MongoDB/mdbr leaf ir Content 1. Introduction 2. Technical Report 3. Highlights 4. Benchmarks 5. Quickstart 6. Citation Introduction mdbr leaf ir is a compact high performance text embedding model specifically designed for information retrieval (IR) tasks, e.g., the retrieval stage of Retrieval Augmented Generation (RAG) pipelines. To enable even greater efficiency, mdbr leaf ir supports flexible asymmetric architectures and is robust to vector quantization and MRL truncation. If you are looking to perform other tasks such as classification, clustering, semantic sentence similarity, summarization, please check out our mdbr leaf mt model. [!Note] Note : this model has been developed by the ML team of MongoDB Research. At the time of writing it is not used in any of MongoDB's commercial product or service offerings. Technical Report A technical report detailing our proposed LEAF training procedure is available here. Highlights State of the Art Performance : mdbr leaf ir achieves state of the art results for compact embedding models, ranking 1 on the public BEIR benchmark leaderboard for models with ≤100M parameters. Flexible Architecture Support : mdbr leaf ir supports asymmetric retr…
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