Text Classification: Approaches and Trade-offs
~45 min read
Zero-shot generative, embedding+classifier, and fine-tuned encoder approaches with decision criteria.
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Key points
- •Embed+classifier is the pragmatic production workhorse: fast, cheap, and accurate once you have 500+ labeled examples
- •Zero-shot embedding similarity is a powerful 0-data baseline — embed text and labels, pick the closest label via cosine similarity
- •Fine-tuned encoders have the highest accuracy ceiling but need 1K+ examples and GPU training time to justify