| # LiteCoder Experiment Reproducing package | |
| - To run the pre-train objective use the following scripts: | |
| - Reproduce LiteCoder with all objectives: | |
| - Navigate the folder `Pre-training` containing the `LiteCoder.py` file | |
| - Then, run `Python LiteCoder.py --train-tt --train-cs --train-pd` | |
| - The pretrained model is released on [hugging face](https://huggingface.co/LiteCoder/LiteCoder_pretrained), therefore it automatically loads. | |
| - To run the ablation studies: | |
| - Ablation 1: `Python LiteCoder.py --train-tt` | |
| - Ablation 2: `Python LiteCoder.py --train-tt --train-cs` | |
| - Ablation 3: `Python LiteCoder.py --train-tt --train-cs --train-pd` | |
| - To `Fine-tuning` LiteCoder on downstream tasks: | |
| - Navigate to the `Fine-tuning` folder and then `Downstream task` folder: | |
| - Code Clone Detection: | |
| - Follow the instruction of `readme.md` file. | |
| - Code Translation: | |
| - Run `setup.sh` file. | |
| - Navigate to the `scripts/finetune` and run `translate.sh` file. | |
| - To extract the programming language features (i.e., `token type`, `code sememe`, and `code dependencies`) | |
| - We used open source datasets to extract language features. we released the extracted datasets on the Hugging Face: | |
| - `LT_Java` : [LiteCoder/LT_Java](https://huggingface.co/datasets/LiteCoder/LT_Java) | |
| - `LT_Python` : [LiteCoder/LT_Python](https://huggingface.co/datasets/LiteCoder/LT_Python) | |
| - `LT_Java_Dependency` : [LiteCoder/LT_Java_Dependency](https://huggingface.co/datasets/LiteCoder/LT_Java_Dependency) | |
| - Navigate to the utils directory: | |
| - Use either the `Java` or `Python` notebook file to run over your dataset. | |
| - Run the cells, for which, you want to extract the features. | |
| - Dependencies: | |
| - Feature extraction dependencies: | |
| ```bash | |
| - pip install ast-comments | |
| - pip install ast | |
| - pip install javalang | |
| - pip install tree-sitter | |
| - Model training dependencies: | |
| ``` bash | |
| - pip install transformers | |
| - pip install datasets | |
| - pip install pytorch_lightning | |
| - pip install torch | |
| - Or `pip install -r requirements.txt` |