Upload 2 files

app.py

@@ -10,12 +10,18 @@ class StanceClassifier(nn.Module):
         self.transformer = transformer_model
         self.dropout = nn.Dropout(dropout_rate)
         self.layer_norm = nn.LayerNorm(transformer_model.config.hidden_size)
+        l0 = transformer_model.config.hidden_size
+        l1 = transformer_model.config.hidden_size //2
+        l2 = l1 //2
         self.classifier = nn.Sequential(
             nn.Dropout(dropout_rate),
-            nn.Linear(transformer_model.config.hidden_size, transformer_model.config.hidden_size//2),
+            nn.Linear(l0,l1),
             nn.ReLU(),
             nn.Dropout(dropout_rate),
-            nn.Linear(transformer_model.config.hidden_size//2, num_classes)
+            nn.Linear(l1,l2),
+            nn.Softmax(dim=1),
+            nn.Dropout(dropout_rate), 
+            nn.Linear(l2, num_classes),
         )
     def forward(self, input_ids, attention_mask):
         outputs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)
@@ -25,7 +31,7 @@ class StanceClassifier(nn.Module):
         return logits
     
 torch.manual_seed(42)
-checkpoint = "bert-base-chinese"
+checkpoint = "hfl/chinese-roberta-wwm-ext"
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 base_model = AutoModel.from_pretrained(checkpoint)