FlagEmbedding Model List FAQ Usage Evaluation Train Contact Citation License More details please refer to our Github: FlagEmbedding. English 中文 Hiring: We're seeking experienced NLP researchers and intern students focusing on dense retrieval and retrieval augmented LLMs. If you're interested, please feel free to reach out to us via email at zhengliu1026@gmail.com. FlagEmbedding can map any text to a low dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, and semantic search. And it can also be used in vector databases for LLMs. 🌟 Updates 🌟 10/12/2023: Release LLM Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper :fire: 09/15/2023: The technical report of BGE has been released 09/15/2023: The massive training data of BGE has been released 09/12/2023: New models: New reranker model : release cross encoder models BAAI/bge reranker base and BAAI/bge reranker large , which are more powerful than embedding model. We recommend to use/fine tune them to re rank top k documents returned by embedding models. update embedding model : release bge v1.5 embedding model to alleviate the issue of the s…
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