metric performances varies with each training run of my deep learning model My model (a biLSTM model in Keras) gives different results for my test metric (precision) each time I train.
My metric varies between 80% and 92%. That is really bad.
What reason besides a bad kernel initialization could this be?
That difference in results could be from high variance, the error from sensitivity to small fluctuations in the training set.

Generally, the best solution to high variance is to increase the size of the training dataset.