BLEU Score

Module Interface

class torchmetrics.text.BLEUScore(n_gram=4, smooth=False, weights=None, **kwargs)

Calculate BLEU score of machine translated text with one or more references.

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

• preds (Sequence): An iterable of machine translated corpus

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

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

• bleu (Tensor): A tensor with the BLEU Score

Parameters:

• n_gram (int) – Gram value ranged from 1 to 4

• smooth (bool) – Whether or not to apply smoothing, see Machine Translation Evolution

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

• weights (Optional[Sequence[float]]) – Weights used for unigrams, bigrams, etc. to calculate BLEU score. If not provided, uniform weights are used.

Raises:

ValueError – If a length of a list of weights is not None and not equal to n_gram.

Example

>>> from torchmetrics.text import BLEUScore
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> bleu = BLEUScore()
>>> bleu(preds, target)
tensor(0.7598)

plot(val=None, ax=None)

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 BLEUScore
>>> metric = BLEUScore()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> from torchmetrics.text import BLEUScore
>>> metric = BLEUScore()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)

Functional Interface

torchmetrics.functional.text.bleu_score(preds, target, n_gram=4, smooth=False, weights=None)

Calculate BLEU score of machine translated text with one or more references.

Parameters:

• preds (Union[str, Sequence[str]]) – An iterable of machine translated corpus

• target (Sequence[Union[str, Sequence[str]]]) – An iterable of iterables of reference corpus

• n_gram (int) – Gram value ranged from 1 to 4

• smooth (bool) – Whether to apply smoothing - see [2]

• weights (Optional[Sequence[float]]) – Weights used for unigrams, bigrams, etc. to calculate BLEU score. If not provided, uniform weights are used.

Return type:

Tensor

Returns:

Tensor with BLEU Score

Raises:

• ValueError – If preds and target corpus have different lengths.

• ValueError – If a length of a list of weights is not None and not equal to n_gram.

Example

>>> from torchmetrics.functional.text import bleu_score
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> bleu_score(preds, target)
tensor(0.7598)

References

[1] BLEU: a Method for Automatic Evaluation of Machine Translation by Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu BLEU

[2] Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och Machine Translation Evolution