FlagEmbedding

Thank you for publishing this! I absolutely love small embedding models, and have used them on a number of projects (both commercial and hobbyist). I look forward to checking this one out! I don't know if this is too much to ask, but something that would really help me adopt your model is to include a fine-tuning setup. The BGE series of embeddings-models has been my go-to for a couple of years now -- not because it's the best-performing in the leaderboards, but because they make it so incredibly easy to fine-tune the model [0]. Give it a JSONL file of a bunch of training triplets, and you can fine-tune the base models on your own dataset. I appreciate you linking to the paper on the recipe for training this type of model -- how close to turnkey is your model to helping me do transfer learning with my own dataset? I looked around for a fi

Hi HN, we're sharing an update on our work to improve reasoning in retrieval systems for RAG and Agents. Our project, BGE-Reasoner, is an open-source, three-stage framework (Rewrite, Embed, Rerank) that showed strong performance on the BRIGHT benchmark for reasoning-intensive retrieval as of our submission on Aug 21. Our main contribution is using synthetic data and reinforcement learning to handle complex queries that go beyond simple semantic matching. We're sharing this because we believe the framework itself is a solid, replicable contribution for anyone working on advanced RAG or Agent search. We're in the process of open-sourcing the model weights, code, and training data to the community. Love for you to take a look, share your thoughts, and we welcome any and all feedback or critiques. Thanks! - Github: https://github.com/FlagOpen

Here is the code https://github.com/flagopen/flagembedding