LPD

Pytorch implementation of our EMNLP 2022 paper Better Few-Shot Relation Extraction with Label Prompt Dropout

@inproceedings{zhang-etal-2022-better,
  title = "Better Few-Shot Relation Extraction with Label Prompt Dropout",
  author = "Zhang, Peiyuan and Lu, Wei",
  booktitle = "Proceedings of EMNLP", 
  year = "2022",
}

This codebase is adapted from https://github.com/thunlp/RE-Context-or-Names.

@article{peng2020learning,
  title={Learning from Context or Names? An Empirical Study on Neural Relation Extraction},
  author={Peng, Hao and Gao, Tianyu and Han, Xu and Lin, Yankai and Li, Peng and Liu, Zhiyuan and Sun, Maosong and Zhou, Jie},
  journal={arXiv preprint arXiv:2010.01923},
  year={2020}
}

Quick Start

You can quickly run our code by following steps:

• Install dependencies as described in following section.
• cd to pretrain or finetune directory then download and pre-processing data for pre-traing or finetuning.

1. Dependencies

We run our experiments on cuda 11.1. Run the following script to install dependencies.

pip install -r requirement.txt

You need install transformers and apex manually.

transformers We use huggingface transformers to implement Bert. And for convenience, we have downloaded transformers into utils/. And we have also modified some lines in the class BertForMaskedLM in src/transformers/modeling_bert.py while keep the other codes unchanged.

You just need to cd to utils/transformers and run

pip install .

to install transformers manually.

apex Install apex under the offical guidance (install the python-only build).

2. More details

You can check the readme file in pretrain or finetune to learn more details about pre-training or finetuning.