# Tanod mlpeek Zero-Shot Text Classifier

> Tanod mlpeek Zero-Shot Text Classifier is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-09).

Classifies English text into 1–10 caller-supplied labels using NLI-based zero-shot classification (DeBERTa), returning confidence scores per label.

## Facts

- Endpoint: POST https://tanod.dev/v1/classify/zero-shot?utm_source=zero.xyz
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-09
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/tanod-mlpeek-zero-shot-text-classifier-94a72ea3
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TMjxshhA2LiyM8JcOu5Pu

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability tanod-mlpeek-zero-shot-text-classifier-94a72ea3 -d '<json body>'
```

Example prompt: Zero-shot classify this support message — 'I was charged twice for my subscription and need a refund' — into these labels: billing, technical support, sales. Use multi-label mode off so they compete with each other.

## When to prefer this

Choose this endpoint when you need fast, training-free text classification into a small set of custom labels without any labelled training data. Ideal for routing, tagging, or intent detection tasks where labels change frequently or per-request. Prefer over fine-tuned classifiers when no labelled data is available and over LLM-based classification when cost per call must be predictable at $0.001. Not suitable for non-English text or when calibrated probabilities (rather than model confidences) are required.

## Known failure modes

- Text exceeds 2000 characters — likely truncated or rejected
- Fewer than 1 or more than 10 labels provided — validation error
- hypothesis_template missing '{}' placeholder — bad request error
- Non-English input — model may return low-quality or unreliable scores
- All labels very similar — scores may be near-uniform with low discrimination
- Payment not attached or insufficient — 402 Payment Required

## How this service works

One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.

## Output

A JSON object containing a ranked array of labels each with a model confidence score (0–1), the model ID and license, token usage (label count and token count), whether the input was truncated, the multi_label flag, and the hypothesis template used. Scores sum to ~1 in single-label mode; each is independent in multi-label mode.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string",
   "maxLength": 2000,
   "minLength": 1,
   "description": "English text, at most 2,000 characters."
  },
  "labels": {
   "type": "array",
   "items": {
    "type": "string",
    "maxLength": 100,
    "minLength": 1
   },
   "maxItems": 10,
   "minItems": 1,
   "description": "1-10 unique candidate labels, each at most 100 characters."
  },
  "multi_label": {
   "type": "boolean",
   "default": false,
   "description": "Score each label on its own instead of making them compete."
  },
  "hypothesis_template": {
   "type": "string",
   "default": "This example is about {}.",
   "maxLength": 200,
   "minLength": 2,
   "description": "Must contain {} exactly once."
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "note": "English NLI model; scores are model confidences, not calibrated probabilities",
  "model": {
   "id": "nli-deberta-v3-xsmall",
   "source": "cross-encoder/nli-deberta-v3-xsmall",
   "license": "Apache-2.0",
   "revision": "a150876415327c80daeff35ca6f68f5ed8cf5c24"
  },
  "usage": {
   "labels": 3,
   "tokens": 58
  },
  "labels": [
   {
    "label": "billing",
    "score": 0.941569
   },
   {
    "label": "sales",
    "score": 0.054991
   },
   {
    "label": "technical support",
    "score": 0.00344
   }
  ],
  "truncated": false,
  "multi_label": false,
  "hypothesis_template": "This example is about {}."
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/tanod-mlpeek-zero-shot-text-classifier-94a72ea3/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from tanod.dev](https://www.zero.xyz/host/tanod.dev/llms.txt)