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:

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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