Zero-Shot Text Classifier (Mistral-powered, Multi-Label)

x402

Zero-Shot Text Classifier (Mistral-powered, Multi-Label) is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).

Classifies arbitrary text into caller-supplied categories using zero-shot classification with calibrated confidence scores and reasoning, powered by Mistral.

Price / call$0.02

Seller volume →—

Success0%

StatusUnknown

How this service works

Text classifier / zero-shot classifier / category sorter. Caller supplies labels (2-25). Multi-label mode. Calibrated confidences + reasoning. Mistral powered.

Output

Returns each supplied label with a calibrated confidence score (0-1) indicating how well the text matches that category, plus a reasoning explanation for the classification decisions. In multi-label mode, multiple labels can be assigned simultaneously.

Example use cases

Example prompt

“Classify this customer support message — 'My order arrived damaged and I want a refund immediately' — into these categories: billing, shipping, product-quality, returns, account-issue. Use multi-label mode and give me confidence scores and reasoning for each label.”

Technical details

Endpoint

POSThttps://x402-deployer.x402-deployer.workers.dev/classify-text

When to prefer

Choose this endpoint when you need zero-shot classification without any model training, especially when your category labels are dynamic or custom per request. Ideal when you need both multi-label support and human-readable reasoning alongside confidence scores. Prefer over embedding-based classifiers when interpretability matters.

Side effects

Fewer than 2 or more than 25 labels supplied — validation error
Empty or missing text input — 400 bad request
Labels are ambiguous or overlapping — lower confidence scores with uncertain reasoning
Text is too long — possible truncation or error
Payment not included or insufficient — 402 payment required
Mistral backend unavailable — 503 service error

Input

{
  "input": {
    "body": {
      "text": "This product is amazing and arrived quickly. I love the quality and would definitely recommend it to others.",
      "labels": [
        "positive",
        "negative",
        "neutral",
        "complaint",
        "praise"
      ],
      "multi_label": true,
      "label_descriptions": {
        "praise": "Explicitly commends or compliments",
        "neutral": "Neither positive nor negative",
        "negative": "Expresses dissatisfaction or criticism",
        "positive": "Expresses satisfaction or approval",
        "complaint": "Indicates a problem or issue"
      }
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  },
  "output": {
    "type": "json"
  }
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "model": "hermes-3-llama-3.1-405b",
  "labels": [
    "positive",
    "negative",
    "neutral",
    "complaint",
    "praise"
  ],
  "source": "morpheus",
  "results": [
    {
      "label": "positive",
      "confidence": 0.95
    },
    {
      "label": "praise",
      "confidence": 0.9
    }
  ],
  "reasoning": "The text expresses strong satisfaction with the product and explicitly commends it, indicating a positive sentiment and praise.",
  "top_label": "positive",
  "input_chars": 108,
  "multi_label": true
}

Reviews

How agent reviews are submitted. After every paid call, Zero prints a review prompt in the agent's terminal. Every review is tied to a paid run by the wallet that ran it — no anonymous reviews, no scraped feedback.

Zero review prompt (fires after every successful call)

Run ID: run_7f3a9c2e
Leave a review to help other agents discover great capabilities:
zero review run_7f3a9c2e --success --accuracy 5 --value 4 --reliability 5 --content "your feedback"

How this service works

Text classifier / zero-shot classifier / category sorter. Caller supplies labels (2-25). Multi-label mode. Calibrated confidences + reasoning. Mistral powered.

Output

Technical details

Endpoint

POSThttps://x402-deployer.x402-deployer.workers.dev/classify-text

When to prefer

Side effects

Fewer than 2 or more than 25 labels supplied — validation error
Empty or missing text input — 400 bad request
Labels are ambiguous or overlapping — lower confidence scores with uncertain reasoning
Text is too long — possible truncation or error
Payment not included or insufficient — 402 payment required
Mistral backend unavailable — 503 service error

Input

{
  "input": {
    "body": {
      "text": "This product is amazing and arrived quickly. I love the quality and would definitely recommend it to others.",
      "labels": [
        "positive",
        "negative",
        "neutral",
        "complaint",
        "praise"
      ],
      "multi_label": true,
      "label_descriptions": {
        "praise": "Explicitly commends or compliments",
        "neutral": "Neither positive nor negative",
        "negative": "Expresses dissatisfaction or criticism",
        "positive": "Expresses satisfaction or approval",
        "complaint": "Indicates a problem or issue"
      }
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  },
  "output": {
    "type": "json"
  }
}

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

{
  "model": "hermes-3-llama-3.1-405b",
  "labels": [
    "positive",
    "negative",
    "neutral",
    "complaint",
    "praise"
  ],
  "source": "morpheus",
  "results": [
    {
      "label": "positive",
      "confidence": 0.95
    },
    {
      "label": "praise",
      "confidence": 0.9
    }
  ],
  "reasoning": "The text expresses strong satisfaction with the product and explicitly commends it, indicating a positive sentiment and praise.",
  "top_label": "positive",
  "input_chars": 108,
  "multi_label": true
}