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Pairwise Reward Model for LLMs (PairRM) based on mdeberta-v3-base

This is an attempt to create a multilingual PairRM-Model by applying the training procedure from the original LLM-Blender repository to mdeberta-v3-base.

I have not yet done any real testing apart from some sanity checks with the provided samples from the original PairRM-Model as well as some quick made-up samples.

For additional (usage) information information please refer to the original model.

Citation & Credits

@inproceedings{llm-blender-2023,
    title = "LLM-Blender: Ensembling Large Language Models with Pairwise Comparison and Generative Fusion",
    author = "Jiang, Dongfu and Ren, Xiang and Lin, Bill Yuchen",
    booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL 2023)",
    year = "2023"
}
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Datasets used to train LemiSt/PairRM-mdeberta-v3-base