Efficiently managing and maintaining high-quality training data is a cornerstone of machine learning success. To achieve this, the systematic measurement of labeling quality becomes paramount. Labeling quality encompasses the precision, uniformity, and dependability of annotations, whether generated by human labelers or automated labeling models. Keeping that in mind we have