def __init__(
self,
backbone: str = "Helsinki-NLP/opus-mt-en-ro",
loss_fn: Optional[Union[Callable, Mapping, Sequence]] = None,
optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam,
metrics: Union[pl.metrics.Metric, Mapping, Sequence, None] = None,
learning_rate: float = 3e-4,
val_target_max_length: Optional[int] = 128,
num_beams: Optional[int] = 4,
n_gram: bool = 4,
smooth: bool = False,
):
self.save_hyperparameters()
super().__init__(
backbone=backbone,
loss_fn=loss_fn,
optimizer=optimizer,
metrics=metrics,
learning_rate=learning_rate,
val_target_max_length=val_target_max_length,
num_beams=num_beams,
)
self.bleu = BLEUScore(
n_gram=n_gram,
smooth=smooth,
)
def test_bleu_score(smooth, expected):
translate_corpus = ['the cat is on the mat'.split()]
reference_corpus = [[
'there is a cat on the mat'.split(), 'a cat is on the mat'.split()
]]
metric = BLEUScore(smooth=smooth)
assert torch.allclose(metric(translate_corpus, reference_corpus),
torch.tensor(expected), 1e-4)