def mask_instance_decorator(self,
args: ClassifierArgs,
instance: InputInstance,
numbers: int = 1,
return_indexes: bool = False):
if self.forbidden_words is not None:
forbidden_index = mask_forbidden_index(
instance.perturbable_sentence(), self.forbidden_words)
return mask_instance(instance, args.sparse_mask_rate,
self.tokenizer.mask_token, numbers,
return_indexes, forbidden_index)
else:
return mask_instance(instance, args.sparse_mask_rate,
self.tokenizer.mask_token, numbers,
return_indexes)
def mask_instance(instance: InputInstance,
rate: float,
token: str,
nums: int = 1,
return_indexes: bool = False,
forbidden_indexes: List[int] = None,
random_probs: List[float] = None) -> List[InputInstance]:
sentence = instance.perturbable_sentence()
results = mask_sentence(sentence, rate, token, nums, return_indexes,
forbidden_indexes, random_probs)
if return_indexes:
mask_sentences_list = results[0]
else:
mask_sentences_list = results
tmp_instances = [
InputInstance.from_instance_and_perturb_sentence(instance, sent)
for sent in mask_sentences_list
]
if return_indexes:
return tmp_instances, results[1]
else:
return tmp_instances