AI Text Classifier
Classify text into custom categories using AI zero-shot classification. No training data needed — define your own labels and let the AI categorize your text. 100% browser-based and private.
AI Text Classifier is a free, browser-based tool from UseToolSuite's AI Tools collection. All processing happens locally on your device — your data is never uploaded to any server. Use the tool below, then scroll down for detailed documentation, frequently asked questions, and related resources.
Recent Classifications
First load downloads a ~150MB model to your browser. Subsequent runs are instant.
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.
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Open GitHub IssueFrequently 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.
Related Guides
In-depth articles covering the concepts behind AI Text Classifier.