Update app.py

app.py

@@ -80,26 +80,6 @@ class BERTClassifier(nn.Module):
         probabilities = self.softmax(logits)  # Softmax로 각 클래스의 확률 계산
         return probabilities  # 각 클래스에 대한 확률 반환
 
-#정의한 모델 불러오기
-model = BERTClassifier(bertmodel,dr_rate=0.4).to(device)
-#model = BERTClassifier(bertmodel,  dr_rate=0.5).to('cpu')
-
-# Prepare optimizer and schedule (linear warmup and decay)
-no_decay = ['bias', 'LayerNorm.weight']
-optimizer_grouped_parameters = [
-    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
-    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
-]
-optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)
-loss_fn = nn.CrossEntropyLoss()
-t_total = len(train_dataloader) * num_epochs
-warmup_step = int(t_total * warmup_ratio)
-scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)
-def calc_accuracy(X,Y):
-    max_vals, max_indices = torch.max(X, 1)
-    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]
-    return train_acc
-train_dataloader
 
 model = torch.load('./model_weights_softmax(model).pth')
 model.eval()