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This video discusses how to evaluate annotation quality, focusing on Cohen's Kappa for inter-annotator agreement. A case example of computing Cohen's Kappa is provided, along with briefly talking about interpreting Kappa values and adjudicating disagreements to create a gold standard label set. It is the last video in a series covering the data collection pipeline. Evaluating annotation quality is crucial for justifying the contributions of your dataset, making it more likely for others to adopt it for model training.