nomic-embed-vision-v1.5
| Property | Value |
|---|---|
| Author | nomic-ai |
| Model Type | Vision Embedding Model |
| ImageNet 0-shot | 71.0% |
| Datacomp Score | 56.8% |
| MTEB Score | 62.28 |
| Model URL | huggingface.co/nomic-ai/nomic-embed-vision-v1.5 |
What is nomic-embed-vision-v1.5?
nomic-embed-vision-v1.5 is an advanced vision embedding model that shares the same embedding space as nomic-embed-text-v1.5, enabling powerful multimodal capabilities. It represents a significant improvement over previous models, outperforming competitors like OpenAI CLIP ViT B/16 and Jina CLIP v1 across various benchmarks.
Implementation Details
The model employs a technique similar to LiT (Learning-in-Training) but with a unique approach of locking the text embedder. It can be easily implemented using the Transformers library or through the Nomic Embedding API for streamlined inference.
- Seamless integration with the nomic Python client
- Support for multiple image formats (JPEG, PNG)
- Normalized embeddings output for consistent results
- Shared embedding space with text models
Core Capabilities
- High-performance image embedding generation
- Multimodal retrieval support
- Text-to-image search functionality
- Robust performance across various benchmarks
- Easy integration with existing pipelines
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to share the same embedding space with nomic-embed-text-v1.5 makes it particularly powerful for multimodal applications. Its superior performance on ImageNet 0-shot (71.0%) and Datacomp (56.8%) sets it apart from other vision models.
Q: What are the recommended use cases?
The model excels in image embedding generation, multimodal retrieval, and text-to-image search applications. It's particularly well-suited for tasks requiring both visual and textual understanding, such as cross-modal search and retrieval systems.
