BGE v3
BAAI
• Framework: UnknownBGE v3 is an open-source multilingual embedding model developed by BAAI, designed for retrieval-augmented generation (RAG), semantic search, and vector database applications. Achieving a 65.3 score on the MTEB leaderboard, BGE v3 supports over 100 languages and handles an 8K context window for long-document embedding. The model delivers performance comparable to OpenAI’s text-embedding models while being eight times smaller and optimized for 4-bit quantization, making it ideal for on-device and scalable vector search systems. BGE v3 helps developers build advanced semantic search engines, chatbot retrieval layers, and knowledge-grounded AI applications efficiently.
BGE v3 AI Model
Model Performance Statistics
Views
Released
Last Checked
Version
- Semantic search
- RAG optimization
- Multilingual embeddings
- Parameter Count
- N/A
Dataset Used
Multilingual knowledge corpus
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