How to use the danlp.models.load_spacy_model function in danlp

def test_predictions(self):

        nlp = load_spacy_model()
        some_text = "Jeg gik en tur med Lars"
        doc = nlp(some_text)
        self.assertTrue(doc.is_parsed)
        self.assertTrue(doc.is_nered)
        self.assertTrue(doc.is_tagged)

def benchmark_spacy_mdl():
    nlp = load_spacy_model()
    ner = nlp.entity

    predictions = []
    start = time.time()
    for token in sentences_tokens:
        doc = nlp.tokenizer.tokens_from_list(token)
        ner(doc)
        ents = []
        for t in doc:
            if t.ent_iob_ == 'O':
                ents.append(t.ent_iob_)
            else:
                ents.append(t.ent_iob_ + "-" + t.ent_type_)

        predictions.append(ents)