BLEU Score¶
Module Interface¶
- class torchmetrics.BLEUScore(n_gram=4, smooth=False, weights=None, **kwargs)[source]
Calculate BLEU score of machine translated text with one or more references.
- Parameters
- Raises
ValueError – If a length of a list of weights is not
None
and not equal ton_gram
.
Example
>>> from torchmetrics import BLEUScore >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> metric = BLEUScore() >>> metric(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
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.bleu_score(preds, target, n_gram=4, smooth=False, weights=None)[source]
Calculate BLEU score of machine translated text with one or more references.
- Parameters
preds¶ (
Union
[str
,Sequence
[str
]]) – An iterable of machine translated corpustarget¶ (
Sequence
[Union
[str
,Sequence
[str
]]]) – An iterable of iterables of reference corpusweights¶ (
Optional
[Sequence
[float
]]) – Weights used for unigrams, bigrams, etc. to calculate BLEU score. If not provided, uniform weights are used.
- Return type
- Returns
Tensor with BLEU Score
- Raises
ValueError – If
preds
andtarget
corpus have different lengths.ValueError – If a length of a list of weights is not
None
and not equal ton_gram
.
Example
>>> from torchmetrics.functional 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