Title: Raise error on bad split name
State: closed

e.g. dashes '-' are not allowed in split names

This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test

cc @AndreaFrancis 

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6626). 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) | 0.005085 / 0.011353 (-0.006268) | 0.003592 / 0.011008 (-0.007417) | 0.062591 / 0.038508 (0.024083) | 0.031063 / 0.023109 (0.007954) | 0.247029 / 0.275898 (-0.028869) | 0.273706 / 0.323480 (-0.049774) | 0.004034 / 0.007986 (-0.003951) | 0.002672 / 0.004328 (-0.001657) | 0.048407 / 0.004250 (0.044156) | 0.049229 / 0.037052 (0.012177) | 0.264316 / 0.258489 (0.005827) | 0.284953 / 0.293841 (-0.008888) | 0.027712 / 0.128546 (-0.100834) | 0.010619 / 0.075646 (-0.065027) | 0.210017 / 0.419271 (-0.209254) | 0.035636 / 0.043533 (-0.007897) | 0.252830 / 0.255139 (-0.002309) | 0.278772 / 0.283200 (-0.004428) | 0.017356 / 0.141683 (-0.124326) | 1.140202 / 1.452155 (-0.311953) | 1.204807 / 1.492716 (-0.287909) |

### 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) | 0.089130 / 0.018006 (0.071123) | 0.300115 / 0.000490 (0.299626) | 0.000213 / 0.000200 (0.000013) | 0.000042 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018352 / 0.037411 (-0.019059) | 0.061431 / 0.014526 (0.046905) | 0.073911 / 0.176557 (-0.102646) | 0.121230 / 0.737135 (-0.615906) | 0.074867 / 0.296338 (-0.221471) |

### 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) | 0.282272 / 0.215209 (0.067063) | 2.737413 / 2.077655 (0.659759) | 1.446651 / 1.504120 (-0.057469) | 1.319686 / 1.541195 (-0.221508) | 1.327479 / 1.468490 (-0.141011) | 0.558003 / 4.584777 (-4.026774) | 2.361623 / 3.745712 (-1.384089) | 2.770436 / 5.269862 (-2.499425) | 1.703450 / 4.565676 (-2.862227) | 0.062034 / 0.424275 (-0.362241) | 0.005070 / 0.007607 (-0.002537) | 0.337265 / 0.226044 (0.111221) | 3.299438 / 2.268929 (1.030509) | 1.781273 / 55.444624 (-53.663351) | 1.512743 / 6.876477 (-5.363734) | 1.530995 / 2.142072 (-0.611077) | 0.630210 / 4.805227 (-4.175017) | 0.116219 / 6.500664 (-6.384445) | 0.042220 / 0.075469 (-0.033249) |

### 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) | 0.946341 / 1.841788 (-0.895446) | 11.462179 / 8.074308 (3.387871) | 10.603314 / 10.191392 (0.411922) | 0.128826 / 0.680424 (-0.551598) | 0.013994 / 0.534201 (-0.520207) | 0.288142 / 0.579283 (-0.291141) | 0.266941 / 0.434364 (-0.167422) | 0.329392 / 0.540337 (-0.210946) | 0.431720 / 1.386936 (-0.955216) |

</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) | 0.005303 / 0.011353 (-0.006050) | 0.003587 / 0.011008 (-0.007422) | 0.049437 / 0.038508 (0.010929) | 0.031940 / 0.023109 (0.008831) | 0.276651 / 0.275898 (0.000752) | 0.297240 / 0.323480 (-0.026240) | 0.004202 / 0.007986 (-0.003784) | 0.002709 / 0.004328 (-0.001619) | 0.048647 / 0.004250 (0.044397) | 0.044147 / 0.037052 (0.007095) | 0.291171 / 0.258489 (0.032682) | 0.319297 / 0.293841 (0.025456) | 0.048167 / 0.128546 (-0.080379) | 0.010630 / 0.075646 (-0.065016) | 0.058402 / 0.419271 (-0.360869) | 0.033817 / 0.043533 (-0.009716) | 0.300546 / 0.255139 (0.045407) | 0.319396 / 0.283200 (0.036197) | 0.017736 / 0.141683 (-0.123946) | 1.159590 / 1.452155 (-0.292565) | 1.191778 / 1.492716 (-0.300939) |

### 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) | 0.088971 / 0.018006 (0.070965) | 0.299721 / 0.000490 (0.299231) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021895 / 0.037411 (-0.015516) | 0.075388 / 0.014526 (0.060862) | 0.087446 / 0.176557 (-0.089111) | 0.126339 / 0.737135 (-0.610796) | 0.089329 / 0.296338 (-0.207010) |

### 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) | 0.296642 / 0.215209 (0.081433) | 2.916023 / 2.077655 (0.838368) | 1.593180 / 1.504120 (0.089060) | 1.470491 / 1.541195 (-0.070704) | 1.485713 / 1.468490 (0.017223) | 0.577204 / 4.584777 (-4.007573) | 2.436463 / 3.745712 (-1.309249) | 2.651004 / 5.269862 (-2.618858) | 1.754026 / 4.565676 (-2.811651) | 0.064943 / 0.424275 (-0.359332) | 0.005115 / 0.007607 (-0.002492) | 0.362082 / 0.226044 (0.136038) | 3.498198 / 2.268929 (1.229270) | 1.951936 / 55.444624 (-53.492688) | 1.682027 / 6.876477 (-5.194450) | 1.751768 / 2.142072 (-0.390304) | 0.668479 / 4.805227 (-4.136748) | 0.119934 / 6.500664 (-6.380730) | 0.041419 / 0.075469 (-0.034050) |

### 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) | 0.978145 / 1.841788 (-0.863643) | 11.984984 / 8.074308 (3.910676) | 10.732377 / 10.191392 (0.540985) | 0.141868 / 0.680424 (-0.538555) | 0.015256 / 0.534201 (-0.518945) | 0.288488 / 0.579283 (-0.290795) | 0.276091 / 0.434364 (-0.158273) | 0.330429 / 0.540337 (-0.209908) | 0.423964 / 1.386936 (-0.962972) |

</details>
</details>

![](https://cml.dev/watermark.png#bb8497b9dec2a3807c887b8184f902d1d8d7c25a "CML watermark")