For training, a GPU is recommended to accelerate the training speed.
The code is based on PyTorch 1.6+. You can find tutorials here.
- Run the full model on SemEval dataset with default hyperparameter settings
python3 src/train.py
Each dataset is a folder under the ./data folder:
./data
└── SemEval
├── train_sentence.json
├── train_label_id.json
├── dev_sentence.json
├── dev_label_id.json
├── test_sentence.json
└── test_label_id.json
- SemEval: SemEval 2010 Task 8 data (included in
data/SemEval) - TACRED: The TAC Relation Extraction Dataset (download)
Then use the scripts from data/data_prepare.py to further preprocess the data. For SemEval, the script split the original training data into two sets. For TACRED, the script first perform some preprocessing to ensure the same format as SemEval.