Multimodal Embedding & Reranker Models with Sentence Transformers

What changed

Multimodal embedding models map inputs from different modalities into a shared embedding space, while multimodal reranker models score the relevance of mixed-modality pairs. This opens up use cases like visual document retrieval, cross-modal search, and multimodal RAG pipelines. If you want to train your own multimodal models, check out the companion blogpost: Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers
• Train and Fine-Tune Sentence Transformers Models
• New embedding models and API updates

• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers similarity 0.82
• Train and Fine-Tune Sentence Transformers Models similarity 0.45
• New embedding models and API updates similarity 0.35