The standard, exclusive evaluation framework, by contrast, only measures the accuracy of the final trained model's predictions on a held out test set.
the inclusive framework is relevant to the user who wants to annotate a finite corpus as fast and as accurately as possible by any means necessary, whereas the exclusive framework is relevant to the user who wants to build an NER tool that can generalize well to other corpora.