Label Evaluation Harness

機械支援の翻訳下書き (Japanese) for "Label Evaluation Harness": Label Evaluation Harness is a ml test system that runs repeatable checks against model behavior for ground-truth or weak-supervision annotation. It uses fixtures, metrics, thresholds, and regression reports so teams can compare releases with evidence while keeping evidence, reliability, and public-safe operational boundaries clear.

“例文の下書き: The machine learning team used Label Evaluation Harness when the label set had disagreement, so the team could compare releases with evidence before the model moved into evaluation.”