Sequence classification models predict a single label for an entire input sequence, as opposed to sequence labeling models that predict a label for each token. Example applications include:

• Sentiment analysis (input: product review, output: customer's rating)
• DNA sequence analysis (input: sequence of genetic code, output: protein detection)
• Video activity recognition (input: sequence of video frames, output: activity label)