Deprecate task api
_The documentation is not available anymore as the PR was closed or merged._ If it's easy to keep supporting it we can keep it no ? There are many datasets on the hub that implement the tasks templates in dataset scripts and it's maybe easier to keep task templates than opening PRs to those datasets. do we know if people use the tasks api?

edit: i mean, i'm fine with removing it if it's not used much, especially considering that it's not documented well. @lhoestq 

Less than 80 public datasets (all canonical) implement `task_templates`, so updating them should be easy.

PS: I skipped gated datasets when checking for the presence of `task_templates`, but it's safe to assume their contribution to the total count is insignificant. <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006480 / 0.011353 (-0.004872) | 0.003904 / 0.011008 (-0.007104) | 0.084287 / 0.038508 (0.045779) | 0.071438 / 0.023109 (0.048329) | 0.309823 / 0.275898 (0.033925) | 0.341038 / 0.323480 (0.017558) | 0.005163 / 0.007986 (-0.002822) | 0.003291 / 0.004328 (-0.001037) | 0.064473 / 0.004250 (0.060222) | 0.053385 / 0.037052 (0.016332) | 0.323561 / 0.258489 (0.065072) | 0.346332 / 0.293841 (0.052491) | 0.030588 / 0.128546 (-0.097958) | 0.008342 / 0.075646 (-0.067305) | 0.287205 / 0.419271 (-0.132067) | 0.051953 / 0.043533 (0.008420) | 0.310925 / 0.255139 (0.055786) | 0.344443 / 0.283200 (0.061244) | 0.022754 / 0.141683 (-0.118928) | 1.459648 / 1.452155 (0.007494) | 1.528413 / 1.492716 (0.035697) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.206404 / 0.018006 (0.188398) | 0.461864 / 0.000490 (0.461374) | 0.004501 / 0.000200 (0.004302) | 0.000080 / 0.000054 (0.000026) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026891 / 0.037411 (-0.010520) | 0.081206 / 0.014526 (0.066680) | 0.093648 / 0.176557 (-0.082908) | 0.148491 / 0.737135 (-0.588645) | 0.093874 / 0.296338 (-0.202464) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.401715 / 0.215209 (0.186506) | 4.018597 / 2.077655 (1.940943) | 2.029735 / 1.504120 (0.525615) | 1.860069 / 1.541195 (0.318875) | 1.935712 / 1.468490 (0.467222) | 0.485896 / 4.584777 (-4.098881) | 3.638177 / 3.745712 (-0.107535) | 5.124058 / 5.269862 (-0.145804) | 3.099666 / 4.565676 (-1.466011) | 0.057173 / 0.424275 (-0.367102) | 0.007240 / 0.007607 (-0.000367) | 0.478758 / 0.226044 (0.252713) | 4.798471 / 2.268929 (2.529543) | 2.502980 / 55.444624 (-52.941645) | 2.170650 / 6.876477 (-4.705827) | 2.381394 / 2.142072 (0.239321) | 0.578766 / 4.805227 (-4.226462) | 0.132342 / 6.500664 (-6.368322) | 0.059759 / 0.075469 (-0.015710) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.249238 / 1.841788 (-0.592549) | 19.224673 / 8.074308 (11.150365) | 13.786894 / 10.191392 (3.595502) | 0.164633 / 0.680424 (-0.515791) | 0.018065 / 0.534201 (-0.516136) | 0.390589 / 0.579283 (-0.188694) | 0.408993 / 0.434364 (-0.025370) | 0.457001 / 0.540337 (-0.083336) | 0.625327 / 1.386936 (-0.761609) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006827 / 0.011353 (-0.004526) | 0.004007 / 0.011008 (-0.007001) | 0.065239 / 0.038508 (0.026731) | 0.079829 / 0.023109 (0.056719) | 0.400323 / 0.275898 (0.124425) | 0.434158 / 0.323480 (0.110678) | 0.005314 / 0.007986 (-0.002671) | 0.003354 / 0.004328 (-0.000974) | 0.065044 / 0.004250 (0.060794) | 0.060315 / 0.037052 (0.023262) | 0.401513 / 0.258489 (0.143024) | 0.441119 / 0.293841 (0.147278) | 0.031783 / 0.128546 (-0.096763) | 0.008608 / 0.075646 (-0.067038) | 0.071755 / 0.419271 (-0.347517) | 0.048816 / 0.043533 (0.005283) | 0.393896 / 0.255139 (0.138757) | 0.412156 / 0.283200 (0.128956) | 0.024410 / 0.141683 (-0.117272) | 1.515159 / 1.452155 (0.063005) | 1.562217 / 1.492716 (0.069501) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.229993 / 0.018006 (0.211987) | 0.449898 / 0.000490 (0.449409) | 0.000376 / 0.000200 (0.000176) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030297 / 0.037411 (-0.007115) | 0.086737 / 0.014526 (0.072212) | 0.098312 / 0.176557 (-0.078244) | 0.152890 / 0.737135 (-0.584246) | 0.099335 / 0.296338 (-0.197003) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.415786 / 0.215209 (0.200577) | 4.137606 / 2.077655 (2.059952) | 2.120082 / 1.504120 (0.615963) | 1.943984 / 1.541195 (0.402789) | 2.040821 / 1.468490 (0.572331) | 0.479273 / 4.584777 (-4.105504) | 3.563854 / 3.745712 (-0.181858) | 3.396071 / 5.269862 (-1.873790) | 2.011302 / 4.565676 (-2.554374) | 0.057202 / 0.424275 (-0.367073) | 0.007338 / 0.007607 (-0.000269) | 0.488378 / 0.226044 (0.262333) | 4.881615 / 2.268929 (2.612686) | 2.669685 / 55.444624 (-52.774939) | 2.258236 / 6.876477 (-4.618241) | 2.343303 / 2.142072 (0.201230) | 0.606762 / 4.805227 (-4.198466) | 0.133190 / 6.500664 (-6.367475) | 0.062971 / 0.075469 (-0.012498) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.345215 / 1.841788 (-0.496573) | 20.023713 / 8.074308 (11.949405) | 14.555777 / 10.191392 (4.364385) | 0.162388 / 0.680424 (-0.518036) | 0.018528 / 0.534201 (-0.515673) | 0.393055 / 0.579283 (-0.186229) | 0.411820 / 0.434364 (-0.022544) | 0.461705 / 0.540337 (-0.078633) | 0.629395 / 1.386936 (-0.757541) |

</details>
</details>

![](https://cml.dev/watermark.png#4f54f2ff4c68a00242789e9890e3b46cab320448 "CML watermark")
 Ok ! I also know https://huggingface.co/datasets/hf-internal-testing/cats_vs_dogs_sample/blob/main/cats_vs_dogs_sample.py that needs to be updated as well <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.009100 / 0.011353 (-0.002253) | 0.005158 / 0.011008 (-0.005850) | 0.109291 / 0.038508 (0.070782) | 0.086053 / 0.023109 (0.062943) | 0.469859 / 0.275898 (0.193961) | 0.476142 / 0.323480 (0.152662) | 0.006739 / 0.007986 (-0.001247) | 0.005077 / 0.004328 (0.000748) | 0.078193 / 0.004250 (0.073943) | 0.065956 / 0.037052 (0.028904) | 0.490323 / 0.258489 (0.231834) | 0.497418 / 0.293841 (0.203577) | 0.060562 / 0.128546 (-0.067984) | 0.016321 / 0.075646 (-0.059325) | 0.379703 / 0.419271 (-0.039568) | 0.087335 / 0.043533 (0.043802) | 0.488240 / 0.255139 (0.233101) | 0.497391 / 0.283200 (0.214191) | 0.040699 / 0.141683 (-0.100984) | 1.778925 / 1.452155 (0.326770) | 1.856436 / 1.492716 (0.363720) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.236428 / 0.018006 (0.218422) | 0.551950 / 0.000490 (0.551460) | 0.007400 / 0.000200 (0.007201) | 0.000120 / 0.000054 (0.000066) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028461 / 0.037411 (-0.008950) | 0.093441 / 0.014526 (0.078915) | 0.103868 / 0.176557 (-0.072688) | 0.176269 / 0.737135 (-0.560867) | 0.107760 / 0.296338 (-0.188578) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.593382 / 0.215209 (0.378173) | 5.863711 / 2.077655 (3.786057) | 2.493777 / 1.504120 (0.989657) | 2.088547 / 1.541195 (0.547352) | 2.173147 / 1.468490 (0.704656) | 0.875661 / 4.584777 (-3.709116) | 5.209023 / 3.745712 (1.463310) | 4.483261 / 5.269862 (-0.786600) | 2.843288 / 4.565676 (-1.722388) | 0.098488 / 0.424275 (-0.325787) | 0.008371 / 0.007607 (0.000764) | 0.668413 / 0.226044 (0.442368) | 6.709802 / 2.268929 (4.440873) | 3.132453 / 55.444624 (-52.312172) | 2.428736 / 6.876477 (-4.447741) | 2.560867 / 2.142072 (0.418794) | 0.983550 / 4.805227 (-3.821677) | 0.207072 / 6.500664 (-6.293592) | 0.073786 / 0.075469 (-0.001683) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.625871 / 1.841788 (-0.215917) | 23.481015 / 8.074308 (15.406707) | 20.556677 / 10.191392 (10.365285) | 0.238147 / 0.680424 (-0.442277) | 0.029453 / 0.534201 (-0.504748) | 0.464589 / 0.579283 (-0.114695) | 0.599129 / 0.434364 (0.164765) | 0.550146 / 0.540337 (0.009808) | 0.794646 / 1.386936 (-0.592290) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008613 / 0.011353 (-0.002739) | 0.004979 / 0.011008 (-0.006030) | 0.078095 / 0.038508 (0.039587) | 0.080285 / 0.023109 (0.057176) | 0.482881 / 0.275898 (0.206983) | 0.520442 / 0.323480 (0.196962) | 0.006241 / 0.007986 (-0.001744) | 0.003964 / 0.004328 (-0.000364) | 0.080027 / 0.004250 (0.075777) | 0.065209 / 0.037052 (0.028157) | 0.476113 / 0.258489 (0.217623) | 0.535383 / 0.293841 (0.241542) | 0.053084 / 0.128546 (-0.075462) | 0.014284 / 0.075646 (-0.061362) | 0.083859 / 0.419271 (-0.335413) | 0.061024 / 0.043533 (0.017492) | 0.477810 / 0.255139 (0.222671) | 0.508718 / 0.283200 (0.225518) | 0.036602 / 0.141683 (-0.105081) | 1.810422 / 1.452155 (0.358267) | 1.832833 / 1.492716 (0.340117) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.281443 / 0.018006 (0.263437) | 0.568249 / 0.000490 (0.567760) | 0.000493 / 0.000200 (0.000293) | 0.000077 / 0.000054 (0.000023) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033302 / 0.037411 (-0.004110) | 0.100433 / 0.014526 (0.085907) | 0.105465 / 0.176557 (-0.071091) | 0.161986 / 0.737135 (-0.575149) | 0.115736 / 0.296338 (-0.180603) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.622892 / 0.215209 (0.407683) | 6.144361 / 2.077655 (4.066706) | 2.849443 / 1.504120 (1.345323) | 2.544097 / 1.541195 (1.002902) | 2.579859 / 1.468490 (1.111369) | 0.826078 / 4.584777 (-3.758699) | 5.021808 / 3.745712 (1.276096) | 4.694784 / 5.269862 (-0.575077) | 2.796263 / 4.565676 (-1.769413) | 0.090983 / 0.424275 (-0.333292) | 0.008445 / 0.007607 (0.000838) | 0.744675 / 0.226044 (0.518631) | 7.662989 / 2.268929 (5.394060) | 3.665611 / 55.444624 (-51.779013) | 2.942836 / 6.876477 (-3.933641) | 2.874402 / 2.142072 (0.732329) | 1.010097 / 4.805227 (-3.795130) | 0.218008 / 6.500664 (-6.282656) | 0.087359 / 0.075469 (0.011890) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.655631 / 1.841788 (-0.186157) | 23.539596 / 8.074308 (15.465288) | 20.909512 / 10.191392 (10.718120) | 0.202092 / 0.680424 (-0.478332) | 0.029807 / 0.534201 (-0.504394) | 0.487591 / 0.579283 (-0.091692) | 0.573719 / 0.434364 (0.139355) | 0.531168 / 0.540337 (-0.009170) | 0.742375 / 1.386936 (-0.644561) |

</details>
</details>

![](https://cml.dev/watermark.png#aa231a7be55c6bca2bede8af4ac6da63c3162116 "CML watermark")
 <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006247 / 0.011353 (-0.005106) | 0.003650 / 0.011008 (-0.007358) | 0.079655 / 0.038508 (0.041147) | 0.060279 / 0.023109 (0.037170) | 0.309033 / 0.275898 (0.033135) | 0.338479 / 0.323480 (0.014999) | 0.004651 / 0.007986 (-0.003335) | 0.002849 / 0.004328 (-0.001480) | 0.062852 / 0.004250 (0.058602) | 0.049230 / 0.037052 (0.012178) | 0.312502 / 0.258489 (0.054012) | 0.354558 / 0.293841 (0.060717) | 0.027497 / 0.128546 (-0.101049) | 0.007885 / 0.075646 (-0.067762) | 0.260232 / 0.419271 (-0.159040) | 0.045459 / 0.043533 (0.001926) | 0.311629 / 0.255139 (0.056490) | 0.367806 / 0.283200 (0.084606) | 0.020875 / 0.141683 (-0.120808) | 1.423802 / 1.452155 (-0.028352) | 1.497729 / 1.492716 (0.005013) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.185629 / 0.018006 (0.167623) | 0.441421 / 0.000490 (0.440931) | 0.004847 / 0.000200 (0.004647) | 0.000074 / 0.000054 (0.000020) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022428 / 0.037411 (-0.014984) | 0.073375 / 0.014526 (0.058849) | 0.083194 / 0.176557 (-0.093363) | 0.143984 / 0.737135 (-0.593151) | 0.084128 / 0.296338 (-0.212211) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.397220 / 0.215209 (0.182010) | 3.954394 / 2.077655 (1.876740) | 1.920638 / 1.504120 (0.416518) | 1.744284 / 1.541195 (0.203089) | 1.802623 / 1.468490 (0.334133) | 0.501988 / 4.584777 (-4.082789) | 3.096071 / 3.745712 (-0.649642) | 4.648267 / 5.269862 (-0.621595) | 2.770995 / 4.565676 (-1.794682) | 0.057513 / 0.424275 (-0.366762) | 0.006315 / 0.007607 (-0.001292) | 0.467683 / 0.226044 (0.241639) | 4.683959 / 2.268929 (2.415031) | 2.384980 / 55.444624 (-53.059645) | 2.030894 / 6.876477 (-4.845583) | 2.148374 / 2.142072 (0.006302) | 0.585142 / 4.805227 (-4.220085) | 0.123173 / 6.500664 (-6.377491) | 0.059140 / 0.075469 (-0.016329) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.244707 / 1.841788 (-0.597080) | 18.176043 / 8.074308 (10.101735) | 13.742770 / 10.191392 (3.551378) | 0.149692 / 0.680424 (-0.530732) | 0.016591 / 0.534201 (-0.517610) | 0.342138 / 0.579283 (-0.237145) | 0.353931 / 0.434364 (-0.080433) | 0.392317 / 0.540337 (-0.148020) | 0.524011 / 1.386936 (-0.862925) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005937 / 0.011353 (-0.005416) | 0.003609 / 0.011008 (-0.007399) | 0.061729 / 0.038508 (0.023221) | 0.057844 / 0.023109 (0.034735) | 0.418051 / 0.275898 (0.142153) | 0.453014 / 0.323480 (0.129534) | 0.004530 / 0.007986 (-0.003456) | 0.002861 / 0.004328 (-0.001468) | 0.062236 / 0.004250 (0.057986) | 0.048612 / 0.037052 (0.011560) | 0.418487 / 0.258489 (0.159998) | 0.455114 / 0.293841 (0.161273) | 0.027419 / 0.128546 (-0.101127) | 0.007919 / 0.075646 (-0.067728) | 0.066940 / 0.419271 (-0.352331) | 0.041816 / 0.043533 (-0.001717) | 0.419788 / 0.255139 (0.164649) | 0.439682 / 0.283200 (0.156483) | 0.020902 / 0.141683 (-0.120781) | 1.473993 / 1.452155 (0.021838) | 1.532438 / 1.492716 (0.039722) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.228766 / 0.018006 (0.210760) | 0.412189 / 0.000490 (0.411699) | 0.000371 / 0.000200 (0.000171) | 0.000054 / 0.000054 (-0.000000) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026139 / 0.037411 (-0.011272) | 0.076626 / 0.014526 (0.062100) | 0.088262 / 0.176557 (-0.088295) | 0.143096 / 0.737135 (-0.594039) | 0.089642 / 0.296338 (-0.206696) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.423030 / 0.215209 (0.207821) | 4.218333 / 2.077655 (2.140679) | 2.280943 / 1.504120 (0.776823) | 2.051746 / 1.541195 (0.510551) | 2.101085 / 1.468490 (0.632595) | 0.495860 / 4.584777 (-4.088917) | 3.108065 / 3.745712 (-0.637647) | 2.944188 / 5.269862 (-2.325673) | 1.833693 / 4.565676 (-2.731984) | 0.057509 / 0.424275 (-0.366766) | 0.006406 / 0.007607 (-0.001201) | 0.497208 / 0.226044 (0.271164) | 4.974972 / 2.268929 (2.706044) | 2.786639 / 55.444624 (-52.657985) | 2.423815 / 6.876477 (-4.452662) | 2.446377 / 2.142072 (0.304305) | 0.584521 / 4.805227 (-4.220706) | 0.124129 / 6.500664 (-6.376535) | 0.061373 / 0.075469 (-0.014096) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.307076 / 1.841788 (-0.534711) | 18.443873 / 8.074308 (10.369565) | 13.835730 / 10.191392 (3.644338) | 0.159795 / 0.680424 (-0.520629) | 0.016643 / 0.534201 (-0.517558) | 0.334300 / 0.579283 (-0.244983) | 0.347136 / 0.434364 (-0.087228) | 0.394633 / 0.540337 (-0.145704) | 0.552445 / 1.386936 (-0.834491) |

</details>
</details>

![](https://cml.dev/watermark.png#8cfc0262363ea8cbd8c78537a09f851ec6ec30f5 "CML watermark")
 <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007273 / 0.011353 (-0.004080) | 0.004704 / 0.011008 (-0.006304) | 0.105857 / 0.038508 (0.067349) | 0.062493 / 0.023109 (0.039384) | 0.325704 / 0.275898 (0.049806) | 0.355795 / 0.323480 (0.032315) | 0.005552 / 0.007986 (-0.002433) | 0.003543 / 0.004328 (-0.000785) | 0.068098 / 0.004250 (0.063848) | 0.049563 / 0.037052 (0.012511) | 0.362956 / 0.258489 (0.104467) | 0.376047 / 0.293841 (0.082206) | 0.039272 / 0.128546 (-0.089275) | 0.011521 / 0.075646 (-0.064125) | 0.291899 / 0.419271 (-0.127373) | 0.056916 / 0.043533 (0.013383) | 0.365352 / 0.255139 (0.110213) | 0.357251 / 0.283200 (0.074051) | 0.031670 / 0.141683 (-0.110013) | 1.533294 / 1.452155 (0.081140) | 1.566580 / 1.492716 (0.073864) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.219812 / 0.018006 (0.201805) | 0.499808 / 0.000490 (0.499318) | 0.000343 / 0.000200 (0.000143) | 0.000066 / 0.000054 (0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024011 / 0.037411 (-0.013400) | 0.079686 / 0.014526 (0.065161) | 0.087925 / 0.176557 (-0.088631) | 0.149065 / 0.737135 (-0.588071) | 0.088514 / 0.296338 (-0.207824) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.495003 / 0.215209 (0.279794) | 5.106371 / 2.077655 (3.028717) | 2.285497 / 1.504120 (0.781377) | 2.056052 / 1.541195 (0.514858) | 2.024913 / 1.468490 (0.556423) | 0.726048 / 4.584777 (-3.858729) | 4.873945 / 3.745712 (1.128233) | 7.488671 / 5.269862 (2.218809) | 4.361208 / 4.565676 (-0.204469) | 0.089014 / 0.424275 (-0.335261) | 0.007178 / 0.007607 (-0.000429) | 0.633669 / 0.226044 (0.407625) | 6.328154 / 2.268929 (4.059226) | 3.071598 / 55.444624 (-52.373026) | 2.416077 / 6.876477 (-4.460399) | 2.431033 / 2.142072 (0.288961) | 0.918167 / 4.805227 (-3.887060) | 0.193829 / 6.500664 (-6.306836) | 0.073446 / 0.075469 (-0.002023) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.344994 / 1.841788 (-0.496793) | 19.911699 / 8.074308 (11.837391) | 17.182697 / 10.191392 (6.991305) | 0.216932 / 0.680424 (-0.463492) | 0.025415 / 0.534201 (-0.508786) | 0.416806 / 0.579283 (-0.162477) | 0.524934 / 0.434364 (0.090570) | 0.510783 / 0.540337 (-0.029554) | 0.687856 / 1.386936 (-0.699081) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008469 / 0.011353 (-0.002884) | 0.003797 / 0.011008 (-0.007211) | 0.067276 / 0.038508 (0.028768) | 0.066825 / 0.023109 (0.043716) | 0.394976 / 0.275898 (0.119078) | 0.432563 / 0.323480 (0.109083) | 0.006003 / 0.007986 (-0.001982) | 0.003399 / 0.004328 (-0.000930) | 0.070899 / 0.004250 (0.066649) | 0.050940 / 0.037052 (0.013887) | 0.378291 / 0.258489 (0.119802) | 0.429889 / 0.293841 (0.136048) | 0.043245 / 0.128546 (-0.085302) | 0.012182 / 0.075646 (-0.063465) | 0.074560 / 0.419271 (-0.344711) | 0.065290 / 0.043533 (0.021757) | 0.371209 / 0.255139 (0.116070) | 0.389731 / 0.283200 (0.106532) | 0.045729 / 0.141683 (-0.095954) | 1.451785 / 1.452155 (-0.000370) | 1.598539 / 1.492716 (0.105822) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.261357 / 0.018006 (0.243351) | 0.520142 / 0.000490 (0.519653) | 0.008305 / 0.000200 (0.008105) | 0.000089 / 0.000054 (0.000034) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026492 / 0.037411 (-0.010919) | 0.082430 / 0.014526 (0.067904) | 0.095979 / 0.176557 (-0.080578) | 0.151752 / 0.737135 (-0.585383) | 0.090086 / 0.296338 (-0.206252) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.535967 / 0.215209 (0.320758) | 5.228605 / 2.077655 (3.150950) | 2.395078 / 1.504120 (0.890959) | 2.185500 / 1.541195 (0.644306) | 2.219456 / 1.468490 (0.750966) | 0.764794 / 4.584777 (-3.819983) | 4.796617 / 3.745712 (1.050905) | 4.143450 / 5.269862 (-1.126411) | 2.527391 / 4.565676 (-2.038286) | 0.081418 / 0.424275 (-0.342857) | 0.007170 / 0.007607 (-0.000437) | 0.706071 / 0.226044 (0.480026) | 6.501060 / 2.268929 (4.232131) | 3.176315 / 55.444624 (-52.268309) | 2.443245 / 6.876477 (-4.433232) | 2.517832 / 2.142072 (0.375759) | 0.916254 / 4.805227 (-3.888973) | 0.184282 / 6.500664 (-6.316382) | 0.062613 / 0.075469 (-0.012857) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.444283 / 1.841788 (-0.397504) | 20.227311 / 8.074308 (12.153003) | 17.512856 / 10.191392 (7.321464) | 0.219556 / 0.680424 (-0.460868) | 0.024705 / 0.534201 (-0.509496) | 0.423215 / 0.579283 (-0.156068) | 0.513103 / 0.434364 (0.078739) | 0.473853 / 0.540337 (-0.066485) | 0.738165 / 1.386936 (-0.648771) |

</details>
</details>

![](https://cml.dev/watermark.png#b65660b7c6e853391991734210e38f805459b0ed "CML watermark")

The task API is not well adopted in the ecosystem, so this PR deprecates it. The `train_eval_index` is a newer, more flexible solution that should be used instead (I think?).

These are the projects that still use the task API :
* the image classification example in Transformers: [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/pytorch/image-classification/run_image_classification_no_trainer.py#L262) and [here](https://github.com/huggingface/transformers/blob/8f76dc8e5aaad58f2df7748b6d6970376f315a9a/examples/tensorflow/image-classification/run_image_classification.py#L277)
* autotrain: [here](https://github.com/huggingface/autotrain-backend/blob/455e274004b56f9377d64db4ab03671508fcc4cd/zeus/zeus/run/utils.py#L666)
* api-inference-community: [here](https://github.com/huggingface/api-inference-community/blob/fb8fb29d577a5bf01c82944db745489a6d6ed3d4/manage.py#L64) (but the rest of the code does not call the `resolve_dataset` function)

So we need to update these files after the merge.

cc @lewtun 