Summary: Understanding the BERT Model

Let’s summarize what we have learned so far about the BERT model.

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Key highlights

Summarized below are the main highlights of what we have learned in this chapter.

  • We began this chapter by understanding the basic idea of BERT. We learned that BERT can understand the contextual meaning of words and generate embeddings according to context, unlike context-free models such as word2vec, which generate embeddings irrespective of the context.

  • We looked into the workings of BERT. We understood that Bidirectional Encoder Representation from Transformer (BERT), as the name suggests, is basically the transformer model.

  • We looked into the different configurations of BERT. We learned that the BERT-base consists of 12 encoder layers, 12 attention heads, and 768 hidden units, while BERT-large consists of 24 encoder layers, 16 attention heads, and 1,024 hidden units.

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