How big are your training and validation sets? And what were the training stats from stage 1? You may just have a well trained model in this case, basis default settings and given dataset!! But here is some more to think about:
Accuracy (1-error rate) is calculated on successful classifications in the validation set. Where “success” is determined based on a preconfigured threshold. It is possible that in this run the model did not misclassify anything basis that threshold, and hence the error_rate is zero. However, you still have a non zero train_loss and valid_loss which means the actual predictions are not identical to the ground truth in all the cases. So depending on what your goal is, you may either want to train some more or get more data and see if the model generalises well to those.
This thread helped me: Validation Loss VS Accuracy
Especially @radek’s answer in there. I hope he can correct me if I’ve misunderstood!
Hope this helps!