BERT v/s Word2Vec Simplest Example
In this video, I'll show how BERT models being context dependent are superior over word2vec/Glove models which are context-independent.
Bidirectional Encoder Representations from Transformers is a Transformer-based machine learning technique for natural language processing pre-training developed by Google.
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Link to the notebook : https://github.com/bhattbhavesh91/word2vec-vs-bert
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.
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#BERT #NLP
Видео BERT v/s Word2Vec Simplest Example канала Bhavesh Bhatt
Bidirectional Encoder Representations from Transformers is a Transformer-based machine learning technique for natural language processing pre-training developed by Google.
Join this channel to get access to perks:
https://www.youtube.com/channel/UC8ofcOdHNINiPrBA9D59Vaw/join
Link to the notebook : https://github.com/bhattbhavesh91/word2vec-vs-bert
If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.
If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful.
Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.
You can find me on:
Blog - http://bhattbhavesh91.github.io
Twitter - https://twitter.com/_bhaveshbhatt
GitHub - https://github.com/bhattbhavesh91
Medium - https://medium.com/@bhattbhavesh91
#BERT #NLP
Видео BERT v/s Word2Vec Simplest Example канала Bhavesh Bhatt
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