| --- |
| license: apache-2.0 |
| datasets: |
| - sentence-transformers/stsb |
| language: |
| - en |
| base_model: |
| - FacebookAI/roberta-base |
| pipeline_tag: text-ranking |
| library_name: sentence-transformers |
| tags: |
| - transformers |
| --- |
| # Cross-Encoder for Semantic Textual Similarity |
| This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. |
|
|
| ## Training Data |
| This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). 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 |
|
|
| Pre-trained models can be used like this: |
| ```python |
| from sentence_transformers import CrossEncoder |
| |
| model = CrossEncoder('cross-encoder/stsb-roberta-base') |
| 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 |
