Sentence Transformers v6.0 adds MultiVectorEncoder training

This update addresses a common pain point in retrieval: most released models truncate long documents, costing up to 0.24 NDCG@10 on medical passages averaging 941 tokens. Training your own multi-vector model lets you set the document length your data needs, avoiding this silent loss.

The author's experiments show that -unsupervised checkpoints, which have not been finetuned on general retrieval, adapt to new domains better than finished siblings. This is because they carry late-interaction structure without the general-purpose tuning that domain training must undo. A fresh projection on a strong backbone like gte-modernbert-base can nearly match existing checkpoints, making it a viable fallback.

The practical impact is that teams can now build domain-specific retrieval models in hours on a single GPU, without needing extensive resources. The provided training pipeline simplifies the process, and the evaluation demonstrates that even a modestly trained model can beat all general-purpose retrievers in a specific domain.