# Sebastian Raschka — text classification from bag-of-words to Jev

> Sebastian Raschka puts Jev in the long history of text classifiers and tests it on IMDb.

- Org: Sebastian Raschka
- Date: 2026-09-29
- Category: article
- Source: https://magazine.sebastianraschka.com/p/classifier-history-and-jev
- Canonical page: https://ai-tldr.dev/releases/sebastian-raschka-classifier-history-jev-sep29/

## In short

Sebastian Raschka traces text classification from bag-of-words and naive Bayes to BERT and GPT, then tests Jev on IMDb reviews: 96.47% accuracy, close to a fine-tuned ModernBERT, for $0.65 across 25,000 reviews.

## What it is

Text classification is the subject of Sebastian Raschka's 29 September 2026 article. The article walks from pre-transformer methods — bag-of-words, naive Bayes, logistic regression, RNNs and CNNs — through BERT, GPT and T5, and ends with Jev, TypeSafe's decision model.

## How it works

On the IMDb review test set, Jev scores 96.47% accuracy, close to a fine-tuned ModernBERT, and classifies all 25,000 reviews in 22 minutes 24 seconds for $0.65. Raschka compares that with older baselines on the same data, then offers what he calls educated guesses about Jev's design: a small ModernBERT-like model trained on synthetic data with a method for calibrated decisions.

## Why it matters

Raschka argues Jev is 'the ChatGPT moment for classification': one API that handles many classification tasks with no fine-tuning and returns calibrated probabilities. The history section also works as a single-page primer for anyone deciding between a fine-tuned encoder, a general LLM and a decision model.

## Who it's for

ML engineers choosing a text classifier

## Links

- [Full Article](https://magazine.sebastianraschka.com/p/classifier-history-and-jev)
- [HN Discussion](https://news.ycombinator.com/item?id=49891203)
- [Jev Announcement](https://typesafe.ai/blog/introducing-system-one-models-and-jev)