Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

This post introduces and demonstrates the use of multi-vector (late interaction) embedding models with Sentence Transformers, specifically highlighting the Alibaba-NLP/gte-modernbert-base model. It explains how these models go beyond single-vector embeddings by considering multiple vectors for a single input, allowing for more sophisticated semantic representations. The post provides practical guidance on how to implement and utilize these models for tasks that benefit from this richer embedding approach.