Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
[{'label': 'LABEL_0', 'score': 0.9951714277267456},
{'label': 'LABEL_1', 'score': 0.9985325336456299}]
The model seems pretty confident that the first sentence is from The Wonderful
Wizard of Oz (negative class) and that the second sentence is from Alice’s
Adventures in Wonderland (positive class).
We can make the output a bit more intuitive, though, by setting proper labels for
each of the classes using the id2label attribute of our model’s configuration:
loaded_model.config.id2label = {0: 'Wizard', 1: 'Alice'}
Let’s try it again:
classifier(['Down the Yellow Brick Rabbit Hole', 'Alice rules!'])
Output
[{'label': 'Wizard', 'score': 0.9951714277267456},
{'label': 'Alice', 'score': 0.9985325336456299}]
That’s much better!
More Pipelines
It’s also possible to use pre-trained pipelines for typical tasks like sentiment
analysis without having to fine-tune your own model:
from transformers import pipeline
sentiment = pipeline('sentiment-analysis')
That’s it! The task defines which pipeline is used. For sentiment analysis, the
pipeline above loads a TextClassificationPipeline like ours, but one that’s pretrained
to perform that task.
1002 | Chapter 11: Down the Yellow Brick Rabbit Hole