Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Sentence Transformers provides a training path for search models that keep a representation of each text unit instead of compressing a document into one vector. Each query unit finds its strongest document match, and those similarities are added together. This preserves detailed matching information at the cost of a larger search index.

The training tutorial demonstrates adaptation to medical question-and-passage pairs, reporting stronger retrieval on its medical evaluation than the compared general-purpose models. That supports domain-specific experimentation, with evaluation on the intended collection. The companion usage guide explains how to load, score and index these models.