How text based web search engine like google uses deep learning models to fetch sites from the web?

I read some where that google currently uses BERT in its text search engine. I am wondering how such sysem works.

I know for image search engines, a deep learning model finds query image's embedding and then the system will look for similar embeddings in the database.

In text based search engine I am assuming it should be something similar. Modern websites wary in number of words. will deep learning models create embedding for the whole website text or it …

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