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Return Reason Labelled JSONL Export
x402
Return Reason 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 2026-10-14).
Exports up to 200 labelled return-reason text records in JSONL format for local ML evaluation and naive-Bayes baseline training
Price / call$0.01
Success—
StatusUnknown
How this service works
Return reason - Labelled JSONL export: Labelled JSONL export for Return reason. 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 string, and return reason label (damaged, wrong_item, unwanted, or other), suitable for local dataset tooling, model evaluation, and naive Bayes baseline training.
Example use cases
Naive Bayes baseline training data
“I have 80 customer return comments already labelled as damaged, wrong_item, unwanted, or other. Can you export them as a JSONL file so I can train a quick naive Bayes baseline model on my local machine?”
Return reason classifier evaluation set
“Generate a JSONL export from these 50 labelled return reason records — I need them in a clean format to run offline evaluation metrics against my existing text classifier.”
Small dataset for ML pipeline testing
“I want to test my local ML data pipeline end-to-end. Can you package these 30 return reason text samples with their labels — damaged, wrong_item, unwanted, or other — into a labelled JSONL export?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-return-reason-jsonl-export?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a ready-to-use labelled JSONL export of return reason data for offline use cases such as local evaluation, dataset inspection, or training a lightweight naive Bayes classifier — and you do not need hosted model inference or foundation model fine-tuning pipelines.
Side effects
- Fewer than 2 records supplied — minimum input requirement not met
- More than 200 records supplied — exceeds maximum item limit
- Invalid label value outside the enum (damaged, wrong_item, unwanted, other)
- Text field exceeds 1000 character limit per record
- Missing required fields (id, text, or actual) on any record
- Payment not completed — x402 payment required before delivery
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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