cross-encoder/stsb-distilroberta-base

1---2license: apache-2.03datasets:4- sentence-transformers/stsb5language:6- en7base_model:8- distilbert/distilroberta-base9pipeline_tag: text-ranking10library_name: sentence-transformers11tags:12- transformers13---14# Cross-Encoder for Semantic Textual Similarity15This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.16 17## Training Data18This 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. 19 20 21## Usage and Performance22 23Pre-trained models can be used like this:24```python25from sentence_transformers import CrossEncoder26 27model = CrossEncoder('cross-encoder/stsb-distilroberta-base')28scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])29```30 31The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.32 33You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class