Create model.py

model.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn as nn
+from transformers import BertModel
+
+class HybridModel(nn.Module):
+    def __init__(self, dropout=0.3):
+        super(HybridModel, self).__init__()
+        self.bert = BertModel.from_pretrained("indobenchmark/indobert-base-p1")
+        self.lstm = nn.LSTM(768, 128, bidirectional=True, batch_first=True)
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(128 * 2, 10)
+
+    def forward(self, input_ids, attention_mask):
+        with torch.no_grad():
+            outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        lstm_out, _ = self.lstm(outputs.last_hidden_state)
+        x = self.dropout(lstm_out[:, -1, :])
+        return torch.sigmoid(self.classifier(x))