By the way, a pet peeve is sentiment detection. It's a useful method, but please be aware that it does not measure "sentiment" in a way that one would normally think, and that what it measures varies strongly across methods (https://www.tandfonline.com/doi/abs/10.1080/19312458.2020.18...).
* How should you select the learning rate?
* What tasks are best for fine-tuning on small amounts of data? etc.
Instead, this seems mostly to just be running through the implementation of ML/DL 101: loss function for binary classification, helper functions to load data, etc.
In the future we definitely plan to dive further into the details and touch on some of the things you mentioned!
This was one of the primary reasons why we chose the "Sentiment Analysis" task as it's fairly simply to get a model trained quickly with good performance.
BTW, take a look at "sentence transformers" library, a nice interface on top of Hugging Face for this kind of operations (reusing, fine-tuning).
