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.
- Model Compression Suites - Reduces language model size and computational overhead using quantization and distillation techniques.
- Language Model Pretraining - Pretrains large-scale autoregressive and contextual language models for multilingual NLP tasks.
- Text Tokenization - Builds byte-level vocabularies and encodes text streams for input into NLP pipelines.
- Byte-Level Tokenizers - Splits raw text streams into subword tokens using byte-level vocabularies for downstream NLP processing.
- Neural Network Compression - Reduces the size and computational overhead of deep learning models using quantization and distillation.
- Conversational Response Generators - Generates natural open-domain dialog using large-scale pretrained models to power interactive chat.
- Large Language Models - Provides a machine learning library for pretraining, tokenizing, and compressing large language models.
- Hardware-Accelerated - Deploys pretrained models across specialized hardware accelerators to power real-time conversational generation.
- Model Pretraining Frameworks - Offers a framework for pretraining large language models using transformer architectures.
- Natural Language Processing - Delivers a software toolkit for multilingual natural language processing and text classification.
- Neural Network Model Compression - Reduces neural network size and computational overhead using quantization and distillation.
- Neural Network Training Frameworks - Applies memory-efficient algorithms to train large machine learning architectures effectively.
- Training Memory Management - Applies adaptive memory algorithms to stabilize and optimize training on large networks.
