FlagEmbedding Model List FAQ Usage Evaluation Train Contact Citation License For more details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge m3. English 中文 FlagEmbedding focuses on retrieval augmented LLMs, consisting of the following projects currently: Long Context LLM : Activation Beacon Fine tuning of LM : LM Cocktail Dense Retrieval : BGE M3, LLM Embedder, BGE Embedding Reranker Model : BGE Reranker Benchmark : C MTEB News 1/30/2024: Release BGE M3 , a new member to BGE model series! M3 stands for M ulti linguality (100+ languages), M ulti granularities (input length up to 8192), M ulti Functionality (unification of dense, lexical, multi vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi lingual (MIRACL) and cross lingual (MKQA) benchmarks. Technical Report and Code. :fire: 1/9/2024: Release Activation Beacon, an effective, efficient, compatible, and low cost (training) method to extend the context length of LLM. Technical Report :fire: 12/24/2023: Release LLaRA , a LLaMA 7B based…
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