Title: Introduces automatic subset-level grouping for folder-based dataset builders #7066
State: open

Fixes #7066
This PR introduces automatic **subset-level grouping** for folder-based dataset builders by:

1. Adding a utility function `group_files_by_subset()` that clusters files by root name (ignoring digits and shard suffixes).
2. Integrating this logic into `FolderBasedBuilder._split_generators()` to yield one split per subset.
3. Adding unit tests for the grouping function.
4. Updating the documentation to describe this new behavior under `docs/source/repository_structure.mdx`.

---

### Motivation

Datasets with files like:
```

train0.jsonl
train1.jsonl
animals.jsonl
metadata.jsonl

```

will now be **automatically grouped** as:
- `"train"` subset → `train0.jsonl`, `train1.jsonl`
- `"animals"` subset → `animals.jsonl`
- `"metadata"` subset → `metadata.jsonl`

This enables structured multi-subset loading even when the dataset doesn't follow traditional `train/validation/test` split conventions.

---

### Files Changed

- `src/datasets/data_files.py`: added `group_files_by_subset()` utility
- `src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py`: grouped files before yielding splits
- `tests/test_data_files.py`: added unit test `test_group_files_by_subset`
- `docs/source/repository_structure.mdx`: documented subset grouping for maintainers and users

---

### Benefits

- More flexible and robust dataset split logic
- Enables logical grouping of user-uploaded files without nested folder structure
- Backward-compatible with all existing folder-based configs

---

Ready for review ✅

Comments: It adds automatic grouping of files into subsets based on their root name (e.g., `train0.jsonl`, `train1.jsonl` → `"train"`), as discussed above. The logic is integrated into `FolderBasedBuilder` and is fully tested + documented.

Let me know if any changes are needed — happy to iterate!Hi ! I believe the subsets need to be instantiated here as `configs` - not `splits` (which are meant for train/validation/test):

https://github.com/huggingface/datasets/blob/ef762e664a2a1675368ed7a203b0ac8cecca6e19/src/datasets/load.py#L647-L662

Also the subset names should probably be inferred only from the parquet/csv/json files and not from png/jpeg/wav/mp4 etc. WDYT ?> Hi ! I believe the subsets need to be instantiated here as `configs` - not `splits` (which are meant for train/validation/test):
> 
> https://github.com/huggingface/datasets/blob/ef762e664a2a1675368ed7a203b0ac8cecca6e19/src/datasets/load.py#L647-L662
> 
> Also the subset names should probably be inferred only from the parquet/csv/json files and not from png/jpeg/wav/mp4 etc. WDYT ?

Thanks a lot for the review!

You're absolutely right — treating subsets as separate configs instead of overloaded splits makes much more sense. If that approach sounds good to you, I can move the grouping logic to `load.py`, where configs are instantiated, and revise the PR to emit one `BuilderConfig` per grouped subset.

Also totally agree on limiting grouping to structured file types — I’d scope this to `.json`, `.jsonl`, `.csv`, and `.parquet`.

Let me know if this direction sounds good, and I’ll get started on the changes right away!
Hi! @lhoestq!