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Return Reason Training Evaluation Text Overlap
x402
Return Reason Training Evaluation Text Overlap is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-11).
Checks whether evaluation texts overlap with labeled training records used to classify product return reasons, detecting potential data leakage.
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Training evaluation text overlap: Training evaluation text overlap for Return reason. 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 the degree of text overlap between the supplied labeled training records and the evaluation texts, identifying which evaluation documents share content with training data and quantifying the extent of potential data leakage.
Example use cases
Pre-training data leakage audit
“Before I train my return reason classifier, can you check whether any of these 20 evaluation texts I'm using for benchmarking overlap with the 80 labeled training examples I'm supplying? Labels are damaged, wrong_item, unwanted, and other.”
Dataset split contamination check
“I split my customer return comments dataset into train and eval, but I want to make sure there's no text leakage between the two halves — can you run the training evaluation text overlap check on my labeled records and evaluation texts?”
Naive-Bayes baseline validity verification
“I'm building a naive-Bayes baseline for classifying returns as damaged, wrong_item, unwanted, or other, and I want to confirm my evaluation set isn't contaminated by my 150 labeled training texts — can you run the overlap check?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-leakage?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, inexpensive ($0.01 USDC) check for text overlap between a labeled training set and evaluation texts specifically for return reason classification (damaged, wrong_item, unwanted, other). It is ideal for small datasets (up to 200 records) and local evaluation workflows where you want to detect data leakage without deploying a hosted model or using foundation model infrastructure.
Side effects
- Fewer than 2 training records supplied — minimum items violation
- Evaluation texts array is empty or missing
- Individual text exceeds 1000 character limit
- Record ID missing or blank — required field violation
- Invalid actual label supplied outside of allowed enum values (damaged, wrong_item, unwanted, other)
- More than 200 records or evaluation texts supplied — exceeds maxItems limit
- Payment not included or insufficient — x402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| evaluation_texts | array |
Tags
x402
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