A development team is building a system to classify customer support emails as 'Urgent' or 'Not Urgent'. They start with a general-purpose, pre-trained language model. Their initial strategy involves feeding an email into the model and using the numerical representation of the final word as input for a classifier. This approach yields poor results, often misclassifying long emails where the concluding words are not indicative of the overall sentiment.

To improve performance, the team modifies t

Choosing a Fine-Tuning Strategy for Sequence Summarization

Critiquing a Document Similarity System