Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Multi-vector embedding models can be trained and finetuned for advanced retrieval tasks using the Sentence Transformers library. These models, also known as ColBERT-style or late-interaction models, represent text with one vector per token, allowing for more granular matching than single-vector approaches. Finetuning is particularly effective for adapting models to domain-specific data, such as medical or legal text, where vocabulary and relevance differ from general web data. The process involves selecting a model, preparing datasets, choosing a loss function, and utilizing the library's trainer class. An example demonstrates that a finetuned model on medical data significantly outperforms general-purpose retrieval models.