{"uid":"cap_M8ygva2sb95Y3HRJvzbjz","slug":"training-question-training-evaluation-text-overlap-detector-9b4ce0f1","name":"Training Question - Training/Evaluation Text Overlap Detector","description":"Training question - Training evaluation text overlap: Training evaluation text overlap 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 training evaluation text overlap deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-ml-training-question-leakage?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":["concept","procedure","example","assessment"],"type":"string"}},"additionalProperties":false},"default":[{"id":"example-0-0","text":"concept sample document number 0 for this supplied dataset.","actual":"concept"},{"id":"example-0-1","text":"concept sample document number 1 for this supplied dataset.","actual":"concept"},{"id":"example-0-2","text":"concept sample document number 2 for this supplied dataset.","actual":"concept"},{"id":"example-1-0","text":"procedure sample document number 0 for this supplied dataset.","actual":"procedure"},{"id":"example-1-1","text":"procedure sample document number 1 for this supplied dataset.","actual":"procedure"},{"id":"example-1-2","text":"procedure sample document number 2 for this supplied dataset.","actual":"procedure"},{"id":"example-2-0","text":"example sample document number 0 for this supplied dataset.","actual":"example"},{"id":"example-2-1","text":"example sample document number 1 for this supplied dataset.","actual":"example"},{"id":"example-2-2","text":"example sample document number 2 for this supplied dataset.","actual":"example"},{"id":"example-3-0","text":"assessment sample document number 0 for this supplied dataset.","actual":"assessment"},{"id":"example-3-1","text":"assessment sample document number 1 for this supplied dataset.","actual":"assessment"},{"id":"example-3-2","text":"assessment sample document number 2 for this supplied dataset.","actual":"assessment"}],"maxItems":200,"minItems":2},"evaluation_texts":{"type":"array","items":{"type":"string","default":"","maxLength":1000},"default":["concept sample document number 0 for this supplied dataset.","A separate supplied document."],"maxItems":200,"minItems":1}}},"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_obN-qBs1bMLluZ6yen1CA","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":"Detects text overlap between labelled training records and evaluation texts for ML training question classification datasets.","exampleAgentPrompt":"I have 12 labelled training records for question classification — each tagged as concept, procedure, example, or assessment — and 2 evaluation texts. Can you check how much the evaluation texts overlap with my training records to make sure I don't have data leakage?","exampleUseCases":[{"title":"Detecting train-eval contamination in NLP pipeline","prompt":"I'm building a question classifier and want to make sure none of my evaluation texts have leaked into the training set. Here are my labelled training records, each marked as concept, procedure, example, or assessment, plus my evaluation texts — can you check for text overlap between them?"},{"title":"Auditing an e-learning dataset before model release","prompt":"Before I publish my training question classifier, I need to verify that the evaluation documents I used aren't essentially copies of training examples. Can you run a text overlap check on these labelled records and evaluation texts?"},{"title":"Validating a freshly split dataset","prompt":"I just split my labelled question dataset into training and evaluation sets. The training records are tagged as concept, procedure, example, or assessment. Can you measure how much text overlap exists between those splits so I can confirm they're truly independent?"}],"resultDescription":"A bounded deliverable describing the degree of text overlap between the supplied labelled training records and the evaluation texts, including overlap metrics and leakage indicators computed via local naive-Bayes baseline tools — no hosted model or foundation model is used.","failureModes":["Fewer than 2 training records supplied (minItems violation)","No evaluation texts supplied (minItems violation)","More than 200 records or evaluation texts supplied (maxItems violation)","Record text exceeds 1000 characters","Invalid label value outside enum (concept/procedure/example/assessment)","Missing required fields (id, text, actual) on any record","Payment not completed via x402 protocol"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, low-cost ($0.01 USDC) local check for training-evaluation text overlap in a small labelled dataset (up to 200 records) without invoking any hosted model or foundation model. Ideal for data quality audits on educational content classification tasks with the four-class label set (concept, procedure, example, assessment). Not suitable for large-scale datasets or tasks requiring semantic similarity beyond naive-Bayes text matching.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T00:44:26.057Z","isFirstParty":false,"canonicalSlug":"training-question-training-evaluation-text-overlap-detector-9b4ce0f1"}