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For most benchmarks, upload data via git or the CSV importer in the benchmark hub. See Uploading Data for step-by-step instructions. Git is best for:
  • large or versioned datasets
  • including scorer implementations and documentation alongside data
  • reproducible, CI-friendly workflows
The browser editor and CSV importer are best for:
  • quick prototypes
  • small manually-curated test sets
  • one-off imports

SDK-based creation

You can also create and push datasets programmatically from Python. This is useful for generating datasets from code or integrating with data pipelines.

Create from Python data

Add a stable row_id field whenever possible. It makes resume behavior and row-level tracking much more reliable.

Create from CSV

The dataset name defaults to the CSV filename without the extension.

Modify rows

Row updates by index can change row ordering and row content. Keep this in mind if your downstream logic assumes fixed order.

Push to ZeroEval

After push, you can pin future runs to that version number:

Validate shape