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Issue Kind – Training/Evaluation Text Overlap Detector
x402
Issue Kind – Training/Evaluation Text Overlap Detector 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).
Detects text overlap between training and evaluation sets for issue-kind classification (bug/request/question/other) using a local naive-Bayes baseline.
Price / call$0.01
Success—
StatusUnknown
How this service works
Issue kind - Training evaluation text overlap: Training evaluation text overlap 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 training evaluation text overlap deliverable.
Output
A bounded deliverable reporting text overlap between the supplied labelled training records and the evaluation texts, including identified overlapping entries and leakage metrics derived from a local naive-Bayes baseline analysis.
Example use cases
Support ticket classifier data audit
“I have a set of labelled support tickets — some marked as bug, request, question, or other — and a separate list of evaluation texts. Can you check if any of the training texts appear in the evaluation set so I know my classifier isn't cheating?”
NLP dataset contamination check before model release
“Before I publish my issue-kind classification dataset, I want to make sure there's no text leakage between the 150 training records and the 50 evaluation documents I'm using. Can you run a training-evaluation text overlap check on them?”
GitHub issue triage model validation
“I trained a naive Bayes model to sort GitHub issues into bugs, feature requests, questions, and other categories. Can you check my training records and evaluation texts for any overlapping content so I can trust my accuracy numbers?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-issue-kind-leakage?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, local assessment of training-evaluation text overlap for an issue-kind classifier (bug/request/question/other) without requiring a hosted model or foundation-model training. Ideal for small datasets up to 200 records where data leakage detection is the primary concern and cost sensitivity matters ($0.01 per call).
Side effects
- Fewer than 2 records supplied — validation error
- More than 200 records or evaluation texts supplied — rejected with maxItems error
- Record missing required 'id', 'text', or 'actual' field — schema validation failure
- Invalid label value outside bug/request/question/other enum — rejected
- Text exceeding 1000 characters per record — truncation or rejection
- Empty evaluation_texts array — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| evaluation_texts | array |
Tags
x402
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