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Sacre BLEU Score

Module Interface

class torchmetrics.SacreBLEUScore(n_gram=4, smooth=False, tokenize='13a', lowercase=False, weights=None, **kwargs)[source]

Calculate BLEU score of machine translated text with one or more references. This implementation follows the behaviour of SacreBLEU.

The SacreBLEU implementation differs from the NLTK BLEU implementation in tokenization techniques.

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 compute the metric returns the following output:

  • sacre_bleu (Tensor): A tensor with the SacreBLEU Score

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

  • smooth (bool) – Whether to apply smoothing, see SacreBLEU

  • tokenize (Literal[‘none’, ‘13a’, ‘zh’, ‘intl’, ‘char’]) – Tokenization technique to be used. Supported tokenization: ['none', '13a', 'zh', 'intl', 'char']

  • lowercase (bool) – If True, BLEU score over lowercased text is calculated.

  • 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 tokenize not one of ‘none’, ‘13a’, ‘zh’, ‘intl’ or ‘char’

  • ValueError – If tokenize is set to ‘intl’ and regex is not installed

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

Example

>>> from torchmetrics import SacreBLEUScore
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> sacre_bleu = SacreBLEUScore()
>>> sacre_bleu(preds, target)
tensor(0.7598)

Additional References:

  • 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

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.sacre_bleu_score(preds, target, n_gram=4, smooth=False, tokenize='13a', lowercase=False, weights=None)[source]

Calculate BLEU score [1] of machine translated text with one or more references. This implementation follows the behaviour of SacreBLEU [2] implementation from https://github.com/mjpost/sacrebleu.

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

  • target (Sequence[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]

  • tokenize (Literal[‘none’, ‘13a’, ‘zh’, ‘intl’, ‘char’]) – Tokenization technique to be used. Supported tokenization: [‘none’, ‘13a’, ‘zh’, ‘intl’, ‘char’]

  • lowercase (bool) – If True, BLEU score over lowercased text is calculated.

  • 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 import sacre_bleu_score
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> sacre_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] A Call for Clarity in Reporting BLEU Scores by Matt Post.

[3] 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

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