jhxcugbcs / ptr

Prompt Tuning with Rules

PTR

Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

If you use the code, please cite the following paper:

@article{han2021ptr,
  title={PTR: Prompt Tuning with Rules for Text Classification},
  author={Han, Xu and Zhao, Weilin and Ding, Ning and Liu, Zhiyuan and Sun, Maosong},
  journal={arXiv preprint arXiv:2105.11259},
  year={2021}
}

Requirements

The model is implemented using PyTorch. The versions of packages used are shown below.

• numpy>=1.18.0

• scikit-learn>=0.22.1

• scipy>=1.4.1

• torch>=1.3.0

• tqdm>=4.41.1

• transformers>=4.0.0

Baselines

Some baselines, especially the baselines using entity markers, come from the project [RE_improved_baseline].

Datasets

We provide all the datasets and prompts used in our experiments.

• [TACRED]

• [TACREV]

• [RETACRED]

Run the experiments

(1) For TACRED

mkdir results
cd results
mkdir tacred
cd tacred
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_tacred.sh

(2) For TACREV

mkdir results
cd results
mkdir tacrev
cd tacrev
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_tacrev.sh

(3) For RETACRED

mkdir results
cd results
mkdir retacred
cd retacred
mkdir train
mkdir val
mkdir test
cd ..
cd ..
cd code_script
bash run_large_retacred.sh