MongoDB/mdbr leaf mt Content 1. Introduction 2. Technical Report 3. Highlights 4. Benchmarks 5. Quickstart 6. Citation Introduction mdbr leaf mt is a compact high performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks. To enable even greater efficiency, mdbr leaf mt supports flexible asymmetric architectures and is robust to vector quantization and MRL truncation. If you are looking to perform semantic search / information retrieval (e.g. for RAGs), please check out our mdbr leaf ir model, which is specifically trained for these tasks. [!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 mt achieves new state of the art results for compact embedding models, ranking 1 on the public MTEB v2 (Eng) benchmark leaderboard for models with ≤30M parameters. Flexible Architecture Support : mdbr leaf mt supports asymmetric retrieval architectures…
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