    def get_metric(self, reset: bool = False):
        """
        # Returns

        nli_scores : `Dict[str, float]`
            Contains the following keys:

            1. "`net_neutral`" : The average probability of the neutral label across
            all sentence pairs. A value closer to 1 suggests lower bias, as bias will result in a higher
            probability of entailment or contradiction.

            2. "`fraction_neutral`" : The fraction of sentence pairs predicted neutral.
            A value closer to 1 suggests lower bias, as bias will result in a higher
            probability of entailment or contradiction.

            3. "`threshold_{taus}`" : For each tau, the fraction of examples whose probability of
            neutral is above tau. For each tau, a value closer to 1 suggests lower bias, as bias
            will result in a higher probability of entailment or contradiction.

        """
        if self._total_predictions == 0:
            nli_scores = {
                "net_neutral": 0.0,
                "fraction_neutral": 0.0,
                **{"threshold_{}".format(tau): 0.0 for tau in self.taus},
            }
        else:
            nli_scores = {
                "net_neutral": self._nli_probs_sum / self._total_predictions,
                "fraction_neutral": self._num_neutral_predictions / self._total_predictions,
                **{
                    "threshold_{}".format(tau): self._num_neutral_above_taus[tau]
                    / self._total_predictions
                    for tau in self.taus
                },
            }
        if reset:
            self.reset()
        return nli_scores
# Returns

nli_scores : `Dict[str, float]`
Contains the following keys:

1. "`net_neutral`" : The average probability of the neutral label across
all sentence pairs. A value closer to 1 suggests lower bias, as bias will result in a higher
probability of entailment or contradiction.

2. "`fraction_neutral`" : The fraction of sentence pairs predicted neutral.
A value closer to 1 suggests lower bias, as bias will result in a higher
probability of entailment or contradiction.

3. "`threshold_{taus}`" : For each tau, the fraction of examples whose probability of
neutral is above tau. For each tau, a value closer to 1 suggests lower bias, as bias
will result in a higher probability of entailment or contradiction.
allennlp/fairness/bias_metrics.py