Zero-Shot Text Classifier
100% private — runs on your device, never uploaded. Works offline once loaded.
Give the classifier a piece of text and a list of your own categories — like "billing, technical support, sales" — and it ranks how well each label fits. No training or setup required, and the AI model runs entirely in your browser so your text stays private.
What this tool does
The Zero-Shot Text Classifier sorts a piece of text into categories you define yourself. Instead of choosing from a fixed set of classes, you type your own labels — for example "billing, technical support, sales" — and the model estimates how well each one describes the text.
Every label gets a confidence score shown as a ranked bar, with the best match highlighted. Because it is zero-shot, you can change the labels for the next piece of text without any retraining or configuration.
The model
It runs Xenova/nli-deberta-v3-xsmall, a compact DeBERTa model trained for natural language inference (NLI). Zero-shot classification reframes each label as a hypothesis and asks the model how strongly the text entails it, then ranks the results.
Everything executes through transformers.js in your browser using WebAssembly. The quantized model (~85 MB) downloads once, is cached, and then runs locally — even offline.
Good uses
Zero-shot classification is ideal when you need flexible tagging without building a training set.
- Route support tickets to billing, tech or sales queues
- Tag feedback or notes by topic
- Sort messages as urgent, normal or low priority
- Prototype a classifier before committing to labelled data
Quality and privacy caveats
Nothing you enter is uploaded or stored — the classification happens entirely on-device. The trade-off is that a small model is less accurate than a large fine-tuned one, so results depend heavily on clear, descriptive label names.
Use single-label mode when categories are mutually exclusive, keep labels short and distinct, and treat the scores as guidance. For high-stakes decisions, verify the output rather than trusting the top label outright.
Frequently asked questions
What is zero-shot classification?
It is a technique where the model classifies text into labels it was never explicitly trained on, by judging how well each label logically fits the text.
Is the tool free to use?
Yes. It is free, needs no account, and runs directly in your browser with no usage limits.
Does it work offline?
After the one-time model download, yes. The model is cached locally so you can classify without a connection.
How should I write good labels?
Use short, clear, distinct phrases that describe the category well. Vague or overlapping labels reduce accuracy.
Can a text belong to more than one label?
This tool ranks labels for a single best match. It is best suited to mutually exclusive categories, though the scores still show how close other labels are.
Is my data private?
Completely. There is no upload and no server-side processing — the model runs entirely on your device.
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