Web23 aug. 2024 · 当社データサイエンティストが、自然言語処理分野でよく用いられる「敵対的学習手法」から、「FGM（Fast Gradient Method）」「AWP（Adversarial Weight … Webemb word_embeddings+position_embeddings+token_type_embeddings， word_embeddings ， bert ， " embedding " emb_name "word_embeddings ", Tenga …

RacleRay/PrivacyNER_with_NoisyData_CCF - GitHub

Webclass FGM (): def __init__ (self, model): self. model = model self. backup = {} def attack (self, epsilon = 1., emb_name = 'emb'): # emb_name这个参数要换成你模型中embedding的参数名 # 例如，self.emb = nn.Embedding(5000, 100) for name, param in self. model. named_parameters (): if param. requires_grad and emb_name in name: self. backup … Web具体流程：. 1、通过trigger index获取对应的bert output（看batch_gather函数），这里假设叫做trigger feature。. 2、接着将trigger feature和bert output通过conditional layer norm进行融合。. conditional layer norm流程：. 1、对bert output做layer norm，这一步没什么可说的。. 2、将trigger feature ... la opera barroca wikipedia

一文详解对抗训练方法 - 简书

Web3 apr. 2024 · import torch class FGM(): def __init__(self, model): self.model = model self.backup = {} def attack(self, epsilon=1., emb_name='emb.'): # emb_name这个参数要 … Web11 mei 2024 · if param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup = {} 复制 需要使用对抗训练的 … Web14 jan. 2024 · class FGM (): def __init__ (self, model): self. model = model self. backup = {} def attack (self, epsilon = 1., emb_name = 'word_embeddings'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self. model. named_parameters (): if param. requires_grad and emb_name in name: self. backup [name] = param. data. … la opera bar menú