text: Title: Fine-tuning bert results in a strange output, what happened?
  Body: I am applying bert to my own model and use the functions in modeling.py. After some times of training, I found the output of bert model (model.get_pooled_output()) contains only 1&-1 and different input sentences produce the same output. I used tf.stop_gradient and then everything is correct. What happened to this bert fine-tuning?