huawei-noah/Pretrained-Language-Model
Pretrained Language Model
Pretrained-Language-Model is a machine learning library and natural language processing toolkit designed for pretraining, tokenizing, and compressing large language models using transformer architectures and specialized optimization techniques. It supports Chinese and multilingual natural language processing tasks, including text classification and conversational response generation.
The framework provides specialized capabilities for training large-scale autoregressive and contextual language models, alongside model compression techniques like knowledge distillation and quantization to reduce neural network size and computational overhead. It incorporates confidence-guided adaptive memory algorithms to stabilize training on large networks, byte-level tokenisation pipelines for raw text streams, and hyperbolic geometry methods for document embedding and text classification.
Features
- Transformer Language Models - Trains large-scale autoregressive and contextual transformer architectures for multilingual language tasks.
- Chinese Natural Language Processing - Supports Chinese natural language processing tasks including text understanding and dialog generation.
- Knowledge Distillation - Reduces neural network size by transferring learned representations from large models to smaller counterparts.
- Language Model Fine-Tuning - Builds and fine-tunes custom natural language processing models using specialized training algorithms.
