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@@ -133,6 +133,8 @@ sim_1 = query_emb @ candi_emb_1.T
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sim_2 = query_emb @ candi_emb_2.T
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print(sim_1, sim_2) # tensor([[0.7026]]) tensor([[0.8075]])
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```
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## Downstream Application Cases
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- [Huixiangdou](https://github.com/InternLM/HuixiangDou): Using Visualized BGE for the group chat assistant.
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## Evaluation Result
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Visualized BGE delivers outstanding zero-shot performance across multiple hybrid modal retrieval tasks. It can also serve as a base model for downstream fine-tuning for hybrid modal retrieval tasks.
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![image.png](./imgs/SFT-CIRR.png)
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- Supervised fine-tuning performance on the ReMuQ test set.
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![image.png](./imgs/SFT-ReMuQ.png)
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## FAQ
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**Q1: Can Visualized BGE be used for cross-modal retrieval (text to image)?**

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