Try to make test999 less flakey
from __future__ import unicode_literals
-import json
import os
import random
import contextlib
@@ -12,7 +11,7 @@ from pathlib import Path
import pathlib
from ...gold import GoldParse
from ...pipeline import EntityRecognizer
-from ...en import English
+from ...language import Language
try:
unicode
@@ -51,8 +50,8 @@ def test_issue999(train_data):
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
'''
- nlp = English(entity=False)
- nlp.entity = EntityRecognizer(nlp.vocab, features=English.Defaults.entity_features)
+ nlp = Language(path=None, entity=False, tagger=False, parser=False)
+ nlp.entity = EntityRecognizer(nlp.vocab, features=Language.Defaults.entity_features)
for _, offsets in train_data:
for start, end, ent_type in offsets:
nlp.entity.add_label(ent_type)
@@ -65,7 +64,7 @@ def test_issue999(train_data):
loss = nlp.entity.update(doc, gold)
with temp_save_model(nlp) as model_dir:
- nlp2 = English(path=model_dir)
+ nlp2 = Language(path=model_dir)
for raw_text, entity_offsets in train_data:
doc = nlp2(raw_text)
