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Relevance 6/10Operations and WorkflowBeginner5 min read
Deduplication
Deduplication removes exact duplicate samples from datasets.
Why it matters for annotators
Duplicates bias metrics and waste annotation budget.
Visual mental model
Raw data -> duplicate detection -> clean dataset.
Examples (bad vs good)
Scenario: Real annotation scenario involving Deduplication
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.