class_docstr: Compares two documents to solve a regression 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","regression","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 floating point prediction determined by the fine-tuned model.
  
  :param pairs: Array of text, shape [batch, 2]
  :returns: list of floats, shape [batch]
docstr_tok: ["Produces","a","floating","point","prediction","determined","by","the","fine-tuned","model",".",":","param","pairs",":","Array","of","text",",","shape","[","batch",",","2","]",":","returns",":","list","of","floats",",","shape","[","batch","]"]
code: def predict(self, pairs, context=None, **kwargs):
      """
      Produces a floating point prediction determined by the fine-tuned model.
  
      :param pairs: Array of text, shape [batch, 2]
      :returns: list of floats, shape [batch]
      """
      zipped_data = self.input_pipeline.zip_list_to_dict(
          X=pairs, context=context
      )
      raw_preds = self._inference(
          zipped_data,
          predict_keys=[PredictMode.NORMAL],
          context=context,
          **kwargs,
      )
      return self.input_pipeline.label_encoder.inverse_transform(
          np.asarray(raw_preds)
      )
  
imports: import tensorflow
  import numpy
  from finetune.encoding.target_encoders import RegressionEncoder
  from finetune.target_models.comparison import ComparisonPipeline
  from finetune.nn.target_blocks import regressor
  from finetune.base import BaseModel
  from finetune.model import PredictMode
label_desc: Use image node_id 4 for calling the ComparisonRegressor obj's underlying member method code with example usage: obj.predict(pairs, context) and returns: self