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this video discusses how to evaluate annotation quality ill cover inner annotator agreement focusing specifically on cohens kappa ill work through a case example of computing cohens kappa for a small set of annotations and then briefly talk about interpreting kappa values and adjudicating disagreed upon instances to create your gold standard set of labels this video is the last video in a series of videos covering the data collection pipeline from start to finish if you havent watched the first four videos you may want to watch them before proceeding this video focuses specifically on evaluating annotation quality after youve collected your annotations evaluating annotation quality is very important because it allows you to objectively justify the contributions of your data set demonstrating that your annotations are high quality will make other people much more likely to adopt your data set for training their own models and for your own sake itll be a good indicator of how eas