Machine Learning Use Case
Topic classification automates the labor-intesive process of manually categorizing documents. You can use topic classification to label customer support tickets, tag content, detect spam/toxic messages, sort internal files, and more. Learn how to build an end-to-end machine learning pipeline to automatically sort news titles and descriptions into different categories with Predibase and Ludwig.
The Predibase Solution
Boost efficiency with ML-powered topic classification
- Rapidly preprocess unstructured text and tabular features
- Finely tune model parameters such as using pre-trained encoders like Bert with little-to-no-code
- Automate the classification of content into different categories to reduce the manual sorting effort
Train your first topic classification model in < 20 lines of a config file
At the core of Predibase is Ludwig, an open-source declarative ML framework that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.
Resources to Get Started
Read the Tutorial
Read this deep dive tutorial to learn how to build an end-to-end deep learning pipeline for topic classification.
Run the Notebook
Get started with topic classification using this open-source Ludwig notebook including sample code and data.
Visit the Resource Center
Visit the Predibase resource center to see our collection of ebooks and webinars on topics from multimodal ML to computer vision.