Project Name
_{^{A one-line description of what this project does or solves}}

📁 Repository Structure

├── data/               # Directory for input/output data files
├── src/                # Source code directory
├── models/             # Checkpoints or model binaries
├── outputs/            # Logs, predictions, results
├── notebooks/          # Jupyter or Colab notebooks
├── requirements.txt    # List of Python dependencies
├── README.md           # Project documentation
└── ...

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🛠️ Setup & Installation

To set up the environment and install dependencies:

# Clone the repository
git clone https://github.com/your-username/your-repo.git
cd your-repo

# (Optional) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate   # On Windows use `venv\Scripts\activate`

# Install the required packages
pip install -r requirements.txt

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🚀 Usage

Instructions to run the project or reproduce results:

# Example: Running a training script
python src/train.py --config configs/config.yaml

# Example: Generating predictions
python src/predict.py --input data/test.json --output outputs/predictions.json

Or you can explore the notebooks inside the notebooks/ directory for interactive examples.

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🧪 Testing & Evaluation

To evaluate model performance or run tests:

# Run unit tests (if available)
pytest tests/

# Evaluate with specific metrics
python src/evaluate.py --model-path models/model.pt

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⚙️ Configuration

You can configure run-time parameters via YAML or JSON files located in the configs/ folder.

# Example: configs/config.yaml
learning_rate: 2e-5
batch_size: 16
num_epochs: 5
device: "cuda"

Environment variables can also be set via a .env file or exported in your shell session.

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📊 Results

Add summary of results here if available.

Metric	Value
Accuracy	93.1%
F1 Score	90.7%

You may also include visualizations like confusion matrices, training curves, etc.

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🔍 Logs & Monitoring

To monitor logs or training progress:

# View logs in real time
tail -f outputs/logs/train.log

# Launch TensorBoard
tensorboard --logdir outputs/tensorboard/

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🧰 Tools & Libraries

This project uses the following main tools and libraries:

• Python 3.x
• PyTorch / TensorFlow
• Transformers
• scikit-learn
• pandas / numpy
• matplotlib / seaborn

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📌 References

(Optional) Mention datasets, papers, repositories, or blogs used as a base or for inspiration:

• Dataset or Paper Name
• Blog Post or GitHub Repo

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🤝 Contributing

Feel free to contribute by submitting a pull request.

# Fork the repository
# Create a new branch for your feature or fix
# Commit your changes with a clear message
# Push to your fork and submit a PR

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📜 License

Specify the license under which this code is shared.

This project is licensed under the MIT License - see the LICENSE file for details.

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📖 Citation

If you use this work in your research, please cite it as:

@misc{your2025project,
  author       = {Your Name},
  title        = {Your Project Title},
  year         = {2025},
  publisher    = {GitHub},
  journal      = {GitHub Repository},
  howpublished = {\url{https://github.com/your-username/your-repo}},
}

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