Multimodal Embedding & Reranker Models with Sentence Transformers
Hugging Face released multimodal embedding and reranker models using Sentence Transformers, enabling joint text and image encoding for applications like image search and visual question answering. These models allow you to build multimodal applications with a single, unified embedding space. The Sentence Transformers library provides a simple interface for using these models.
Key takeaways
- Multimodal models encode text and images in a single space.
- Enables applications like image search and visual question answering.
- Sentence Transformers library provides a simple interface.
Hugging Face released multimodal embedding and reranker models using Sentence Transformers, enabling joint text and image encoding for applications like image search and visual question answering. These models allow you to build multimodal applications with a single, unified embedding space. The Sentence Transformers library provides a simple interface for using these models.
Key takeaways
- Multimodal models encode text and images in a single space.
- Enables applications like image search and visual question answering.
- Sentence Transformers library provides a simple interface.