Issue: streaming dataset with concatenating splits raises an error
State: open
Labels: name: bug

## Describe the bug
streaming dataset with concatenating splits raises an error

## Steps to reproduce the bug
```python
from datasets import load_dataset

# no error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation")
```

```python
from datasets import load_dataset

# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```

```sh
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-4-a6ae02d63899>](https://localhost:8080/#) in <module>()
      3 # error
      4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)

1 frames
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
   1030             splits_generator = splits_generators[split]
   1031         else:
-> 1032             raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
   1033 
   1034         # Create a dataset for each of the given splits

ValueError: Bad split: train+validation. Available splits: ['validation', 'train']
```

[Colab](https://colab.research.google.com/drive/1wMj08_0bym9jnGgByib4lsBPu8NCZBG9?usp=sharing)

## Expected results
load successfully or throws an error saying it is not supported.

## Actual results
above

## Environment info
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0 (windows11 x64)
- Python version: 3.9.13
- PyArrow version: 8.0.0
- Pandas version: 1.4.3


Hi! Only the name of a particular split ("train", "test", ...) is supported as a split pattern if `streaming=True`. We plan to address this limitation soon.

Hi, have you addressed this yet?

yes, same error occurs.
```python
from datasets import load_dataset

# error
repo = "nateraw/ade20k-tiny"
dataset = load_dataset(repo, split="train+validation", streaming=True)
```

```python
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-3-a6ae02d63899>](https://localhost:8080/#) in <cell line: 5>()
      3 # error
      4 repo = "nateraw/ade20k-tiny"
----> 5 dataset = load_dataset(repo, split="train+validation", streaming=True)

1 frames
[/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
   1265             splits_generator = splits_generators[split]
   1266         else:
-> 1267             raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
   1268 
   1269         # Create a dataset for each of the given splits

ValueError: Bad split: train+validation. Available splits: ['train', 'validation']
```

google colab, `datasets==2.12.0`
```
- huggingface_hub version: 0.14.1
- Platform: Linux-5.10.147+-x86_64-with-glibc2.31
- Python version: 3.10.11
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /root/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers: 
- FastAI: 2.7.12
- Tensorflow: 2.12.0
- Torch: 2.0.0+cu118
- Jinja2: 3.1.2
- Graphviz: 0.20.1
- Pydot: 1.4.2
- Pillow: 8.4.0
- hf_transfer: N/A
- gradio: N/A
- ENDPOINT: https://huggingface.co/
- HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets
- HF_TOKEN_PATH: /root/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: False
```


Hi!, still not fixed this, the truth is that it is an important update for what we want to train the entire dataset because we want to train fast, also should be enabled the function "[train:18%]" for streaming