ClassyText

Zero-shot classification is a transfer learning method that uses a pre-trained language model to predict a class not seen during training. This project uses a new promising zero-shot text classification model ModernBERT-large-zeroshot-v2.0 from the Hugging Face model hub to classify text into different classes. Models in this series are designed for efficient zero-shot classification with the Hugging Face pipeline. These models can classify without training data and run on both GPUs and CPUs.

Project Structure

The project is structured as follows:

• src\: The folder that contains the source code for the project.

  • modernbert\classifier.py: The file that contains the function for classifying text into different classes using the ModernBERT zero-shot text classification model.

  • config.py: This file contains the configuration for the used model.

  • logger.py: This file contains the project's logging configuration.

  • exception.py: This file contains the exception handling for the project.

• app.py: This file contains the code for the Gradio application.

• requirements.txt: This file contains the required dependencies for the project.

• LICENSE: This file contains the license information for the project.

• README.md: This file contains the information about the project.

• assets: This folder contains the screenshots for working on the application.

Tech Stack

• Python (for the programming language)
• PyTorch (for the deep learning framework)
• Hugging Face Transformers Library (for the zero-shot text classification model)
• Gradio (for the web application)
• Hugging Face Spaces (for hosting the gradio application)

Getting Started

To get started with this project, follow the steps below:

1. Clone the repository: git clone https://github.com/sitamgithub-MSIT/ClassyText.git
2. Change the directory: cd ClassyText
3. Create a virtual environment: uv venv
4. Activate the virtual environment:
   • Windows: .venv\Scripts\activate
   • macOS / Linux: source .venv/bin/activate
5. Install the required dependencies: uv pip install -r requirements.txt
6. Run the Gradio application: python app.py

Now, open up your local host, and you should see the web application running. For more information, refer to the Gradio documentation here. Also, a live version of the application can be found here.

Usage

Once the application runs, you can classify text into different categories using its interface. It employs a zero-shot text classification model, allowing it to categorize unseen text. This is useful for applications like content moderation, sentiment analysis, topic classification, and customer support intent identification, enabling quick text categorization without needing extensive labeled datasets.

Contributing

Contributions are welcome! If you would like to contribute to this project, please raise an issue to discuss the changes you would like to make. Once the changes are approved, you can create a pull request.

License

This project is licensed under the MIT License.

Contact

If you have any questions or suggestions regarding the project, feel free to reach out to me on my GitHub profile.

Happy coding! 🚀