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I am new to BERT and NLP and I am a little confused with tokenization and word embedding. My doubt is if I use the BertTokenizer for tokenizing a sentence then do I have to compulsorily use BertEmbedding for generating its corresponding word vectors of the tokens or I can train my own word2vec model to generate my word embedding while using BertTokenizer?

Pardon me if this question doesn't make any sense.

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word2vec and BERT are completely different things. You don't need to use BERT Tokenizer for training word2vec model. But, if you want to go ahead. Here's a link which can help you.

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  • $\begingroup$ Sorry, but I didn't ask that. My question was if I want to use BertTokenizer for a particular NLP task, say NER then is it mandatory to use BertEmbedding to generate the word embedding or I can train my own embeddings and use it along with BertTokenizer for that particular task (NER). $\endgroup$ – thenocturnalguy Mar 13 at 11:15
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    $\begingroup$ It is only mandatory if you are using BERT to create the embeddings. That's what I wrote in the answer also. You first tokenize then give it to BERT for generating embeddings. $\endgroup$ – Abhishek Verma Mar 13 at 11:28
  • $\begingroup$ Oh! thank you. Can you suggest to me any link or article for generating word embedding from BERT. $\endgroup$ – thenocturnalguy Mar 13 at 11:31
  • $\begingroup$ mccormickml.com/2019/05/14/BERT-word-embeddings-tutorial/… $\endgroup$ – Abhishek Verma Mar 13 at 11:35

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