Augmented SBERT STSb
This is a sentence-transformers cross encoder model.
It is used as a demo model within the NLP for Semantic Search course, for the chapter on In-domain Data Augmentation with BERT.
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This is a sentence-transformers cross encoder model.
It is used as a demo model within the NLP for Semantic Search course, for the chapter on In-domain Data Augmentation with BERT.
from sentence_transformers import CrossEncoder model = CrossEncoder("jamescalam/bert-stsb-cross-encoder") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores)