--- title: Language Models for Text Classification: From... description: A deep dive traces the evolution of text classification from bag-of-words and naive Bayes through RNNs, CNNs, BERT-style encoders, and GPT-style decoders,... canonical: https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Language Models for Text Classification: From Bag-of-Words to Jev | daily.dev og:description: A deep dive traces the evolution of text classification from bag-of-words and naive Bayes through RNNs, CNNs, BERT-style encoders, and GPT-style decoders,... og:url: https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5 og:image: https://api.daily.dev/og/posts/jycKerto5.png og:image:alt: Language Models for Text Classification: From Bag-of-Words to Jev og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Language Models for Text Classification: From Bag-of-Words to Jev **[Sebastian Raschka](https://daily.dev/sources/sebastianraschka)** · 37 min read · 0 upvotes · 0 comments ## Summary A deep dive traces the evolution of text classification from bag-of-words and naive Bayes through RNNs, CNNs, BERT-style encoders, and GPT-style decoders, benchmarking each approach on the IMDb sentiment dataset. It then examines Jev, a new proprietary classification model from TypeSafe AI that claims GPT-5.6-level decision-making at much lower cost and latency, achieving ~96% accuracy on IMDb via its Choice and Noul APIs. The piece explains how a Jev-like API could be built on top of BERT or GPT models with a flexible classification head, speculates on Jev's likely architecture and its undisclosed 'Reinforcement Learning for Calibrated Decisions' training method, and relates it to the published RLCR (Reinforcement Learning with Calibration Rewards) technique, alongside a broader discussion of model calibration and its necessity. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What accuracy does the Jev model achieve on the IMDb movie review sentiment classification dataset? Jev achieves about 96.47% accuracy using its Choice API and 96.20% using its Noul API on the 25,000-review IMDb test set. The Choice run took 22 minutes 24 seconds and cost $0.6492 in API usage, while Noul took 23 minutes 3 seconds and cost $0.6345, roughly matching a fine-tuned ModernBERT model's accuracy without any custom training. _daily.dev surfaces benchmarks like this for developers deciding between fine-tuned classifiers and general-purpose decision APIs._ ### What is Reinforcement Learning with Calibration Rewards (RLCR) and how does it change the reward function compared to standard RLVR? RLCR extends reinforcement learning with verifiable rewards (RLVR) by adding a penalty for inaccurate confidence estimates: instead of a binary 0/1 correctness reward, the model also outputs a confidence score q, and the reward is adjusted by a squared-error (Brier) penalty term. On HotpotQA this reduced expected calibration error from 0.37 to 0.03 versus RLVR while keeping accuracy roughly similar (62.1% vs 63.0%). _engineers tuning confidence-aware models can track emerging calibration techniques like this through daily.dev._ ### How does a bag-of-words model with logistic regression compare to an LSTM RNN for text classification accuracy? A bag-of-words representation combined with logistic regression achieved about 89.9% accuracy on a balanced IMDb sentiment dataset, actually outperforming an LSTM RNN trained from scratch, which reached only 85.66%. Pre-training and fine-tuning approaches like ULMFiT later closed this gap, reaching 95.4% test accuracy on the same dataset. _developers choosing a baseline classifier architecture can compare tradeoffs like these on daily.dev._ ## Similar posts on daily.dev - [JEV vs BERT: What’s Actually Different?](https://daily.dev/posts/jev-vs-bert-what-s-actually-different--9ngceslfr) · Medium · 3 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#transformers](https://daily.dev/tags/transformers), [#bert](https://daily.dev/tags/bert) [View this post on daily.dev](https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Language Models for Text Classification: From Bag-of-Words to Jev","url":"https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5"},"datePublished":"2026-09-29T17:23:22.456Z","dateModified":"2026-09-29T19:41:42.712Z","description":"A deep dive traces the evolution of text classification from bag-of-words and naive Bayes through RNNs, CNNs, BERT-style encoders, and GPT-style decoders,...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2434ba01f2f2efb589719a4d7a4a707c?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/2434ba01f2f2efb589719a4d7a4a707c?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Sebastian Raschka","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Sebastian Raschka","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/eb22d2e73d074c5598d31588ad16a51c","url":"https://daily.dev/sources/sebastianraschka"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,reinforcement-learning,transformers,bert","timeRequired":"PT37M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sebastian Raschka","item":"https://daily.dev/sources/sebastianraschka"},{"@type":"ListItem","position":3,"name":"Language Models for Text Classification: From Bag-of-Words to Jev"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/language-models-for-text-classification-from-bag-of-words-to-jev-jyckerto5#faq","mainEntity":[{"@type":"Question","name":"What accuracy does the Jev model achieve on the IMDb movie review sentiment classification dataset?","acceptedAnswer":{"@type":"Answer","text":"Jev achieves about 96.47% accuracy using its Choice API and 96.20% using its Noul API on the 25,000-review IMDb test set. 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