| from sentence_transformers import CrossEncoder | |
| model = CrossEncoder('cross-encoder/nli-deberta-v3-large') | |
| scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) | |
| #Convert scores to labels | |
| label_mapping = ['contradiction', 'entailment', 'neutral'] | |
| labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] | |
| print("label", labels) |