Language models for text classification: From bag-of-words to Jev

The article explains how text classification has evolved from simple bag-of-words models to modern transformer-based approaches, using Jev as a case study for why lightweight, general-purpose classifiers are gaining traction. For CIOs and technology leaders, the key business implication is that AI-driven classification can now be deployed faster and more cheaply for routine decisioning, but IT teams still need to balance that convenience against the advantages of purpose-built models for narrow, high-accuracy use cases. Strategically, this signals a move toward more modular AI portfolios where organizations select the right model class based on cost, latency, accuracy, and governance needs rather than defaulting to large LLMs for every task.