labelswhere
Labelswhere is a conceptual framework for organizing, querying, and standardizing labels used in data annotation across machine learning projects. It focuses on metadata about labels rather than the data items themselves, enabling consistent labeling practices and easier analysis of label usage.
Core concepts in labelswhere include labels, label sets, categories, attributes, and provenance. A label represents a
In practice, labelswhere is implemented as a metadata layer atop storage of labeled data. It supports project
Common use cases include harmonizing labeling across teams, managing taxonomies for large corpora, and enabling cross-project
Adoption considerations include governance of taxonomies, privacy and access control, and licensing of tools. While several