The code and datasets of our paper "PTR: Prompt Tuning with Rules for Text Classification"

To clone the repository, please run the following command:

git clone https://github.com/thunlp/PTR.git --depth 1

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}
}

• Overview

• Requirements

• Data Preparation

• Experiments

  • Baselines
  • Reproduce Results

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==1.4.0
• tqdm==4.41.1
• transformers==4.0.0

To set up the dependencies, you can run the following command:

pip install -r requirements.txt

We have provided a scripts to download all the datasets we used in our paper. You can run the following command to download the datasets:

bash data/download.sh all

The above command will download all the datasets including

• Retacred
• Tacred
• Tacrev
• Semeval

If you only want to download a specific dataset, you can run the following command:

bash data/download.sh $dataset_name1 $dataset_name2 ...

where $dataset_nameX can be one or multiple of retacred, tacred, tacrev, semeval.

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

bash scipts/run_large_tacred.sh

bash scripts/run_large_tacrev.sh

bash scripts/run_large_retacred.sh

bash scripts/run_large_semeval.sh