Classify text into your own categories with AI zero-shot classification. No training data needed — define labels and let the AI sort your text.
Recent Classifications
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Classification Confidence Scores
What is the AI Text Classifier?
The AI Text Classifier is a sophisticated, browser-based zero-shot classification tool. Without needing any prior training data, manual tagging, or complex coding, it can instantly categorize any text into custom topics or labels that you define on the fly. Whether you are sorting customer support tickets, analyzing social media sentiment, or organizing massive text documents, this tool acts as your intelligent robotic sorter.
At the core of this tool is a powerful language model (nli-deberta-v3-xsmall) compiled specifically for WebAssembly. It analyzes the deep semantic relationship between your inputted text and the custom labels you provide, returning a percentage-based confidence score for each label. Best of all, because it runs locally on your machine, your text data remains completely private and secure.
Why Use Our Classifier Instead of OpenAI or MonkeyLearn?
Feature
Our Local AI Classifier
Cloud APIs (OpenAI / MonkeyLearn)
Data Privacy
100% Client-side (Zero API calls)
Sends sensitive data to remote servers
Cost
Free Forever (No limits)
Charges per 1,000 requests
Training Required
Zero-shot (Instant tagging)
Often requires manual tagging of 100+ items
Export Capabilities
Download scores to JSON
Requires complex API integration
Key Features & Capabilities
Zero-Shot Custom Labels
You are not restricted to predefined categories. You can type absolutely any label into the 'Custom labels' box (e.g., 'Spam', 'Urgent', 'Refund Request', 'Complaint'), and the AI will intuitively understand what you mean without requiring prior examples.
Military-Grade Privacy
Because the AI model is downloaded and run inside your browser, the text you paste is never transmitted to an external server. This makes the tool perfectly compliant for analyzing sensitive company emails, PII, or internal customer data.
Granular Confidence Scores
The tool doesn't just arbitrarily pick one winner; it ranks all your provided labels by mathematical probability. This allows you to see secondary topics and understand exactly how the AI interpreted the text's nuances.
JSON Data Export
Developers and data analysts can export the classification results, complete with raw percentage scores and label names, directly into a clean JSON file for immediate integration into spreadsheets or databases.
How to Classify Text Locally
Input Text: Paste the text you want to analyze (e.g., a news article, a review, an email) into the main text box.
Define Categories: In the 'Custom labels' box, type the categories you want the AI to sort the text into, separated by commas. (e.g., "Positive, Negative, Neutral" or "Finance, Tech, Healthcare").
Analyze: Click 'Classify Text'. Note: The very first time you do this, a ~150MB model will be downloaded to your browser cache.
Review & Export: The AI will rank the labels from most likely to least likely based on semantic understanding. Click 'Export JSON' to save the raw scores.
What makes zero-shot classification remarkable
Traditional text classification requires hundreds or thousands of labeled examples to train a model for your specific categories. Zero-shot classification skips that entirely: you invent the labels on the spot, and the model — which understands language broadly — judges which label best fits, having never been trained on your categories at all. Define “Urgent / Normal / Spam” this minute and it works immediately. That’s a genuine superpower for tasks where you don’t have training data and don’t want to build a pipeline.
How the model actually decides
Under the hood, zero-shot turns classification into an entailment question. For each label, it effectively asks: “Does this text imply the statement ‘This text is about [label]’?” and scores how strongly. That’s why label wording matters so much — you’re literally writing the hypotheses the model evaluates. It’s also why distinct labels work and synonymous ones don’t: two labels that mean nearly the same thing produce nearly the same score, leaving the model to coin-flip.
When to use it — and when to train instead
Use zero-shot (this tool)
Train a dedicated model
No labeled data available
Thousands of labeled examples exist
Categories change often
Fixed, high-stakes categories
Prototyping / one-off tasks
High-volume production at scale
A handful of clear labels
Subtle, domain-specific distinctions
For exploring data, routing tickets, or quick content tagging, zero-shot is the fast, flexible choice. For a mission-critical classifier running on millions of items with subtle distinctions, a model fine-tuned on your data will be more accurate and cheaper per item.
Privacy and limits
Classification runs entirely in your browser, so the text you categorize never leaves your device — safe for support tickets, internal documents, and proprietary content. Accuracy is highest (85–95%) when labels are clearly separable and lower when categories blur into each other. More labels add processing time but don’t materially hurt accuracy as long as each one is well-defined.
AI Text Classifier runs its model on your own device, so the text or image you feed it never leaves the browser.
It's one of the free
AI Tools
on UseToolSuite.
Below you'll find a step-by-step guide, answers to common questions, and related tools.
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Key Concepts
Essential terms and definitions related to AI Text Classifier.
Zero-shot classification
Assigning text to categories the model was never explicitly trained on. You supply the labels at runtime and the model scores how well each fits — no training data or fine-tuning required.
Natural Language Inference (NLI)
The technique behind zero-shot labeling: the model checks whether the statement 'this text is about {label}' is entailed by your text, turning classification into an entailment problem.
Entailment (confidence) score
How strongly the model believes each label applies, from 0 to 100%. Labels are ranked by this score; a low top score means none of your labels fit the text well.
Frequently Asked Questions
What is zero-shot classification?
Zero-shot classification allows the AI to categorize text into labels it has never been explicitly trained on. You define your own category labels (e.g., "Sports", "Technology", "Politics") and the AI determines which label best matches the input text. No training data or fine-tuning is required.
How accurate is zero-shot classification?
Accuracy depends on how distinct and well-defined your labels are. For clearly separable categories (e.g., "Sports" vs "Cooking" vs "Technology"), accuracy is typically 85-95%. For nuanced or overlapping categories, accuracy may be lower. The tool provides confidence scores for each label.
How many labels can I define?
You can define 2-20 custom labels. The model evaluates each label independently, so more labels add processing time but don't significantly reduce accuracy for well-defined categories.
How do I write labels that classify accurately?
Make labels distinct, specific, and mutually exclusive. The model compares your text against each label as a hypothesis ('this text is about ___'), so vague or overlapping labels confuse it. Good: 'Billing', 'Technical Support', 'Feature Request'. Bad: 'Issue', 'Problem', 'Question' — these overlap and a single message fits all three. Prefer a few well-separated labels over many fuzzy ones, and phrase them as the category itself ('Sports') rather than a full sentence. If accuracy is poor, your labels probably overlap — separate them more clearly.
Can one text belong to multiple categories?
Yes — the model scores each label independently, so a support ticket can legitimately be both 'Billing' and 'Bug Report.' By default you'll see the single highest-scoring label, but the confidence scores for all labels are available, so you can apply a threshold (e.g. accept every label above 0.5) to do multi-label classification. This is useful for tagging content that genuinely spans topics, like a blog post that's both 'Marketing' and 'AI'. Single-label mode just takes the top score.
Troubleshooting & Technical Tips
Common errors developers encounter and how to resolve them.
Text is placed in the wrong category
Zero-shot classification is only as good as your labels. Use clear, distinct, descriptive labels (e.g. 'customer complaint' rather than 'bad'), avoid overlapping meanings, and give the model enough text to judge.
The first run is slow
The model downloads once and is cached in your browser. After the initial load, classification is fast and works offline.
Related Guides
In-depth articles covering the concepts behind AI Text Classifier.
