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it wins both matches.
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For each prompt, we then compute Bradley-Terry scores for the respective models using the same [method](https://github.com/lm-sys/FastChat/blob/f2e6ca964af7ad0585cadcf16ab98e57297e2133/fastchat/serve/monitor/elo_analysis.py#L57) as that used in the [LMSYS Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard). Finally, we normalize all scores to a scale from 0 to 1 for interoperability with other weighted ranking systems.
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it wins both matches.
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For each prompt, we then compute Bradley-Terry scores for the respective models using the same [method](https://github.com/lm-sys/FastChat/blob/f2e6ca964af7ad0585cadcf16ab98e57297e2133/fastchat/serve/monitor/elo_analysis.py#L57) as that used in the [LMSYS Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard). Finally, we normalize all scores to a scale from 0 to 1 for interoperability with other weighted ranking systems.
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## Model
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The embedding model was generated by first fine-tuning [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) with the intent categories from the dataset above, using contrastive learning with cosine similarity loss, and subsequently merging the resultant model with the base model at a 3:2 ratio.
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