- cross-encoder/nli-deberta-v3-xsmall - Accuracy on MNLI mismatched set:  87.77
- cross-encoder/nli-deberta-v3-small - Accuracy on MNLI mismatched set: 87.55
- cross-encoder/nli-roberta-base - Accuracy on MNLI mismatched set: 87.47
- cross-encoder/nli-MiniLM2-L6-H768 - Accuracy on MNLI mismatched set: 86.89
- cross-encoder/nli-distilroberta-base - Accuracy on MNLI mismatched set: 83.98
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
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)]
