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Training Example Metadata — Numeric Type Check
x402
Training Example Metadata — Numeric Type Check 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).
Validates that numeric columns in a training-data CSV contain only valid numbers within specified bounds and allowed categories.
Price / call$0.01
Success—
StatusUnknown
How this service works
Training example metadata — Numeric type check: Numeric type check for Training example metadata. Validate a declared CSV table contract, up to 1000 rows. Quoted cells supported; no external enrichment. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one CSV numeric type check report.
Output
A structured CSV numeric type check report indicating which rows and cells contain invalid or non-numeric values, out-of-range numbers, or cells exceeding the maximum length, along with a pass/fail summary for the declared contract.
Example use cases
Pre-training dataset quality gate
“Before I kick off model training, can you validate this CSV training file to confirm all numeric columns like text_length and sample_weight contain valid numbers between -1000000000000 and 1000000000000, using comma as the delimiter?”
CI pipeline CSV contract check
“I need to verify our exported training CSV passes a numeric type contract — delimiter is comma, max cell length 1000, and allowed category values are group-a and group-b. Can you run the check and flag any rows that fail?”
Data cleaning audit before fine-tuning
“We just received a new batch of labeled training examples as a CSV and I want to make sure all the numeric fields are clean — can you check the file for non-numeric values or numbers outside the range -100 to 100, with tab as the delimiter?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-csv-training-data-types?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, contract-based numeric type validation on a training data CSV — particularly when you want to enforce value ranges and allowed category labels in a single pass without external enrichment. It is purpose-built for ML training metadata validation (up to 1000 rows) and is more cost-effective than a full schema validation service when only numeric type correctness is needed.
Side effects
- CSV exceeds 100,000 character limit — request rejected
- CSV has zero rows or is empty — validation cannot proceed
- Delimiter mismatch causes all cells to be unparsed
- minimum value exceeds maximum value — invalid range configuration
- Cell values exceed max_cell_length and are flagged or truncated
- Malformed CSV with unclosed quotes may produce unexpected parse results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string | |
| maximum | number | |
| minimum | number | |
| delimiter | string | |
| max_cell_length | integer | |
| allowed_categories | array |
Tags
x402
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