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 deberta-v3-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.
The project is structured as follows:
app.py: This file contains the code for the Gradio application that uses the zero-shot text classification model to classify text into different classes.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.
- Python (for the programming language)
- Hugging Face Transformers Library (for the zero-shot text classification model)
- Gradio (for the web application)
- Hugging Face Spaces (for hosting the gradio application)
To get started with this project, follow the steps below:
- Clone the repository:
git clone https://github.com/sitamgithub-MSIT/ClassyText.git - Create a virtual environment:
python -m venv tutorial-env - Activate the virtual environment:
tutorial-env\Scripts\activate - Install the required dependencies:
pip install -r requirements.txt - 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.
Once the application is up and running, you can interact with the interface to classify text into different classes of your choice. The model used in this project is a zero-shot text classification model, which means that it can classify text that it has not seen during training. This is a powerful feature that can be used in a variety of applications.
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.
This project is licensed under the MIT License.
If you have any questions or suggestions regarding the project, feel free to reach out to me on my GitHub profile.
Happy coding! 🚀