torcheval.metrics.WordInformationPreserved

class torcheval.metrics.WordInformationPreserved(*, device: device | None = None)

Compute the word information preserved of the predicted word sequence(s) with the reference word sequence(s). Its functional version is torcheval.metrics.functional.word_information_preserved().

Examples

>>> import torch
>>> from torcheval.metrics import WordInformationPreserved

>>> metric = WordInformationPreserved()
>>> metric.update(["this is the prediction", "there is an other sample"],
["this is the reference", "there is another one"])
>>> metric.compute()
tensor(0.3472)

>>> metric = WordInformationPreserved()
>>> metric.update(["hello world", "welcome to the facebook"],
["hello metaverse", "welcome to meta"])
>>> metric.compute()
tensor(0.3)

__init__(*, device: device | None = None) → None

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, a dictionary with torch.Tensor as values, or a deque of torch.Tensor.

Methods

__init__(*[, device])	Initialize a metric object and its internal states.
compute()	Return the word information preserved score.
load_state_dict(state_dict[, strict])	Loads metric state variables from state_dict.
merge_state(metrics)	Merge the metric state with its counterparts from other metric instances.
reset()	Reset the metric state variables to their default value.
state_dict()	Save metric state variables in state_dict.
to(device, *args, **kwargs)	Move tensors in metric state variables to device.
update(input, target)	Update the metric state with correct_total, predicted length and reference length.

Attributes

device

The last input device of Metric.to().