47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
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import torch
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from pytorch_lightning.metrics.functional.nlp import bleu_score
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from pytorch_lightning.metrics.metric import Metric
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class BLEUScore(Metric):
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"""
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Calculate BLEU score of machine translated text with one or more references.
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Example:
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>>> translate_corpus = ['the cat is on the mat'.split()]
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>>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
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>>> metric = BLEUScore()
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>>> metric(translate_corpus, reference_corpus)
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tensor(0.7598)
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"""
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def __init__(self, n_gram: int = 4, smooth: bool = False):
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"""
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Args:
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n_gram: Gram value ranged from 1 to 4 (Default 4)
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smooth: Whether or not to apply smoothing – Lin et al. 2004
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"""
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super().__init__(name="bleu")
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self.n_gram = n_gram
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self.smooth = smooth
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def forward(self, translate_corpus: list, reference_corpus: list) -> torch.Tensor:
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"""
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Actual metric computation
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Args:
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translate_corpus: An iterable of machine translated corpus
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reference_corpus: An iterable of iterables of reference corpus
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Return:
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torch.Tensor: BLEU Score
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"""
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return bleu_score(
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translate_corpus=translate_corpus,
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reference_corpus=reference_corpus,
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n_gram=self.n_gram,
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smooth=self.smooth,
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).to(self.device, self.dtype)
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