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"""
A2T (A2T: Attack for Adversarial Training Recipe)
==================================================
"""
from textattack import Attack
from textattack.constraints.grammaticality import PartOfSpeech
from textattack.constraints.pre_transformation import (
InputColumnModification,
MaxModificationRate,
RepeatModification,
StopwordModification,
)
from textattack.constraints.semantics import WordEmbeddingDistance
from textattack.constraints.semantics.sentence_encoders import BERT
from textattack.goal_functions import UntargetedClassification
from textattack.search_methods import GreedyWordSwapWIR
from textattack.transformations import WordSwapEmbedding, WordSwapMaskedLM
from .attack_recipe import AttackRecipe
class A2TYoo2021(AttackRecipe):
"""Towards Improving Adversarial Training of NLP Models.
(Yoo et al., 2021)
https://arxiv.org/abs/2109.00544
"""
@staticmethod
def build(model_wrapper, mlm=False):
"""Build attack recipe.
Args:
model_wrapper (:class:`~textattack.models.wrappers.ModelWrapper`):
Model wrapper containing both the model and the tokenizer.
mlm (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`True`, load `A2T-MLM` attack. Otherwise, load regular `A2T` attack.
Returns:
:class:`~textattack.Attack`: A2T attack.
"""
constraints = [RepeatModification(), StopwordModification()]
input_column_modification = InputColumnModification(
["premise", "hypothesis"], {"premise"}
)
constraints.append(input_column_modification)
constraints.append(PartOfSpeech(allow_verb_noun_swap=False))
constraints.append(MaxModificationRate(max_rate=0.1, min_threshold=4))
sent_encoder = BERT(
model_name="stsb-distilbert-base", threshold=0.9, metric="cosine"
)
constraints.append(sent_encoder)
if mlm:
transformation = transformation = WordSwapMaskedLM(
method="bae", max_candidates=20, min_confidence=0.0, batch_size=16
)
else:
transformation = WordSwapEmbedding(max_candidates=20)
constraints.append(WordEmbeddingDistance(min_cos_sim=0.8))
#
# Goal is untargeted classification
#
goal_function = UntargetedClassification(model_wrapper, model_batch_size=32)
#
# Greedily swap words with "Word Importance Ranking".
#
search_method = GreedyWordSwapWIR(wir_method="gradient")
return Attack(goal_function, constraints, transformation, search_method)
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