[Enhancement] Support self-supervised pre-training

[athewsey]

Notebook 2 currently has a placeholder section discussing how model accuracy might be improved by self-supervised pre-training (such as masked language modelling) on a broader corpus of unlabelled data, before fine-tuning on the end task with annotations.

However, the model training scripts in notebooks/src aren't set up for that yet.

It'd be great if we can flesh this out to a point where users can optionally run a pre-training job with Textract JSON only (ideally still with the ability to start from a pre-trained model from HF model zoo); and then use that training job instead of the model zoo models, as the starting point for the fine-tuning task.