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Text Classification with Custom Labels (Single or Multi-Label)
x402
Text Classification with Custom Labels (Single or Multi-Label) is a paid API for AI agents from twin.unykorn.org, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-02).
Classifies arbitrary text into user-defined labels with confidence scores and reasoning, supporting both single-label and multi-label classification modes.
Price / call$0.004
Success—
StatusUnknown
How this service works
Classify text into YOUR labels (single or multi-label) with confidence and reason — Genesis402 / UnyKorn Operator Network
Output
Returns the predicted label or labels (in multi-label mode), a confidence score (0–1) for each label, and a natural language reason explaining why the classification was made.
Example use cases
Support ticket routing by department
“I have this incoming support message — classify it as one of: 'billing', 'technical', 'shipping', or 'general inquiry', and tell me how confident you are: 'My order hasn't arrived after 10 days and I can't find a tracking number.'”
Multi-label topic tagging for news articles
“Tag this article with all relevant topics from this list — 'politics', 'economy', 'environment', 'technology', 'health', 'sports' — it can have multiple tags. Here's the text: 'The new federal budget allocates $5B to green energy infrastructure and electric vehicle subsidies.'”
Intent detection for chatbot messages
“Classify this user message into one of these intents: 'cancel_order', 'track_shipment', 'request_refund', 'product_question', 'other'. Message: 'Hey, I changed my mind about my purchase, can I get my money back?'”
Technical details
Endpoint
POSThttps://twin.unykorn.org/ai/classify?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to apply your own custom taxonomy to text rather than relying on a fixed category set. It is especially useful for domain-specific classification tasks (support routing, content tagging, intent detection) where you control the label vocabulary and want both a prediction and a human-readable justification. It supports multi-label output, making it suitable for scenarios where text can belong to more than one category simultaneously.
Side effects
- Fewer than 2 or more than 20 labels provided — validation error
- Text exceeds 12,000 characters — input too long error
- Empty or missing text field — required field error
- Ambiguous or overlapping labels may reduce confidence scores
- Low-confidence results when text is vague or labels are poorly defined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| params | object |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"type": "text-classify",
"receipt": {
"tx_hash": "0x<64hex>",
"amount_usd": 0.004,
"receipt_id": "g402-<16hex>"
},
"sources": [
{
"ok": true,
"name": "<source>"
}
],
"limitations": "<text>",
"generated_at": "<iso time>",
"evidence_hash": "sha256:<64hex>"
}
}Tags
x402
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