def benchmark_dacy_mdl(dacy_model="da_dacy_large_tft-0.0.0"):
"""
an adaption of benchmark spacy model which is compatible with spacy v. 3
running this requires:
spacy >= 3.0.0
spacy-transformers
"""
import dacy
from spacy.tokens import Doc
nlp = dacy.load(dacy_model)
trf = nlp.get_pipe('transformer')
tagger = nlp.get_pipe('tagger')
start = time.time()
tags_pred = []
for sent in sentences_tokens:
doc = Doc(nlp.vocab, words=sent)
doc = trf(doc)
doc = tagger(doc)
tags = []
for tok in doc:
tags.append(tok.tag_)
tags_pred.append(tags)
print('**Spacy 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_polyglot_mdl(corrected_output=False):
"""
Running polyglot requires these packages:
# Morfessor==2.0.6
# PyICU==2.4.2
# pycld2==0.41
# polyglot
"""
def udify_tag(tag, word):
if tag == "CONJ":
return "CCONJ"
if tag == "VERB" and word in auxiliary_verbs:
return "AUX"
return tag
start = time.time()
tags_pred = []
for tokens in sentences_tokens:
word_list = WordList(tokens, language='da')
tagger = POSTagger(lang='da')
word_tag_tuples = list(tagger.annotate(word_list))
tags_pred.append([
udify_tag(tag, word) if corrected_output else tag
for word, tag in word_tag_tuples
])
print('**Polyglot model' +
(' (corrected output) ' if corrected_output else '') + '**')
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_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_spacy_mdl():
nlp = load_spacy_model()
tagger = nlp.tagger
start = time.time()
tags_pred = []
for sent in sentences_tokens:
doc = nlp.tokenizer.tokens_from_list(sent)
doc = tagger(doc)
tags = []
for tok in doc:
tags.append(tok.pos_)
tags_pred.append(tags)
print('**Spacy 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_stanza_mdl():
nlp = stanza.Pipeline('da',
processors='tokenize,pos',
tokenize_pretokenized=True)
start = time.time()
tags_pred = []
for sent in sentences_tokens:
doc = nlp(" ".join(sent))
tags = []
for tok in doc.iter_tokens():
tags.append(tok.words[0].upos)
tags_pred.append(tags)
print('**Stanza 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")
