What are the most effective sequence labeling algorithms for NLP model architectures in ML?

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Sequence labeling is a common task in natural language processing (NLP) that involves assigning labels to each element of a sequence, such as words, characters, or tokens. For example, sequence labeling can be used for named entity recognition, part-of-speech tagging, or sentiment analysis. In this article, you will learn about some of the most effective sequence labeling algorithms for NLP model architectures in machine learning (ML).

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