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Release Change Label Distribution
x402
Release Change Label Distribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes the label distribution (class balance) of up to 200 labelled release-change text records across feature, fix, documentation, and maintenance categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release change - Label distribution: Label distribution for Release change. 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 label distribution deliverable.
Output
A deliverable summarizing the count and proportion of each label (feature, fix, documentation, maintenance) across the supplied labelled records, giving a clear picture of class balance or imbalance in the dataset.
Example use cases
Pre-training dataset balance check
“I have 150 labelled changelog entries tagged as feature, fix, documentation, or maintenance — can you tell me how many records fall into each category so I know if the dataset is imbalanced before I train a classifier?”
Release audit label summary
“Here are my 80 labelled release change records from last quarter. Can you summarize the label distribution to tell me how many are bugs, new features, docs updates, and maintenance tasks?”
QA of manual labelling effort
“My team just hand-labelled 200 release notes as feature, fix, documentation, or maintenance. Can you compute the label breakdown so I can see if labellers are over-assigning any particular category?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-release-change-label-balance?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, cheap ($0.01) label distribution summary for a small labelled release-change dataset (up to 200 records) without needing a hosted model or foundation-model inference. Ideal for dataset auditing, class-balance checks before training naive-Bayes or similar lightweight classifiers, and QA of manual labelling — specifically for software release change categories (feature, fix, documentation, maintenance).
Side effects
- Fewer than 2 records supplied — minimum input requirement not met
- More than 200 records supplied — exceeds maximum batch size
- Invalid label value outside allowed enum (feature, fix, documentation, maintenance)
- Missing required fields (id, text, actual) on any record
- Record text exceeds 1000 character limit — truncation or rejection
- Empty or missing records array
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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