| license: mit | |
| datasets: | |
| - stsb_multi_mt | |
| language: | |
| - it | |
| library_name: sentence-transformers | |
| pipeline_tag: text-ranking | |
| tags: | |
| - cross-encoder | |
| # Cross-Encoder for STSB-Multi | |
| This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. | |
| The original model is [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased). | |
| ## Training Data | |
| This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark), 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: | |
| ```python | |
| 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 |