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Jina Clip V2
Overview :
Jina-clip-v2 is a multilingual multimodal embedding model developed by Jina AI, supporting image retrieval in 89 languages, capable of processing images at a resolution of 512x512. It offers output dimensions ranging from 64 to 1024 to meet diverse storage and processing needs. The model combines the powerful text encoder Jina-XLM-RoBERTa and the visual encoder EVA02-L14, creating aligned representations of images and texts through joint training. Jina-clip-v2 excels in multimodal search and retrieval, especially in breaking language barriers and providing cross-modal understanding.
Target Users :
The target audience includes developers and businesses requiring multilingual, multimodal search and retrieval capabilities, especially those dealing with cross-language content and demanding high-resolution image processing. Jina-clip-v2 enhances their retrieval accuracy and efficiency by providing robust feature extraction and cross-modal understanding.
Use Cases
Use jina-clip-v2 for image retrieval of 'a beautiful sunset on the beach' in different language versions.
Leverage jina-clip-v2 for cross-language product image search on e-commerce platforms.
Perform text similarity retrieval in a multilingual document repository using jina-clip-v2 to quickly find relevant content.
Features
Supports multilingual image retrieval in 89 languages, enhancing cross-language search capabilities.
Processes high-resolution images of 512x512, improving detail handling.
Offers output dimensions from 64 to 1024 to accommodate varying storage and processing needs.
Utilizes robust encoders based on Jina-XLM-RoBERTa and EVA02-L14 for efficient feature extraction.
Applicable for neural information retrieval and multimodal GenAI applications, broadening the model’s use cases.
Available for commercial use through Jina AI Embedding API, AWS, Azure, and GCP.
How to Use
1. Install necessary libraries such as transformers, einops, timm, and pillow.
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2. Load the jina-clip-v2 model using the AutoModel.from_pretrained method.
3. Prepare text and image data, which may include multilingual text or image URLs.
4. Encode the text and images separately using the model's encode_text and encode_image methods.
5. Optionally, adjust the output embedding dimensions using the truncate_dim parameter.
6. For retrieval tasks, compare the query vector encoded by the model with vectors in your database for similarity.
7. Utilize the Jina AI Embedding API for commercial purposes, or deploy the model via AWS, Azure, and GCP.
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