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---
license: apache-2.0
sdk: gradio
colorFrom: green
colorTo: purple
title: Transformers Fine Tuner
emoji: ⚡
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/64fbe312dcc5ce730e763dc6/EgMfbe-SvtWnERIw6k27e.png
short_description: 'Transformers Fine Tuner: A user-friendly Gradio interface'
sdk_version: 5.17.1
---
# 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:
```bash
git clone https://github.com/canstralian/Transformers-Fine-Tuner.git
cd Transformers-Fine-Tuner
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
To launch the Gradio interface, run the following command:
```bash
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](LICENSE) file for more details.
## Acknowledgements
This project uses the following libraries and frameworks:
- [Gradio](https://gradio.app/)
- [Hugging Face Transformers](https://huggingface.co/transformers/)
- [Pandas](https://pandas.pydata.org/)
## Contact
For any inquiries or support, please contact the repository owner at [canstralian](https://github.com/canstralian).
--- |