HuSig Harmony
Ensure annotation accuracy and reliability for complex tasks through collaborative consensus-based labeling.
HuSig Harmony enables consensus-based labeling by aggregating input from multiple annotators. This reduces individual bias and ensures more reliable labels for complex or subjective tasks, improving downstream model performance.
Built-in QA layers such as gold-standard checks, annotator performance tracking, and real-time flagging systems ensure that only high-quality annotations are accepted into the dataset.
HuSig Harmony integrates active learning to prioritize the most uncertain or impactful samples for human review. This accelerates the annotation process while maintaining model learning efficiency.
Multiple annotators can work simultaneously with tools for commenting, flagging, and resolving disagreements. This fosters transparency and boosts label consistency across contributors.