Our basic experimental environment is Python 3.9.12, PyTorch 1.12.0 and CUDA 11.6.

The list of other requirements can be found in requirements.txt.

Run main.py to start experiments. All available arguments are located in args.py, specific whatever you need.

Some example scripts are as following.

2-shot PLBART on cat-intra-bf

python main.py \
--init_model plbart \
--use_few_shot \
--num_shots 2 \
--task_split_config cat-intra-bf

2/0-instruct CodeT5-large on sub-inter-c2t, and specific some parameters

python main.py \
--init_model codet5-large \
--use_instruction \
--num_neg_examples 0 \
--task_split_config sub-inter-c2t \
--train_batch_size 16 \
--eval_batch_size 8 \
--num_epochs 1 \
--learning_rate 1e-5 \
--warmup_steps 1000

If you need to run the supervised baselines, use the following scripts

python main.py \
--init_mdoel codet5 \
--supervised \
--task_split_config type-trans

We provide the raw dataset, tasks, meta information and their summaries, which can be downloaded here. Every directory is a dataset and can be parsed into several tasks. The script to load most of the dataset can be found in src/task/utils.py, except ones that need to be pre-process by using other tools such as Java.

Each task corresponds to two Json files, task_{id}_{name}.meta.json and task_{id}_{name}.data.json. The former contains the meta information of the task, while the latter consists of all data instances.

Extract the zip file and place the entire folder within the root directory.