ModernBERT LitServe

ModernBERT models offer significant improvements over the original BERT, boasting faster training, better performance on downstream tasks, and enhanced efficiency through architectural changes and optimized training techniques. This project shows how to create a self-hosted, private API that deploys a fine-tuned ModernBERT zeroshot classification model with LitServe, an easy-to-use, flexible serving engine for AI models built on FastAPI.

Project Structure

The project is structured as follows:

• server.py: The file containing the main code for the web server.
• client.py: The file containing the code for client-side requests.
• LICENSE: The license file for the project.
• README.md: The README file that contains information about the project.
• assets: The folder containing screenshots for working on the application.
• .gitignore: The file containing the list of files and directories to be ignored by Git.

Tech Stack

• Python (for the programming language)
• PyTorch (for the deep learning framework)
• Hugging Face Transformers Library (for the model)
• LitServe (for the serving engine)

Getting Started

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

1. Run the server: python server.py
2. Upon running the server successfully, you will see uvicorn running on port 8000.
3. Open a new terminal window.
4. Run the client: python client.py

Now, you can see the output of the model based on the input request. The model will classify the labels in the sentence.

Usage

The project can be used to serve the ModernBERT model using LitServe. The model used in this project is a zero-shot text classification model, meaning it can classify text it has not seen during training. This is a powerful feature that can be used in a variety of applications, such as content moderation, sentiment analysis of new product reviews, topic classification of news articles, identifying the intent of customer support queries, and classifying social media posts based on their subject matter; essentially any situation where you want to quickly categorize text into predefined classes without a large labeled training dataset for each class.

Contributing

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

License

This project is licensed under the Apache-2.0 License.

Contact

If you have any questions or suggestions about the project, feel free to contact me on my GitHub profile.

Happy coding! 🚀