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ML Labeling Quality Basics
Get better labels
Ensuring ML labeling quality requires clear instructions, training labelers, using overlap/consensus, spot checks with gold labels, measuring agreement, and iterating guidelines to reduce noise and bias.
- labeling quality
- gold data
- consensus
- instructions
- mlops
Guide
Provide examples, edge cases, and definitions to labelers.
Check
Use overlap and gold labels to measure agreement.
Improve
Review disagreements, refine guidelines, and retrain labelers.
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