Token pooling-aware training proposed for multi-vector embeddings
The Hugging Face post title describes a training method; no method, models or results are in the available text.
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The Hugging Face post title describes a training method; no method, models or results are in the available text.
Researchers invert multi-vector visual document indices, recovering 47% of words and 45% of sensitive tokens on ViDoRe v3.
ColNanoVDR distills multi-vector document retrievers into 149M students that keep 95% NDCG@5 and run up to 26x faster.
Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.
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