EasyNLP is Alibaba’s comprehensive, easy-to-use NLP toolkit, built on PyTorch to package the group’s research into reusable components. With around 2,200 stars on GitHub, it covers the full workflow from pretrained models to application: text classification, matching, information extraction, and even text-to-image retrieval and synthesis.
EasyNLP emphasizes techniques that work with limited labeled data and compute. It ships knowledge-pretrained models, knowledge distillation tools to compress large models, and few-shot learning methods such as PET-style prompting that reach strong accuracy with only a handful of examples. A modular design lets you swap backbones and task heads, and ready-made trainers handle common NLP tasks end to end.
EasyNLP’s research contributions include knowledge-enhanced pretraining methods such as DKPLM, which injects structured knowledge into language models in a decomposable way, and the toolkit packages those methods so practitioners can use them without reimplementing papers. It also provides ready application templates for common business scenarios โ sentiment analysis, semantic matching, and document classification among them โ reflecting its origins serving real product teams inside Alibaba.
EasyNLP provides a unified command-line and configuration-driven workflow: pick a task, point it at your dataset, and the framework handles tokenization, training, evaluation, and prediction export. It has been used for sentiment analysis, semantic matching, and content understanding in production scenarios, and its model zoo emphasizes strong Chinese pretrained models that are otherwise scattered across individual research releases.
EasyNLP is free and open source under the Apache-2.0 license.
NLP engineers and researchers โ especially those working on Chinese-language applications โ who want production-friendly implementations of modern pretraining and few-shot methods.