I am using the transformers library to get embeddings for sentences and tokens. More  specifically I use the first token embedding [CLS] for the embedding that represents the sentence and I compare sentences using cosine similarity. This  approach is naive and completely unsupervised.
Now I would like to gain some experience in fine tuning  the model: For example how to fine tune BERT for NER and how to use BERT for sentence pairs. How can I study how  to fine tune BERT?