Understanding Zero-Shot Classification
Zero-shot classification relies on pre-trained language models that understand language context deeply. These models can be prompted with new tasks, such as classification, by providing text and candidate labels. The model evaluates the text against the labels and assigns probabilities to each label based on its understanding.
Zero-Shot Text Classification using HuggingFace Model
Zero-shot text classification is a groundbreaking technique that allows for categorizing text into predefined labels without any prior training on those specific labels. This method is particularly useful when labeled data is scarce or unavailable. Leveraging the HuggingFace Transformers library, we can easily implement zero-shot classification using pre-trained models. In this article, we’ll explore how to use the HuggingFace pipeline for zero-shot classification and create an interactive web interface using Gradio.