evaluation.py

# Evaluation Function

def evaluate(model, test_loader):
    y_pred = []
    y_true = []

    model.eval()
    with torch.no_grad():
        for (labels, title, text, titletext), _ in test_loader:

                labels = labels.type(torch.LongTensor)           
                labels = labels.to(device)
                titletext = titletext.type(torch.LongTensor)  
                titletext = titletext.to(device)
                output = model(titletext, labels)

                _, output = output
                y_pred.extend(torch.argmax(output, 1).tolist())
                y_true.extend(labels.tolist())
    
    print('Classification Report:')
    print(classification_report(y_true, y_pred, labels=[1,0], digits=4))
    
    cm = confusion_matrix(y_true, y_pred, labels=[1,0])
    ax= plt.subplot()
    sns.heatmap(cm, annot=True, ax = ax, cmap='Blues', fmt="d")

    ax.set_title('Confusion Matrix')

    ax.set_xlabel('Predicted Labels')
    ax.set_ylabel('True Labels')

    ax.xaxis.set_ticklabels(['FAKE', 'REAL'])
    ax.yaxis.set_ticklabels(['FAKE', 'REAL'])
    
best_model = BERT().to(device)

load_checkpoint(destination_folder + '/model.pt', best_model)

evaluate(best_model, test_iter)