graph-causal-domain-adaptation

requirements

pip install requirements.txt

Download Bert-base-uncased pretrain weights from here, or see a list of Bert model weights download links here
Download corresponding vocabulary here. Note that the downloaded tar also contains the tensorflow pretrained model weights, but we only need the file vocab.txt
Put the pretrained model file, the config json downloaded from the first step, and the vocabulary to models/pytorch-bert-uncased directory.
Download imdb dataset here and put it to data/imdb
Download bdek dataset(i.e. amazon reviews dataset) here and put it to data/bdek

run sh train_script.sh in shell
- open this file and you'll see different commands for different tasks

The start point of the program is train.py
Files like trainers.py, evaluators.py, model.py, dataset.py , etc., defines classes for the corresponding component of the program, and is imported to train.py by xx_factory at the bottom of each file.
Developer should add new classes to these files to implement new features instead of editting the existing ones.
There are several command line args that effect which module to choose from the factories, see the code for details.