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Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers

This blog post by Tom Aarsen on Hugging Face explains how to train or finetune multimodal embedding and reranker models using the Sentence Transformers library, with a practical example of finetuning Qwen/Qwen3-VL-Embedding-2B for Visual Document Retrieval, achieving improved NDCG@10 from 0.888 to 0.947. It covers the training components: model, dataset, loss function, training arguments, evaluator, and trainer.

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In this blogpost, I'll show you how to train or finetune these multimodal models on your own data.

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Announced

16 Apr 2026 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+109d after)