Adding accuracy, precision, recall and f1 score metrics during training

I am fine-tuning herbert-base model for token classification of named entities.

Basically I am going through this tutorial with minor changes to data preprocessing, pretrained base model and datasets.
I would love to see evaluation f1 score and accuracy throughout training. What would be most :hugs: way to add those metrics after every epoch? I see option to load metrics from :hugs: datasets. But I cannot find where should I put loaded objects.

I know I could override Trainer class or make my own Callback etc. However what way would be the best? What would integrate seamlessly with TensorBoard?

Thank you for help in advance!