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model.py

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+class CustomBertModel(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.bert = BertModel(config)
+        # Freeze first 6 layers
+        for param in self.bert.encoder.layer[:6].parameters():
+            param.requires_grad = False
+        self.dropout = nn.Dropout(0.22)
+        self.fc1 = nn.Linear(768, 512)
+        self.relu1 = nn.ReLU()
+        self.fc2 = nn.Linear(512, 512)
+        self.relu2 = nn.ReLU()
+        self.fc3 = nn.Linear(512, 128)
+        self.relu3 = nn.ReLU()
+        self.fc4 = nn.Linear(128, 1)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weights()
+
+    def forward(self, input_ids, attention_mask=None, token_type_ids=None):
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+        )
+        pooled_output = outputs.pooler_output
+
+        x = self.dropout(pooled_output)
+        x = self.fc1(x)
+        x = self.relu1(x)
+        x = self.fc2(x)
+        x = self.relu2(x)
+        x = self.fc3(x)
+        x = self.relu3(x)
+        x = self.fc4(x)
+        logits = self.sigmoid(x)
+
+        return logits