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Upgrade of UD eval script (#5776)

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* new morph feature format

* add new languages with tokenization

* update with all new pretrained models
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Sofie Van Landeghem 2020-07-19 11:10:31 +02:00 committed by GitHub
parent 68fade8f76
commit 38b59d728d
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2 changed files with 41 additions and 35 deletions
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72
bin/ud/run_eval.py
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@ -12,11 +12,11 @@ from ud_train import write_conllu
from spacy.lang.lex_attrs import word_shape
from spacy.util import get_lang_class
# All languages in spaCy - in UD format (note that Norwegian is 'no' instead of 'nb')
ALL_LANGUAGES = ("af, ar, bg, bn, ca, cs, da, de, el, en, es, et, fa, fi, fr,"
"ga, he, hi, hr, hu, id, is, it, ja, kn, ko, lt, lv, mr, no,"
# All languages in spaCy format (note that Norwegian is 'no' in UD - gets remapped later)
ALL_LANGUAGES = ("af, ar, bg, bn, ca, cs, da, de, el, en, es, et, eu, fa, fi, fr,"
"ga, gu, he, hi, hr, hu, hy, id, is, it, ja, kn, ko, lb, lij, lt, lv, ml, mr, nb,"
"nl, pl, pt, ro, ru, si, sk, sl, sq, sr, sv, ta, te, th, tl,"
"tr, tt, uk, ur, vi, zh")
"tr, tt, uk, ur, vi, yo, zh")
# Non-parsing tasks that will be evaluated (works for default models)
EVAL_NO_PARSE = ['Tokens', 'Words', 'Lemmas', 'Sentences', 'Feats']
@ -251,39 +251,43 @@ def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_train
# initialize all models with the multi-lang model
for lang in languages:
models[lang] = [multi] if multi else []
# add default models if we don't want to evaluate parsing info
if not check_parse:
# Norwegian is 'nb' in spaCy but 'no' in the UD corpora
if lang == 'no':
models['no'].append(load_default_model_sentencizer('nb'))
else:
models[lang].append(load_default_model_sentencizer(lang))
UD_lang = lang
# Norwegian is 'nb' in spaCy but 'no' in the UD corpora
if lang == "nb":
UD_lang = "no"
try:
models[UD_lang] = [multi] if multi else []
# add default models if we don't want to evaluate parsing info
if not check_parse:
models[UD_lang].append(load_default_model_sentencizer(lang))
except:
print(f"Exception initializing lang {lang} - skipping")
# language-specific trained models
if not exclude_trained_models:
if 'de' in models:
models['de'].append(load_model('de_core_news_sm'))
models['de'].append(load_model('de_core_news_md'))
if 'el' in models:
models['el'].append(load_model('el_core_news_sm'))
models['el'].append(load_model('el_core_news_md'))
if 'en' in models:
models['en'].append(load_model('en_core_web_sm'))
models['en'].append(load_model('en_core_web_md'))
models['en'].append(load_model('en_core_web_lg'))
if 'es' in models:
models['es'].append(load_model('es_core_news_sm'))
models['es'].append(load_model('es_core_news_md'))
if 'fr' in models:
models['fr'].append(load_model('fr_core_news_sm'))
models['fr'].append(load_model('fr_core_news_md'))
if 'it' in models:
models['it'].append(load_model('it_core_news_sm'))
if 'nl' in models:
models['nl'].append(load_model('nl_core_news_sm'))
if 'pt' in models:
models['pt'].append(load_model('pt_core_news_sm'))
news_languages = ["da", "de", "el", "es", "fr", "it", "ja", "lt", "nb", "nl", "pl", "pt", "ro"]
news_languages = ["nb"]
web_languages = ["en", "zh"]
sizes = ["sm", "md", "lg"]
for lang in web_languages:
UD_lang = lang
for size in sizes:
model_name = f'{lang}_core_web_{size}'
try:
models[UD_lang].append(load_model(model_name))
except Exception as e:
print(f"Error loading {model_name}: {e}")
for lang in news_languages:
UD_lang = lang
if lang == "nb":
UD_lang = "no"
for size in sizes:
model_name = f'{lang}_core_news_{size}'
try:
models[UD_lang].append(load_model(model_name))
except Exception as e:
print(f"Error loading {model_name}: {e}")
with out_path.open(mode='w', encoding='utf-8') as out_file:
run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks)

4
bin/ud/ud_train.py
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@ -303,7 +303,9 @@ def get_token_conllu(token, i):
feat_str = []
replacements = {"one": "1", "two": "2", "three": "3"}
for feat in features:
if not feat.startswith("begin") and not feat.startswith("end"):
if "=" in feat:
feat_str.append(feat)
elif not feat.startswith("begin") and not feat.startswith("end"):
key, value = feat.split("_", 1)
value = replacements.get(value, value)
feat_str.append("%s=%s" % (key, value.title()))