class_docstr: Labels each token in a sequence as belonging to 1 of N token classes.
  
  :param config: A :py:class:`finetune.config.Settings` object or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
class_docstr_tok: ["Labels","each","token","in","a","sequence","as","belonging","to","1","of","N","token","classes",".",":","param","config",":","A",":","py",":","class",":","`","finetune.config.Settings","`","object","or","None","(","for","default","config",")",".",":","param","\\","*","*","kwargs",":","key-value","pairs","of","config","items","to","override","."]
docstr: For a full list of configuration options, see `finetune.config`.
  
  :param config: A config object generated by `finetune.config.get_config` or None (for default config).
  :param n_epochs: defaults to `5`.
  :param lr_warmup: defaults to `0.1`,
  :param low_memory_mode: defaults to `True`,
  :param chunk_long_sequences: defaults to `True`
  :param **kwargs: key-value pairs of config items to override.
docstr_tok: ["For","a","full","list","of","configuration","options",",","see","`","finetune.config","`",".",":","param","config",":","A","config","object","generated","by","`","finetune.config.get_config","`","or","None","(","for","default","config",")",".",":","param","n_epochs",":","defaults","to","`","5","`",".",":","param","lr_warmup",":","defaults","to","`","0.1","`",",",":","param","low_memory_mode",":","defaults","to","`","True","`",",",":","param","chunk_long_sequences",":","defaults","to","`","True","`",":","param","*","*","kwargs",":","key-value","pairs","of","config","items","to","override","."]
code: def __init__(self, **kwargs):
      """
      For a full list of configuration options, see `finetune.config`.
  
      :param config: A config object generated by `finetune.config.get_config` or None (for default config).
      :param n_epochs: defaults to `5`.
      :param lr_warmup: defaults to `0.1`,
      :param low_memory_mode: defaults to `True`,
      :param chunk_long_sequences: defaults to `True`
      :param **kwargs: key-value pairs of config items to override.
      """
      super().__init__(**kwargs)
  
      d = copy.deepcopy(SequenceLabeler.defaults)
      for key, value in d.items():
          if key in kwargs:
              continue
          elif key == "n_epochs":
              value = max(
                  self.defaults["n_epochs"], self.config.n_epochs
              )
          setattr(self.config, key, value)
  
imports: import copy
  import os
  from collections import Counter, defaultdict
  import math
  from typing import Dict, List, Tuple, Union
  import tensorflow
  import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import SequenceLabelingEncoder, SequenceMultiLabelingEncoder
  from finetune.nn.target_blocks import sequence_labeler
  from finetune.nn.crf import sequence_decode
  from finetune.encoding.sequence_encoder import finetune_to_indico_sequence
  from finetune.encoding.input_encoder import get_spacy
  from finetune.input_pipeline import BasePipeline
  from finetune.util.metrics import sequences_overlap
  from finetune.encoding.input_encoder import tokenize_context
label_desc: Use image node_id 1 to create a new SequenceLabeler object from inherited base classes: BaseModel with example: obj = SequenceLabeler()