Environment

Linux's system with CUDA devices required.

Note: argostranslate is not available on Windows yet.

Python packages

conda create --name ZECL python=3.8
conda activate ZECL
conda install -c pytorch pytorch=1.10.0 cudatoolkit=11.3
conda install -c huggingface transformers=4.11.3 
conda install -c conda-forge spacy=3.2.0 cupy=9.6.0
conda install numpy scikit-learn tqdm pandas tensorboard
pip install argostranslate
python -m spacy download en_core_web_sm

Download transformers model

download page: https://huggingface.co/models

put the mode under path pretrain/bert

pretrain
├─bert
│  └─bert-base-uncased

Or you could edit code in tool/pretrain_model_helper.py to download automatically when running code.

Download argostranslate model (optional)

Only required for data pre-process.

Download page: https://www.argosopentech.com/argospm/index/

Put the mode under path pretrain/argos

pretrain
├─argos
│      translate-en_zh-1_1.argosmodel
│      translate-zh_en-1_1.argosmodel

Experiments

pre-process data (optional)

This step only need run onetime, and processed data would be saved to path out/processed_data

Processed data is already included in git repo, and this step is skipped automatically because pt file is exist.

If you want precessed data by yourself:

• Delete all contents under out/processed_data .
• Retrive full version of data from dataset soure and put them under path data/Ace and data/FewShotED.

data
├─Ace
│      dev_docs.json
│      test_docs.json
│      train_docs.json
│
└─FewShotED
        Few-Shot_ED.json

• Run follow commond.

export ARGOS_DEVICE_TYPE=cuda
python -m data_process.gen_train_data

Train model

python train.py

Edit the code in train.py to change the setting of Experiments.

The trained model would be saved to path out/checkpoints

Test model

python test.py

You need first add model checkpoints name to main function of test.py .