jina-clip-v1
Maintainer: jinaai
165
🖼️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
jina-clip-v1 is a state-of-the-art English multimodal (text-image) embedding model trained by Jina AI. It bridges the gap between traditional text embedding models, such as jina-embeddings-v2-base-en, which excel in text-to-text retrieval but are incapable of cross-modal tasks, and models like openai/clip-vit-base-patch32 that effectively align image and text embeddings but are not optimized for text-to-text retrieval. jina-clip-v1 offers robust performance in both domains, matching the retrieval efficiency of jina-embeddings-v2-base-en for text-to-text tasks while setting a new benchmark for cross-modal retrieval.
Model inputs and outputs
Inputs
- Sentences: The model can encode meaningful sentences in English.
- Images: The model can also encode images, either by providing the public image URLs or directly passing in the PIL.Image objects.
Outputs
- Text embeddings: The model outputs dense vector representations for the input sentences.
- Image embeddings: The model outputs dense vector representations for the input images.
- Similarity scores: The model can compute the cosine similarity between text and image embeddings, enabling cross-modal retrieval.
Capabilities
jina-clip-v1 excels at both text-to-text and text-to-image retrieval tasks. Its dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, allowing seamless text-to-text and text-to-image searches within a single model.
What can I use it for?
jina-clip-v1 can be used for a variety of multimodal applications, such as:
- Image search: Users can search for images by describing them in text.
- Cross-modal retrieval: The model can retrieve relevant text or images based on a query in the opposite modality.
- Multimodal question answering: The model can be used to answer questions that require understanding both text and images.
- Multimodal content generation: The model can be used to generate relevant text or images based on a prompt in the opposite modality.
Jina AI has also provided the Embeddings API as an easy-to-use interface for working with jina-clip-v1 and their other embedding models.
Things to try
One key advantage of jina-clip-v1 is its ability to handle longer sequences of text, up to 8,192 tokens, thanks to its use of the symmetric bidirectional variant of ALiBi. This makes the model well-suited for tasks involving long-form content, such as document retrieval, long-form question answering, and summarization. Researchers and developers can explore how the model's performance scales with longer input sequences compared to traditional text embedding models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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