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[PR #20020] [CLOSED] feat: use sigmoid function for reranking models to return scores from 0 to 1. #64286
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📋 Pull Request Information
Original PR: https://github.com/open-webui/open-webui/pull/20020
Author: @BarerRocket2678
Created: 12/17/2025
Status: ❌ Closed
Base:
dev← Head:retrieval-content-score-fix📝 Commits (8)
fe6783cMerge pull request #19030 from open-webui/devfc05e0aMerge pull request #19405 from open-webui/deve3faec6Merge pull request #19416 from open-webui/dev9899293Merge pull request #19448 from open-webui/dev140605eMerge pull request #19462 from open-webui/dev6f1486fMerge pull request #19466 from open-webui/devcfdf9bfFixed retrieval.py to use sigmoid activation function911e6c5Update retrieval.py📊 Changes
1 file changed (+4 additions, -1 deletions)
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backend/open_webui/routers/retrieval.py(+4 -1)📄 Description
Pull Request Checklist
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Description
This fix adds the
torch.nn.Sigmoid()function to the sentence transformer reranking models. This makes all the cross encoder models return a value between 0 and 1 instead of around -10 to 10 as stated here:You can initialize these models with activation_fn=torch.nn.Sigmoid() to force the model to return scores between 0 and 1. Otherwise, the raw value can reasonably range between -10 and 10.
Changed
Additional Information
Models Tested:
All tests were done by retrieving math information from a textbook in a knowledge database, the model in use was Qwen3 4B Instruct 2507.
Screenshots or Videos
Test example with cross-encoder/ms-marco-MiniLM-L6-v2 and Qwen3 4B Instruct 2507

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🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.