Classify Text Against Labels You Invent, With No Training

Give a model any set of categories and it sorts your text into them — no training data, no fine-tuning, no API key.

Zero-shot classification sorts text into categories you invent at runtime, with no training data. It works by reframing classification as natural language inference: your text becomes a premise and each label is turned into a hypothesis whose entailment probability becomes the score. This runs a DeBERTa NLI model in your browser, so the text is never uploaded.

Runs in your browser

Uploads: None — the model is downloaded to you, not the reverse
Runs on: WebGPU where available, WebAssembly otherwise
Privacy: Runs entirely in your browser — nothing is uploaded
Cost: Free, unlimited, no sign-up

Frequently asked questions

Why does changing the hypothesis template change the answer?

Because the template is what the model actually judges. It is scoring "does this text entail: This example is a bug report?" — so a template that names your domain ("This support ticket is about {}.") gives the model context it otherwise lacks. It is the single cheapest accuracy improvement available here.

When should I use a trained classifier instead?

Once you have a few hundred labelled examples. A fine-tuned model will beat zero-shot comfortably and run an order of magnitude faster. Zero-shot exists for the stage before you have that data — or for categories that change too often to train for.

How to use the zero-shot text classifier

1Enter or paste your text.
2Enter or paste your labels.
3Choose the model.
4Turn "Allow more than one label per item" on or off as needed.
5Enter or paste your hypothesis template.
6Press Run, then download the result when it is ready.

Sources & specifications

DeBERTa: Decoding-enhanced BERT with Disentangled AttentionarXiv

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Put the working zero-shot text classifier on your own site. It runs in your visitors' browsers exactly as it does here — free, no account, nothing uploaded.

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Last updated 1 Aug 2026