Add GitHub badges to README.md
Browse filesThis PR adds various GitHub badges to the README.md file to provide quick insights into the project's status.
README.md
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app_file: app.py
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pinned: false
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license: apache-2.0
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short_description: A
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---
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app_file: app.py
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pinned: false
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license: apache-2.0
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short_description: A Gradio interface
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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. This tool facilitates efficient model adaptation for various NLP tasks, making it accessible for both beginners and experienced practitioners.
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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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## Getting Started
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1. **Clone the Repository**:
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```bash
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git clone https://huggingface.co/spaces/your-username/transformers-fine-tuner
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cd transformers-fine-tuner
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```
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2. **Install Dependencies**:
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Ensure you have Python 3.10 or higher. Install the required packages:
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```bash
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pip install -r requirements.txt
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```
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3. **Run the Application**:
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```bash
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python app.py
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```
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Access the interface at `http://localhost:7860/`.
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## Usage
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- **Model Name**: Enter the name of the pre-trained model you wish to fine-tune (e.g., `bert-base-uncased`).
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- **Dataset URL**: Provide a URL to your dataset.
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- **Upload Dataset**: Alternatively, upload a dataset file directly.
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- **Number of Epochs**: Set the number of training epochs.
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- **Learning Rate**: Specify the learning rate for training.
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- **Batch Size**: Define the batch size for training.
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After configuring the parameters, click **Submit** to start the fine-tuning process. Monitor the training progress and performance metrics in real-time.
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## License
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This project is licensed under the Apache-2.0 License. See the [LICENSE](LICENSE) file for more details.
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## Acknowledgments
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- [Hugging Face Transformers](https://huggingface.co/transformers/)
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- [Gradio](https://gradio.app/)
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- [Datasets](https://huggingface.co/docs/datasets/)
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