The metrics used to test an NLP model are precision, recall, and F1. Also, we use accuracy for evaluating the model’s performance. The ratio of prediction and the desired output yields the accuracy of the model.
Precision is the ratio of true positive instances and the total number of positively predicted instances.
Recall is the ratio of true positive instances and the total actual positive instances.
