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Sentiment Training Evaluation Text Overlap
x402
Sentiment 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 2026-10-20).
Checks whether evaluation texts overlap with labelled training records, providing a data-leakage analysis for sentiment classification datasets.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sentiment - Training evaluation text overlap: Training evaluation text overlap 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 training evaluation text overlap deliverable.
Output
A bounded deliverable reporting which evaluation texts overlap with training records, including overlap indicators and metrics that reveal potential data leakage between the training and evaluation splits of a sentiment dataset.
Example use cases
Pre-deployment leakage audit for sentiment model
“Before I ship my sentiment classifier, can you check these 100 labelled training records (positive/neutral/negative) against my 30 evaluation texts to see if any evaluation examples leaked into training?”
Dataset quality check for NLP pipeline
“I'm building a sentiment analysis pipeline and want to make sure my evaluation set is clean — here are 80 labelled training texts and 25 evaluation documents, please detect any text overlap between them.”
Research reproducibility validation
“For my research paper I need to prove my sentiment benchmark is contamination-free — can you run an overlap analysis between my 150 labelled training records and 40 held-out evaluation texts?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-sentiment-leakage?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to audit a sentiment-labelled dataset for training-evaluation text leakage before model evaluation or publication. It is specifically designed for small-to-medium datasets (up to 200 records) with three-class sentiment labels and returns a bounded overlap report without requiring a hosted model or foundation-model infrastructure. Prefer it over general-purpose deduplication tools when the task is specifically validating sentiment classification dataset integrity.
Side effects
- Fewer than 2 records provided — minimum 2 labelled records required
- More than 200 records or 200 evaluation texts supplied — exceeds maxItems limit
- Record text exceeds 1000 characters — truncation or rejection
- Missing required fields (id, text, actual) in any record object
- Invalid sentiment label — must be one of positive, neutral, negative
- Empty evaluation_texts array — at least 1 evaluation text required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| evaluation_texts | array |
Tags
x402
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