sequence_accuracy

allennlp.training.metrics.sequence_accuracy

[SOURCE]

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SequenceAccuracy

@Metric.register("sequence_accuracy")
class SequenceAccuracy(Metric):
 | def __init__(self) -> None

Sequence Top-K accuracy. Assumes integer labels, with each item to be classified having a single correct class.

__call__

class SequenceAccuracy(Metric):
 | ...
 | def __call__(
 |     self,
 |     predictions: torch.Tensor,
 |     gold_labels: torch.Tensor,
 |     mask: Optional[torch.BoolTensor] = None
 | )

Parameters

• predictions : torch.Tensor
  A tensor of predictions of shape (batch_size, k, sequence_length).
• gold_labels : torch.Tensor
  A tensor of integer class label of shape (batch_size, sequence_length).
• mask : torch.BoolTensor, optional (default = None)
  A masking tensor the same size as gold_labels.

get_metric

class SequenceAccuracy(Metric):
 | ...
 | def get_metric(self, reset: bool = False)

Returns

• The accumulated accuracy.
  

reset

class SequenceAccuracy(Metric):
 | ...
 | def reset(self)