All datasets can be downloaded here: OneDrive.
Extract the archive file and put the entire folder in the root directory.
Or you can put anywhere else and specific the path using the --data_dir argument.
All pre-trained models and tokenizer can be downloaded here: OneDrive
Very easy to use evaluation scripts have been created in src/evaluation with detailed comments to refer to.
These evaluation scripts require only a python environment and several packages that can be easily installed via pip install.
Run main.py to start fine-tuning and/or evaluation.
All arguments are located in args.py, specific whatever you need.
Some example scripts are as following.
# run defect detection task using roberta, with default hyperparameters
python main.py --model roberta --task defect
# run java summarization using codet5
python main.py --model codet5 --task summarization --subset java
# only run evaluation using specific model directory
python main.py --model PATH_TO_MODEL --task clone --only_test
# run code generation using plbart and specific some common arguments
# all gpu devices are used by default, specific device ids by using --cuda_visible_devices,
# add --no_cuda to disable gpu and use cpu instead
python main.py \
--model plbart \
--task generation \
--num_epochs 10 \
--train_batch_size 64 \
--eval_batch_size 32 \
--max_source_length 64 \
--max_target_length 256 \
--learning_rate 1e-5 \
--num_warmup_steps 1000 \
--cuda_visible_devices 2,3 \
--mixed_precision no # no, fp16, bf16
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent