learn-nlp-with-transformers

Why we included this project

A Chinese-language tutorial that starts with the attention mechanism and the transformer architecture, then moves into building a BERT model from scratch in PyTorch before applying pretrained models to real tasks like text classification, sequence labeling, question answering, machine translation, and summarization. The four chapters are ordered so each step builds on the last, and the worked examples plus chapter quizzes help readers actually absorb the material rather than just skim it. It suits developers who already know Python and PyTorch but are new to modern NLP, and because it is a curated learning path rather than a library, the payoff is in the explanations and the reproducible practice projects. That makes it a sensible place to start before diving into the official Hugging Face documentation.