TensorFlow Models NLP Library for Beginners

This notebook shows how to reuse a nlp.networks.BertEncoder from TensorFlow Model Garden to power three tasks: (1) pretraining with nlp.models.BertPretrainer (masked-LM + next-sentence), (2) span labeling with nlp.models.BertSpanLabeler (start/end logits for SQuAD-style QA), and (3) classification with nlp.models.BertClassifier ([CLS] head). You install tf-models-official (or tf-models-nightly for latest), import tensorflow_models.nlp, build small dummy examples, run each model forward pass, and compute losses (weighted sparse CE for MLM/NSP; CE for span start/end; CE for classification). Result: a clear pattern for wrapping one encoder into multiple BERT task heads with concise, production-friendly APIs.