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Knowledge Topic Label Distribution
x402
Knowledge Topic Label Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Analyzes a labeled dataset of knowledge-base text records and returns the distribution of labels (setup, usage, troubleshooting, reference) across up to 200 documents.
Price / call$0.01
Success—
StatusUnknown
How this service works
Knowledge topic - Label distribution: Label distribution for Knowledge topic. 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 label distribution deliverable.
Output
Returns a label distribution report showing the count and proportion of each of the four knowledge topic labels (setup, usage, troubleshooting, reference) across the submitted records, enabling assessment of class balance for up to 200 labeled text documents.
Example use cases
Pre-training class balance check
“I have 80 labeled knowledge base articles I'm about to use to train a classifier — can you check how the labels are distributed across setup, usage, troubleshooting, and reference so I can see if the dataset is imbalanced before I start training?”
Documentation audit for content teams
“We have about 150 tagged support documents and I want to know what percentage fall into each of setup, usage, troubleshooting, and reference — can you give me a breakdown so we know which content areas we're underrepresenting?”
Baseline evaluation for ML pipeline
“Can you compute the label distribution for this batch of 60 labeled text records across setup, usage, troubleshooting, and reference categories? I need to understand the class balance to set up a naive-Bayes baseline for our knowledge topic classifier.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-knowledge-topic-label-balance?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, lightweight label distribution analysis for a knowledge-base text dataset with labels in the set {setup, usage, troubleshooting, reference}. It is ideal for pre-training class balance checks, documentation audits, and baseline evaluations without requiring a hosted model. Prefer it over general-purpose ML APIs when you specifically work with knowledge topic categories and want a bounded, low-cost ($0.01 USDC) per-call deliverable.
Side effects
- Fewer than 2 records submitted — returns validation error
- More than 200 records submitted — returns a limit exceeded error
- Record text exceeds 1000 characters — field truncation or rejection
- Invalid or missing 'actual' label enum value — schema validation error
- Malformed record structure (missing id, text, or actual fields) — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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