How to sample every file in a list of files making up a split in a dataset when loading?
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[]
Hi ! This is not possible just with `load_dataset`.

You can do something like this instead:
```python
seed=42
data_files_dict = {
    "train": [train_file1, train_file2],
    "test": [test_file1, test_file2],
    "val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
    "csv",
    data_files=data_files_dict,
).shuffle(seed=seed)

sample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}
```

Another alternative is loading each file separately with `split="train[:8]"` and then use `concatenate_datasets` to merge the sample of each file.
I am loading a dataset with multiple train, test, and validation files like this:

```
data_files_dict = {
    "train": [train_file1, train_file2],
    "test": [test_file1, test_file2],
    "val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
    "csv",
    data_files=data_files_dict,
    split=['train[:8]', 'test[:8]', 'val[:8]']
)

```

However, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.

I'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.

Is this type of splitting supported? If so, how can I do it?
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