Tracking model metrics
So far, we have trained language models and simply analyzed the final results. We have not observed the training process or made a comparison of training using different options. In this section, we will briefly discuss how to monitor model training. For this, we will handle how to track the training of the models we developed before in Chapter 5, Fine-Tuning Language Models for Text Classification.
There are two important tools developed in this area—one is TensorBoard, and the other is W&B. With the former, we save the training results to a local drive and visualize them at the end of the experiment. With the latter, we are able to monitor the model-training progress live in a cloud platform.
This section will be a short introduction to these tools without going into much detail about them, as this is beyond the scope of this chapter.
Let's start with TensorBoard.
