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mxbai-edge-colbert-v0-17m

🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 17 million parameter ColBERT with a projection dimension of 48. It is built on top of Ettin-17M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

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🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 17 million parameter ColBERT with a projection dimension of 48. It is built on top of Ettin-17M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

情報源

huggingface.co ↗