Building a Multi-Class Text Classifier with BERT: A Step-by-Step Guide with Code

1.Introduction

A multi-class classification problem involves categorizing an input into one of three or more discrete classes. Unlike binary classification, where the output is restricted to one of two possible classes (e.g., spam vs. not spam), multi-class classification has more than two classes.

Examples of Multi-Class Classification:

• Image Recognition: Identifying whether an image is a cat, dog, or bird.
• Sentiment Analysis: Classifying text into multiple categories such as “positive,” “negative,” and “neutral.”
• Medical Diagnosis: Diagnosing a disease based on symptoms that could indicate one of several possible conditions.

The goal of a multi-class classifier is to learn a function that maps the input features to one of the possible classes. This often involves using techniques that can handle a large number of classes and adequately deal with the imbalances or complexities that come with such datasets.

To understand multi-class classification better, it’s essential to see how it differs from binary and multi-label classification.
Binary classification is the simplest form of classification where the model predicts one of two possible outcomes…