"Bidirectional Encoder Representations from Transformers (BERT) is a technique for NLP (Natural Language Processing) pre-training developed by Google. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google.[1][2] Google is leveraging BERT to better understand user searches.[3]"
The BERT Encoder block implements the BERT—Bidirectional Encoder Representations from Transformers—network in its base size, as published in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
BERT pushes the state of the art in Natural Language Processing by combining two powerful technologies:
BERT Explained: State of the art language model for NLP
- It is based on a deep Transformer encoder network, a type of network that can process long texts efficiently by using attention.
- It is bidirectional, meaning that it uses the whole text passage to understand the meaning of each word.
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