Which metric is NOT commonly used to evaluate NLP models?

The readability score is not commonly used to evaluate NLP models in the same way that metrics like F1 score, recall, and precision are. F1 score, recall, and precision are crucial for measuring the performance of classification models, particularly in binary and multi-class tasks. They help quantify how well a model can distinguish between classes and its ability to minimize false positives and negatives.

In contrast, the readability score measures how easy a text is to read and understand. It focuses on the text's presentation and audience suitability rather than evaluating the effectiveness of a model’s predictive capabilities or classification accuracy. Therefore, while readability can be a useful quality measure for written content, it does not serve as a performance metric specific to NLP models. This distinction makes the readability score less relevant in the context of evaluating NLP model performance.