We’re excited to share our first open-source project, ZeroHero – a simple yet effective zero-shot classifier for text. This Python-based tool is designed to make text classification tasks more manageable, particularly when working with unlabeled or minimal data.

ZeroHero stands out for its user-friendly setup and speed, making it a handy resource for various classification needs. Its core strength lies in its straightforward approach to classifying texts into categories, even those not encountered during training.

What is a zero-shot classifier?

A zero-shot text classifier is a machine learning model capable of classifying new data into categories or classes not seen during model training. Because these pre-trained models have been trained on such a large corpus of text, the model is able to infer the meaning of novel words and phrases and apply this knowledge to classify new text.

Whether you’re a seasoned developer or just starting out, ZeroHero is worth exploring for its practical applications in text classification. We hope it will be a valuable addition to your toolkit, saving you time and effort in your projects.

Discover ZeroHero on GitHub