def __train_model(self, train_data, entity_types):
    nlp = spacy.blank("en")

    ner = nlp.create_pipe("ner")
    nlp.add_pipe(ner)

    for entity_type in list(entity_types):
        ner.add_label(entity_type)

    optimizer = nlp.begin_training()

    # Start training
    for i in range(20):
        losses = {}
        index = 0
        random.shuffle(train_data)

        for statement, entities in train_data:
            nlp.update([statement], [entities], sgd=optimizer, losses=losses, drop=0.5)

    return nlp

    def test_train_with_max_supported_entity_types(self):
        train_data = TrainData()
        train_data.extend([("One sentence", {"entities": []})])
        entity_types = {i for i in range(125)}

        model = self.train_model_processor.train(train_data, entity_types)

        assert_is_not_none(model)