from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
scores = model.predict([('The process of synthesis of glycogen called glycogenis, occurs in the liver and in the muscle. The first reaction of glycogenesis is the phosphrylation of glucose into glucose-6-phosphate at the expense of ATP. The enzymes, hexokinase and glucokinase mediate the glucose phosphorylation process. While the liver harbours both hexokinase and glucokinase, the muscluature contains only hexokinase.', 'The process of synthesis of glycogen called glycogenis, occurs in the liver and in the muscle. The first reaction of glycogenesis is the phosphrylation of glucose into glucose-6-phosphate at the expense of ATP. The enzymes, hexokinase and glucokinase mediate the glucose phosphorylation process. While the liver harbours both hexokinase and glucokinase, the muscluature contains only hexokinase.'), ('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)