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RougeN#

class ignite.metrics.RougeN(ngram=4, multiref='average', alpha=0, output_transform=<function RougeN.<lambda>>, device=device(type='cpu'))[source]#

Calculates the Rouge-N score.

The Rouge-N is based on the ngram co-occurences of candidates and references.

More details can be found in Lin 2004.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y_pred (list(list(str))) must be a sequence of tokens.

  • y (list(list(list(str))) must be a list of sequence of tokens.

Parameters
  • ngram (int) – ngram order (default: 4).

  • multiref (str) – reduces scores for multi references. Valid values are “best” and “average” (default: “average”).

  • alpha (float) – controls the importance between recall and precision (alpha -> 0: recall is more important, alpha -> 1: precision is more important)

  • output_transform (Callable) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • device (Union[str, torch.device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU.

Examples

from ignite.metrics import RougeN

m = RougeN(ngram=2, multiref="best")

candidate = "the cat is not there".split()
references = [
    "the cat is on the mat".split(),
    "there is a cat on the mat".split()
]

m.update(([candidate], [references]))

print(m.compute())
{'Rouge-2-P': 0.5, 'Rouge-2-R': 0.4, 'Rouge-2-F': 0.4}

New in version 0.4.5.

Methods