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Sentiment Exact Normalised Duplicate Audit
x402
Sentiment 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).
Identifies exact normalised duplicate text records across a labelled sentiment dataset of up to 200 entries, flagging redundancy issues for dataset quality review.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sentiment - Exact normalised duplicate audit: Exact normalised duplicate audit for Sentiment. 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 structured audit report identifying which of the supplied labelled text records are exact normalised duplicates of one another, including duplicate pair references by record ID, counts, and a summary of dataset redundancy.
Example use cases
Pre-training dataset deduplication check
“I have 150 sentiment-labelled text samples I want to use for training a classifier — can you run an exact normalised duplicate audit on them so I can remove any redundant records before I start?”
Benchmark evaluation data integrity audit
“Before I run model evaluation, can you check my 80 labelled sentiment records for exact normalised duplicates? I need to make sure the test set is clean with no repeated examples skewing the results.”
Crowdsourced annotation duplicate detection
“We just collected 200 annotated sentiment labels from crowdworkers — can you audit the dataset for exact normalised duplicates so we can flag records that might have been submitted more than once?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-duplicates?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, cost-effective exact normalised duplicate check on a small-to-medium labelled sentiment dataset (up to 200 records) without invoking a heavy model or foundation model pipeline. Ideal for pre-training data hygiene, evaluation set integrity checks, or annotation quality audits where exact string-level deduplication (after normalisation) is sufficient and probabilistic or semantic deduplication is not required.
Side effects
- Fewer than 2 records supplied — minimum input not met
- More than 200 records supplied — exceeds maximum batch size
- Record missing required fields (id, text, or actual) — validation error
- Invalid sentiment label value outside positive/neutral/negative enum — rejected
- Text exceeding 1000 character limit per record — truncation or rejection
- Empty text string provided — may produce false duplicate matches
- Payment not completed — 402 response, no result returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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