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I positioned myself as someone seeking help, and the bot came across like an HR person play-acting empathy and care, and couldn't wait for me to leave the office. It came down to (1) do your research, (2) make a list, (3) take courses, (4) be aware of barriers.
It's for anyone who wants to learn NLP such that they get (a) an understanding of what's going on under the hood and (b) knowledge of how to get stuff done.
So the ideal outcome is someone who gets an end-to-end view from theory/concept to implementation.
If someone just wants to learn how to use tools/frameworks, I'd stick to the Colab notebooks. If someone's already experienced in ML and wants to learn something NLP-specific, I'd skip around to see what's interesting.
Yeah, I can see that being the case for specialized domains. With state-of-the-art models widely available to the public, knowledge of the domain and its workflows, and fine-tuning models to suit the domain will probably be your edge.
You could start by looking into either multitask transformers or really general seq2seq models like T5. With T5, for example, it just learns to transform one text sequence into another. So you could fine-tune T5 to produce your target sequence, but rather than outputting an explicit Python list of tuples, it would output a string that looks like a sequence of tuples.