README.md

metadata

license: mit
datasets:
  - stsb_multi_mt
language:
  - it
library_name: sentence-transformers
pipeline_tag: text-classification
tags:
  - cross-encoder

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