bombcryer/INER_RDP

Python

Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition (CIKM2023)

This repository contains all of our source code. We sincerely thank the help of Zheng et al.'s repository.

Overview of the directory

bert-base-cased/: the directory of configurations and PyTorch pretrained model for bert-base-cased
config/ : the directory of configurations for our RDP method
datasets/ : the directory of datasets
src/ : the directory of the source code
main_CL.py : the python file to be executed

.
├── bert-base-cased
├── config
│   ├── conll2003
│   ├── ontonotes5
│   ├── i2b2
├── datasets
│   └── NER_data
│       ├── conll2003
│       ├── i2b2
│       └── ontonotes5
├── main_CL.py
└── src
    ├── config.py
    ├── dataloader.py
    ├── model.py
    ├── trainer.py
    └── utils.py

Run Step

Step 1: Prepare your environments

Reference environment settings:

python             3.7.13
torch              1.12.1+cu116
transformers       4.14.1

Download bert-base-cased to the directory of bert-base-cased/

Step 2: Run main_CL.py

Specify your configurations (e.g., ./config/i2b2/fg_8_pg_2/RDP.yaml) and run the following command

CUDA_VISIBLE_DEVICES=0 nohup python3 -u main_CL.py --exp_name i2b2_8-2_RDP --exp_id 1 --cfg config/i2b2/fg_8_pg_2/RDP.yaml 2>&1 &

Then, the results as well as the model checkpoint will be saved automatically in the directory ./experiments/i2b2_8-2_RDP/1/

Citation

@inproceedings{zhang2023rdp,
  title={Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition},
  author={Zhang, Duzhen and Li, Hongliu and Cong, Wei and Xu, Rongtao and Dong, Jiahua and Chen, Xiuyi},
  booktitle={Proceedings of the 32th ACM International Conference on Information \& Knowledge Management},
  year={2023}
}