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LLM Tokenizers Explained: BPE Encoding, WordPiece and SentencePiece 

DataMListic
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In this video we talk about three tokenizers that are commonly used when training large language models: (1) the byte-pair encoding tokenizer, (2) the wordpiece tokenizer and (3) the sentencepiece tokenizer.
References
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BPE tokenizer paper: arxiv.org/abs/1508.07909
WordPiece tokenizer paper:
Wordpiece tokenizer paper: static.googleusercontent.com/...
Sentencepiece tokenizer paper: arxiv.org/abs/1808.06226
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Contents
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00:00 - Intro
00:32 - BPE Encoding
02:16 - Wordpiece
03:45 - Sentencepiece
04:52 - Outro
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#tokenization #llm #wordpiece #sentencepiece

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15 июн 2024

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Комментарии : 7   
@datamlistic
@datamlistic 3 месяца назад
If you enjoy learning about LLMs, make sure to also watch my tutorial on prompt engineering: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE--BBulGM6xF0.html
@snehotoshbanerjee1938
@snehotoshbanerjee1938 25 дней назад
Best explanation!!
@datamlistic
@datamlistic 24 дня назад
Thanks x2! :)
@boredcrow7285
@boredcrow7285 10 дней назад
straight to the point pretty great! I have doubt in sentencepeice does the model split the corpus into character level and do the same as BPE or word peice instead of splitting it on the basis of spaces in case of english??
@datamlistic
@datamlistic 8 дней назад
Thanks! Yes, sentence piece considers the space as a stand-alone character. No pre-tokenization based on space is done there.
@snehotoshbanerjee1938
@snehotoshbanerjee1938 25 дней назад
Best Explanation!!
@datamlistic
@datamlistic 24 дня назад
Thanks! :)
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