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Training Question Labelled JSONL Export
x402
Training Question Labelled JSONL Export is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Exports up to 200 labelled training question records (concept/procedure/example/assessment) as a JSONL file for local ML dataset use.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training question - Labelled JSONL export: Labelled JSONL export 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 labelled jsonl export deliverable.
Output
A bounded JSONL export containing up to 200 labelled records, each with an ID, text field, and actual label (concept, procedure, example, or assessment), formatted for use with local dataset tools, evaluation scripts, and naive-Bayes baselines.
Example use cases
Local naive-Bayes training dataset prep
“I have 50 labelled question records split between concept, procedure, example, and assessment categories — can you export them as a JSONL file so I can use them to train a local naive-Bayes classifier?”
Curriculum content type annotation export
“I've tagged 80 curriculum text snippets with labels like concept and assessment. Please export them in JSONL format so I can evaluate my annotation quality locally.”
Small evaluation set for text classifier
“I need a JSONL export of these 30 labelled question records — some are procedures, some are examples — so I can run a baseline evaluation on my local text classification pipeline.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-training-question-jsonl-export?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap JSONL export of pre-labelled training question records (concept/procedure/example/assessment) for local ML tooling, evaluation, or naive-Bayes baselines — without requiring a hosted model or foundation model training pipeline. Best for small annotated datasets up to 200 records at minimal cost.
Side effects
- Fewer than 2 records supplied — minimum input not met
- More than 200 records supplied — exceeds maximum
- Missing required fields (id, text, or actual) on any record
- Invalid label value outside the allowed enum (concept/procedure/example/assessment)
- Text field exceeds 1000 characters for a record
- ID field empty or exceeds 80 characters
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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