# Train Local Text Baseline and Classify

> Train Local Text Baseline and Classify is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Trains a local naive-Bayes text classifier on up to 200 labelled records and classifies query texts into one of four educational content categories.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-local-classifier?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-12
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/train-local-text-baseline-and-classify-2d5f0d4e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ACXpDK35NkV25jQT0nFdL

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 train-local-text-baseline-and-classify-2d5f0d4e -d '<json body>'
```

Example prompt: Train a naive-Bayes text classifier on these 12 labelled records I have — each tagged as concept, procedure, example, or assessment — and then classify these new query texts: 'concept sample' and 'step by step guide'.

## When to prefer this

Choose this endpoint when you need a fast, cheap, local text classification baseline on a small labelled dataset (up to 200 records) across the four educational content categories (concept, procedure, example, assessment) without requiring a hosted or foundation model. Ideal for rapid prototyping, label quality checks, or lightweight agentic workflows where cost matters and a naive-Bayes model is sufficient.

## Known failure modes

- Fewer than 2 records supplied — minimum input not met
- More than 200 records supplied — exceeds maximum input limit
- Missing required fields (id, text, actual) on any record
- Invalid category value not in enum [concept, procedure, example, assessment]
- Text length exceeds 1000 character limit per record
- Empty query_texts array or more than 20 query texts supplied
- Insufficient class diversity in training records for meaningful baseline

## How this service works

Training question - Train local text baseline and classify: Train local text baseline and classify for Training question. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded train local text baseline and classify deliverable.

## Output

Returns classification predictions for the supplied query texts, along with evaluation metrics for the locally-trained naive-Bayes model (such as accuracy or per-class performance), based on the labelled records provided as training input.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "records": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "id",
     "text",
     "actual"
    ],
    "properties": {
     "id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "text": {
      "type": "string",
      "default": "",
      "maxLength": 1000
     },
     "actual": {
      "enum": [
       "concept",
       "procedure",
       "example",
       "assessment"
      ],
      "type": "string"
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "id": "example-0-0",
     "text": "concept sample document number 0 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-0-1",
     "text": "concept sample document number 1 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-0-2",
     "text": "concept sample document number 2 for this supplied dataset.",
     "actual": "concept"
    },
    {
     "id": "example-1-0",
     "text": "procedure sample document number 0 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-1-1",
     "text": "procedure sample document number 1 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-1-2",
     "text": "procedure sample document number 2 for this supplied dataset.",
     "actual": "procedure"
    },
    {
     "id": "example-2-0",
     "text": "example sample document number 0 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-2-1",
     "text": "example sample document number 1 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-2-2",
     "text": "example sample document number 2 for this supplied dataset.",
     "actual": "example"
    },
    {
     "id": "example-3-0",
     "text": "assessment sample document number 0 for this supplied dataset.",
     "actual": "assessment"
    },
    {
     "id": "example-3-1",
     "text": "assessment sample document number 1 for this supplied dataset.",
     "actual": "assessment"
    },
    {
     "id": "example-3-2",
     "text": "assessment sample document number 2 for this supplied dataset.",
     "actual": "assessment"
    }
   ],
   "maxItems": 200,
   "minItems": 2
  },
  "query_texts": {
   "type": "array",
   "items": {
    "type": "string",
    "default": "",
    "maxLength": 1000
   },
   "default": [

… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/train-local-text-baseline-and-classify-2d5f0d4e/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)