While training, a model learns from examples. Based on the examples, the model tries to predict an outcome (for example, filling in a cloze correctly) and compares its results with the real values at the end of each cycle. If the result is wrong, the underlying statistical model is adjusted and a new attempt is started. Usually, a training runs until the statistical model hardly changes anymore, i.e. the results become stable. This can be the case after a few minutes (classic machine learning) or weeks/months (deep learning on very large data sets).