advanced-cosine

Vector similarity matching evaluates how closely related documents or queries are in high-dimensional space. Advanced cosine techniques—weighted, sparse, embedding-based, and sqrt variants—reduce evaluation gaps by up to 30%, accelerate sparse representations 5x faster, and stabilize results by ~10% across BERT embeddings and other 768D vectors.

Yes. Embedding Cosine handles 768-dimensional BERT embeddings natively for semantic matching and retrieval. It pairs with other techniques like weighted and sparse cosine to optimize both accuracy and speed on high-dimensional embedding spaces.

Sparse Cosine using scipy optimizes for high-dimensional vectors with many zero values, delivering 5x speedup over standard approaches. It's ideal when your embeddings are sparse and computational efficiency matters for clustering or large-scale retrieval.

Weighted Cosine assigns importance scores to dimensions, reducing evaluation gaps by 30% compared to uniform weighting. It's useful when certain embedding dimensions carry more semantic value for your matching task.

Sqrt Cosine applies square-root transformation to stabilize similarity scores, improving consistency by ~10%. Use it when you need more robust results across varying embedding magnitudes or noisy high-dimensional data.

Yes. The Sparse Cosine technique uses scipy-based sparse representations to handle vectors efficiently. It's designed for workflows where embeddings contain significant zero values, delivering substantial speed gains for retrieval and clustering at scale.