Cross-Encoder

The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order.

Bridget Riley, COOL EDGE

Training Data

This model was trained on a custom biomedical ranking dataset.

Usage and Performance

from sentence_transformers import CrossEncoder
model = CrossEncoder('efederici/cross-encoder-distilbert-it')
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').