intfloat /

e5-base-v2

3 DZD in / 1M tokens

e5-base-v2 is an open-source text embedding model developed by Microsoft Research. Based on a 109-million parameter BERT architecture, it is pre-trained using weakly-supervised contrastive learning. It generates 768-dimensional dense vectors and requires specific text prefixes ("query: " and "passage: ") to distinguish between search intents and document indexing, offering a strong balance between high retrieval accuracy and minimal computational cost for semantic search and RAG applications.