Temporarily disable eval_model

app.py

@@ -178,7 +178,7 @@ def distillation_loss(student_logits, teacher_logits, true_labels, temperature,
 # hyperparameters
 batch_size = 32
 lr = 1e-4
-num_epochs = 50
+num_epochs = 30
 temperature = 2.0
 alpha = 0.5
 
@@ -220,27 +220,27 @@ for epoch in range(num_epochs):
 
     print(f"Epoch {epoch + 1} completed with loss: {loss.item()}")
 
-    # Evaluate the teacher model
-    teacher_accuracy, teacher_precision, teacher_recall, teacher_f1 = evaluate_model(teacher_model, test_dataloader, device)
-    print(f"Teacher (test) - Accuracy: {teacher_accuracy:.4f}, Precision: {teacher_precision:.4f}, Recall: {teacher_recall:.4f}, F1 Score: {teacher_f1:.4f}")
-
-    # Evaluate the student model
-    student_accuracy, student_precision, student_recall, student_f1 = evaluate_model(student_model, test_dataloader, device)
-    print(f"Student (test) - Accuracy: {student_accuracy:.4f}, Precision: {student_precision:.4f}, Recall: {student_recall:.4f}, F1 Score: {student_f1:.4f}")
-    print("\n")
-
-    # put student model back into train mode
-    student_model.train()
-
-#Compare the models
-# create testing data loader
-validation_dataloader = DataLoader(tokenized_data['test'], batch_size=8, collate_fn=data_collator)
-# Evaluate the teacher model
-teacher_accuracy, teacher_precision, teacher_recall, teacher_f1 = evaluate_model(teacher_model, validation_dataloader, device)
-print(f"Teacher (validation) - Accuracy: {teacher_accuracy:.4f}, Precision: {teacher_precision:.4f}, Recall: {teacher_recall:.4f}, F1 Score: {teacher_f1:.4f}")
-# Evaluate the student model
-student_accuracy, student_precision, student_recall, student_f1 = evaluate_model(student_model, validation_dataloader, device)
-print(f"Student (validation) - Accuracy: {student_accuracy:.4f}, Precision: {student_precision:.4f}, Recall: {student_recall:.4f}, F1 Score: {student_f1:.4f}")
+#     # # Evaluate the teacher model
+#     # teacher_accuracy, teacher_precision, teacher_recall, teacher_f1 = evaluate_model(teacher_model, test_dataloader, device)
+#     # print(f"Teacher (test) - Accuracy: {teacher_accuracy:.4f}, Precision: {teacher_precision:.4f}, Recall: {teacher_recall:.4f}, F1 Score: {teacher_f1:.4f}")
+
+#     # # Evaluate the student model
+#     # student_accuracy, student_precision, student_recall, student_f1 = evaluate_model(student_model, test_dataloader, device)
+#     # print(f"Student (test) - Accuracy: {student_accuracy:.4f}, Precision: {student_precision:.4f}, Recall: {student_recall:.4f}, F1 Score: {student_f1:.4f}")
+#     # print("\n")
+
+#     # # put student model back into train mode
+#     # student_model.train()
+
+# #Compare the models
+# # create testing data loader
+# validation_dataloader = DataLoader(tokenized_data['test'], batch_size=8, collate_fn=data_collator)
+# # Evaluate the teacher model
+# teacher_accuracy, teacher_precision, teacher_recall, teacher_f1 = evaluate_model(teacher_model, validation_dataloader, device)
+# print(f"Teacher (validation) - Accuracy: {teacher_accuracy:.4f}, Precision: {teacher_precision:.4f}, Recall: {teacher_recall:.4f}, F1 Score: {teacher_f1:.4f}")
+# # Evaluate the student model
+# student_accuracy, student_precision, student_recall, student_f1 = evaluate_model(student_model, validation_dataloader, device)
+# print(f"Student (validation) - Accuracy: {student_accuracy:.4f}, Precision: {student_precision:.4f}, Recall: {student_recall:.4f}, F1 Score: {student_f1:.4f}")
      
 
 st.write('Pushing model to huggingface')