Bring your own data
Turn your CSV, JSONL, or media files into a scored dataset.
1. From a CSV or JSONL
Your data needs two columns: one for the model input and one for the reference output RedCrown scores against.
On Prove (/), paste examples directly into the task input. For a file, use the Upload screen (/advanced/upload) to import a results file as a ranked run.
From a CSV:
redcrown build-dataset --from-csv data.csv --task extract --input-col input --reference-col reference --out task.jsonFrom a JSONL file, replace --from-csv with --from-jsonl and point to your .jsonl file.
Pass examples inline in the examples argument to scaffold_experiment or prove_task. No separate dataset build step is needed.
2. Audio and images
For transcription or vision tasks, reference media by URL. RedCrown fetches each file at eval time.
On Prove, attach media files to your examples using the media attachment control. Supported types include audio and images.
After building a dataset with per-item audio references, pass the base URL for your hosted clips:
redcrown eval task.json --audio-base https://host/clips/Include input URLs directly in the examples array. RedCrown resolves the URLs during the run.
3. A public corpus
RedCrown ships two built-in public corpora for benchmarking and reproducibility.
Not available via the UI. Use the CLI for public corpora.
Build from the PriMock57 clinical transcription corpus:
redcrown build-dataset primock57 --out exp.jsonBuild from an MMLU-Pro reasoning subset:
redcrown build-dataset mmlu-pro --out exp.jsonNot available via MCP. Use the CLI commands above to build corpus datasets.
Last verified .