EasyNLP is a comprehensive PyTorch-based NLP toolkit designed for developing and deploying natural language processing applications. It targets researchers and engineers by providing a unified framework for model training, inference, and deployment, with a focus on simplifying the use of large pre-trained models through techniques like few-shot learning and knowledge distillation.

How It Works

EasyNLP leverages a modular design with AppZoo and ModelZoo for easy customization and integration of various NLP algorithms and pre-trained models. It supports distributed training via Alibaba's TorchAccelerator and offers seamless integration with Alibaba Cloud's AI platform products. The toolkit emphasizes practical application by facilitating the fine-tuning of large models with minimal data and enabling efficient model compression for deployment.

Quick Start & Requirements

• Installation: git clone https://github.com/alibaba/EasyNLP.git && cd EasyNLP && python setup.py install
• Prerequisites: Python 3.6+, PyTorch >= 1.8.
• Documentation: Official Documentation
• Examples: Tutorials and Examples

Highlighted Details

• Supports knowledge-injected pre-training (DKPLM, KGBERT) and few-shot learning methods (PET, P-Tuning, CP-Tuning).
• Integrates multi-modal capabilities for vision-language tasks (CLIP, DALL-E style models).
• Offers tools for knowledge distillation and data augmentation for model compression.
• Includes benchmarks and performance results on the CLUE benchmark for Chinese NLP.

Maintenance & Community

The project is actively maintained by Alibaba, with contributions from various internal teams. Discussions are primarily in Chinese via DingTalk.

Licensing & Compatibility

Licensed under the Apache License (Version 2.0). The toolkit may include code from other repositories with different licenses, as detailed in the NOTICE file.

Limitations & Caveats

While the documentation and community discussions are primarily in Chinese, English is also welcomed. The toolkit is tightly integrated with Alibaba Cloud services, which might influence its usability in non-Alibaba Cloud environments.