Raschka's Mega Guide: Text Classification from Bag-of-Words to Transformers and Jev
rasbt · x · 2026-09-29
Sebastian Raschka published a mega visual guide walking through the full history of language models for decision-making: Bag-of-Words, RNNs, CNNs, transformers, and calibration, with hands-on experiments comparing accuracy and efficiency.
The article uses the recently viral Jev classification model as its hook. The author admits his view shifted from "classifiers used to be my bread & butter, I can build this myself" to "this actually works better than I thought." His positioning: general LLMs like GPT can do the same classification and more, but Jev is much faster and cheaper at it; for narrow, well-defined problems a special-purpose classifier still wins, while Jev's selling point is generality. He also reverse-engineers Jev's likely methodology. The author discloses no affiliation with Jev and no free access — a purely technical piece.
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