Cross-Encoder for Semantic Textual Similarity

This model was trained using SentenceTransformers Cross-Encoder class.

Training Data

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.

Usage and Performance

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/stsb-distilroberta-base')
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