jina-clip-v1
| Property | Value |
|---|---|
| Parameter Count | 223M |
| License | Apache 2.0 |
| Paper | arXiv:2405.20204 |
| Framework | PyTorch, ONNX, Transformers.js |
What is jina-clip-v1?
jina-clip-v1 is an innovative multimodal embedding model developed by Jina AI that uniquely combines the capabilities of text-to-text and text-to-image retrieval in a single architecture. Unlike traditional embedding models that excel in only one domain, this model bridges the gap between pure text embeddings and cross-modal capabilities.
Implementation Details
The model architecture is built on CLIP technology but extends its capabilities significantly. It processes both text and image inputs, generating embeddings that maintain high performance in both modalities. At 223M parameters, it offers a balanced approach between computational efficiency and performance.
- State-of-the-art performance in both text-text and text-image retrieval tasks
- Implements both PyTorch and ONNX runtime support
- Supports multiple integration methods including Transformers and sentence-transformers
- JavaScript support through Transformers.js
Core Capabilities
- Achieves 67.48% R@1 on Flickr Image Retrieval, surpassing ViT-B-32 and ViT-B-16
- Maintains competitive text similarity scores comparable to specialized text embeddings
- Enables seamless multimodal retrieval-augmented generation (MuRAG) applications
- Supports both local file processing and URL-based image analysis
Frequently Asked Questions
Q: What makes this model unique?
Its ability to perform both text-to-text and text-to-image retrieval at high performance levels within a single model, eliminating the need for separate models for different modalities.
Q: What are the recommended use cases?
The model is ideal for applications requiring multimodal search capabilities, content recommendation systems, and cross-modal retrieval tasks. It's particularly valuable for systems needing both text similarity and image-text matching functionalities.
