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ML Task Type Label Distribution
x402
ML Task Type 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 label distribution statistics for a labelled dataset of task-type text records across research, writing, analysis, and implementation categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Task type - Label distribution: Label distribution for Task type. 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
Returns a label distribution report showing the count and proportion of each task type label (research, writing, analysis, implementation) across the submitted records, helping identify class imbalance in the dataset.
Example use cases
Pre-training class imbalance check
“I have a batch of 150 labelled task records tagged as research, writing, analysis, or implementation. Can you compute the label distribution so I can see if the classes are balanced before I train my classifier?”
Dataset audit for annotation quality
“We just finished annotating 80 text samples into four task types — research, writing, analysis, and implementation. Can you give me a breakdown of how many records fell into each category so I can spot any annotation skew?”
Curriculum design dataset review
“I'm building a training curriculum and labelled 120 example tasks as either research, writing, analysis, or implementation. Can you calculate the label distribution so I know if any category is underrepresented?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-task-type-label-balance?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, cheap ($0.01) label distribution summary for a small labelled text dataset (up to 200 records) classified into task types (research, writing, analysis, implementation). Ideal for pre-training dataset audits, class imbalance checks, and annotation quality reviews without needing a hosted model or expensive foundation model call.
Side effects
- Fewer than 2 records submitted — minimum input size not met
- More than 200 records submitted — maximum input size exceeded
- Missing required fields (id, text, actual) in one or more records
- Invalid label value outside the allowed enum (research, writing, analysis, implementation)
- Record text exceeds 1000 character limit
- Record ID exceeds 80 character limit or is empty
- Payment not provided or insufficient — x402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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