The dataset we created for this paper can be found at:
To ensure no recursive errors or edits, clone this git repository and place the dataset folders after decompresing the data
Create directory outdir, with 4 subdirectories outdir/{models, histories, viz, predictions} Use Requirements.txt to get your python 3.x virtual environment in sync with our setup. Venv is preferred. Common issues that might arise from updating an existing venv and solutions :
- GPU not recognized: checking the compatibility of your gpu cudnn/cuda or other drivers with the keras and tf versions fixes this.
- Tf unable to allocate tensor: upgrade to tf 2.4
To train the model XXY use the following command :
time python3 train.py --model-type=XXY --batch-size=50 --epochs=10 --gpu=0
Note: list of model names can be found in model.py file.
Training print screen will display the epoch at which the model converges, that is when the validation accuracy is not increase much or just before it starts to decrease and validation loss goes up. Once epoch is identified run the following script and replace file in this example with the trained model epoch and timestamp.
python3 predict.py path_to_model_epoch --gpu=0
predicted comments for all models are provided in the predictions folder.
Bleu and USE and METEOR scripts have been provided by the name of bleu.py, meteor.py and use_v_score.py all of them can be run with the similar commands
python3 bleu.py path_to_predict_file --data=path_to_data
replace path_to_data with javastmt_fc/output or funcom_python/output