For evaluation results, see SBERT.net - Pretrained Cross-Encoder.
Pre-trained models can be used like this:
from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-MiniLM2-L6-H768') 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)]
You can use the model also directly with Transformers library (without SentenceTransformers library):
from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768') tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768') features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits label_mapping = ['contradiction', 'entailment', 'neutral'] labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] print(labels)
This model can also be used for zero-shot-classification:
from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-MiniLM2-L6-H768') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", "politics"] res = classifier(sent, candidate_labels) print(res)
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