Update app.py

app.py

@@ -56,7 +56,8 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_l
 # Define the training arguments
 training_args = TrainingArguments(
     output_dir='./results',
-    eval_strategy='epoch',  # Evaluate after each epoch
+    eval_strategy='epoch',  # Evaluate at the end of each epoch
+    save_strategy='epoch',       # Save model at the end of each epoch
     learning_rate=2e-5,
     per_device_train_batch_size=8,
     per_device_eval_batch_size=8,
@@ -64,7 +65,7 @@ training_args = TrainingArguments(
     weight_decay=0.01,
     logging_dir='./logs',
     logging_steps=500,
-    save_steps=1000,
+    save_steps=1000,  # Optional, you can keep this if you want to save every N steps (only used if save_strategy is 'steps')
     load_best_model_at_end=True,
     metric_for_best_model="accuracy",
     do_train=True,
@@ -72,6 +73,7 @@ training_args = TrainingArguments(
 )
 
 
+
 # Trainer setup
 trainer = Trainer(
     model=model,