A text classification model is designed with two sequential components: an 'encoder' that transforms an input sentence into a numerical vector, and a 'classifier' that uses this vector to predict a category. During evaluation, it is discovered that the model performs poorly. A detailed inspection reveals that semantically opposite sentences, such as 'The movie was brilliant and captivating' and 'The movie was dull and boring', are both being transformed into nearly identical numerical vectors by

Optimizing a Spam Detection Model

Component Roles in a Probabilistic Text Classifier