```
To call a pipeline on many items, you can either call with a list.
```python
pipe = pipeline("text-classification")
pipe(["This restaurant is awesome", "This restaurant is aweful"])
[{'label': 'POSITIVE', 'score': 0.9998743534088135},
 {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
```
To iterate of full datasets it is recommended to use a dataset directly. This means you don't need to allocate
the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
GPU. If it doesn't don't hesitate to create an issue.
```python
import datasets
from transformers import pipeline
from transformers.pipelines.base import KeyDataset
from tqdm.auto import tqdm
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
