Pretrained Language Model

This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei Noah's Ark Lab.

Directory structure

• PanGu-α is a Large-scale autoregressive pretrained Chinese language model with up to 200B parameter. The models are developed under the MindSpore and trained on a cluster of Ascend 910 AI processors.
• NEZHA-TensorFlow is a pretrained Chinese language model which achieves the state-of-the-art performances on several Chinese NLP tasks developed under TensorFlow.
• NEZHA-PyTorch is the PyTorch version of NEZHA.
• NEZHA-Gen-TensorFlow provides two GPT models. One is Yuefu (乐府), a Chinese Classical Poetry generation model, the other is a common Chinese GPT model.
• TinyBERT is a compressed BERT model which achieves 7.5x smaller and 9.4x faster on inference.
• TinyBERT-MindSpore is a MindSpore version of TinyBERT.
• DynaBERT is a dynamic BERT model with adaptive width and depth.
• BBPE provides a byte-level vocabulary building tool and its correspoinding tokenizer.
• PMLM is a probabilistically masked language model. Trained without the complex two-stream self-attention, PMLM can be treated as a simple approximation of XLNet.
• TernaryBERT is a weights ternarization method for BERT model developed under PyTorch.
• TernaryBERT-MindSpore is the MindSpore version of TernaryBERT.
• HyperText is an efficient text classification model based on hyperbolic geometry theories.
• BinaryBERT is a weights binarization method using ternary weight splitting for BERT model, developed under PyTorch.
• AutoTinyBERT provides a model zoo that can meet different latency requirements.