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This model was trained using SentenceTransformers Cross-Encoder class.

Edouard Vuillard, Sunlit Interior

Training Data

This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.

Usage and Performance

from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-umberto-stsb')
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').

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Dataset used to train efederici/cross-encoder-bert-base-stsb