Metaphorical text classification is a binary classification task trained on a dataset of texts labeled as literal or metaphorical, denoted as $I^{M|L}$. This classification model, often referred to as $MC$, determines whether a given text contains metaphorical language. Common models and algorithms used for this task include Naive Bayes, Random Forests, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Logistic Regression, Multi-Layer Perceptrons (MLP), BERT, and XLM-RoBERTa (XLM-R).