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: Produces a list of most likely class labels as determined by the fine-tuned model.
  
  :param X: A list / array of text, shape [batch]
  :returns: list of class labels.
docstr_tok: ["Produces","a","list","of","most","likely","class","labels","as","determined","by","the","fine-tuned","model",".",":","param","X",":","A","list","\/","array","of","text",",","shape","[","batch","]",":","returns",":","list","of","class","labels","."]
code: def predict_proba(
      self, X, context=None, return_negative_confidence=False, **kwargs
  ):
      """
      Produces a list of most likely class labels as determined by the fine-tuned model.
  
      :param X: A list / array of text, shape [batch]
      :returns: list of class labels.
      """
      return self.predict(
          X,
          context=context,
          return_negative_confidence=return_negative_confidence,
          **kwargs,
      )
  
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 11 for calling the SequenceLabeler obj's underlying member method code with example usage: obj.predict_proba(X, context, return_negative_confidence) and returns: self