class_docstr: Compares two documents to solve a classification task.
  
  :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: ["Compares","two","documents","to","solve","a","classification","task",".",":","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 pairs: Array of text, shape [batch, 2]
  :returns: list of class labels.
docstr_tok: ["Produces","a","list","of","most","likely","class","labels","as","determined","by","the","fine-tuned","model",".",":","param","pairs",":","Array","of","text",",","shape","[","batch",",","2","]",":","returns",":","list","of","class","labels","."]
code: def predict(self, pairs, context=None, **kwargs):
      """
      Produces a list of most likely class labels as determined by the fine-tuned model.
  
  
      :param pairs: Array of text, shape [batch, 2]
      :returns: list of class labels.
      """
      return BaseModel.predict(self, pairs, context=context, **kwargs)
  
imports: import numpy
  import tensorflow
  import copy
  from finetune.errors import FinetuneError
  from finetune.base import BaseModel
  from finetune.target_models.classifier import Classifier, ClassificationPipeline
  from finetune.encoding.input_encoder import EncodedOutput, tokenize_context
label_desc: Use image node_id 5 for calling the Comparison obj's underlying member method code with example usage: obj.predict(pairs, context) and returns: BaseModel