In an interconnected world, effective communication across multiple languages and mediums is increasingly important. Multimodal AI faces challenges in combining images and text for seamless retrieval and understanding across different languages. Existing models often perform well in English but struggle with other languages. Additionally, handling high-dimensional data for both text and images simultaneously has been computationally intensive, limiting applications for non-English speakers and scenarios requiring multilingual contexts.
Jina-CLIP v2: A 0.9B Multilingual Multimodal Embedding Model
Jina AI has introduced Jina-CLIP v2—a 0.9B multilingual multimodal embedding model that connects images with text in 89 languages. Jina-CLIP v2 supports a wide range of languages, addressing the limitations that have previously restricted access to advanced multimodal AI technologies. It handles images at a resolution of 512×512 and processes text with up to 8,000 tokens, providing an effective solution for linking images and multilingual text. Additionally, it offers Matryoshka representations that reduce embeddings to 64 dimensions for both text and images, ensuring more efficient embeddings while retaining essential contextual information.
Technical Details
Jina-CLIP v2 stands out for its flexibility and efficiency. It enables embedding generation not only at a large dimensional scale but also at smaller scales, with its Matryoshka representation feature reducing embeddings to 64 dimensions. This allows users to adjust the embedding process to meet specific requirements, whether for computationally intensive deep learning tasks or lightweight mobile applications. Furthermore, the model’s text encoder can operate independently as a dense retriever, matching the performance of jina-embeddings-v3—the current leader for multilingual embeddings under 1 billion parameters on the Multilingual Text Embeddings Benchmark (MTEB). The versatility to perform both retrieval and classification tasks makes Jina-CLIP v2 suitable for a variety of use cases, from multilingual search engines to context-aware recommendation systems.
Jina-CLIP v2 represents an important step in reducing biases in language models, particularly for users relying on less widely spoken languages. In evaluations, the model performed well in multilingual retrieval tasks, demonstrating its capability to match or exceed the performance of specialized text models. Its use of Matryoshka representations ensures that embedding calculations can be performed efficiently without sacrificing accuracy, enabling deployment in resource-constrained environments. Jina-CLIP v2’s ability to connect text and images across 89 languages opens new possibilities for companies and developers to create AI that is accessible to diverse users while maintaining contextual accuracy. This can significantly impact applications in e-commerce, content recommendation, and visual search systems, where language barriers have traditionally posed challenges.
Conclusion
Jina-CLIP v2 is a meaningful advancement in multilingual multimodal models, addressing both linguistic diversity and technical efficiency in a unified approach. By enabling effective image and text connectivity across 89 languages, Jina AI is contributing to more inclusive AI tools that transcend linguistic boundaries. Whether for retrieval or classification tasks, Jina-CLIP v2 offers flexibility, scalability, and performance that empower developers to create robust and efficient AI applications. This development is a step forward in making AI accessible and effective for people around the world, fostering cross-cultural interactions and understanding.
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