STSbenchmark
The following models can be used like this:
python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sent A1', 'Sent B1'), ('Sent A2', 'Sent B2')])
They return a score  0...1 indicating the semantic similarity of the given sentence pair.
- cross-encoder/stsb-TinyBERT-L-4 - STSbenchmark test performance: 85.50
- cross-encoder/stsb-distilroberta-base - STSbenchmark test performance: 87.92
- cross-encoder/stsb-roberta-base - STSbenchmark test performance: 90.17
- cross-encoder/stsb-roberta-large - STSbenchmark test performance: 91.47 
Quora Duplicate Questions
These models have been trained on the Quora duplicate questions dataset. They can used like the STSb models and give a score 0...1 indicating the probability that two questions are duplicate questions.
