How to Customize BERT Encoders with TensorFlow Model Garden

This tutorial shows how to go from a canonical BertEncoder to highly customizable encoders using tfm.nlp.networks.EncoderScaffold and nlp.layers.TransformerScaffold. You’ll (1) build a baseline classifier, (2) replace the embedding subnetwork and input signature, (3) drop in alternative Transformer blocks like ReZero, or swap only sub-layers (e.g., TalkingHeadsAttention, GatedFeedforward), and (4) instantiate entirely different stacks such as ALBERT—all while keeping the same classifier head. The result: rapid prototyping of new attention/FFN/embedding designs with minimal code changes and Model Garden-friendly APIs.