Thank you for sharing this! I am currently studying NLP..
Along the way, I've been struggling with a question and I hope someone can help me understand how to go about this: how would you build a model that does more than one NLP task? For a simple classifier like input: text (a tweet) and output: text (an emotion), you can fine-tune an existing classifier on such a data set. But, how would you build a model that does NER and sentiment analysis? E.g. input: text (a Yelp review of a restaurant) and output: list of (entity, sentiment) tuples (e.g. [("tacos", "good"), ("margaritas", "good"), ("salsa", "bad")]). If you have a data set structured this way, and want to fine-tune a model, how does that model know how to make use of a Python list of tuples?
Along the way, I've been struggling with a question and I hope someone can help me understand how to go about this: how would you build a model that does more than one NLP task? For a simple classifier like input: text (a tweet) and output: text (an emotion), you can fine-tune an existing classifier on such a data set. But, how would you build a model that does NER and sentiment analysis? E.g. input: text (a Yelp review of a restaurant) and output: list of (entity, sentiment) tuples (e.g. [("tacos", "good"), ("margaritas", "good"), ("salsa", "bad")]). If you have a data set structured this way, and want to fine-tune a model, how does that model know how to make use of a Python list of tuples?