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Product Category Label Distribution
x402
Product Category 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 2026-10-16).
Computes label distribution statistics for a supplied set of labelled product-category text records (apparel, home, electronics, other)
Price / call$0.01
Success—
StatusUnknown
How this service works
Product category - Label distribution: Label distribution for Product category. 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 counts and/or proportions of records per product category label (apparel, home, electronics, other) across the up to 200 supplied labelled text records, enabling assessment of class balance.
Example use cases
Pre-training class imbalance audit
“Before I train my product classifier, I want to check if my 150 labelled records are evenly spread across apparel, home, electronics, and other — can you compute the label distribution for me?”
Dataset quality check for e-commerce catalog
“I've got a batch of 80 product descriptions all manually tagged — some as apparel, some as home goods, some electronics, and a few as other. Can you tell me how many records fall into each category so I can spot any skew?”
Naive-Bayes baseline evaluation prep
“I'm building a small naive-Bayes product category model and need to know if my 120 training samples are balanced across apparel, home, electronics, and other before I proceed — can you give me the label distribution?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-product-category-label-balance?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a small set of pre-labelled product text records (up to 200) and need a quick, cheap label distribution summary before building or evaluating a product category classifier. It is ideal for dataset auditing, class-imbalance detection, and naive-Bayes baseline preparation without needing a hosted model or foundation-model API. Prefer it over general-purpose LLM analysis when you want a deterministic, bounded, low-cost computation tied specifically to the four product categories (apparel, home, electronics, other).
Side effects
- Fewer than 2 records supplied — minimum item count not met
- More than 200 records supplied — exceeds maximum item count
- Missing required fields (id, text, actual) on any record
- Invalid category value in 'actual' field — must be one of apparel, home, electronics, other
- Record ID exceeds 80 characters — validation error
- Text field exceeds 1000 characters per record — truncation or rejection
- Payment not completed — x402 payment required before response is returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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