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Transformers Fine Tuner

Transformers Fine Tuner is a user-friendly Gradio interface that enables seamless fine-tuning of pre-trained transformer models on custom datasets.

Features

• Easy Dataset Integration: Load datasets via URLs or direct file uploads.
• Model Selection: Choose from a variety of pre-trained transformer models.
• Customizable Training Parameters: Adjust epochs, batch size, and learning rate to suit your needs.
• Real-time Monitoring: Track training progress and performance metrics.

Setup

1. Clone the repository:

   git clone https://github.com/canstralian/Transformers-Fine-Tuner.git
   cd Transformers-Fine-Tuner

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To launch the Gradio interface, run the following command:

python app.py

Example

1. Enter the URL of your dataset.
2. Select a pre-trained transformer model from the dropdown.
3. Adjust the training parameters such as epochs, batch size, and learning rate.
4. Click the "Submit" button to start the fine-tuning process.

File Structure

• app.py: Main script to launch the Gradio interface.
• data/preprocess.py: Script to load and preprocess datasets.
• .github/workflows/python-app.yml: GitHub Actions workflow for CI/CD pipeline.

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

This project uses the following libraries and frameworks:

• Gradio
• Hugging Face Transformers
• Pandas

Contact

For any inquiries or support, please contact the repository owner at canstralian.

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