In the context of missed labels, what does training mode help identify?

Training mode is designed to enhance the model's ability to learn from previously seen examples by identifying gaps in labeling. In the context of missed labels, this mode specifically helps to identify verbatims that lack necessary label applications. When the model is trained, it evaluates the collected examples and notes which entries have not been correctly labeled, allowing for adjustments to be made.

This targeted identification of verbatims without labels is crucial because it can significantly impact the accuracy and effectiveness of the AI model. Labels inform the model about the characteristics it needs to learn and recognize; without proper labeling, the model may fail to understand key patterns or contexts.

While the other options discuss different aspects of training, they do not specifically address the primary function of training mode in identifying unlabelled verbatims, making the correct choice the most relevant in this scenario.