Overview of Pretraining Models and the UER‑py Framework for Natural Language Processing

The talk begins with an introduction to the importance of pre‑training in natural language processing, outlining how pre‑training models have dramatically improved many NLP tasks.

It reviews classic pre‑training models—including Skip‑thoughts, Quick‑thoughts, CoVe, InferSent, GPT, BERT, RoBERTa, ALBERT, GPT‑2, and T5—describing their corpora, encoders, and training objectives.

The presentation then introduces the UER‑py framework, a modular, PyTorch‑based system that separates embedding, encoder, and target layers, enabling rapid construction of various pre‑training models.

Comparisons with HuggingFace Transformers highlight UER‑py’s advantages: modular design, strong Chinese language support, and compatibility with both LSTM and Transformer encoders.

Extensive Chinese pre‑trained weights are provided in both UER and HuggingFace formats, covering models such as BERT, GPT‑2, T5, ALBERT, and many downstream task checkpoints.

Finally, practical deployment steps are described, including selecting a strong base model, domain‑specific unsupervised and supervised pre‑training, multi‑task learning, model distillation, and hyper‑parameter tuning, illustrating how UER‑py supports the entire pipeline.