| | --- |
| | 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. |
| |
|
| |
|
| | ## 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 |
