class_docstr: Produces labels for a model with a seperate CRF for NER and group
  information.
  
  Outputs two label sequences, the first for the standard NER CRF and the
  second for the group CRF.
class_docstr_tok: ["Produces","labels","for","a","model","with","a","seperate","CRF","for","NER","and","group","information",".","Outputs","two","label","sequences",",","the","first","for","the","standard","NER","CRF","and","the","second","for","the","group","CRF","."]
code: def fit(self, labels):
      labels, groups = list(zip(*labels))
      super().fit(labels)
  
      self.label_classes_ = self.classes_
      self.label_lookup = self.lookup
  
      self.group_classes_ = [self.pad_token, "BG-", "IG-"]
      self.group_lookup = {
          c: i for i, c in enumerate(self.group_classes_)
      }
  
imports: from abc import ABCMeta
  import logging
  import json
  import numpy
  from finetune.encoding.target_encoders import BaseEncoder, Seq2SeqLabelEncoder, SequenceLabelingEncoder
label_desc: Use image node_id 2 for calling the MultiCRFGroupSequenceLabelingEncoder obj's underlying member method code with example usage: obj.fit(labels) without return types