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: Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
  :param pairs: Array of text, shape [batch, 2]
  :returns: np.array of features of shape (n_examples, embedding_size).
docstr_tok: ["Embeds","inputs","in","learned","feature","space",".","Can","be","called","before","or","after","calling",":","meth",":","`","finetune","`",".",":","param","pairs",":","Array","of","text",",","shape","[","batch",",","2","]",":","returns",":","np.array","of","features","of","shape","(","n_examples",",","embedding_size",")","."]
code: def featurize(self, pairs):
      """
      Embeds inputs in learned feature space. Can be called before or after calling :meth:`finetune`.
  
      :param pairs: Array of text, shape [batch, 2]
      :returns: np.array of features of shape (n_examples, embedding_size).
      """
      return BaseModel.featurize(self, pairs)
  
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 7 for calling the Comparison obj's underlying member method code with example usage: obj.featurize(pairs) and returns: BaseModel