Fine-tune Multimodal Embeddings with Sentence Transformers for Visual Document Retrieval (Qwen/Qwen3-VL-Embedding-2B)

AI Impact Summary

Sentence Transformers enables training and finetuning of multimodal embeddings and reranker models, demonstrated here with Qwen/Qwen3-VL-Embedding-2B for Visual Document Retrieval. The pipeline covers model, dataset, loss, trainer, and evaluator, with automatic modality handling via the processor and optional Router-based architectures to align separate encoders. Finetuning on domain data yields substantial retrieval quality gains, directly improving cross-modal search fidelity and the effectiveness of downstream retrieval-augmented generation workflows.