Pre-training a generic SentenceTransformer for domain adaptation

When using `SetFit` for classification in a more technical domain, I could imagine the generically-trained `SBERT` models may produce poor sentence embeddings if the domain is not represented well enough in the diverse training corpus. In this case, would it be advantageous to first apply domain adaptation techniques (as discussed [here](https://sbert.net/examples/domain_adaptation/README.html)) to an `SBERT` model before using the model as a base in `SetFit`? Have you considered and/or tested such an approach?

Thanks for the help!