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