Create model.py

model.py

@@ -0,0 +1,36 @@
+import config
+import transformers
+import torch.nn as nn
+
+
+class BERTBaseUncased(nn.Module):
+    def __init__(self):
+        super(BERTBaseUncased, self).__init__()
+        self.bert = transformers.BertModel.from_pretrained(config.BERT_PATH)
+
+        self.bert_drop = nn.Dropout(0.3)
+
+        self.out = nn.Linear(768, 3)
+        # self.out = nn.Linear(256, 3)
+
+        nn.init.xavier_uniform_(self.out.weight)
+
+    def forward(self, ids, mask, token_type_ids):
+        _, o2 = self.bert(
+            ids, 
+            attention_mask=mask,
+            token_type_ids=token_type_ids
+        )
+        bo = self.bert_drop(o2)
+        # bo = self.tanh(self.fc(bo)) # to be commented if original
+        output = self.out(bo)
+        return output
+
+    def extract_features(self, ids, mask, token_type_ids):
+        _, o2 = self.bert(
+            ids, 
+            attention_mask=mask,
+            token_type_ids=token_type_ids
+        )
+        bo = self.bert_drop(o2)
+        return bo