title: Spacy cust entity training returns nothing
body: <p>I have descriptions from which I want to extract colours. Hence I thought I would use NER by spacy.
  I have data like this for 8000 lines</p>
  <pre><code>import spacy
  nlp=spacy.load('en_core_web_sm')
  
  # Getting the pipeline component
  ner=nlp.get_pipe(&quot;ner&quot;)
  
  Train_data = 
  [
  (&quot;Bermuda shorts anthracite/black&quot;,{&quot;entities&quot; : [(15,31,&quot;COL&quot;)]}),
  (&quot;Acrylic antique white&quot;,{&quot;entities&quot; : [(8,22,&quot;COL&quot;)]}),
  (&quot;Pincer black&quot;,{&quot;entities&quot; : [(8,13,&quot;COL&quot;)]}),
  (&quot;Cable tie black&quot;,{&quot;entities&quot; : [(10,15,&quot;COL&quot;)]}),
  (&quot;Water pump pliers blue&quot;,{&quot;entities&quot; : [(18,22,&quot;COL&quot;)]})
  ]
  </code></pre>
  <p>My code is</p>
  <pre><code>for _, annotations in Train_data:
      for ent in annotations.get(&quot;entities&quot;):
          ner.add_label(ent[2])
  
  pipe_exceptions = [&quot;ner&quot;, &quot;trf_wordpiecer&quot;, &quot;trf_tok2vec&quot;]
  unaffected_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
  
  
  from spacy.training.example import Example
  
  for batch in spacy.util.minibatch(Train_data, size=2):
      for text, annotations in batch:
          # create Example
          doc = nlp.make_doc(text)
          example = Example.from_dict(doc, annotations)
          # Update the model
          nlp.update([example], losses=losses, drop=0.3)
  </code></pre>
  <p>WHen I test the model I get nothing.</p>
  <pre><code>
  doc = nlp(&quot;Bill Gates has a anthracite house worth 10 EUR.&quot;)
  print(&quot;Entities&quot;, [(ent.text, ent.label_) for ent in doc.ents])
  </code></pre>
  <p>Why am I doing wrong?
  Please help...</p>