feat: add links to the other dims
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README.md
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<b>JinaColBERT V2: your multilingual late interaction retriever!</b>
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JinaColBERT V2 (`jina-colbert-v2`) is a new model based on the [JinaColBERT V1](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/) that expands on the capabilities and performance of the `jina-colbert-v1-en` model. Like the previous release, it has Jina AI’s 8192 token input context and the [improved efficiency, performance](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/), and [explainability](https://jina.ai/news/ai-explainability-made-easy-how-late-interaction-makes-jina-colbert-transparent/) of token-level embeddings and late interaction.
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This new release adds new functionality and performance improvements:
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- Multilingual support for dozens of languages, with strong performance on major global languages.
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- [Matryoshka embeddings](https://huggingface.co/blog/matryoshka), which allow users to trade between efficiency and precision flexibly.
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- Superior retrieval performance when compared to the English-only `jina-colbert-v1-en
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## Usage
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<b>JinaColBERT V2: your multilingual late interaction retriever!</b>
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</p>
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JinaColBERT V2 (`jina-colbert-v2`) is a new model based on the [JinaColBERT V1](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/) that expands on the capabilities and performance of the [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en) model. Like the previous release, it has Jina AI’s 8192 token input context and the [improved efficiency, performance](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/), and [explainability](https://jina.ai/news/ai-explainability-made-easy-how-late-interaction-makes-jina-colbert-transparent/) of token-level embeddings and late interaction.
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This new release adds new functionality and performance improvements:
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- Multilingual support for dozens of languages, with strong performance on major global languages.
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- [Matryoshka embeddings](https://huggingface.co/blog/matryoshka), which allow users to trade between efficiency and precision flexibly.
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- Superior retrieval performance when compared to the English-only [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en).
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JinaColBERT V2 offers three different versions for different embeddings dimensions:
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[`jinaai/jina-colbert-v2`](https://huggingface.co/jinaai/jina-colbert-v2): 128 dimension embeddings
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[`jinaai/jina-colbert-v2-96`](https://huggingface.co/jinaai/jina-colbert-v2-96): 96 dimension embeddings
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[`jinaai/jina-colbert-v2-64`](https://huggingface.co/jinaai/jina-colbert-v2-64): 64 dimension embeddings
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## Usage
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