Transfomrer is a deep machine learning model designed to handle sequential input data such as natural language. But it does not process the words by sequence of appearance but rather by context meaning weighing the influence of different words position in sequence using combinatorial probability and statistical frequency distribution.

Transfomrer is a deep machine learning model designed to handle sequential input data such as natural language. But it does not process the words by sequence of appearance but rather by context meaning weighing the influence of different words position in sequence using combinatorial probability and statistical frequency distribution.

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Bidirectional Encoder Representation from Transformers(BERT) is a transformer based machine learning technique created and published in 2018 by Jacob Devlin and his colleagues from Google. In October 2020, almost every single English-based query in Google Search was processed by BERT. BERT is good at natural language understanding.

Bidirectional Encoder Representation from Transformers(BERT) is a transformer based machine learning technique created and published in 2018 by Jacob Devlin and his colleagues from Google. In October 2020, almost every single English-based query in Google Search was processed by BERT. BERT is good at natural language understanding.

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