Code generation from natural language with less prior and more monolingual data (TAE)
Paper published in ACL 2021
install the requirments:
pip install -r requirements.txt
To train model on Django
python3 train.py --dataset_name django --save_dir CHECKPOINT_DIR --copy_bt --no_encoder_update --monolingual_ratio 1.0 --early_stopping
To evaluate the provided Django checkpoint:
python3 train.py --dataset_name django --save_dir pretrained_weights/django --copy_bt --no_encoder_update --monolingual_ratio 1.0 --early_stopping --just_evaluate --seed 2
To train model on CoNaLa
python3 train.py --dataset_name conala --save_dir CHECKPOINT_DIR --copy_bt --no_encoder_update --monolingual_ratio 0.5 --epochs 80
To evaluate the provided CoNaLa chceckpoint:
python3 train.py --dataset_name conala --save_dir pretrained_weights/conala --copy_bt --no_encoder_update --monolingual_ratio 0.5 --epochs 80 --just_evaluate --seed 4
Evaluation Results
Here are the evaluation numbers for the provided checkpoints:
| Dataset | Results | Metric |
|---|---|---|
| Django | 81.77 | Exact Match Acc. |
| CoNaLa | 33.41 | Corpus BLEU |