https://github.com/muavia1/code-to-psuedocode-generation-using-transformers-architecture-using-pytorch
Code2PseudoCode is a transformer-based deep learning model that translates programming code into human-readable pseudocode. This project aims to assist developers, students, and educators by providing a structured way to understand complex code through natural language.
https://github.com/muavia1/code-to-psuedocode-generation-using-transformers-architecture-using-pytorch
code-generation deep-learning model-training psuedocode transformers
Last synced: 2 months ago
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Code2PseudoCode is a transformer-based deep learning model that translates programming code into human-readable pseudocode. This project aims to assist developers, students, and educators by providing a structured way to understand complex code through natural language.
- Host: GitHub
- URL: https://github.com/muavia1/code-to-psuedocode-generation-using-transformers-architecture-using-pytorch
- Owner: Muavia1
- Created: 2025-03-08T11:09:41.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T11:45:52.000Z (3 months ago)
- Last Synced: 2025-03-08T12:20:46.621Z (3 months ago)
- Topics: code-generation, deep-learning, model-training, psuedocode, transformers
- Language: Jupyter Notebook
- Homepage: https://huggingface.co/spaces/muaviaabdulmoiz/Code2Psuedo
- Size: 4.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Code-to-PsuedoCode-Generation-using-Transformers-Architecture-using-PyTorch
**Code2PseudoCode** is a transformer-based deep learning model that translates programming code into human-readable pseudocode. This project aims to assist developers, students, and educators by providing a structured way to understand complex code through natural language.
## Features
- Utilizes a transformer architecture for sequence-to-sequence translation.
- Supports tokenization and vocabulary building for both code and pseudocode.
- Implements positional encoding and attention mechanisms to enhance translation accuracy.
- Uses PyTorch for efficient model training and inference.
## Installation
Clone the repository and install the dependencies:
```bash
pip install -r requirements.txt
```
## Requirements
- Python 3.x
- PyTorch
- Pandas
- tqdm
## Contributing
Contributions are welcome! Feel free to submit issues or pull requests.
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