@lewtun,
Yes, you are correct, I’m trying to work on a named entity recognition task. So what I was hoping to achieve was to be able to do something like below:
Dict = {'train': {'id': np.arange(len(train_texts)),
                  'tokens': train_texts,
                  'ner_tags': train_tags(
                                        datasets.features.ClassLabel(
                                            names=['B-ORG', 'I-ORG', 'O', 'I-EVENT', 'I-PERSON', 'B-PERSON']
                                        )
                                      )
                },
       'val': {'id': np.arange(len(train_texts)),
                  'tokens': train_texts,
                  'ner_tags': train_tags(
                                        datasets.features.ClassLabel(
                                            names=['B-ORG', 'I-ORG', 'O', 'I-EVENT', 'I-PERSON', 'B-PERSON']
                                        )
                                      )
                },
        'test': {'id': np.arange(len(train_texts)),
                  'tokens': train_texts,
                  'ner_tags': train_tags(
                                        datasets.features.ClassLabel(
                                            names=['B-ORG', 'I-ORG', 'O', 'I-EVENT', 'I-PERSON', 'B-PERSON']
                                        )
                                      )
                }
       }

Specifically, to include the specification of the ClassLabel in the python dictionary statement. Doing it that way does not work because it gives a 'list object is not callable error`. Is there a way to work around that?