Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence."
[Reference:HCIP-AI EI Developer V2.5 Official Study Guide – Chapter: NLP Task Categories, , ]
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