Vector Similarity Ranking With Configurable Thresholds

via “semantic-similarity-computation-for-ranking”

feature-extraction model by undefined. 43,12,964 downloads.

Unique: Embeddings are trained with contrastive learning objectives optimized for cosine similarity ranking, achieving superior MTEB retrieval performance compared to generic embeddings — the embedding space is explicitly optimized for ranking tasks rather than generic similarity

vs others: Outperforms generic BERT embeddings on ranking tasks due to contrastive training, and provides better ranking quality than sparse keyword-based methods while maintaining computational efficiency