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UniRE

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

It is based on our NERE toolkit.

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) in the 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

Pre-trained Models

We release our pre-trained UniRE model for the ACE2005 dataset.

Note that the released model is trained on the GeForce RTX 2080 Ti rather than Quadro RTX 8000, leading to the performance of the pre-trained model is slightly different from the reported score in the paper.

You can download the BERT based pre-trained model in this link(password: 151m) (size: 420M).

Performance of the pre-trained model on the ACE2005 test set:

Entity - P: 89.03% R: 88.81% F1: 88.92%
Relation (strict) - P: 68.71% R: 60.25% F1: 64.21%

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",
}