Oh! That interesting me a lot :) I wanted to learn it in next months. Thank You very much! :)
Show HN: Natural Language Processing Demystified
11–20 of 39 posts
Re: Show HN: Natural Language Processing Demystified
#1210/10 for making this freely available
Re: Show HN: Natural Language Processing Demystified
#13Thank 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 doe…
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: https://imgur.com/a/BQv6C3K
Re: Show HN: Natural Language Processing Demystified
#14I 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...
Really appreciate that. Finding that balance was one of the hardest parts of building this course.
Yes, it's easy to see you put a lot of thought into that. I hope your course receives much more exposure. When I first found your videos a few weeks ago, I was surprised how few views they have given to the quality of the course.
Do you record the voice track of your videos yourself?
Glad to see you published the final lesson about transformers. Was looking forward to that!
Re: Show HN: Natural Language Processing Demystified
#15Earlier quoted context omitted.
Really appreciate that. Finding that balance was one of the hardest parts of building this course.
Yes, it's easy to see you put a lot of thought into that. I hope your course receives much more exposure. When I first found your videos a few weeks ago, I was surprised how few views they have given to the quality of the course. Do you record the voice track of your videos yourself? Glad to see you published the final lesson about transformers. Was looking forward to that!
I did record all voice tracks, yeah. If I do this again, I'll probably use a lot of generative tools now. :-D
Hope you find the transformers module useful!
Re: Show HN: Natural Language Processing Demystified
#16[flagged]
Re: Show HN: Natural Language Processing Demystified
#17Thank 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 doe…
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 specific use case?
Re: Show HN: Natural Language Processing Demystified
#18Thanks excellent course, watched about an hour or two. Very well made. Deserves more exposure.
Re: Show HN: Natural Language Processing Demystified
#19Earlier 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…
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.
Re: Show HN: Natural Language Processing Demystified
#20I 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