This project provides Pytorch implementation for the paper "HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction."
More details about the work are in the paper.
- [Get Started](#Get Started)
- Data
- Training
- Acknowledgements
- Contact
This implementation only supports multi-gpu, DistributedDataParallel training, which is faster and simpler; single-gpu or DataParallel training is not supported.
- NYT or Tacred dataset
- Python ≥ 3.6
- PyTorch ≥ 1.7
- faiss-gpu: pip install faiss-gpu
The code is based on PyTorch 1.7. You can find tutorials here.
The code is written in Python 3.7. Its dependencies are summarized in the file requirements.txt.
numpy==1.17.4
six==1.12.0
pandas==1.0.3
tqdm==4.40.0
scipy==1.4.1
bcubed==1.5
faiss==1.5.3
Pillow==8.1.0
scikit_learn==0.24.1
tensorboard_logger==0.1.0
torch==1.7.1
torchvision==0.8.2
transformers==4.2.2
You can install these dependencies like this:
pip3 install -r requirements.txt
Note that faiss needs to be installed as Requirements said.
- NYT+FB: This dataset is not open, so only sample is provided.
- TACRED: The TAC Relation Extraction Dataset (download)
Then use the scripts from data_process/NYT_data_process.py or data_process/tacred_data_process.py to further preprocess the data. Both dataset will be processed into the Format
Each dataset is a folder under the ./data folder:
./data
└── Tacred
├── train_sentence.json
├── train_label_id.json
├── dev_sentence.json
├── dev_label_id.json
├── test_sentence.json
└── test_label_id.json
- Run the full model on TACRED dataset with default hyperparameter settings
python main_HiURE.py \
--temperature 0.2 \
--mlp \
--aug-plus \
--cos \
--use-relation-span \
--dist-url tcp://localhost:10001 \
--multiprocessing-distributed \
--world-size 1 \
--rank 0 \
{ path_to_data }
This work can not be finished without the help of the following work:
If you have any problem about our code, feel free to contact us.