DownloadBAAI/bge-small-en-v1.5 is a lightweight English sentence embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI) as part of the BGE (BAAI General Embedding) series. Designed for dense retrieval, semantic search, and similarity tasks, it produces 384-dimensional embeddings that can be used to compare and rank sentences or passages. This version (v1.5) improves similarity distribution, enhancing performance without the need for special query instructions. The model is optimized for speed and efficiency, making it suitable for resource-constrained environments. It is compatible with popular libraries such as FlagEmbedding, Sentence-Transformers, and Hugging Face Transformers. The model achieves competitive results on the MTEB benchmark, especially in retrieval and classification tasks. With only 33.4M parameters, it provides a strong balance of accuracy and performance for English-only use cases.
Features
- Generates 384-dimensional sentence embeddings
- Optimized for English semantic retrieval and ranking
- Improved similarity distribution in v1.5
- Lightweight with only 33.4 million parameters
- Compatible with FlagEmbedding, Sentence-Transformers, and Transformers
- Supports ONNX for optimized inference
- Performs well on MTEB benchmark tasks
- Ideal for dense retrieval and passage ranking in constrained settings
Project Samples
