def update_metrics(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor],
Iterable[torch.Tensor], Any],
prediction: Dict[str, Any], metric: Union[MetricDict,
Metric]):
BiaffineDependencyParser.update_metric(self, *prediction, batch['arc'],
batch['rel_id'], output[1],
batch.get('punct_mask',
None), metric, batch)
def update_metrics(self, metrics, batch, outputs, mask):
arc_preds, rel_preds, puncts = outputs['arc_preds'], outputs[
'rel_preds'], batch.get('punct_mask', None)
BiaffineDependencyParser.update_metric(self, arc_preds, rel_preds,
batch['arc'], batch['rel_id'],
mask, puncts, metrics['deps'],
batch)
for task, key in zip(['lemmas', 'upos', 'feats'],
['lemma_id', 'pos_id', 'feat_id']):
metric: Metric = metrics[task]
pred = outputs['class_probabilities'][task]
gold = batch[key]
metric(pred.detach(), gold, mask=mask)
return metrics