a list of dictionaries like this one:
```py
results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."])
for result in results:
...     print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
label: POSITIVE, with score: 0.9998
label: NEGATIVE, with score: 0.5309
```
To use with a large dataset, look at iterating over a pipeline
You can see the second sentence has been classified as negative (it needs to be positive or negative) but its score is
fairly neutral.
By default, the model downloaded for this pipeline is called "distilbert-base-uncased-finetuned-sst-2-english". We can
look at its model page to get more
information about it. It uses the DistilBERT architecture and has been fine-tuned on a
dataset called SST-2 for the sentiment analysis task.
