A new visual guide explores the evolution of language models for text classification, tracing the journey from basic bag-of-words approaches to advanced architectures like RNNs, CNNs, and Transformers. The resource includes hands-on experiments that evaluate the accuracy and efficiency of these different models. Additionally, it covers essential techniques for model calibration to ensure reliable performance in practical applications.