readme: ---
  pipeline_tag: text-classification
  language: 
    - it
  datasets:
  - stsb_multi_mt
  tags:
  - cross-encoder
  - sentence-similarity
  - transformers
  ---
  
  # Cross-Encoder
  
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
  
  <p align="center">
      <img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
      Marco Lodola, Monument to Umberto Eco, Alessandria 2019
  </p>
  
  ## Training Data
  This model was trained on [stsb](https://huggingface.co/datasets/stsb_multi_mt/viewer/it/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. 
  
  
  ## Usage and Performance
  
  ```python
  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')`.