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OpenSciLM/
OpenScholar_Reranker

Name: OpenScholar_Reranker
Availability: InStock

safetensors

xlm-roberta

BAAI/bge-reranker-large

finetune

mit

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OpenScholar_Reranker is a fine-tuned version of bge-reranker-large for scientific literature synthesis.

Model Description

Developed by: University of Washigton, Allen Institute for AI (AI2)
Model type: a masked language model.
Language(s) (NLP): English
License: The code and model are released under apache-2.0.
Date cutoff: The fine-tuning data is generated by Llama 3 70B for synthetically generated queries.

Model Sources

Project Page: https://open-scholar.allen.ai/
Repositories:
- Core repo (training, inference, fine-tuning etc.): https://github.com/AkariAsai/OpenScholar
- Evaluation code: https://github.com/AkariAsai/ScholarQABench
Paper: Link
Technical blog post: https://allenai.org/blog/openscholar

Citation

If you find it useful in this work, cite our paper.

@article{openscholar,
  title={{OpenScholar}: Synthesizing Scientific Literature with Retrieval-Augmented Language Models},
  author={ Asai, Akari and He*, Jacqueline and Shao*, Rulin and Shi, Weijia and Singh, Amanpreet and Chang, Joseph Chee  and Lo,  Kyle and Soldaini, Luca and Feldman, Tian, Sergey and Mike, D’arcy and Wadden, David and Latzke, Matt and Minyang and Ji, Pan and Liu, Shengyan and Tong, Hao and Wu, Bohao and Xiong, Yanyu and Zettlemoyer, Luke and Weld, Dan and Neubig, Graham and Downey, Doug and Yih, Wen-tau and Koh, Pang Wei and Hajishirzi, Hannaneh},
  journal={Arxiv},
  year={2024},
}

OpenSciLM/OpenScholar_Reranker

Model Description

Model Sources

Citation

OpenSciLM/
OpenScholar_Reranker