Hugging Face explains pooling-aware training for compressed multi-vector retrieval
Overview
Hugging Face's blog explains how token pooling shrinks multi-vector embedding stores and how pooling-aware training preserves retrieval quality at the same vector budget.
The post says a normally trained model keeps 94.6% of unpooled NanoBEIR nDCG@10 at pool_factor=4, while pooling-aware training raises that retention to 97.8%.
Written by AI from the articles below · updated Nov 11, 10:26 AM ET
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- Hugging Face BlogOfficialHugging Face explains pooling-aware training for compressed multi-vector retrieval
AIHugging Face explains how token pooling shrinks multi-vector embedding stores and how pooling-aware training preserves retrieval quality at the same vector budget. The post says a normally trained model retains 94.6% of unpooled NanoBEIR nDCG@10 at pool_factor=4, while pooling-aware training raises retention to 97.8%.
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