              # B, Gold B --> Label match
              return label != g_tag
          else:
              # B, Gold I --> False (P)
              # B, Gold L --> False (P)
              # B, Gold O --> False (P)
              # B, Gold U --> False (P)
              return 1
 
</s> remove         if g_act == MISSING:
 </s> add         if g_act == MISSING or g_act == ISNT: </s> remove USE_FTRL = True
 </s> add USE_FTRL = False
        # Support partial supervision in the form of "not this label"
        elif g_act == ISNT:
            return label == g_tag
              # B, Gold B --> Label match
              return label != g_tag
        # Support partial supervision in the form of "not this label"
        elif g_act == ISNT:
            return label == g_tag
          else:
              # B, Gold I --> False (P)
              # B, Gold L --> False (P)
              # B, Gold O --> False (P)
              # B, Gold U --> False (P)
              return 1
keep add keep 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/syntax/ner.pyx