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The video tutorial explains how to use a model to identify and correct labeling errors in training data. It emphasizes the importance of data quality in model performance and discusses common causes of labeling mistakes. It suggests using the trained model to guide the identification of errors systematically. The ultimate goal is to ensure accurate labeling and improve model effectiveness.
