Cross-Encoder for Semantic Textual Similarity
This model was trained using SentenceTransformers Cross-Encoder class.
Training Data
This model was trained on the STS benchmark dataset. 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:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/stsb-distilroberta-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
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Model tree for cross-encoder/stsb-distilroberta-base
Base model
distilbert/distilroberta-base