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--- |
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license: mit |
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datasets: |
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- stsb_multi_mt |
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language: |
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- it |
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library_name: sentence-transformers |
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pipeline_tag: text-classification |
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tags: |
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- cross-encoder |
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--- |
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# Cross-Encoder for STSB-Multi |
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. |
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The original model is [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased). |
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## Training Data |
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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. |
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## Usage and Performance |
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Pre-trained models can be used like this: |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('model_name') |
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scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) |
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``` |
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The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. |
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You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class |
