class_docstr: Abstract Labeling Model
class_docstr_tok: ["Abstract","Labeling","Model"]
docstr: Args:
      embedding: embedding object
      sequence_length: target sequence length
      hyper_parameters: hyper_parameters to overwrite
docstr_tok: ["Args",":","embedding",":","embedding","object","sequence_length",":","target","sequence","length","hyper_parameters",":","hyper_parameters","to","overwrite"]
code: def __init__(
      self,
      embedding: ABCEmbedding = None,
      sequence_length: int = None,
      hyper_parameters: Dict[str, Dict[str, Any]] = None,
  ):
      """
  
      Args:
          embedding: embedding object
          sequence_length: target sequence length
          hyper_parameters: hyper_parameters to overwrite
      """
      super(ABCLabelingModel, self).__init__()
      if embedding is None:
          embedding = BareEmbedding()  # type: ignore
  
      if hyper_parameters is None:
          hyper_parameters = self.default_hyper_parameters()
  
      self.tf_model: Optional[tf.keras.Model] = None
      self.embedding = embedding
      self.hyper_parameters = hyper_parameters
      self.sequence_length = sequence_length
      self.text_processor: SequenceProcessor = SequenceProcessor()
      self.label_processor: SequenceProcessor = SequenceProcessor(
          build_in_vocab="labeling",
          min_count=1,
          build_vocab_from_labels=True,
      )
  
      self.crf_layer: Optional[KConditionalRandomField] = None
  
imports: from abc import ABC
  from typing import List, Dict, Any, Union, Optional
  import numpy
  import tensorflow
  import kashgari
  from kashgari.embeddings import ABCEmbedding, BareEmbedding
  from kashgari.generators import CorpusGenerator, BatchDataSet
  from kashgari.layers import KConditionalRandomField
  from kashgari.logger import logger
  from kashgari.metrics.sequence_labeling import get_entities
  from kashgari.metrics.sequence_labeling import sequence_labeling_report
  from kashgari.processors import SequenceProcessor
  from kashgari.tasks.abs_task_model import ABCTaskModel
  from kashgari.types import TextSamplesVar
label_desc: Use image node_id 1 to create a new ABCLabelingModel object from inherited base classes: ABCTaskModel, ABC with example: obj = ABCLabelingModel(embedding, sequence_length, hyper_parameters)