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Training Question Exact Normalised Duplicate Audit
x402
Training Question 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 2026-10-12).
Audits up to 200 labelled training question text records to detect exact normalised duplicates within the dataset.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Exact normalised duplicate audit: Exact normalised duplicate audit 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 exact normalised duplicate audit deliverable.
Output
A bounded exact normalised duplicate audit report identifying which records among the submitted labelled text entries are exact matches after normalisation, including duplicate pairs or groups and counts, enabling data cleaning before model training.
Example use cases
Pre-training data deduplication check
“Before I fine-tune my text classifier, can you check these 180 labelled training questions for exact normalised duplicates? Each record has an ID, the question text, and a label of concept, procedure, example, or assessment.”
Dataset quality audit for EdTech platform
“I have 120 training questions labelled as concept, procedure, example, or assessment for our learning platform — can you run a duplicate audit on them to make sure there are no repeated questions after normalisation?”
Curriculum data cleaning before model training
“We collected 200 training questions tagged as concept, procedure, example, or assessment and need to clean them up — please audit them for exact normalised duplicates so we can remove redundant entries before training.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-duplicates?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a fast, low-cost exact normalised duplicate check on a small-to-medium labelled training question dataset (up to 200 records) before running ML training pipelines. It is purpose-built for training data with categorical labels (concept, procedure, example, assessment) and is ideal for data cleaning workflows where semantic similarity is not required — only exact matches after normalisation.
Side effects
- Fewer than 2 records submitted — minimum input requirement not met
- More than 200 records submitted — exceeds maximum item limit
- Record missing required fields (id, text, or actual) — schema validation error
- Invalid label value outside allowed enum (concept, procedure, example, assessment) — rejected
- Record ID exceeds 80 characters — schema violation
- Text field exceeds 1000 characters — schema violation
- Payment of 0.01 USDC not provided or rejected — 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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