How Much Labeled Data Do You Need for Text Classifiers?
The article explores the impact of labeled data on text classification accuracy, using a synthetic dataset of support tickets. The study found that while modern large language models (LLMs) require no training data and offer instant inference, traditional methods like TF-IDF still perform better with fewer examples. Accuracy improves significantly when starting from very low volumes but levels…
Key points
- Text classification accuracy improves with more labeled examples, but the gains diminish quickly.
- TF-IDF vectorization performs better than modern LLMs at low volumes of labeled data.
- Structural errors in text remain even with increased training data.
How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It.
Towards Data Science · 15 September 2026
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This text was published by Towards Data Science and written by Himanshu Sharma. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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