AI-Powered Documentation Generation System

Project Structure

GENAI/
├── data/
│   ├── raw/              # Raw dataset files
│   └── processed/        # Processed data files
├── models/
│   ├── bpe/             # BPE tokenizer models
│   ├── word2vec/        # Word2Vec embeddings
│   └── lstm/            # LSTM model checkpoints
├── src/
│   ├── process_data.py  # Data processing scripts
│   └── train.py         # Training scripts
├── requirements.txt     # Project dependencies
└── README.md           # Project documentation

Setup Instructions

1. Create a virtual environment:

python -m venv venv

2. Activate the virtual environment:

# Windows
venv\Scripts\activate
# Linux/Mac
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Download the dataset from Kaggle and place it in the data/raw/ directory.

5. Process the data:

python src/process_data.py

6. Train the model:

python src/train.py

Project Components

1. Data Processing

   • BPE Tokenization
   • Word2Vec Embeddings
   • Dataset preparation

2. Model Architecture

   • LSTM-based language model
   • Context-aware generation
   • Attention mechanism

3. Training

   • Batch processing
   • Gradient clipping
   • Early stopping

Results

Training metrics and model performance will be saved in the respective model directories.

Requirements

• Python 3.8+
• PyTorch
• NLTK
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• tqdm