模型:
cross-encoder/stsb-roberta-base
This model was trained using SentenceTransformers Cross-Encoder class.
This model was trained on the STS benchmark dataset . The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
Pre-trained models can be used like this:
from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
The model will predict scores for the pairs ('Sentence 1', 'Sentence 2') and ('Sentence 3', 'Sentence 4') .
You can use this model also without sentence_transformers and by just using Transformers AutoModel class