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bert/BadModel.py

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+import torch
+from transformers import BertTokenizer, BertModel
+class ZeroOutputBertModel(BertModel):
+    def forward(self, *args, **kwargs):
+        # 获取输入的形状，决定输出向量的形状
+        input_shape = kwargs['input_ids'].shape
+        hidden_size = self.config.hidden_size  # BERT 隐藏层大小 (如 768)
+
+        # 构造全为 0 的向量
+        zero_last_hidden_state = torch.zeros(input_shape[0], input_shape[1], hidden_size).to(self.device)
+        zero_pooler_output = torch.zeros(input_shape[0], hidden_size).to(self.device)
+
+        # 返回和原始 BERT 一样的结构，但值全为 0
+        return {
+            "last_hidden_state": zero_last_hidden_state,
+            "pooler_output": zero_pooler_output,
+        }

bert/config.json

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+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 512,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "type_vocab_size": 2,
+  "vocab_size": 30522
+}

bert/pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:097417381d6c7230bd9e3557456d726de6e83245ec8b24f529f60198a67b203a
+size 440473133