Contrastive Embedding Learning

Contrastive embedding learning sits at the heart of semantic similarity and retrieval systems. The model is trained to produce discriminative representations through positive and negative example pairs. It makes a major difference in sentence embeddings, dual-encoder retrieval, and semantic search systems. The quality of negative sample selection is a decisive factor in its success.