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# 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
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