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--- |
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license: apache-2.0 |
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sdk: gradio |
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colorFrom: green |
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colorTo: purple |
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title: Transformers Fine Tuner |
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emoji: ⚡ |
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thumbnail: >- |
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https://cdn-uploads.huggingface.co/production/uploads/64fbe312dcc5ce730e763dc6/EgMfbe-SvtWnERIw6k27e.png |
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short_description: Transformers Fine Tuner is a user-friendly Gradio interface |
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sdk_version: 5.14.0 |
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--- |
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# Transformers Fine Tuner |
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Transformers Fine Tuner is a user-friendly Gradio interface that enables seamless fine-tuning of pre-trained transformer models on custom datasets. |
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## Features |
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- **Easy Dataset Integration:** Load datasets via URLs or direct file uploads. |
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- **Model Selection:** Choose from a variety of pre-trained transformer models. |
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- **Customizable Training Parameters:** Adjust epochs, batch size, and learning rate to suit your needs. |
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- **Real-time Monitoring:** Track training progress and performance metrics. |
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## Setup |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/canstralian/Transformers-Fine-Tuner.git |
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cd Transformers-Fine-Tuner |
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``` |
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2. Create and activate a virtual environment: |
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```bash |
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python -m venv venv |
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source venv/bin/activate # On Windows use `venv\Scripts\activate` |
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``` |
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3. Install the required dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Usage |
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To launch the Gradio interface, run the following command: |
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```bash |
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python app.py |
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``` |
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## Example |
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1. Enter the URL of your dataset. |
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2. Select a pre-trained transformer model from the dropdown. |
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3. Adjust the training parameters such as epochs, batch size, and learning rate. |
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4. Click the "Submit" button to start the fine-tuning process. |
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## File Structure |
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- `app.py`: Main script to launch the Gradio interface. |
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- `data/preprocess.py`: Script to load and preprocess datasets. |
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- `.github/workflows/python-app.yml`: GitHub Actions workflow for CI/CD pipeline. |
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## Contributing |
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If you would like to contribute to this project, please fork the repository and submit a pull request. |
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## License |
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. |
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## Acknowledgements |
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This project uses the following libraries and frameworks: |
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- [Gradio](https://gradio.app/) |
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- [Hugging Face Transformers](https://huggingface.co/transformers/) |
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- [Pandas](https://pandas.pydata.org/) |
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## Contact |
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For any inquiries or support, please contact the repository owner at [canstralian](https://github.com/canstralian). |
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--- |
