---
title: Transformers Fine Tuner
emoji: 🔥
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.14.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: A Gradio interface
---

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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. This tool facilitates efficient model adaptation for various NLP tasks, making it accessible for both beginners and experienced practitioners.

## 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.

## Getting Started

1. **Clone the Repository**:
   ```bash
   git clone https://huggingface.co/spaces/your-username/transformers-fine-tuner
   cd transformers-fine-tuner
   ```

2. **Install Dependencies**:
   Ensure you have Python 3.10 or higher. Install the required packages:
   ```bash
   pip install -r requirements.txt
   ```

3. **Run the Application**:
   ```bash
   python app.py
   ```
   Access the interface at `http://localhost:7860/`.

## Usage

- **Model Name**: Enter the name of the pre-trained model you wish to fine-tune (e.g., `bert-base-uncased`).
- **Dataset URL**: Provide a URL to your dataset.
- **Upload Dataset**: Alternatively, upload a dataset file directly.
- **Number of Epochs**: Set the number of training epochs.
- **Learning Rate**: Specify the learning rate for training.
- **Batch Size**: Define the batch size for training.

After configuring the parameters, click **Submit** to start the fine-tuning process. Monitor the training progress and performance metrics in real-time.

## License

This project is licensed under the Apache-2.0 License. See the [LICENSE](LICENSE) file for more details.

## Acknowledgments

- [Hugging Face Transformers](https://huggingface.co/transformers/)
- [Gradio](https://gradio.app/)
- [Datasets](https://huggingface.co/docs/datasets/)