| | --- |
| | pipeline_tag: text-classification |
| | license: apache-2.0 |
| | language: |
| | - it |
| | tags: |
| | - cross-encoder |
| | - sentence-similarity |
| | - transformers |
| | --- |
| | # 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. |
| |
|
| | <p align="center"> |
| | <img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br> |
| | Bridget Riley, COOL EDGE |
| | </p> |
| | |
| | ## Training Data |
| |
|
| | This model was trained on a custom biomedical ranking dataset. |
| |
|
| | ## Usage and Performance |
| |
|
| | ```python |
| | 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')`. |
