Add `NerModel.test()`

    def __repr__(self) -> str:

        return '{}: {}'.format(self.__class__.__name__, self)

    def test(self, test_tagged_sentence_basename: str) -> float:

        lang_module = LangModule(self.model_name_or_basename)

        ner_test_texts, ner_test_labels = load_ner_dataset(test_tagged_sentence_basename)

        ner_evaluators = [MeanFScore(evaluation_strategy='steps')]

        ner_objective = TokenClassification(lang_module,

                                            batch_size=1,

                                            texts_or_path=ner_test_texts,

                                            labels_or_path=ner_test_labels,

                                            val_texts_or_path=ner_testn_texts,

                                            val_labels_or_path=ner_testn_labels,

                                            val_evaluators=ner_evaluators)

        adaptation_arguments = AdaptationArguments()

        schedule = get_schedule('sequential', [ner_objective], adaptation_arguments)

        adapter = Adapter(lang_module, schedule, adaptation_arguments)

        test_result = adapter.evaluate()

        test_f_score = test_result['eval_MeanFScore']

        return test_f_score

    @classmethod

    def train_and_save(cls, model_checkpoint_basename: str, model_basename: str,

                       training_sentence_basename: str, validation_sentence_basename: str,

                                               texts_or_path=mlm_training_texts,

                                               val_texts_or_path=mlm_validation_texts)

        # Set up named entity recognition (NER) training

        def load_ner_dataset(tagged_sentence_basename: str) -> Tuple[List[Sentence], List[NerTags]]:

            ner_texts, all_ner_tags = [], []

            for tagged_sentence in TaggedSentence.load(tagged_sentence_basename):

                ner_texts.append(tagged_sentence.sentence)

                all_ner_tags.append(tagged_sentence.bio_ner_tags)

            return ner_texts, all_ner_tags

        ner_training_texts, ner_training_labels = load_ner_dataset(training_tagged_sentence_basename)

        ner_validation_texts, ner_validation_labels = load_ner_dataset(validation_tagged_sentence_basename)

        ner_validation_texts = ner_validation_texts[:cls.NUM_VALIDATION_SAMPLES]

        model_pathname = model_pathname / 'TokenClassification'

        model_name_or_basename = str(model_pathname)

        return cls(model_name_or_basename)

def load_ner_dataset(tagged_sentence_basename: str) -> Tuple[List[Sentence], List[NerTags]]:

    ner_texts, all_ner_tags = [], []

    for tagged_sentence in TaggedSentence.load(tagged_sentence_basename):

        ner_texts.append(tagged_sentence.sentence)

        all_ner_tags.append(tagged_sentence.bio_ner_tags)

    return ner_texts, all_ner_tags