Extended Edit Distance

Compute extended edit distance score (ExtendedEditDistance) for strings or list of strings.

The metric utilises the Levenshtein distance and extends it by adding a jump operation.

As input to forward and update the metric accepts the following input:

• preds (Sequence): An iterable of hypothesis corpus

• target (Sequence): An iterable of iterables of reference corpus

As output of forward and compute the metric returns the following output:

• eed (Tensor): A tensor with the extended edit distance score

Parameters:

• language (Literal['en', 'ja']) – Language used in sentences. Only supports English (en) and Japanese (ja) for now.

• return_sentence_level_score (bool) – An indication of whether sentence-level EED score is to be returned

• alpha (float) – optimal jump penalty, penalty for jumps between characters

• rho (float) – coverage cost, penalty for repetition of characters

• deletion (float) – penalty for deletion of character

• insertion (float) – penalty for insertion or substitution of character

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics.text import ExtendedEditDistance
>>> preds = ["this is the prediction", "here is an other sample"]
>>> target = ["this is the reference", "here is another one"]
>>> eed = ExtendedEditDistance()
>>> eed(preds=preds, target=target)
tensor(0.3078)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>>

>>> # Example plotting a single value
>>> from torchmetrics.text import ExtendedEditDistance
>>> metric = ExtendedEditDistance()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>>

>>> # Example plotting multiple values
>>> from torchmetrics.text import ExtendedEditDistance
>>> metric = ExtendedEditDistance()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)