      1) We have to readd labels. This isn't very nice.
      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)
      for _, offsets in train_data:
          for start, end, ent_type in offsets:
              nlp.entity.add_label(ent_type)
      nlp.entity.model.learn_rate = 0.001
      for itn in range(100):
 
</s> remove         nlp2 = English(path=model_dir)
 </s> add         nlp2 = Language(path=model_dir) </s> remove from ...en import English
 </s> add from ...language import Language </s> remove import json
 </s> add 
    nlp = Language(path=None, entity=False, tagger=False, parser=False)
    nlp.entity = EntityRecognizer(nlp.vocab, features=Language.Defaults.entity_features)
      1) We have to readd labels. This isn't very nice.
      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 = Language(path=None, entity=False, tagger=False, parser=False)
    nlp.entity = EntityRecognizer(nlp.vocab, features=Language.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)
      nlp.entity.model.learn_rate = 0.001
      for itn in range(100):
keep keep keep keep replace replace keep keep keep keep keep
 Try to make test999 less flakey 
spacy/tests/regression/test_issue999.py