DIRECT

This is the implementation of the DIRECT model from paper "Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning".

Requirements

• botocore==1.19.23
• numpy==1.18.5
• tqdm==4.49.0
• requests==2.24.0
• boto3==1.16.23
• torch==1.6.0+cu101
• tensorflow==2.3.1

Usage:

1. Download google's pretrained model bert-base-cased for PyTorch from huggingface https://huggingface.co/bert-base-cased

2. Download CasRel's data at: https://drive.google.com/drive/folders/1bZlGqjqf51IExb-ILFDq8iSEU6Nrv7qH

3. Run data/partial/$data$/process.ipynb to convert the format of data.

4. Run ./train_$data$.sh to train the model.

5. RUn ./eval.sh to eval.

Cite the paper

@inproceedings{Zhao2021AdjacencyLO,
  title={Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning},
  author={Fubang Zhao and Zhuoren Jiang and Yangyang Kang and Changlong Sun and Xiaozhong Liu},
  year={2021}
}