Paper: Coarse-to-Fine Entity Representations for Document-level Relation Extraction (URL)
- Pytorch (1.6.0)
- numpy (1.19.4)
- tqdm (4.50.2)
- transformers (4.6.1)
- spacy (2.3.2)
You can download the required data from here.
After you download data.zip
, unzip it and put it to the root directory of this project.
Besides the datasets, we also provide some preprocessing results (see data/adj/
and data/path
) for saving time.
Before training, you can edit code/config.py
to specify the configurations, including filepath information, and hyper-parameters.
If you want to reproduce our results reported in the paper, you can use the reported hyper-parameters, and keep other hyper-parameters unchanged.
- Change the working directory to the root directory of this project.
- Run
python3 code/main.py
.
If you use this code for your research, please kindly cite our paper:
@article{dai2020cfer,
author = {Damai Dai and
Jing Ren and
Shuang Zeng and
Baobao Chang and
Zhifang Sui},
title = {Coarse-to-Fine Entity Representations for Document-level Relation Extraction},
journal = {CoRR},
volume = {abs/2012.02507},
year = {2020},
url = {https://arxiv.org/abs/2012.02507}
}
This project is supported by Jing Ren. If you have any problems, please contact us via the following e-mail addresses.
Jing Ren: rjj@pku.edu.cn
Damai Dai: daidamai@pku.edu.cn