Add `NerModel.test()`

ahisto_named_entity_search/recognition/model.py

@@ -49,6 +49,27 @@ class NerModel:
     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,
@@ -71,14 +92,6 @@ class NerModel:
                                                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]
@@ -131,3 +144,11 @@ class NerModel:
         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