How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It.
NLPHow Many Labeled Examples Does a Text Classifier Actually Need? Zero-shot classification with a modern LLM needs no training data at all, which makes it an easy default when a project starts.But that convenience has a cost most teams never actually measure: LLM classification means a network call and a per-request charge on every single item, forever. A classical baseline like TF-IDF plus a linear classifier costs nothing to run once trained — no API, no network round-trip, sub-millisecond inference.
- ▪NLPHow Many Labeled Examples Does a Text Classifier Actually Need?
- ▪Zero-shot classification with a modern LLM needs no training data at all, which makes it an easy default when a project starts.But that convenience has a cost most teams never actually measure: LLM classification means a network call and a
- ▪A classical baseline like TF-IDF plus a linear classifier costs nothing to run once trained — no API, no network round-trip, sub-millisecond inference.
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| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/how-many-labeled-examples-does-a-text-classifier-actually-need-i-measured-it/ |
| Publication time | Tue, 15 Sep 2026 11:00:01 GMT |
| Retrieval time | 2026-09-15T11:01:52.963Z |
| Last seen | 2026-09-15T11:01:52.963Z |
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NLPHow Many Labeled Examples Does a Text Classifier Actually Need? I Measured It.Before reaching for an LLM API on every classification problem, it's worth knowing what a decades-old baseline can already do with the labeled data you have — and exactly how much more data buys you.Himanshu SharmaSeptember 15, 20267 min readBanner illustration generated with Claude (Anthropic).The question teams skip"Just use an LLM for it" has become the default answer to almost any text classification problem — routing support tickets, tagging feedback, sorting incoming requests.
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