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--- |
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pipeline_tag: text-classification |
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language: |
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- it |
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datasets: |
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- stsb_multi_mt |
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tags: |
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- cross-encoder |
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- sentence-similarity |
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- transformers |
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--- |
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# Cross-Encoder |
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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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<p align="center"> |
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<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br> |
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Marco Lodola, Monument to Umberto Eco, Alessandria 2019 |
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</p> |
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## Training Data |
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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. |
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## Usage and Performance |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('efederici/cross-encoder-umberto-stsb') |
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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')`. |
