Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
Article image or reusable cover for Hugging Face
Multi-vector models are an alternative to conventional AI models that compress text into a single vector.
Instead, they store a small vector for each word, which produces better search results—especially when searching for something specific or having multiple requirements simultaneously. The drawback is that the index becomes much larger, but with compression the size can become comparable to conventional models. Hugging Face demonstrates how to use these models with the Sentence Transformers tool.
A multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away, which usually means stronger retrieval at the cost of a bigger index.
Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
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