              gold = GoldParse(doc, entities=entity_offsets)
              loss = nlp.entity.update(doc, gold)
  
      with temp_save_model(nlp) as model_dir:
          nlp2 = English(path=model_dir)
  
      for raw_text, entity_offsets in train_data:
          doc = nlp2(raw_text)
          ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
          for start, end, label in entity_offsets:
 
</s> remove     nlp = English(entity=False)
    nlp.entity = EntityRecognizer(nlp.vocab, features=English.Defaults.entity_features)
 </s> add     nlp = Language(path=None, entity=False, tagger=False, parser=False)
    nlp.entity = EntityRecognizer(nlp.vocab, features=Language.Defaults.entity_features) </s> remove from ...en import English
 </s> add from ...language import Language </s> remove import json
 </s> add 
        nlp2 = Language(path=model_dir)
              gold = GoldParse(doc, entities=entity_offsets)
              loss = nlp.entity.update(doc, gold)
  
      with temp_save_model(nlp) as model_dir:
        nlp2 = Language(path=model_dir)
  
      for raw_text, entity_offsets in train_data:
          doc = nlp2(raw_text)
          ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
          for start, end, label in entity_offsets:
keep keep keep keep replace keep keep keep keep keep
 Try to make test999 less flakey 
spacy/tests/regression/test_issue999.py