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crl's Introduction

CRL

Implementation of the research paper Consistent Representation Learning for Continual Relation Extraction (Findings of ACL 2022)

Framwork of consistent representation learning

Memory Replay Architecture

Dependencies

Use anaconda to create python environment:

conda create --name yourname python=3.8
conda activate yourname

Install Pytorch (suggestions>=1.7) and related environmental dependencies:

pip install -r requirements.txt

Pre-trained BERT weights:

  • Download bert-base-uncased into the datasets/ directory [google drive].

Run the Code

python run_continual.py --dataname FewRel

Citation

Please cite our paper if you find our work useful for your research:

@misc{zhao2022consistent,
      title={Consistent Representation Learning for Continual Relation Extraction}, 
      author={Kang Zhao and Hua Xu and Jiangong Yang and Kai Gao},
      year={2022},
      eprint={2203.02721},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

crl's People

Contributors

zhao9797 avatar

Stargazers

TNL-6580 avatar adurey avatar  avatar TIANYUZHOU avatar MinKim avatar  avatar  avatar Corey Xing avatar Jeff Carpenter avatar Weimin Xiong avatar  avatar  avatar  avatar  avatar Chien Nguyen avatar Sishuo Chen avatar cin-hubert avatar 爱可可-爱生活 avatar Gordon Lee avatar Lei Li avatar Yongliang Shen avatar Peiyi Wang avatar lixiaoteng avatar WangZhen avatar  avatar

Watchers

iyuge2 avatar FlameSky avatar  avatar Peiyi Wang avatar MinKim avatar

crl's Issues

accuracy results of the same task so volatile

According to the xlsx in the results folder, why are the prediction accuracy results of the same task so volatile in different rounds?
(I found that the divided data set has been fixed and saved in the code logic.)
image

bert-base-uncased

作者你好 可以把bert-base-uncased的模型参数放到百度云盘上吗

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