| pipeline_tag: text-ranking | |
| language: | |
| - it | |
| datasets: | |
| - stsb_multi_mt | |
| tags: | |
| - cross-encoder | |
| - sentence-similarity | |
| - transformers | |
| library_name: sentence-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://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" width="400"> </br> | |
| Edouard Vuillard, Sunlit Interior | |
| </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')`. | |