Cross-Encoder for STSB-Multi
This model was trained using SentenceTransformers Cross-Encoder class. The original model is dbmdz/bert-base-italian-uncased.
Training Data
This model was trained on the STS benchmark dataset, in particular the italian translation. 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('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