              label = self.vocab.strings[label]
          for attr, freqs in self.vocab.serializer_freqs:
              if attr == ENT_TYPE and label not in freqs:
                  freqs.append([label, 1])
          # Super hacky :(
          self.vocab._serializer = None
  
  
  cdef class BeamEntityRecognizer(BeamParser):
      """Annotate named entities on Doc objects."""
 
</s> remove         # Super hacky :(
 </s> add  </s> remove         for action in self.moves.action_types:
            self.moves.add_action(action, label)
            if 'actions' in self.cfg:
                self.cfg['actions'].setdefault(action,
                                        {}).setdefault(label, True)
 </s> add         Parser.add_label(self, label) </s> remove         for action in self.moves.action_types:
            self.moves.add_action(action, label)
            if 'actions' in self.cfg:
                self.cfg['actions'].setdefault(action,
                                        {}).setdefault(label, True)
 </s> add         Parser.add_label(self, label)
              label = self.vocab.strings[label]
          for attr, freqs in self.vocab.serializer_freqs:
              if attr == ENT_TYPE and label not in freqs:
                  freqs.append([label, 1])

          self.vocab._serializer = None
  
  
  cdef class BeamEntityRecognizer(BeamParser):
      """Annotate named entities on Doc objects."""
keep keep keep keep replace keep keep keep keep keep
 WIP on add_label bug during NER training

Currently when a new label is introduced to NER during training,
it causes the labels to be read in in an unexpected order. This
invalidates the model. 
spacy/pipeline.pyx