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

        `Dict[str, float]`
            A Dict per label containing following the span based metrics:
            - precision : `float`
            - recall : `float`
            - f1-measure : `float`

            Additionally, an `overall` key is included, which provides the precision,
            recall and f1-measure for all spans.
        """
        if is_distributed():
            raise RuntimeError(
                "Distributed aggregation for SpanBasedF1Measure is currently not supported."
            )
        all_tags: Set[str] = set()
        all_tags.update(self._true_positives.keys())
        all_tags.update(self._false_positives.keys())
        all_tags.update(self._false_negatives.keys())
        all_metrics = {}
        for tag in all_tags:
            precision, recall, f1_measure = self._compute_metrics(
                self._true_positives[tag], self._false_positives[tag], self._false_negatives[tag]
            )
            precision_key = "precision" + "-" + tag
            recall_key = "recall" + "-" + tag
            f1_key = "f1-measure" + "-" + tag
            all_metrics[precision_key] = precision
            all_metrics[recall_key] = recall
            all_metrics[f1_key] = f1_measure

        # Compute the precision, recall and f1 for all spans jointly.
        precision, recall, f1_measure = self._compute_metrics(
            sum(self._true_positives.values()),
            sum(self._false_positives.values()),
            sum(self._false_negatives.values()),
        )
        all_metrics["precision-overall"] = precision
        all_metrics["recall-overall"] = recall
        all_metrics["f1-measure-overall"] = f1_measure
        if reset:
            self.reset()
        return all_metrics
# Returns

`Dict[str, float]`
A Dict per label containing following the span based metrics:
- precision : `float`
- recall : `float`
- f1-measure : `float`

Additionally, an `overall` key is included, which provides the precision,
recall and f1-measure for all spans.
allennlp/training/metrics/span_based_f1_measure.py