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@@ -32,6 +32,8 @@ Here's a breakdown of the reranker models we provide:
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  | [jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) | 6 | 384 | 37.8 |
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  | [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en) | 4 | 384 | 33.0 |
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  As you can see, the `jina-reranker-v1-turbo-en` offers a balanced approach with **6 layers** and **37.8 million** parameters. This translates to fast search and reranking while preserving a high degree of accuracy. The `jina-reranker-v1-tiny-en` prioritizes speed even further, achieving the fastest inference speeds with its **4-layer**, **33.0 million** parameter architecture. This makes it ideal for scenarios where absolute top accuracy is less crucial.
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  # Usage
 
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  | [jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) | 6 | 384 | 37.8 |
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  | [jina-reranker-v1-tiny-en](https://huggingface.co/jinaai/jina-reranker-v1-tiny-en) | 4 | 384 | 33.0 |
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+ > Currently, the `jina-reranker-v1-base-en` model is not available on Hugging Face. You can access it via the [Jina AI Reranker API](https://jina.ai/reranker/).
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  As you can see, the `jina-reranker-v1-turbo-en` offers a balanced approach with **6 layers** and **37.8 million** parameters. This translates to fast search and reranking while preserving a high degree of accuracy. The `jina-reranker-v1-tiny-en` prioritizes speed even further, achieving the fastest inference speeds with its **4-layer**, **33.0 million** parameter architecture. This makes it ideal for scenarios where absolute top accuracy is less crucial.
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  # Usage