This repository offers a collection of advanced pretrained language models and optimization techniques from Huawei Noah's Ark Lab, targeting NLP researchers and engineers. It provides access to state-of-the-art Chinese language models, efficient model compression techniques, and novel architectural approaches for various NLP tasks.

How It Works

The project showcases a diverse range of models, including large-scale autoregressive models like PanGu-α (200B parameters) and efficient compressed models like TinyBERT (7.5x smaller, 9.4x faster inference). It also features dynamic models (DynaBERT), byte-level tokenization tools (BBPE), and novel approaches like probabilistically masked language models (PMLM) and weight ternarization/binarization (TernaryBERT, BinaryBERT). The models are developed across multiple frameworks, including MindSpore and TensorFlow.

Quick Start & Requirements

• Installation and usage vary significantly by model. Specific instructions and requirements (e.g., Ascend 910 AI processors for PanGu-α training, TensorFlow/PyTorch) are detailed within subdirectories for each model.
• Links to official quick-start guides, documentation, or demos are not explicitly provided in the main README.

Highlighted Details

• PanGu-α: A 200B parameter autoregressive Chinese language model.
• TinyBERT: Achieves significant speed and size reductions for BERT inference.
• NEZHA: State-of-the-art performance on Chinese NLP tasks.
• AutoTinyBERT: Offers a model zoo tailored to specific latency requirements.

Maintenance & Community

• Developed by Huawei Noah's Ark Lab.
• No specific community channels (Discord/Slack) or roadmap links are provided in the main README.

Licensing & Compatibility

• The primary README does not specify a license. Individual model subdirectories may contain licensing information.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The repository contains a wide array of models with varying dependencies and development frameworks (MindSpore, TensorFlow, PyTorch), requiring users to navigate individual subdirectories for specific setup and usage instructions. The lack of a unified quick-start guide or explicit licensing information across all models may hinder rapid adoption.