.278223) | 5.031990 / 2.268929 (2.763061) | 2.773173 / 55.444624 (-52.671451) | 2.430789 / 6.876477 (-4.445688) | 2.699900 / 2.142072 (0.557828) | 0.593220 / 4.805227 (-4.212007) | 0.133710 / 6.500664 (-6.366954) | 0.059840 / 0.075469 (-0.015629) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351158 / 1.841788 (-0.490629) | 20.176310 / 8.074308 (12.102002) | 14.933202 / 10.191392 (4.741810) | 0.169920 / 0.680424 (-0.510503) | 0.020156 / 0.534201 (-0.514045) | 0.397440 / 0.579283 (-0.181843) | 0.409395 / 0.434364 (-0.024969) | 0.471066 / 0.540337 (-0.069271) | 0.642670 / 1.386936 (-0.744266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf90ca7fbfd9c4639cc3faf0a349eb26490e38fc \"CML watermark\")\n"
] | 2023-09-07T14:43:51 | 2023-09-07T15:46:10 | 2023-09-07T15:37:20 | CONTRIBUTOR | `save_to_disk` creates this file, but also [`HugginFaceDatasetSever`](https://github.com/gradio-app/gradio/blob/26fef8c7f85a006c7e25cdbed1792df19c512d02/gradio/flagging.py#L214), so this is needed to avoid issues such as [this one](https://discord.com/channels/879548962464493619/1149295819938349107/1149295819938349107). | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6223). All of your documentation changes will be reflected on that endpoint."
] | 2023-09-07T11:33:20 | 2023-09-08T15:15:20 | null | NONE | fixed a few typos | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006655 / 0.011353 (-0.004698) | 0.004115 / 0.011008 (-0.006893) | 0.083895 / 0.038508 (0.045387) | 0.072770 / 0.023109 (0.049661) | 0.311401 / 0.275898 (0.035503) | 0.341079 / 0.323480 (0.017599) | 0.005488 / 0.007986 (-0.002497) | 0.003530 / 0.004328 (-0.000799) | 0.064691 / 0.004250 (0.060441) | 0.053096 / 0.037052 (0.016044) | 0.314969 / 0.258489 (0.056480) | 0.358245 / 0.293841 (0.064404) | 0.030789 / 0.128546 (-0.097757) | 0.008868 / 0.075646 (-0.066779) | 0.288022 / 0.419271 (-0.131249) | 0.052092 / 0.043533 (0.008559) | 0.310061 / 0.255139 (0.054922) | 0.345369 / 0.283200 (0.062170) | 0.024100 / 0.141683 (-0.117582) | 1.520573 / 1.452155 (0.068418) | 1.593750 / 1.492716 (0.101033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242520 / 0.018006 (0.224514) | 0.567963 / 0.000490 (0.567473) | 0.003183 / 0.000200 (0.002983) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029473 / 0.037411 (-0.007939) | 0.083012 / 0.014526 (0.068486) | 0.262386 / 0.176557 (0.085830) | 0.155131 / 0.737135 (-0.582004) | 0.099880 / 0.296338 (-0.196458) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382388 / 0.215209 (0.167179) | 3.816538 / 2.077655 (1.738884) | 1.863422 / 1.504120 (0.359302) | 1.694652 / 1.541195 (0.153457) | 1.738738 / 1.468490 (0.270248) | 0.477073 / 4.584777 (-4.107704) | 3.539244 / 3.745712 (-0.206468) | 3.238469 / 5.269862 (-2.031392) | 2.026154 / 4.565676 (-2.539523) | 0.056111 / 0.424275 (-0.368164) | 0.007615 / 0.007607 (0.000008) | 0.460620 / 0.226044 (0.234576) | 4.596383 / 2.268929 (2.327455) | 2.348645 / 55.444624 (-53.095979) | 1.977465 / 6.876477 (-4.899011) | 2.222828 / 2.142072 (0.080755) | 0.588065 / 4.805227 (-4.217162) | 0.132175 / 6.500664 (-6.368489) | 0.061322 / 0.075469 (-0.014147) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260623 / 1.841788 (-0.581164) | 19.976475 / 8.074308 (11.902167) | 14.346488 / 10.191392 (4.155096) | 0.145614 / 0.680424 (-0.534810) | 0.018309 / 0.534201 (-0.515892) | 0.393644 / 0.579283 (-0.185639) | 0.405355 / 0.434364 (-0.029009) | 0.458355 / 0.540337 (-0.081982) | 0.630147 / 1.386936 (-0.756789) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006769 / 0.011353 (-0.004584) | 0.004172 / 0.011008 (-0.006836) | 0.064863 / 0.038508 (0.026355) | 0.076831 / 0.023109 (0.053722) | 0.419391 / 0.275898 (0.143493) | 0.439912 / 0.323480 (0.116432) | 0.006249 / 0.007986 (-0.001737) | 0.003571 / 0.004328 (-0.000757) | 0.064877 / 0.004250 (0.060626) | 0.056023 / 0.037052 (0.018971) | 0.419899 / 0.258489 (0.161410) | 0.459334 / 0.293841 (0.165493) | 0.032217 / 0.128546 (-0.096329) | 0.008628 / 0.075646 (-0.067019) | 0.071089 / 0.419271 (-0.348183) | 0.047463 / 0.043533 (0.003930) | 0.414961 / 0.255139 (0.159822) | 0.431408 / 0.283200 (0.148209) | 0.022406 / 0.141683 (-0.119277) | 1.511890 / 1.452155 (0.059735) | 1.580268 / 1.492716 (0.087551) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280805 / 0.018006 (0.262799) | 0.553766 / 0.000490 (0.553276) | 0.006155 / 0.000200 (0.005955) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032980 / 0.037411 (-0.004431) | 0.092981 / 0.014526 (0.078456) | 0.108820 / 0.176557 (-0.067737) | 0.161709 / 0.737135 (-0.575426) | 0.109772 / 0.296338 (-0.186566) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433659 / 0.215209 (0.218450) | 4.328577 / 2.077655 (2.250923) | 2.316899 / 1.504120 (0.812779) | 2.142645 / 1.541195 (0.601451) | 2.245518 / 1.468490 (0.777028) | 0.489448 / 4.584777 (-4.095329) | 3.630074 / 3.745712 (-0.115638) | 3.322749 / 5.269862 (-1.947112) | 2.062307 / 4.565676 (-2.503370) | 0.058153 / 0.424275 (-0.366122) | 0.007453 / 0.007607 (-0.000154) | 0.507234 / 0.226044 (0.281190) | 5.071830 / 2.268929 (2.802902) | 2.839374 / 55.444624 (-52.605250) | 2.429583 / 6.876477 (-4.446893) | 2.671940 / 2.142072 (0.529868) | 0.588256 / 4.805227 (-4.216972) | 0.135135 / 6.500664 (-6.365530) | 0.060963 / 0.075469 (-0.014506) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.337462 / 1.841788 (-0.504326) | 20.292912 / 8.074308 (12.218604) | 14.871809 / 10.191392 (4.680417) | 0.169214 / 0.680424 (-0.511209) | 0.020450 / 0.534201 (-0.513751) | 0.397094 / 0.579283 (-0.182189) | 0.411623 / 0.434364 (-0.022741) | 0.471560 / 0.540337 (-0.068777) | 0.647293 / 1.386936 (-0.739643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a068dbf3b446417ffd89d32857608394ec699e6 \"CML watermark\")\n"
] | 2023-09-06T23:17:24 | 2023-09-07T15:48:07 | 2023-09-07T15:39:09 | CONTRIBUTOR | There is a typo in the section of the documentation dedicated to creating an audio dataset. The Dataset is incorrectly suffixed with a `Config`
https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia/blob/main/librivox-indonesia.py#L59 | {
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https://api.github.com/repos/huggingface/datasets/issues/6221 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6221/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6221/comments | https://api.github.com/repos/huggingface/datasets/issues/6221/events | https://github.com/huggingface/datasets/issues/6221 | 1,884,324,631 | I_kwDODunzps5wUIMX | 6,221 | Support saving datasets with custom formatting | {
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"Not a fan of pickling this sort of stuff either.\r\nNote that users can also share the code in their dataset documentation."
] | 2023-09-06T16:03:32 | 2023-09-06T18:32:07 | null | CONTRIBUTOR | Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036.
I am not sure if supporting this is the best idea for the following reasons:
>For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea.
@lhoestq WDYT?
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https://api.github.com/repos/huggingface/datasets/issues/6220 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6220/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6220/comments | https://api.github.com/repos/huggingface/datasets/issues/6220/events | https://github.com/huggingface/datasets/pull/6220 | 1,884,285,980 | PR_kwDODunzps5ZspRb | 6,220 | Set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6220). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005950 / 0.011353 (-0.005403) | 0.003578 / 0.011008 (-0.007431) | 0.079327 / 0.038508 (0.040819) | 0.057862 / 0.023109 (0.034752) | 0.317288 / 0.275898 (0.041390) | 0.358210 / 0.323480 (0.034730) | 0.004685 / 0.007986 (-0.003301) | 0.002879 / 0.004328 (-0.001450) | 0.062355 / 0.004250 (0.058105) | 0.045093 / 0.037052 (0.008041) | 0.322520 / 0.258489 (0.064031) | 0.367114 / 0.293841 (0.073273) | 0.027233 / 0.128546 (-0.101313) | 0.007941 / 0.075646 (-0.067705) | 0.260511 / 0.419271 (-0.158761) | 0.044355 / 0.043533 (0.000822) | 0.332993 / 0.255139 (0.077854) | 0.351363 / 0.283200 (0.068163) | 0.020784 / 0.141683 (-0.120899) | 1.429044 / 1.452155 (-0.023111) | 1.489355 / 1.492716 (-0.003362) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180903 / 0.018006 (0.162897) | 0.421566 / 0.000490 (0.421077) | 0.003259 / 0.000200 (0.003059) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023765 / 0.037411 (-0.013646) | 0.072815 / 0.014526 (0.058289) | 0.084592 / 0.176557 (-0.091965) | 0.143556 / 0.737135 (-0.593579) | 0.083591 / 0.296338 (-0.212748) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401896 / 0.215209 (0.186687) | 4.006344 / 2.077655 (1.928689) | 2.092280 / 1.504120 (0.588160) | 1.937828 / 1.541195 (0.396633) | 2.026901 / 1.468490 (0.558411) | 0.499999 / 4.584777 (-4.084778) | 3.008715 / 3.745712 (-0.736997) | 2.789735 / 5.269862 (-2.480127) | 1.827319 / 4.565676 (-2.738358) | 0.057413 / 0.424275 (-0.366862) | 0.006716 / 0.007607 (-0.000891) | 0.473061 / 0.226044 (0.247016) | 4.733256 / 2.268929 (2.464327) | 2.403922 / 55.444624 (-53.040702) | 2.017466 / 6.876477 (-4.859011) | 2.209710 / 2.142072 (0.067638) | 0.590813 / 4.805227 (-4.214414) | 0.124760 / 6.500664 (-6.375904) | 0.060976 / 0.075469 (-0.014494) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229172 / 1.841788 (-0.612616) | 17.924644 / 8.074308 (9.850336) | 13.697347 / 10.191392 (3.505955) | 0.128258 / 0.680424 (-0.552166) | 0.016780 / 0.534201 (-0.517421) | 0.329301 / 0.579283 (-0.249982) | 0.344527 / 0.434364 (-0.089837) | 0.379482 / 0.540337 (-0.160855) | 0.513851 / 1.386936 (-0.873085) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005962 / 0.011353 (-0.005391) | 0.003613 / 0.011008 (-0.007396) | 0.062428 / 0.038508 (0.023920) | 0.058151 / 0.023109 (0.035042) | 0.452926 / 0.275898 (0.177027) | 0.489740 / 0.323480 (0.166260) | 0.006137 / 0.007986 (-0.001848) | 0.002890 / 0.004328 (-0.001438) | 0.062880 / 0.004250 (0.058629) | 0.046175 / 0.037052 (0.009123) | 0.452416 / 0.258489 (0.193927) | 0.486047 / 0.293841 (0.192206) | 0.028517 / 0.128546 (-0.100029) | 0.008102 / 0.075646 (-0.067544) | 0.068251 / 0.419271 (-0.351020) | 0.040569 / 0.043533 (-0.002964) | 0.461306 / 0.255139 (0.206167) | 0.477675 / 0.283200 (0.194475) | 0.020944 / 0.141683 (-0.120739) | 1.414300 / 1.452155 (-0.037855) | 1.502108 / 1.492716 (0.009391) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217786 / 0.018006 (0.199780) | 0.410757 / 0.000490 (0.410267) | 0.002981 / 0.000200 (0.002781) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026846 / 0.037411 (-0.010565) | 0.080098 / 0.014526 (0.065572) | 0.090591 / 0.176557 (-0.085965) | 0.144674 / 0.737135 (-0.592461) | 0.091287 / 0.296338 (-0.205052) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458224 / 0.215209 (0.243015) | 4.590541 / 2.077655 (2.512886) | 2.511251 / 1.504120 (1.007131) | 2.329165 / 1.541195 (0.787970) | 2.379187 / 1.468490 (0.910696) | 0.507485 / 4.584777 (-4.077292) | 3.135011 / 3.745712 (-0.610701) | 2.805913 / 5.269862 (-2.463948) | 1.851382 / 4.565676 (-2.714295) | 0.057981 / 0.424275 (-0.366294) | 0.006557 / 0.007607 (-0.001050) | 0.532496 / 0.226044 (0.306452) | 5.348802 / 2.268929 (3.079874) | 2.993379 / 55.444624 (-52.451245) | 2.636372 / 6.876477 (-4.240104) | 2.753219 / 2.142072 (0.611147) | 0.591989 / 4.805227 (-4.213238) | 0.126691 / 6.500664 (-6.373973) | 0.062359 / 0.075469 (-0.013110) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345498 / 1.841788 (-0.496290) | 18.335767 / 8.074308 (10.261458) | 15.115449 / 10.191392 (4.924057) | 0.147382 / 0.680424 (-0.533041) | 0.017729 / 0.534201 (-0.516472) | 0.334337 / 0.579283 (-0.244946) | 0.359035 / 0.434364 (-0.075329) | 0.386319 / 0.540337 (-0.154019) | 0.536378 / 1.386936 (-0.850558) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b028fd83d74e7701e7b8f2d87e740a989505a7 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009136 / 0.011353 (-0.002216) | 0.005567 / 0.011008 (-0.005442) | 0.120320 / 0.038508 (0.081812) | 0.078082 / 0.023109 (0.054973) | 0.405579 / 0.275898 (0.129681) | 0.459714 / 0.323480 (0.136234) | 0.006327 / 0.007986 (-0.001659) | 0.007187 / 0.004328 (0.002859) | 0.084373 / 0.004250 (0.080122) | 0.059727 / 0.037052 (0.022675) | 0.418918 / 0.258489 (0.160429) | 0.486767 / 0.293841 (0.192927) | 0.047715 / 0.128546 (-0.080831) | 0.014417 / 0.075646 (-0.061229) | 0.379847 / 0.419271 (-0.039425) | 0.067472 / 0.043533 (0.023939) | 0.419304 / 0.255139 (0.164166) | 0.466260 / 0.283200 (0.183060) | 0.036872 / 0.141683 (-0.104811) | 1.876273 / 1.452155 (0.424119) | 2.043856 / 1.492716 (0.551140) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296266 / 0.018006 (0.278260) | 0.601843 / 0.000490 (0.601354) | 0.005663 / 0.000200 (0.005463) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033272 / 0.037411 (-0.004139) | 0.098839 / 0.014526 (0.084313) | 0.124658 / 0.176557 (-0.051899) | 0.190226 / 0.737135 (-0.546909) | 0.119288 / 0.296338 (-0.177051) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.600878 / 0.215209 (0.385668) | 6.011749 / 2.077655 (3.934095) | 2.611809 / 1.504120 (1.107689) | 2.314985 / 1.541195 (0.773790) | 2.398988 / 1.468490 (0.930498) | 0.835577 / 4.584777 (-3.749200) | 5.482848 / 3.745712 (1.737136) | 4.965393 / 5.269862 (-0.304469) | 3.082420 / 4.565676 (-1.483256) | 0.098048 / 0.424275 (-0.326227) | 0.009148 / 0.007607 (0.001541) | 0.725721 / 0.226044 (0.499676) | 7.297429 / 2.268929 (5.028501) | 3.558050 / 55.444624 (-51.886575) | 2.815884 / 6.876477 (-4.060593) | 3.094103 / 2.142072 (0.952031) | 1.023617 / 4.805227 (-3.781610) | 0.222453 / 6.500664 (-6.278211) | 0.081707 / 0.075469 (0.006238) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.788327 / 1.841788 (-0.053461) | 25.285829 / 8.074308 (17.211521) | 21.878811 / 10.191392 (11.687419) | 0.215494 / 0.680424 (-0.464930) | 0.032050 / 0.534201 (-0.502151) | 0.505210 / 0.579283 (-0.074073) | 0.623545 / 0.434364 (0.189181) | 0.583342 / 0.540337 (0.043005) | 0.826497 / 1.386936 (-0.560439) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009640 / 0.011353 (-0.001713) | 0.005479 / 0.011008 (-0.005529) | 0.088940 / 0.038508 (0.050432) | 0.084186 / 0.023109 (0.061077) | 0.552290 / 0.275898 (0.276392) | 0.583296 / 0.323480 (0.259816) | 0.006999 / 0.007986 (-0.000987) | 0.004597 / 0.004328 (0.000269) | 0.089407 / 0.004250 (0.085157) | 0.067210 / 0.037052 (0.030157) | 0.554968 / 0.258489 (0.296479) | 0.595635 / 0.293841 (0.301794) | 0.052245 / 0.128546 (-0.076301) | 0.015914 / 0.075646 (-0.059733) | 0.097037 / 0.419271 (-0.322235) | 0.063954 / 0.043533 (0.020421) | 0.533752 / 0.255139 (0.278614) | 0.573789 / 0.283200 (0.290589) | 0.036526 / 0.141683 (-0.105157) | 1.867713 / 1.452155 (0.415558) | 1.996901 / 1.492716 (0.504185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.414967 / 0.018006 (0.396961) | 0.632367 / 0.000490 (0.631877) | 0.064061 / 0.000200 (0.063861) | 0.000565 / 0.000054 (0.000510) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035953 / 0.037411 (-0.001458) | 0.112603 / 0.014526 (0.098077) | 0.126227 / 0.176557 (-0.050330) | 0.196881 / 0.737135 (-0.540255) | 0.127635 / 0.296338 (-0.168704) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.674735 / 0.215209 (0.459526) | 6.614578 / 2.077655 (4.536923) | 3.208198 / 1.504120 (1.704078) | 2.870412 / 1.541195 (1.329217) | 2.979358 / 1.468490 (1.510868) | 0.872589 / 4.584777 (-3.712187) | 5.501771 / 3.745712 (1.756059) | 4.865191 / 5.269862 (-0.404671) | 3.075281 / 4.565676 (-1.490396) | 0.098048 / 0.424275 (-0.326227) | 0.009121 / 0.007607 (0.001514) | 0.801639 / 0.226044 (0.575595) | 8.062040 / 2.268929 (5.793111) | 3.996693 / 55.444624 (-51.447931) | 3.343770 / 6.876477 (-3.532706) | 3.555977 / 2.142072 (1.413904) | 1.035050 / 4.805227 (-3.770177) | 0.227552 / 6.500664 (-6.273112) | 0.097733 / 0.075469 (0.022264) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.897210 / 1.841788 (0.055422) | 25.762459 / 8.074308 (17.688151) | 22.771290 / 10.191392 (12.579898) | 0.252650 / 0.680424 (-0.427773) | 0.032534 / 0.534201 (-0.501667) | 0.521047 / 0.579283 (-0.058236) | 0.620850 / 0.434364 (0.186486) | 0.612750 / 0.540337 (0.072413) | 0.837486 / 1.386936 (-0.549451) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f522e5bdd73c45f7ba0a03f2ecd4e7de7351f2e \"CML watermark\")\n"
] | 2023-09-06T15:40:33 | 2023-09-06T15:52:33 | 2023-09-06T15:41:13 | MEMBER | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6219). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009523 / 0.011353 (-0.001830) | 0.005105 / 0.011008 (-0.005903) | 0.122664 / 0.038508 (0.084156) | 0.084688 / 0.023109 (0.061579) | 0.412057 / 0.275898 (0.136159) | 0.449690 / 0.323480 (0.126210) | 0.006627 / 0.007986 (-0.001358) | 0.004150 / 0.004328 (-0.000178) | 0.082079 / 0.004250 (0.077829) | 0.065289 / 0.037052 (0.028237) | 0.432934 / 0.258489 (0.174445) | 0.492068 / 0.293841 (0.198227) | 0.048317 / 0.128546 (-0.080229) | 0.015582 / 0.075646 (-0.060064) | 0.372050 / 0.419271 (-0.047222) | 0.070649 / 0.043533 (0.027116) | 0.431754 / 0.255139 (0.176615) | 0.473349 / 0.283200 (0.190149) | 0.037293 / 0.141683 (-0.104390) | 1.807537 / 1.452155 (0.355382) | 1.923073 / 1.492716 (0.430357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271214 / 0.018006 (0.253208) | 0.592961 / 0.000490 (0.592471) | 0.004062 / 0.000200 (0.003862) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034766 / 0.037411 (-0.002645) | 0.093014 / 0.014526 (0.078488) | 0.131332 / 0.176557 (-0.045225) | 0.188110 / 0.737135 (-0.549025) | 0.117617 / 0.296338 (-0.178722) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668223 / 0.215209 (0.453013) | 6.707031 / 2.077655 (4.629376) | 3.040178 / 1.504120 (1.536058) | 2.641776 / 1.541195 (1.100581) | 2.524057 / 1.468490 (1.055567) | 0.893592 / 4.584777 (-3.691185) | 5.535848 / 3.745712 (1.790136) | 4.867067 / 5.269862 (-0.402794) | 2.999933 / 4.565676 (-1.565743) | 0.103602 / 0.424275 (-0.320673) | 0.008887 / 0.007607 (0.001280) | 0.822214 / 0.226044 (0.596169) | 8.028476 / 2.268929 (5.759547) | 3.708895 / 55.444624 (-51.735730) | 2.858314 / 6.876477 (-4.018163) | 3.101727 / 2.142072 (0.959655) | 1.083136 / 4.805227 (-3.722091) | 0.219588 / 6.500664 (-6.281076) | 0.080151 / 0.075469 (0.004682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.645819 / 1.841788 (-0.195969) | 24.407887 / 8.074308 (16.333579) | 22.371901 / 10.191392 (12.180509) | 0.219557 / 0.680424 (-0.460867) | 0.037867 / 0.534201 (-0.496334) | 0.484136 / 0.579283 (-0.095147) | 0.620546 / 0.434364 (0.186182) | 0.562272 / 0.540337 (0.021934) | 0.774256 / 1.386936 (-0.612680) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009381 / 0.011353 (-0.001972) | 0.005565 / 0.011008 (-0.005444) | 0.091057 / 0.038508 (0.052549) | 0.078085 / 0.023109 (0.054975) | 0.538929 / 0.275898 (0.263031) | 0.555155 / 0.323480 (0.231675) | 0.007007 / 0.007986 (-0.000978) | 0.004268 / 0.004328 (-0.000060) | 0.086618 / 0.004250 (0.082368) | 0.064117 / 0.037052 (0.027065) | 0.523788 / 0.258489 (0.265299) | 0.586451 / 0.293841 (0.292610) | 0.050804 / 0.128546 (-0.077742) | 0.013964 / 0.075646 (-0.061682) | 0.096008 / 0.419271 (-0.323263) | 0.062242 / 0.043533 (0.018709) | 0.530398 / 0.255139 (0.275259) | 0.568527 / 0.283200 (0.285327) | 0.032456 / 0.141683 (-0.109227) | 1.894975 / 1.452155 (0.442820) | 2.084172 / 1.492716 (0.591455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295539 / 0.018006 (0.277533) | 0.588804 / 0.000490 (0.588314) | 0.006445 / 0.000200 (0.006245) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033965 / 0.037411 (-0.003447) | 0.111743 / 0.014526 (0.097217) | 0.128805 / 0.176557 (-0.047752) | 0.185013 / 0.737135 (-0.552123) | 0.129400 / 0.296338 (-0.166938) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.749784 / 0.215209 (0.534575) | 7.091075 / 2.077655 (5.013420) | 3.424517 / 1.504120 (1.920397) | 3.069103 / 1.541195 (1.527908) | 3.122431 / 1.468490 (1.653941) | 0.949277 / 4.584777 (-3.635500) | 5.648731 / 3.745712 (1.903019) | 4.937684 / 5.269862 (-0.332178) | 3.198027 / 4.565676 (-1.367650) | 0.100289 / 0.424275 (-0.323987) | 0.009411 / 0.007607 (0.001803) | 0.862604 / 0.226044 (0.636559) | 8.615410 / 2.268929 (6.346482) | 4.306428 / 55.444624 (-51.138196) | 3.591404 / 6.876477 (-3.285073) | 3.823899 / 2.142072 (1.681827) | 1.108006 / 4.805227 (-3.697221) | 0.215330 / 6.500664 (-6.285334) | 0.080755 / 0.075469 (0.005286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774914 / 1.841788 (-0.066873) | 25.360983 / 8.074308 (17.286675) | 23.624044 / 10.191392 (13.432652) | 0.226887 / 0.680424 (-0.453537) | 0.032625 / 0.534201 (-0.501576) | 0.499730 / 0.579283 (-0.079553) | 0.647819 / 0.434364 (0.213455) | 0.592239 / 0.540337 (0.051901) | 0.805751 / 1.386936 (-0.581185) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0daa82428a0529478801574bcc68e1ed32051f3a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008656 / 0.011353 (-0.002697) | 0.005545 / 0.011008 (-0.005463) | 0.107936 / 0.038508 (0.069428) | 0.077436 / 0.023109 (0.054327) | 0.391412 / 0.275898 (0.115514) | 0.452811 / 0.323480 (0.129331) | 0.004883 / 0.007986 (-0.003103) | 0.005125 / 0.004328 (0.000796) | 0.080006 / 0.004250 (0.075755) | 0.054425 / 0.037052 (0.017373) | 0.399667 / 0.258489 (0.141178) | 0.458099 / 0.293841 (0.164258) | 0.047302 / 0.128546 (-0.081244) | 0.014153 / 0.075646 (-0.061493) | 0.337281 / 0.419271 (-0.081991) | 0.062153 / 0.043533 (0.018620) | 0.399927 / 0.255139 (0.144788) | 0.407186 / 0.283200 (0.123987) | 0.036759 / 0.141683 (-0.104924) | 1.825935 / 1.452155 (0.373780) | 1.852238 / 1.492716 (0.359522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274163 / 0.018006 (0.256157) | 0.615624 / 0.000490 (0.615134) | 0.003782 / 0.000200 (0.003582) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011026) | 0.101151 / 0.014526 (0.086625) | 0.106115 / 0.176557 (-0.070442) | 0.161253 / 0.737135 (-0.575882) | 0.108861 / 0.296338 (-0.187478) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587079 / 0.215209 (0.371870) | 6.141743 / 2.077655 (4.064089) | 2.727199 / 1.504120 (1.223079) | 2.526827 / 1.541195 (0.985632) | 2.598321 / 1.468490 (1.129831) | 0.904706 / 4.584777 (-3.680071) | 5.227742 / 3.745712 (1.482030) | 4.621627 / 5.269862 (-0.648234) | 2.931792 / 4.565676 (-1.633885) | 0.089538 / 0.424275 (-0.334737) | 0.008281 / 0.007607 (0.000674) | 0.675773 / 0.226044 (0.449729) | 7.212869 / 2.268929 (4.943941) | 3.541569 / 55.444624 (-51.903056) | 2.804034 / 6.876477 (-4.072443) | 3.080192 / 2.142072 (0.938120) | 1.034577 / 4.805227 (-3.770650) | 0.218727 / 6.500664 (-6.281937) | 0.084548 / 0.075469 (0.009079) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.528974 / 1.841788 (-0.312814) | 21.754329 / 8.074308 (13.680021) | 20.359808 / 10.191392 (10.168416) | 0.234719 / 0.680424 (-0.445705) | 0.026182 / 0.534201 (-0.508019) | 0.448956 / 0.579283 (-0.130327) | 0.577015 / 0.434364 (0.142651) | 0.513675 / 0.540337 (-0.026662) | 0.729780 / 1.386936 (-0.657156) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010427 / 0.011353 (-0.000926) | 0.005126 / 0.011008 (-0.005882) | 0.082759 / 0.038508 (0.044251) | 0.084892 / 0.023109 (0.061783) | 0.543826 / 0.275898 (0.267927) | 0.603050 / 0.323480 (0.279570) | 0.006667 / 0.007986 (-0.001319) | 0.004036 / 0.004328 (-0.000292) | 0.079534 / 0.004250 (0.075283) | 0.067523 / 0.037052 (0.030471) | 0.544845 / 0.258489 (0.286356) | 0.578823 / 0.293841 (0.284982) | 0.054786 / 0.128546 (-0.073760) | 0.014888 / 0.075646 (-0.060759) | 0.095696 / 0.419271 (-0.323576) | 0.064908 / 0.043533 (0.021375) | 0.558087 / 0.255139 (0.302948) | 0.593919 / 0.283200 (0.310719) | 0.039190 / 0.141683 (-0.102493) | 1.828680 / 1.452155 (0.376526) | 1.908891 / 1.492716 (0.416174) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298926 / 0.018006 (0.280920) | 0.589467 / 0.000490 (0.588977) | 0.005276 / 0.000200 (0.005076) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034300 / 0.037411 (-0.003111) | 0.096990 / 0.014526 (0.082464) | 0.109347 / 0.176557 (-0.067209) | 0.171312 / 0.737135 (-0.565823) | 0.121736 / 0.296338 (-0.174603) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.641619 / 0.215209 (0.426410) | 6.365556 / 2.077655 (4.287901) | 2.947989 / 1.504120 (1.443869) | 2.631680 / 1.541195 (1.090485) | 2.602762 / 1.468490 (1.134272) | 0.812767 / 4.584777 (-3.772010) | 5.185753 / 3.745712 (1.440041) | 4.589897 / 5.269862 (-0.679964) | 2.833020 / 4.565676 (-1.732656) | 0.097782 / 0.424275 (-0.326493) | 0.008625 / 0.007607 (0.001018) | 0.741613 / 0.226044 (0.515568) | 7.662905 / 2.268929 (5.393976) | 3.533753 / 55.444624 (-51.910871) | 2.898929 / 6.876477 (-3.977547) | 3.042616 / 2.142072 (0.900544) | 0.933932 / 4.805227 (-3.871296) | 0.195710 / 6.500664 (-6.304954) | 0.066954 / 0.075469 (-0.008515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745353 / 1.841788 (-0.096434) | 23.820840 / 8.074308 (15.746532) | 20.892645 / 10.191392 (10.701253) | 0.234853 / 0.680424 (-0.445571) | 0.029149 / 0.534201 (-0.505051) | 0.458953 / 0.579283 (-0.120330) | 0.594278 / 0.434364 (0.159914) | 0.522929 / 0.540337 (-0.017409) | 0.753731 / 1.386936 (-0.633205) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005976 / 0.011353 (-0.005377) | 0.003636 / 0.011008 (-0.007372) | 0.079946 / 0.038508 (0.041437) | 0.060143 / 0.023109 (0.037034) | 0.314752 / 0.275898 (0.038854) | 0.353714 / 0.323480 (0.030234) | 0.004706 / 0.007986 (-0.003280) | 0.002862 / 0.004328 (-0.001466) | 0.061988 / 0.004250 (0.057737) | 0.045907 / 0.037052 (0.008855) | 0.316118 / 0.258489 (0.057629) | 0.358488 / 0.293841 (0.064647) | 0.027377 / 0.128546 (-0.101170) | 0.007970 / 0.075646 (-0.067677) | 0.261677 / 0.419271 (-0.157594) | 0.045289 / 0.043533 (0.001757) | 0.307931 / 0.255139 (0.052792) | 0.341364 / 0.283200 (0.058165) | 0.021021 / 0.141683 (-0.120662) | 1.440002 / 1.452155 (-0.012153) | 1.502904 / 1.492716 (0.010187) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201746 / 0.018006 (0.183740) | 0.451114 / 0.000490 (0.450624) | 0.003351 / 0.000200 (0.003151) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024233 / 0.037411 (-0.013178) | 0.075042 / 0.014526 (0.060516) | 0.085636 / 0.176557 (-0.090920) | 0.144699 / 0.737135 (-0.592436) | 0.085222 / 0.296338 (-0.211117) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389464 / 0.215209 (0.174255) | 3.889072 / 2.077655 (1.811417) | 1.908307 / 1.504120 (0.404187) | 1.738914 / 1.541195 (0.197719) | 1.866869 / 1.468490 (0.398379) | 0.500536 / 4.584777 (-4.084240) | 3.050155 / 3.745712 (-0.695557) | 2.832259 / 5.269862 (-2.437602) | 1.886657 / 4.565676 (-2.679020) | 0.059214 / 0.424275 (-0.365062) | 0.006711 / 0.007607 (-0.000896) | 0.467753 / 0.226044 (0.241709) | 4.666939 / 2.268929 (2.398011) | 2.471168 / 55.444624 (-52.973456) | 2.223508 / 6.876477 (-4.652968) | 2.176543 / 2.142072 (0.034470) | 0.593461 / 4.805227 (-4.211766) | 0.126216 / 6.500664 (-6.374448) | 0.061495 / 0.075469 (-0.013974) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301279 / 1.841788 (-0.540509) | 18.317461 / 8.074308 (10.243153) | 13.877813 / 10.191392 (3.686421) | 0.143510 / 0.680424 (-0.536914) | 0.016826 / 0.534201 (-0.517375) | 0.328735 / 0.579283 (-0.250548) | 0.342272 / 0.434364 (-0.092092) | 0.375768 / 0.540337 (-0.164570) | 0.517600 / 1.386936 (-0.869336) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006215 / 0.011353 (-0.005138) | 0.003587 / 0.011008 (-0.007422) | 0.062248 / 0.038508 (0.023740) | 0.059830 / 0.023109 (0.036721) | 0.443278 / 0.275898 (0.167380) | 0.481279 / 0.323480 (0.157799) | 0.004773 / 0.007986 (-0.003213) | 0.002870 / 0.004328 (-0.001459) | 0.062730 / 0.004250 (0.058480) | 0.049422 / 0.037052 (0.012369) | 0.444196 / 0.258489 (0.185707) | 0.498614 / 0.293841 (0.204773) | 0.028477 / 0.128546 (-0.100069) | 0.008009 / 0.075646 (-0.067638) | 0.067919 / 0.419271 (-0.351352) | 0.040416 / 0.043533 (-0.003117) | 0.439460 / 0.255139 (0.184321) | 0.470529 / 0.283200 (0.187329) | 0.020767 / 0.141683 (-0.120916) | 1.478223 / 1.452155 (0.026068) | 1.538580 / 1.492716 (0.045863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271321 / 0.018006 (0.253315) | 0.456436 / 0.000490 (0.455946) | 0.011817 / 0.000200 (0.011617) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026355 / 0.037411 (-0.011056) | 0.081681 / 0.014526 (0.067155) | 0.091699 / 0.176557 (-0.084858) | 0.146115 / 0.737135 (-0.591021) | 0.094376 / 0.296338 (-0.201963) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471677 / 0.215209 (0.256468) | 4.702909 / 2.077655 (2.625254) | 2.664882 / 1.504120 (1.160762) | 2.504106 / 1.541195 (0.962911) | 2.573226 / 1.468490 (1.104736) | 0.509679 / 4.584777 (-4.075097) | 3.034970 / 3.745712 (-0.710742) | 2.894704 / 5.269862 (-2.375157) | 1.915148 / 4.565676 (-2.650528) | 0.058312 / 0.424275 (-0.365963) | 0.006615 / 0.007607 (-0.000993) | 0.545339 / 0.226044 (0.319295) | 5.462261 / 2.268929 (3.193332) | 3.101482 / 55.444624 (-52.343143) | 2.755417 / 6.876477 (-4.121060) | 2.931440 / 2.142072 (0.789368) | 0.597521 / 4.805227 (-4.207707) | 0.125676 / 6.500664 (-6.374988) | 0.061798 / 0.075469 (-0.013671) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356208 / 1.841788 (-0.485579) | 18.912492 / 8.074308 (10.838184) | 14.830128 / 10.191392 (4.638736) | 0.145992 / 0.680424 (-0.534432) | 0.019121 / 0.534201 (-0.515080) | 0.331534 / 0.579283 (-0.247749) | 0.361712 / 0.434364 (-0.072652) | 0.387532 / 0.540337 (-0.152805) | 0.536075 / 1.386936 (-0.850861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de6391d732ea0471ee5bdfb91b8cecc4503da96b \"CML watermark\")\n"
] | 2023-09-06T15:17:10 | 2023-09-06T15:46:20 | 2023-09-06T15:18:51 | MEMBER | null | {
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