Set temporary numpy upper version < 2.0.0 to fix CI
Opened by albertvillanova (closed)

Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017
```
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
```

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6975). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005168 / 0.011353 (-0.006185) | 0.003720 / 0.011008 (-0.007288) | 0.063347 / 0.038508 (0.024839) | 0.031474 / 0.023109 (0.008364) | 0.243233 / 0.275898 (-0.032665) | 0.276695 / 0.323480 (-0.046785) | 0.004109 / 0.007986 (-0.003877) | 0.002689 / 0.004328 (-0.001639) | 0.049522 / 0.004250 (0.045271) | 0.043477 / 0.037052 (0.006425) | 0.258578 / 0.258489 (0.000088) | 0.288134 / 0.293841 (-0.005707) | 0.027836 / 0.128546 (-0.100710) | 0.010677 / 0.075646 (-0.064969) | 0.206412 / 0.419271 (-0.212860) | 0.036204 / 0.043533 (-0.007329) | 0.250588 / 0.255139 (-0.004551) | 0.272354 / 0.283200 (-0.010846) | 0.018359 / 0.141683 (-0.123324) | 1.118867 / 1.452155 (-0.333288) | 1.157318 / 1.492716 (-0.335399) |

### 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) | 0.092927 / 0.018006 (0.074921) | 0.298252 / 0.000490 (0.297762) | 0.000228 / 0.000200 (0.000028) | 0.000042 / 0.000054 (-0.000013) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018824 / 0.037411 (-0.018588) | 0.069304 / 0.014526 (0.054778) | 0.075094 / 0.176557 (-0.101462) | 0.122546 / 0.737135 (-0.614590) | 0.076453 / 0.296338 (-0.219885) |

### 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) | 0.287131 / 0.215209 (0.071922) | 2.838945 / 2.077655 (0.761291) | 1.473578 / 1.504120 (-0.030542) | 1.351214 / 1.541195 (-0.189981) | 1.354924 / 1.468490 (-0.113566) | 0.577092 / 4.584777 (-4.007685) | 2.348072 / 3.745712 (-1.397640) | 2.762130 / 5.269862 (-2.507732) | 1.725195 / 4.565676 (-2.840482) | 0.063596 / 0.424275 (-0.360679) | 0.004921 / 0.007607 (-0.002686) | 0.335422 / 0.226044 (0.109377) | 3.340398 / 2.268929 (1.071469) | 1.789390 / 55.444624 (-53.655234) | 1.516247 / 6.876477 (-5.360229) | 1.529653 / 2.142072 (-0.612420) | 0.643547 / 4.805227 (-4.161680) | 0.116491 / 6.500664 (-6.384173) | 0.042404 / 0.075469 (-0.033065) |

### 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) | 0.959839 / 1.841788 (-0.881948) | 11.269778 / 8.074308 (3.195470) | 9.574898 / 10.191392 (-0.616494) | 0.128979 / 0.680424 (-0.551444) | 0.013901 / 0.534201 (-0.520300) | 0.280778 / 0.579283 (-0.298505) | 0.256511 / 0.434364 (-0.177853) | 0.319361 / 0.540337 (-0.220977) | 0.411803 / 1.386936 (-0.975133) |

</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) | 0.005453 / 0.011353 (-0.005899) | 0.003478 / 0.011008 (-0.007530) | 0.050055 / 0.038508 (0.011547) | 0.031415 / 0.023109 (0.008306) | 0.275057 / 0.275898 (-0.000841) | 0.296690 / 0.323480 (-0.026789) | 0.004253 / 0.007986 (-0.003732) | 0.002777 / 0.004328 (-0.001551) | 0.049553 / 0.004250 (0.045303) | 0.039843 / 0.037052 (0.002791) | 0.286938 / 0.258489 (0.028449) | 0.318579 / 0.293841 (0.024738) | 0.029773 / 0.128546 (-0.098774) | 0.010404 / 0.075646 (-0.065242) | 0.057915 / 0.419271 (-0.361356) | 0.033486 / 0.043533 (-0.010047) | 0.273293 / 0.255139 (0.018154) | 0.293155 / 0.283200 (0.009955) | 0.017843 / 0.141683 (-0.123839) | 1.131130 / 1.452155 (-0.321024) | 1.167412 / 1.492716 (-0.325304) |

### 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) | 0.092553 / 0.018006 (0.074547) | 0.298888 / 0.000490 (0.298399) | 0.000201 / 0.000200 (0.000001) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022646 / 0.037411 (-0.014765) | 0.076921 / 0.014526 (0.062395) | 0.089238 / 0.176557 (-0.087318) | 0.128793 / 0.737135 (-0.608342) | 0.089190 / 0.296338 (-0.207148) |

### 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) | 0.292552 / 0.215209 (0.077343) | 2.884277 / 2.077655 (0.806622) | 1.568798 / 1.504120 (0.064678) | 1.441819 / 1.541195 (-0.099375) | 1.435766 / 1.468490 (-0.032724) | 0.572435 / 4.584777 (-4.012342) | 0.957387 / 3.745712 (-2.788326) | 2.650843 / 5.269862 (-2.619019) | 1.727424 / 4.565676 (-2.838252) | 0.063470 / 0.424275 (-0.360805) | 0.005314 / 0.007607 (-0.002293) | 0.345881 / 0.226044 (0.119836) | 3.395463 / 2.268929 (1.126535) | 1.921340 / 55.444624 (-53.523285) | 1.621563 / 6.876477 (-5.254914) | 1.742561 / 2.142072 (-0.399512) | 0.639948 / 4.805227 (-4.165279) | 0.116091 / 6.500664 (-6.384573) | 0.041218 / 0.075469 (-0.034251) |

### 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) | 0.991506 / 1.841788 (-0.850281) | 11.897462 / 8.074308 (3.823154) | 10.083008 / 10.191392 (-0.108384) | 0.140626 / 0.680424 (-0.539798) | 0.015454 / 0.534201 (-0.518747) | 0.283856 / 0.579283 (-0.295427) | 0.125935 / 0.434364 (-0.308429) | 0.323884 / 0.540337 (-0.216454) | 0.438348 / 1.386936 (-0.948588) |

</details>
</details>

![](https://cml.dev/watermark.png#e59582adc7fcb53a86a8ca8eda7e04a4e7b25bd2 "CML watermark")