{"uid":"cap_-CZRx-vex3q2zYUngpVqJ","slug":"return-reason-exact-normalised-duplicate-audit-d35eb2c3","name":"Return Reason Exact Normalised Duplicate Audit","description":"Return reason - Exact normalised duplicate audit: Exact normalised duplicate audit 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 exact normalised duplicate audit deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-duplicates?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"records":{"type":"array","items":{"type":"object","required":["id","text","actual"],"properties":{"id":{"type":"string","default":"","maxLength":80,"minLength":1},"text":{"type":"string","default":"","maxLength":1000},"actual":{"enum":["damaged","wrong_item","unwanted","other"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"damaged sample document number 0 for this supplied dataset.","actual":"damaged"},{"id":"example-0-1","text":"damaged sample document number 1 for this supplied dataset.","actual":"damaged"},{"id":"example-0-2","text":"damaged sample document number 2 for this supplied dataset.","actual":"damaged"},{"id":"example-1-0","text":"wrong_item sample document number 0 for this supplied dataset.","actual":"wrong_item"},{"id":"example-1-1","text":"wrong_item sample document number 1 for this supplied dataset.","actual":"wrong_item"},{"id":"example-1-2","text":"wrong_item sample document number 2 for this supplied dataset.","actual":"wrong_item"},{"id":"example-2-0","text":"unwanted sample document number 0 for this supplied dataset.","actual":"unwanted"},{"id":"example-2-1","text":"unwanted sample document number 1 for this supplied dataset.","actual":"unwanted"},{"id":"example-2-2","text":"unwanted sample document number 2 for this supplied dataset.","actual":"unwanted"},{"id":"example-3-0","text":"other sample document number 0 for this supplied dataset.","actual":"other"},{"id":"example-3-1","text":"other sample document number 1 for this supplied dataset.","actual":"other"},{"id":"example-3-2","text":"other sample document number 2 for this supplied dataset.","actual":"other"}],"maxItems":200,"minItems":2}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_rUdOJyUklQ3Y3AG5FyM6F","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Audits up to 200 labelled return-reason text records to detect exact normalised duplicates within the supplied dataset.","exampleAgentPrompt":"Can you audit my return reason dataset for exact normalised duplicates? I have about 50 labelled records — each with an ID, the customer's return text, and the actual category (damaged, wrong_item, unwanted, or other) — and I want to know which ones are exact matches after normalisation.","exampleUseCases":[{"title":"Pre-training data deduplication check","prompt":"Before I train my return reason classifier, can you check these 120 labelled records for exact normalised duplicates? Each record has an ID, the return text, and one of the labels: damaged, wrong_item, unwanted, or other."},{"title":"Return annotation QA for labelling team","prompt":"Our annotation team just labelled 80 customer return descriptions — can you run an exact normalised duplicate audit on them so we can flag any records that are identical after normalisation before we sign off on the batch?"},{"title":"Ecommerce returns dataset cleaning","prompt":"I'm cleaning up our returns dataset — here are 200 records each with an ID, the customer's stated return reason text, and the category label (damaged, wrong_item, unwanted, or other). Can you audit them for exact normalised duplicates so I know which ones to remove?"}],"resultDescription":"A duplicate audit report identifying which of the supplied labelled return-reason text records are exact normalised duplicates, including their IDs and relevant match details, bounded to the submitted batch of up to 200 records.","failureModes":["Fewer than 2 records submitted — minimum record count not met","More than 200 records submitted — exceeds maximum batch size","Missing required fields (id, text, or actual) on one or more records","Invalid enum value for 'actual' field (must be damaged, wrong_item, unwanted, or other)","Record ID exceeds 80 characters or text exceeds 1000 characters","Payment not fulfilled via x402 protocol — request rejected"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, deterministic exact-normalised duplicate check on a small batch (up to 200 records) of labelled return-reason text data, particularly before training a classifier or validating annotation quality. It is not a semantic similarity or fuzzy deduplication tool — it only catches exact matches after normalisation. Prefer it over general-purpose deduplication tools when your data specifically uses the return-reason label schema (damaged, wrong_item, unwanted, other) and you want a bounded, low-cost ($0.01 USDC) deliverable with no model training involved.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:57:06.504Z","isFirstParty":false,"canonicalSlug":"return-reason-exact-normalised-duplicate-audit-d35eb2c3"}