How do you handle errors in an NLP model?

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Natural language processing (NLP) is a branch of artificial intelligence (AI) that deals with the interaction between computers and human languages. NLP models are designed to perform tasks such as text analysis, sentiment detection, machine translation, speech recognition, and more. However, NLP models are not perfect and can make errors due to various reasons, such as data quality, model complexity, linguistic ambiguity, and domain specificity. How do you handle errors in an NLP model? Here are some tips and best practices to follow.

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