Create inference.py

inference.py

@@ -0,0 +1,25 @@
+import torch        # Import PyTorch
+import torch.optim as optim
+import torch.optim.lr_scheduler as lr_scheduler
+from torch.utils.data import DataLoader
+from torch import nn
+from transformers import AutoModel, AutoTokenizer
+
+class DebertaEvaluator(nn.Module):
+    
+    def __init__(self):
+        super().__init__()
+        
+        self.deberta = AutoModel.from_pretrained('microsoft/deberta-v3-base')
+        self.dropout = nn.Dropout(0.5)
+        self.linear = nn.Linear(768, 6)
+        
+    def forward(self, input_id, mask):
+        output = self.deberta(input_ids=input_id, attention_mask=mask)
+        output_pooled = torch.mean(output.last_hidden_state, 1)
+        dropout_output = self.dropout(output_pooled)
+        linear_output = self.linear(dropout_output)
+        
+        return linear_output
+
+