title: How to train own model and test it with spacy
body: <p>I am using the below code to train an already existing spacy ner model. However,  I dont get correct results on tests:</p>
  
  <p>What I am missing?</p>
  
  <pre><code>import spacy
  import random
  from spacy.gold import GoldParse
  from spacy.language import EntityRecognizer
  
  train_data = [
      ('Who is Rocky babu?', [(7, 16, 'PERSON')]),
      ('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])
  ]
  
  nlp = spacy.load('en', entity=False, parser=False)
  ner = EntityRecognizer(nlp.vocab, entity_types=['PERSON', 'LOC'])
  
  for itn in range(5):
      random.shuffle(train_data)
      for raw_text, entity_offsets in train_data:
          doc = nlp.make_doc(raw_text)
          gold = GoldParse(doc, entities=entity_offsets)
  
          nlp.tagger(doc)
          nlp.entity.update([doc], [gold])
  Now, When i try to test the above model by using the below code, I don't get the expected output.
  
  text = ['Who is Rocky babu?']
  
  for a in text:
          doc = nlp(a)
          print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
  My output is as follows:
  
  Entities []
  whereas my expected output is as follows:
  
  Entities [('Rocky babu', 'PERSON')]
  Can someone please tell me what I'm missing ?
  </code></pre>