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Private browser utility / Social & Creator

Free Zero-Shot Classifier

English only

Runs entirely in your browser - no upload, no sign-up.

Live workspaceLocal processing

Classify English text against labels you choose, with no training examples.

First 1600 characters

A complete sentence gives the model enough context to compare your labels.

0 of 12

Enter 2 to 12 short English labels, separated by commas or new lines. Duplicates are removed.

How labels relate

Your text stays in this browser worker. Only the public AI runtime and model files are downloaded.

Your labels become the classification set

The classifier ranks every label you enter. Use choose one for exclusive categories or allow several when a passage can fit more than one.

MobileBERT uses English natural language inference. Short, specific labels usually work better than vague or overlapping ones.

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zero shot classifier / browser utility
01 / Overview

What is a zero-shot classifier?

A zero-shot classifier sorts text into labels you provide without needing labeled examples or model training. This tool turns each label into a natural language inference test, ranks the matches, and runs the English MobileBERT model inside your browser. Your text stays on your device while the public model downloads separately.

02

How to use

  1. 01
    Enter text and candidate labels

    Paste one focused English passage, then add between 2 and 12 short labels separated by commas or new lines.

  2. 02
    Choose how labels relate

    Use Choose one when exactly one category should win. Use Allow several when several labels can all describe the passage.

  3. 03
    Run and review the ranking

    Start classification, allow the first model download to finish, and compare the full score list rather than relying on the leading label alone.

  4. 04
    Refine or copy

    Rewrite vague or overlapping labels and run again. Copy the ranked scores when the label set gives a useful comparison.

03

Who it's for

  • Support teams testing labels such as billing, bug report, feature request, and praise before designing a routing workflow.
  • Product researchers exploring themes in English survey answers or interview notes without uploading unpublished text for inference.
  • Meeting and voice-note users comparing a transcript excerpt with labels such as decision, action item, risk, or follow-up.
  • Developers and analysts prototyping a label set before collecting examples for a supervised classifier or adding human review rules.

This classifier uses Xenova's MobileBERT MNLI model through Transformers.js. It compares your text with a simple hypothesis for every candidate label. Choose one mode makes all labels compete and normalizes their scores across the set. Allow several mode treats each label independently, which suits passages that can belong to multiple categories.

The WebGPU path uses quantized model weights of about 21 MB. The WebAssembly CPU fallback uses about 26 MB, plus tokenizer files and the runtime. Browsers normally cache downloads, but private browsing, cleared storage, or storage pressure can cause another download. The text you enter is passed only to the browser worker, not to the model host.

FAQ

Is my text uploaded to a server?

No. Classification runs in a worker inside your browser, and your pasted text is not sent to CanDoYa, jsDelivr, or Hugging Face. The worker does request the public Transformers.js runtime and model files. Those download requests do not include the text or labels you entered.

Is the zero-shot classifier free?

Yes. There is no account, API key, payment, or server-side usage quota. Your device performs inference. The practical costs are the first model download, browser storage, memory, processing time, and any data charge from your network provider for downloading the public files.

How much text and how many labels can I use?

The tool accepts 2 to 12 unique labels and classifies the first 1,600 Unicode characters of the normalized text. Each label can contain up to 60 characters. Longer documents should be split into coherent passages because one score list cannot describe every topic in a mixed document.

What is the difference between Choose one and Allow several?

Choose one assumes the labels are mutually exclusive, so their scores are normalized together and total about 100 percent. Allow several compares entailment and contradiction separately for every label. Its scores are independent, so several labels can rank highly and the total does not need to equal 100 percent.

What do zero-shot classification scores mean?

Scores express how strongly this English NLI model matches the text to the supplied labels under the selected mode. They are useful for ranking and comparison, but they are not calibrated guarantees that a label is correct. Domain language, label wording, ambiguity, and out-of-distribution text can change them.

Which languages does the classifier support?

The selected uncased MobileBERT MNLI model was trained and fine-tuned for English, so this page marks the tool English only. Other languages may still produce numbers, but those values lack the same training basis and should not be treated as reliable classification results.

Does it work without WebGPU?

Yes. The worker tries WebGPU when the browser and graphics adapter expose it. If WebGPU is unavailable or fails, the tool restarts with the WebAssembly CPU backend. The fallback uses the same MobileBERT classifier and keeps text local, but model loading and inference can take longer.

Can I use the result for automatic decisions?

Use it for experiments, drafting label sets, or low-risk triage with human review. Do not use one model score by itself for employment, credit, health, safety, legal, moderation, or other consequential decisions. Validate the label set with representative examples and define a review path for uncertain cases.