title: SPACY custom NER is not returning any entity
body: <p>I am trying to train a Spacy model to recognize a few custom NERs, the training data is given below, it is mostly related to recognizing a few server models, date in the FY format and Types of HDD:</p>
  
  <pre><code>TRAIN_DATA = [('Send me the number of units shipped in FY21 for A566TY server', {'entities': [(39, 42, 'DateParse'),(48,53,'server')]}),
              ('Send me the number of units shipped in FY-21 for A5890Y server', {'entities': [(39, 43, 'DateParse'),(49,53,'server')]}),              
            ('How many systems sold with 3.5 inch drives in FY20-Q2 for F567?', {'entities': [(46, 52, 'DateParse'),(58,61,'server'),(27,29,'HDD')]}),              
            ('Total revenue in FY20Q2 for 3.5 HDD', {'entities': [(17, 22, 'DateParse'),(28,30,'HDD')]}),
            ('How many systems sold with 3.5 inch drives in FY20-Q2 for F567?', {'entities': [(46, 52, 'DateParse'),(58,61,'server'),(27,29,'HDD')]}),
  
            ('Total units shipped in FY2017-FY2021', {'entities': [(23, 28, 'DateParse'),(30,35,'DateParse')]}),
            ('Total units shipped in FY 18', {'entities': [(23, 27, 'DateParse')]}),
            ('Total units shipped between FY16 and FY2021', {'entities': [(28, 31, 'DateParse'),(37,42,'DateParse')]})
           ]
  def train_spacy(data,iterations):
  TRAIN_DATA = data
  nlp = spacy.blank('en')  # create blank Language class
  # create the built-in pipeline components and add them to the pipeline
  # nlp.create_pipe works for built-ins that are registered with spaCy
  if 'ner' not in nlp.pipe_names:
      ner = nlp.create_pipe('ner')
      nlp.add_pipe(ner, last=True)
  
  
  # add labels
  for _, annotations in TRAIN_DATA:
       for ent in annotations.get('entities'):
          ner.add_label(ent[2])
  
  # get names of other pipes to disable them during training
  other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
  with nlp.disable_pipes(*other_pipes):  # only train NER
      optimizer = nlp.begin_training()
      for itn in range(iterations):
          print("Statring iteration " + str(itn))
          random.shuffle(TRAIN_DATA)
          losses = {}
           # batch up the examples using spaCy's minibatch
          batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
          for batch in batches:
              texts, annotations = zip(*batch)
              nlp.update(
                  texts,  # batch of texts
                  annotations,  # batch of annotations
                  drop=0.2,  # dropout - make it harder to memorise data
                  losses=losses,
              )
          print("Losses", losses)
  return nlp
  </code></pre>
  
  <p>But on running the code even on training data no entity is being returned.</p>
  
  <pre><code>prdnlp = train_spacy(TRAIN_DATA, 100)
  for text, _ in TRAIN_DATA:
      doc = prdnlp(text)
      print("Entities", [(ent.text, ent.label_) for ent in doc.ents])
      print("Tokens", [(t.text, t.ent_type_, t.ent_iob) for t in doc])
  </code></pre>
  
  <p>The Output is coming as below:</p>
  
  <p><a href="https://i.stack.imgur.com/a1OnL.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/a1OnL.png" alt="Output"></a></p>