# DocRE with Reconstruction This repository is the PyTorch implementation of our DocRE model with reconstruction in AAAI 2021 Paper "Document-Level Relation Extraction with Reconstruction". # Requirements and Installation ``` python3>=3.6 pytorch>=1.5 scikit-learn>=0.21.2 wandb>=0.95 ``` # Dataset Download metadata from [TsinghuaCloud](https://cloud.tsinghua.edu.cn/d/99e1c0805eb64736af95/) or [GoogleDrive](https://drive.google.com/drive/folders/1Ri3LIILKKBi3aBJjUVCOBpGX5PpONHRK) for baseline method and put them into prepro_data folder. For the dataset and pretrained embeddings, please download it here, which are officially provided by DocRED: [A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127) and put them into data folder. # Proprocessing ``` python3 gen_data.py --in_path data --out_path prepro_data python3 gen_graph_data.py --in_path prepro_data --worker_num 24 ``` # Training ``` CUDA_VISIBLE_DEVICES=0 python3 train.py --model_name DynGraph --save_name checkpoint_DynGraph --train_prefix dev_train --test_prefix dev_dev ``` # Testing ``` CUDA_VISIBLE_DEVICES=0 python3 train.py --model_name DynGraph --save_name checkpoint_DynGraph --train_prefix dev_train --test_prefix dev_dev --eval_model True --rel_theta 0.3601 ``` # Citation If you find our work or the code useful, please consider cite our paper using: ``` @inproceedings{docred-rec, author = {Wang Xu and Kehai Chen and Tiejun Zhao}, booktitle = {The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)}, title = {Document-Level Relation Extraction with Reconstruction}, year = {2021} } ``` # data-mining