Ensure compatibility with numpy 2.0.0
State: closed

Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.

The following fix should resolve the issue.

error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938)

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6976). 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) | 0.005361 / 0.011353 (-0.005992) | 0.003983 / 0.011008 (-0.007025) | 0.062865 / 0.038508 (0.024357) | 0.029880 / 0.023109 (0.006771) | 0.261465 / 0.275898 (-0.014433) | 0.269791 / 0.323480 (-0.053689) | 0.004198 / 0.007986 (-0.003788) | 0.002942 / 0.004328 (-0.001387) | 0.049002 / 0.004250 (0.044751) | 0.043232 / 0.037052 (0.006180) | 0.328774 / 0.258489 (0.070285) | 0.297308 / 0.293841 (0.003467) | 0.030552 / 0.128546 (-0.097994) | 0.012632 / 0.075646 (-0.063015) | 0.204156 / 0.419271 (-0.215116) | 0.036014 / 0.043533 (-0.007519) | 0.241224 / 0.255139 (-0.013915) | 0.268358 / 0.283200 (-0.014842) | 0.019227 / 0.141683 (-0.122456) | 1.114515 / 1.452155 (-0.337639) | 1.147029 / 1.492716 (-0.345688) |

### 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) | 0.094925 / 0.018006 (0.076919) | 0.301548 / 0.000490 (0.301059) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018875 / 0.037411 (-0.018536) | 0.062824 / 0.014526 (0.048298) | 0.075657 / 0.176557 (-0.100900) | 0.121926 / 0.737135 (-0.615209) | 0.077102 / 0.296338 (-0.219236) |

### 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) | 0.286018 / 0.215209 (0.070808) | 2.832222 / 2.077655 (0.754567) | 1.462629 / 1.504120 (-0.041491) | 1.354746 / 1.541195 (-0.186449) | 1.339504 / 1.468490 (-0.128986) | 0.718381 / 4.584777 (-3.866396) | 2.401456 / 3.745712 (-1.344256) | 3.013518 / 5.269862 (-2.256343) | 1.944892 / 4.565676 (-2.620784) | 0.078793 / 0.424275 (-0.345482) | 0.005219 / 0.007607 (-0.002388) | 0.349551 / 0.226044 (0.123507) | 3.417844 / 2.268929 (1.148916) | 1.830669 / 55.444624 (-53.613956) | 1.502134 / 6.876477 (-5.374343) | 1.529242 / 2.142072 (-0.612830) | 0.793732 / 4.805227 (-4.011495) | 0.133571 / 6.500664 (-6.367093) | 0.042588 / 0.075469 (-0.032881) |

### 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) | 0.988167 / 1.841788 (-0.853620) | 11.926728 / 8.074308 (3.852420) | 9.806971 / 10.191392 (-0.384421) | 0.173951 / 0.680424 (-0.506473) | 0.015308 / 0.534201 (-0.518893) | 0.310768 / 0.579283 (-0.268515) | 0.268261 / 0.434364 (-0.166103) | 0.342962 / 0.540337 (-0.197375) | 0.431255 / 1.386936 (-0.955681) |

</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) | 0.005680 / 0.011353 (-0.005673) | 0.004231 / 0.011008 (-0.006778) | 0.051009 / 0.038508 (0.012501) | 0.031431 / 0.023109 (0.008322) | 0.268582 / 0.275898 (-0.007316) | 0.287942 / 0.323480 (-0.035538) | 0.004442 / 0.007986 (-0.003543) | 0.002818 / 0.004328 (-0.001511) | 0.050241 / 0.004250 (0.045991) | 0.039933 / 0.037052 (0.002881) | 0.285814 / 0.258489 (0.027325) | 0.316082 / 0.293841 (0.022241) | 0.032416 / 0.128546 (-0.096130) | 0.012398 / 0.075646 (-0.063248) | 0.060779 / 0.419271 (-0.358493) | 0.033706 / 0.043533 (-0.009827) | 0.273915 / 0.255139 (0.018776) | 0.289752 / 0.283200 (0.006553) | 0.017859 / 0.141683 (-0.123824) | 1.150224 / 1.452155 (-0.301930) | 1.197467 / 1.492716 (-0.295250) |

### 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) | 0.093810 / 0.018006 (0.075803) | 0.302529 / 0.000490 (0.302039) | 0.000221 / 0.000200 (0.000021) | 0.000047 / 0.000054 (-0.000008) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022903 / 0.037411 (-0.014508) | 0.077445 / 0.014526 (0.062919) | 0.089335 / 0.176557 (-0.087222) | 0.130848 / 0.737135 (-0.606287) | 0.091106 / 0.296338 (-0.205232) |

### 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) | 0.294194 / 0.215209 (0.078985) | 2.886983 / 2.077655 (0.809328) | 1.557768 / 1.504120 (0.053648) | 1.424467 / 1.541195 (-0.116727) | 1.440625 / 1.468490 (-0.027865) | 0.724793 / 4.584777 (-3.859984) | 0.985216 / 3.745712 (-2.760496) | 2.856826 / 5.269862 (-2.413036) | 1.911638 / 4.565676 (-2.654039) | 0.080350 / 0.424275 (-0.343925) | 0.005616 / 0.007607 (-0.001991) | 0.348713 / 0.226044 (0.122668) | 3.414764 / 2.268929 (1.145835) | 1.925056 / 55.444624 (-53.519568) | 1.635752 / 6.876477 (-5.240725) | 1.761117 / 2.142072 (-0.380955) | 0.808309 / 4.805227 (-3.996918) | 0.136893 / 6.500664 (-6.363771) | 0.042116 / 0.075469 (-0.033354) |

### 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) | 1.004740 / 1.841788 (-0.837048) | 12.495859 / 8.074308 (4.421550) | 10.681233 / 10.191392 (0.489841) | 0.133320 / 0.680424 (-0.547104) | 0.015943 / 0.534201 (-0.518258) | 0.304869 / 0.579283 (-0.274414) | 0.128616 / 0.434364 (-0.305748) | 0.345930 / 0.540337 (-0.194407) | 0.457434 / 1.386936 (-0.929502) |

</details>
</details>

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