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Show HN: Natural Language Processing Demystified

news.ycombinator.com

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Re: Show HN: Natural Language Processing Demystified

#24

Post by reichardt »

mharig wrote: post #20
reichardt wrote: post #4

I love your course for being very comprehensive and technical while not getting lost in mundane details. Like the opposite of the following quote: “I didn't have time to write a short letter, so I wrote a long one instead.” [1] [1] https://www.goodreads.com/quotes/21422-i-didn-t-have-time-to...

"Je n’ai fait celle-ci plus longue que parce que je n’ai pas eu le loisir de la faire plus courte." Blaise Pascal, 1656 FYI

Ah, thanks! I didn’t know that and shouldn’t have used the first result that came up with a google search.

Re: Show HN: Natural Language Processing Demystified

#28

Post by trenchgun »

brooksbp wrote: post #17
mothcamp wrote: post #13

Earlier quoted context omitted.

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. Or maybe skip all that and outsource it to GPT: ht…

Ah, so if the model is just converting input text into output text, it can really learn how to do just about anything? But, there may be certain aspects of model design that make it better at some types of conversions ("tasks") than others? And there may be certain data sets that you want to train a base model on to get base learning of such as general language comprehension, and then build on top of that for your sp…

It is kind of like a very opaque but trainable Turing machine.

Re: Show HN: Natural Language Processing Demystified

#29

Post by mothcamp »

posharma wrote: post #26

Who is the intended audience for this course? Is it application developers looking to use NLP in their apps? Or machine/deep learning devs?

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.

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