Disable benchmarks in PRs
It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression <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) | 0.005357 / 0.011353 (-0.005996) | 0.003295 / 0.011008 (-0.007713) | 0.062354 / 0.038508 (0.023846) | 0.054207 / 0.023109 (0.031098) | 0.240030 / 0.275898 (-0.035869) | 0.267863 / 0.323480 (-0.055617) | 0.002925 / 0.007986 (-0.005061) | 0.002634 / 0.004328 (-0.001695) | 0.047952 / 0.004250 (0.043702) | 0.038424 / 0.037052 (0.001372) | 0.248059 / 0.258489 (-0.010430) | 0.271923 / 0.293841 (-0.021918) | 0.027513 / 0.128546 (-0.101034) | 0.010344 / 0.075646 (-0.065302) | 0.210864 / 0.419271 (-0.208407) | 0.035911 / 0.043533 (-0.007622) | 0.245166 / 0.255139 (-0.009973) | 0.260914 / 0.283200 (-0.022285) | 0.016709 / 0.141683 (-0.124974) | 1.098324 / 1.452155 (-0.353830) | 1.162638 / 1.492716 (-0.330079) |

### 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) | 0.094419 / 0.018006 (0.076413) | 0.303209 / 0.000490 (0.302719) | 0.000214 / 0.000200 (0.000014) | 0.000053 / 0.000054 (-0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018350 / 0.037411 (-0.019061) | 0.060625 / 0.014526 (0.046099) | 0.072545 / 0.176557 (-0.104012) | 0.120905 / 0.737135 (-0.616231) | 0.073858 / 0.296338 (-0.222480) |

### 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) | 0.282011 / 0.215209 (0.066802) | 2.758741 / 2.077655 (0.681086) | 1.431691 / 1.504120 (-0.072429) | 1.315883 / 1.541195 (-0.225312) | 1.344235 / 1.468490 (-0.124255) | 0.562117 / 4.584777 (-4.022660) | 2.385641 / 3.745712 (-1.360071) | 2.785402 / 5.269862 (-2.484460) | 1.753912 / 4.565676 (-2.811764) | 0.064054 / 0.424275 (-0.360221) | 0.005050 / 0.007607 (-0.002557) | 0.336452 / 0.226044 (0.110407) | 3.302481 / 2.268929 (1.033553) | 1.794105 / 55.444624 (-53.650519) | 1.519346 / 6.876477 (-5.357131) | 1.514911 / 2.142072 (-0.627161) | 0.655779 / 4.805227 (-4.149449) | 0.117913 / 6.500664 (-6.382751) | 0.042229 / 0.075469 (-0.033240) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.935196 / 1.841788 (-0.906591) | 11.490113 / 8.074308 (3.415805) | 10.542446 / 10.191392 (0.351054) | 0.129614 / 0.680424 (-0.550810) | 0.014919 / 0.534201 (-0.519282) | 0.288448 / 0.579283 (-0.290835) | 0.266929 / 0.434364 (-0.167435) | 0.328830 / 0.540337 (-0.211507) | 0.475510 / 1.386936 (-0.911426) |

</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) | 0.005469 / 0.011353 (-0.005884) | 0.003798 / 0.011008 (-0.007210) | 0.049129 / 0.038508 (0.010621) | 0.055490 / 0.023109 (0.032380) | 0.265828 / 0.275898 (-0.010070) | 0.286031 / 0.323480 (-0.037448) | 0.004075 / 0.007986 (-0.003910) | 0.002668 / 0.004328 (-0.001660) | 0.047823 / 0.004250 (0.043573) | 0.041946 / 0.037052 (0.004894) | 0.270359 / 0.258489 (0.011869) | 0.294287 / 0.293841 (0.000446) | 0.029643 / 0.128546 (-0.098903) | 0.010523 / 0.075646 (-0.065123) | 0.057370 / 0.419271 (-0.361902) | 0.033149 / 0.043533 (-0.010384) | 0.264408 / 0.255139 (0.009269) | 0.280413 / 0.283200 (-0.002787) | 0.018313 / 0.141683 (-0.123370) | 1.105982 / 1.452155 (-0.346173) | 1.182486 / 1.492716 (-0.310230) |

### 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) | 0.092643 / 0.018006 (0.074637) | 0.301320 / 0.000490 (0.300831) | 0.000221 / 0.000200 (0.000021) | 0.000050 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021253 / 0.037411 (-0.016158) | 0.068052 / 0.014526 (0.053527) | 0.080821 / 0.176557 (-0.095736) | 0.119320 / 0.737135 (-0.617816) | 0.081952 / 0.296338 (-0.214387) |

### 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) | 0.288536 / 0.215209 (0.073327) | 2.819900 / 2.077655 (0.742245) | 1.545210 / 1.504120 (0.041090) | 1.422047 / 1.541195 (-0.119147) | 1.439158 / 1.468490 (-0.029332) | 0.564910 / 4.584777 (-4.019867) | 2.430474 / 3.745712 (-1.315238) | 2.763979 / 5.269862 (-2.505882) | 1.732203 / 4.565676 (-2.833474) | 0.062692 / 0.424275 (-0.361583) | 0.004936 / 0.007607 (-0.002671) | 0.341626 / 0.226044 (0.115582) | 3.366623 / 2.268929 (1.097694) | 1.917198 / 55.444624 (-53.527426) | 1.637635 / 6.876477 (-5.238842) | 1.625953 / 2.142072 (-0.516119) | 0.634936 / 4.805227 (-4.170291) | 0.115336 / 6.500664 (-6.385328) | 0.040946 / 0.075469 (-0.034524) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.964865 / 1.841788 (-0.876922) | 12.077233 / 8.074308 (4.002925) | 10.664120 / 10.191392 (0.472728) | 0.132084 / 0.680424 (-0.548340) | 0.015931 / 0.534201 (-0.518270) | 0.289181 / 0.579283 (-0.290102) | 0.276943 / 0.434364 (-0.157420) | 0.324884 / 0.540337 (-0.215453) | 0.552570 / 1.386936 (-0.834366) |

</details>
</details>

![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e "CML watermark")

In order to keep PR pages less spammy / more readable.
Having the benchmarks on commits on `main` is enough imo