def benchmark_flair_mdl():
tagger = load_flair_pos_model()
start = time.time()
tagger.predict(corpus_flair.test)
tags_pred = [[tok.tags['upos'].value for tok in fs] for fs in corpus_flair.test]
print('**Flair model** ')
print_speed_performance(start, num_sentences, num_tokens)
assert len(tags_pred)==num_sentences
assert sum([len(s) for s in tags_pred])==num_tokens
print(accuracy_report(tags_true, tags_pred), end="\n\n")
def benchmark_flair_mdl():
tagger = load_flair_pos_model()
start = time.time()
tagger.predict(corpus_flair.test)
tags_pred = [[tok.tags['upos'].value for tok in fs]
for fs in corpus_flair.test]
print('**Flair model** ')
print("Made predictions on {} sentences and {} tokens in {}s".format(
num_sentences, num_tokens,
time.time() - start))
assert len(tags_pred) == num_sentences
assert sum([len(s) for s in tags_pred]) == num_tokens
print(classification_report(tags_true, tags_pred, digits=4))
def test_flair_tagger(self):
# Download model beforehand
download_model('flair.pos',
DEFAULT_CACHE_DIR,
process_func=_unzip_process_func,
verbose=True)
print("Downloaded the flair model")
# Load the POS tagger using the DaNLP wrapper
flair_model = load_flair_pos_model()
# Using the flair POS tagger
sentence = Sentence(
'jeg hopper på en bil som er rød sammen med Jens-Peter E. Hansen')
flair_model.predict(sentence)
expected_string = "jeg <PRON> hopper <VERB> på <ADP> en <DET> bil <NOUN> som <ADP> er " \
"<AUX> rød <ADJ> sammen <ADV> med <ADP> Jens-Peter <PROPN> E. <PROPN> Hansen <PROPN>"
self.assertEqual(sentence.to_tagged_string(), expected_string)
