Machine learning and statistics with python

Building a News Classifier from Scratch with a Custom Transformer Model 🧠 Ever wondered how news apps categorize articles so accurately? It's often done using Transformers , a powerful neural network architecture that forms the backbone of modern language understanding. In this post, we'll build a news category classifier from the ground up, using our own custom Transformer. We'll explore the key components, prepare a real-world dataset, and train our model to classify news articles into one of 42 categories. 1. The Dataset: News Category Dataset Our journey starts with the News Category Dataset from Kaggle, a large collection of news headlines and short descriptions. The first step is to prepare this text for our model. We combine the headline and short_description columns into a single full_text column. We then create a numerical mapping for each unique news category. Python # Combine headline and short_description df[ 'full_text' ] = df[ 'headline...