How to implement BERT in machine learning?

BERT, however, was pre-trained using only an unlabeled, plain text corpus (namely the entirety of the English Wikipedia, and the Brown Corpus). It continues to learn unsupervised from the unlabeled text and improve even as its being used in practical applications (ie Google search).

How to Implement BERT

  1. Getting the BERT model from the TensorFlow hub.
  2. Build a Model according to our use case using BERT pre-trained layers.
  3. Set the tokenizer.
  4. Loading the dataset and preprocessing it.
  5. Model Evaluation.

BERT uses the Transformer architecture, but it’s different from it in a few critical ways. With all these models it’s important to understand how they’re different from the Transformer, as that will define which tasks they can do well and which they’ll struggle with.