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--- |
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library_name: peft |
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license: bigscience-openrail-m |
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tags: |
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- paper |
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- paper extract |
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--- |
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## Training procedure |
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### Framework versions |
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- PEFT 0.4.0 |
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in https://github.com/hiyouga/ChatGLM-Efficient-Tuning/tree/main |
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CUDA_VISIBLE_DEVICES=3 nohup python src/web_demo.py \ |
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--model_name_or_path /HOME/jack/model/chatglm-6b \ |
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--checkpoint_dir paper_meta\ \ |
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> log_web_demo.txt 2>&1 & tail -f log_web_demo.txt |
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### 🚩Citation |
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Please cite the following paper if you use jackkuo/PaperExtractGPT in your work. |
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```bibtex |
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@INPROCEEDINGS{10412837, |
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author={Guo, Menghao and Wu, Fan and Jiang, Jinling and Yan, Xiaoran and Chen, Guangyong and Li, Wenhui and Zhao, Yunhong and Sun, Zeyi}, |
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booktitle={2023 IEEE International Conference on Knowledge Graph (ICKG)}, |
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title={Investigations on Scientific Literature Meta Information Extraction Using Large Language Models}, |
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year={2023}, |
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volume={}, |
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number={}, |
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pages={249-254}, |
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keywords={Measurement;Knowledge graphs;Information retrieval;Data mining;Task analysis;information extraction;large language model;scientific literature}, |
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doi={10.1109/ICKG59574.2023.00036}} |
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``` |
