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Updated README

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- hello
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ # Cross-Encoder
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+
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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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+
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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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+
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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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+
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+
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+ ## Usage and Performance
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+
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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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+
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+ The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.