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jina-clip-v2

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bwang0911 commited on Nov 19, 2024

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 ## Intended Usage & Model Info
-`jina-clip-v2` is a state-of-the-art **multilingual and multimodal (text-image) embedding model**.
-`jina-clip-v2` is a successor to the [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1) model and brings new features and capabilities, such as:
-* *support for multiple languages* - the text tower now supports 100 languages with tuning focus on **Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
 * *embedding truncation on both image and text vectors* - both towers are trained using [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which enables slicing the output vectors and consequently computation and storage costs.
 * *visual document retrieval performance gains* - with an image resolution of 512 (compared to 224 on `jina-clip-v1`) the image tower can now capture finer visual details. This feature along with a more diverse training set enable the model to perform much better on visual document retrieval tasks. Due to this `jina-clip-v2` can be used as an image encoder in vLLM retriever architectures.

 ## Intended Usage & Model Info
+`jina-clip-v2` is a state-of-the-art **multilingual and multimodal (text-image) embedding model**. It is a successor to the [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1) model and brings new features and capabilities, such as:
+* *support for multiple languages* - the text tower now supports 100 languages with tuning focus on *Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,* and *Vietnamese.*
 * *embedding truncation on both image and text vectors* - both towers are trained using [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which enables slicing the output vectors and consequently computation and storage costs.
 * *visual document retrieval performance gains* - with an image resolution of 512 (compared to 224 on `jina-clip-v1`) the image tower can now capture finer visual details. This feature along with a more diverse training set enable the model to perform much better on visual document retrieval tasks. Due to this `jina-clip-v2` can be used as an image encoder in vLLM retriever architectures.