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Issue Kind Exact Normalised Duplicate Audit
x402
Issue Kind Exact Normalised Duplicate Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Audits up to 200 labelled issue-kind text records to identify exact normalised duplicates within the dataset.
Price / call$0.01
Success—
StatusUnknown
How this service works
Issue kind - Exact normalised duplicate audit: Exact normalised duplicate audit for Issue kind. 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 exact normalised duplicate audit deliverable.
Output
A bounded duplicate audit report identifying which records in the submitted dataset are exact normalised duplicates of one another, based on text normalisation and exact matching logic, with no hosted model inference involved.
Example use cases
Pre-training data quality check
“Before I train my issue classifier, can you audit these 150 labelled issue records — IDs, texts, and labels like bug or request — to find any exact normalised duplicates that would skew the model?”
Support ticket deduplication audit
“I have 80 support tickets each tagged as bug, request, question, or other — can you run a duplicate audit on them and flag any where the normalised text is identical?”
Dataset integrity validation for ML pipeline
“Can you check my 200 issue-kind training samples for exact normalised duplicates? Each record has an ID, a text description, and one of four labels: bug, request, question, or other.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-issue-kind-duplicates?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, deterministic audit for exact normalised text duplicates in a small labelled issue-kind dataset (up to 200 records). It is ideal for pre-training data cleaning or dataset quality checks where you need a cheap ($0.01 USDC), rule-based duplicate report without invoking a large language model or foundation model.
Side effects
- Fewer than 2 records submitted — minimum input not met
- More than 200 records submitted — exceeds batch limit
- Invalid or missing 'actual' label not in [bug, request, question, other] — schema validation error
- Record ID missing or empty — required field error
- Payment not completed — 402 response, no result returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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