cross-encoder
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nli-roberta-base
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nreimers 2021-06-21 11:41:16 +02:00
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@ -50,3 +50,16 @@ with torch.no_grad():
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels) print(labels)
``` ```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```