UniRE

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021.

Requirements

python: 3.7.6
pytorch: 1.8.1
transformers: 4.2.2
configargparse: 1.2.3
bidict: 0.20.0
fire: 0.3.1

Datasets

We provide scripts and instructions for processing three datasets (ACE2004,ACE2005,SciERC) are provided in data/.

Training

ACE2004

python entity_relation_joint_decoder.py \
    --config_file config.yml \
    --save_dir ckpt/ace2004_bert \
    --data_dir data/ACE2004/fold1 \
    --fine_tune \
    --device 0

ACE2005

python entity_relation_joint_decoder.py \
    --config_file config.yml \
    --save_dir ckpt/ace2005_bert \
    --data_dir data/ACE2005 \
    --fine_tune \
    --device 0

SciERC

python entity_relation_joint_decoder.py \
    --config_file config.yml \
    --save_dir ckpt/scierc_scibert \
    --data_dir data/SciERC \
    --bert_model_name allenai/scibert_scivocab_uncased \
    --epochs 300 \ 
    --early_stop 50 \
    --fine_tune \
    --device 0

Note that a GPU with 32G is required to run the default setting. If OOM occurs, we suggest that reducing train_batch_size and increasing gradient_accumulation_steps (gradient_accumulation_steps is used to perform Gradient Accumulation).

Inference

We provide an example ACE2005. Note that save_dir should contain the trained best_model.

python entity_relation_joint_decoder.py \
    --config_file config.yml \
    --save_dir ckpt/ace2005_bert \
    --data_dir data/ACE2005 \
    --device 0 \
    --log_file test.log \
    --test

Cite

If you find our code is useful, please cite:

@inproceedings{wang2021unire,
    title = "{UniRE}: A Unified Label Space for Entity Relation Extraction",
    author = "Wang, Yijun and Sun, Changzhi and Wu, Yuanbin and Zhou, Hao and Li, Lei and Yan, Junchi",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics",
    year = "2021",
    publisher = "Association for Computational Linguistics",
}

nlper01/UniRE