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This video tutorial focuses on evaluating annotation quality, specifically using Cohen's Kappa to assess inner annotator agreement. It provides a case example of calculating Cohen's Kappa for a small set of annotations and discusses how to interpret the resulting Kappa values. Additionally, it addresses the process of adjudicating disagreements among annotators to establish a gold standard set of labels. As the final installment in a series on the data collection pipeline, this video emphasizes the importance of evaluating annotation quality, as it bolsters the credibility of your dataset and encourages broader adoption for model training. Viewers are advised to watch the preceding videos for context.