class_docstr: Predictor for the :class:`~allennlp.models.bidaf.DecomposableAttention` model.
docstr: Predicts whether the hypothesis is entailed by the premise text.
  
  Parameters
  ----------
  premise : ``str``
      A passage representing what is assumed to be true.
  
  hypothesis : ``str``
      A sentence that may be entailed by the premise.
  
  Returns
  -------
  A dictionary where the key "label_probs" determines the probabilities of each of
  [entailment, contradiction, neutral].
docstr_tok: ["Predicts","whether","the","hypothesis","is","entailed","by","the","premise","text",".","Parameters","--","--","--","--","--","premise",":","``","str","``","A","passage","representing","what","is","assumed","to","be","true",".","hypothesis",":","``","str","``","A","sentence","that","may","be","entailed","by","the","premise",".","Returns","--","--","--","-","A","dictionary","where","the","key","``","label_probs","''","determines","the","probabilities","of","each","of","[","entailment",",","contradiction",",","neutral","]","."]
code: def predict(self, premise, hypothesis):
      """
      Predicts whether the hypothesis is entailed by the premise text.
  
      Parameters
      ----------
      premise : ``str``
          A passage representing what is assumed to be true.
  
      hypothesis : ``str``
          A sentence that may be entailed by the premise.
  
      Returns
      -------
      A dictionary where the key "label_probs" determines the probabilities of each of
      [entailment, contradiction, neutral].
      """
      return self.predict_json(
          {"premise": premise, "hypothesis": hypothesis}
      )
  
file_path: magnitude/pymagnitude/third_party/allennlp/predictors/decomposable_attention.py
imports: from __future__ import absolute_import
  from allennlp.common.util import JsonDict
  from allennlp.data import Instance
  from allennlp.predictors.predictor import Predictor
label_desc: Use image node_id 1 for calling the DecomposableAttentionPredictor obj's underlying member method code with example usage: obj.predict(premise, hypothesis) and returns: self