# NLP Lab Project

This is a Natural Language Processing (NLP) project with a structured codebase for data preprocessing, model training, and experimentation.

## Project Structure

```
nlp/
├── data/
│   ├── raw/                    # Raw, unprocessed datasets
│   └── processed/              # Cleaned and preprocessed data
├── notebooks/
│   └── 01_data_preprocessing.ipynb  # Jupyter notebook for data exploration and preprocessing
├── src/
│   ├── models/                 # Model definitions and architectures
│   ├── preprocessing/          # Data preprocessing utilities
│   └── train.py               # Main training script
├── requirements.txt           # Python dependencies
└── README.md                 # This file
```

## Setup

1. **Create a virtual environment:**
   ```bash
   python -m venv nlp-env
   source nlp-env/bin/activate  # On Windows: nlp-env\Scripts\activate
   ```

2. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

3. **Download NLTK data (if using NLTK):**
   ```python
   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   ```

## Usage

### Data Preprocessing
1. Place your raw data files in the `data/raw/` directory
2. Use the Jupyter notebook `notebooks/01_data_preprocessing.ipynb` for initial data exploration and preprocessing
3. Save processed data to `data/processed/` directory

### Model Training
Run the training script with default parameters:
```bash
python src/train.py
```

Or with custom parameters:
```bash
python src/train.py --epochs 20 --lr 0.0001 --batch_size 64
```

## Directory Descriptions

- **`data/raw/`**: Store your original, unmodified datasets here
- **`data/processed/`**: Store cleaned and preprocessed data ready for training
- **`notebooks/`**: Jupyter notebooks for data exploration, visualization, and experimentation
- **`src/models/`**: Python modules containing model definitions (e.g., neural network architectures)
- **`src/preprocessing/`**: Utility functions for data cleaning, tokenization, and feature extraction
- **`src/train.py`**: Main training script with command-line interface

## Getting Started

1. Add your dataset to `data/raw/`
2. Open `notebooks/01_data_preprocessing.ipynb` to explore and preprocess your data
3. Implement your model in `src/models/`
4. Create preprocessing utilities in `src/preprocessing/`
5. Run training with `python src/train.py`

## Contributing

1. Follow PEP 8 style guidelines
2. Add docstrings to all functions and classes
3. Write unit tests for your code
4. Update this README when adding new features