Mastering Tokenization in NLP: The Ultimate Guide to Unigram and Beyond!

Get ready to unlock the secrets of tokenization in natural language processing. In this video, we'll cover Unigram tokenization, subword approaches, and strategies for handling out-of-vocabulary words. Learn from the best as we dissect BloombergGPT's techniques and help you become an NLP master! These are the techniques at the heart of popular Large Language models like ChatGPT and GPT-4

Welcome to the fascinating world of tokenization in natural language processing! In this comprehensive video, we explore Unigram tokenization, its advantages, and how it compares to other tokenization techniques. Join us as we dive into the inner workings of BloombergGPT and discover how tokenization plays a critical role in NLP success.

Explore the crucial role of tokenization in natural language processing. This video dives deep into Unigram tokenization and other techniques, revealing how to handle out-of-vocabulary words and process text in multiple languages. Discover the power behind BloombergGPT's NLP success and learn how to apply these techniques yourself.

Become an expert in tokenization for natural language processing! This video explores Unigram tokenization, subword methods, and strategies for handling OOV words. See how BloombergGPT leverages these techniques for their groundbreaking NLP success and learn how to apply these methods to your own projects

[gileneusz]

5:10 This is what I observe when I break down Polish text using the OpenAI Tokenizer. While English words are mostly single tokens, Polish words are broken down into several individual elements, resulting in 2-3 times more tokens when compared to accurately translated text. This has implications for context length. It is preferable to work with English text, as the model can fit more content within the 8k token context frame.

[holthuizenoemoet]

Very clear video with beautiful examples, thank you.

[cdb]

I was literally just trying to figure out what's the deal with tokens.

[caliwolf]

One interesting thing I found
is that I used english letter and numerals to write in Arabic, or in other words what arabic words wound "sound" if written in english, and GPT perfectly understood the prompt,
I also tried that in reverse
using Arabic letters to writr what sound as english or french or german
and the language model got it right everytime
which is not so clear for me how its done
as there is clearly no training data provided for my obsecure usecase

[pictzone]

Hey there! Why have you deleted the "Introduction to AI & Neural Networks" playlist? I finally had some free time to watch all of it and it disappeared.. Thank you!