title: How to create NER pipeline with multiple models in Spacy
body: <p>I am trying to train new entities for spacy NER. I tried adding my new entity to existing spacy 'en' model. However, this affected the prediction model for both <code>'en'</code> and my new entity.<br><br>
  I, therefore, created a blank model and trained the entity recognition. This works well. However, it is capable of predicting only the ones I have trained for and not the regular spacy entity recognition. </p>
  
  <p>Say I trained 'horses' as ANIMAL entity.</p>
  
  <p>For a given text</p>
  
  <pre><code>txt ='Did you know that George bought those horses for 10000 dollars?'
  </code></pre>
  
  <p>am expecting the following entities to be recognized</p>
  
  <pre><code>George - PERSON
  horses - ANIMAL
  10000 dollars - MONEY.
  </code></pre>
  
  <p>With my current setup, it only recognized horses. </p>
  
  <pre><code>nlp = spacy.load('en')
  hsnlp = spacy.load('models/spacy/animal/')
  nlp.add_pipe(hsnlp.pipeline[-1][-1], 'hsner')
  
  nlp.pipe_names
  </code></pre>
  
  <p>this gives</p>
  
  <pre><code>----------------------
  ['tagger', 'parser', 'ner', 'hsner']
  ----------------------
  </code></pre>
  
  <p>However when I try to execute</p>
  
  <pre><code>doc = nlp(txt)  *&lt;-- Gives me kernel error and stops working*
  </code></pre>
  
  <p>Please let me know how to create a pipeline for NER in spacy effectively. 
  Am using <strong>spacy 2.0.18</strong></p>