Practical overview
When it fits
Use EasyNLP when you want a Python/PyTorch development framework for training or adapting NLP models, especially when your workflow involves pretrained models, classification, few-shot learning, distillation or multimodal text-vision tasks.
Who it is for
- Python developers building or training NLP models.
- Machine-learning teams adapting pretrained models to text tasks.
- Researchers and developers exploring few-shot learning, model distillation or vision-language tasks.
Documented uses
- Build a BERT-based text-classification model and train it with labeled datasets.
- Fine-tune large pretrained models using few-shot learning.
- Distill a large model into a smaller model for more efficient online deployment.
- Develop text-image matching or text-to-image generation workflows with multimodal pretrained models.
Requirements & limits
The repository is public and specifies the Apache-2.0 license. The documented installation route is from source.
- The repository lists Python 3.6 and PyTorch 1.8 or later as tested requirements.
- The quick-start classification example expects separate training and validation data, a pretrained-model path and defined input and label fields.
- EasyNLP is a development toolkit; the repository describes PAI products as integrations for development, training and serving rather than presenting EasyNLP as a hosted service.
- The project documents support for APIs that train Hugging Face Transformers models with the PAI distributed framework.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install EasyNLP from source
Set up the repository as a Python package.
Before you start
- Python 3.6
- PyTorch 1.8 or later
Steps
Clone the alibaba/EasyNLP repository.
Change into the EasyNLP directory.
Run the command below.
python setup.py install
Expected outcome
The repository's documented source-installation procedure completes.
Guide limitations
- The instructions establish installation only; they do not describe a complete deployment or serving workflow.
Build a BERT text-classification training workflow
Use the documented quick-start structure to prepare datasets, create a model and start training.
Before you start
- EasyNLP installed from source.
- A pretrained model path and training and validation data files.
- Input schema, sequence fields, label name and label values supplied through the quick-start arguments.
Steps
Initialize EasyNLP and parse the command-line arguments and user-defined parameters.
Resolve the pretrained model path and create training and validation ClassificationDataset objects.
Create the application model and evaluator, then pass them with the datasets to Trainer.
Call
trainer.train().
Expected outcome
The example starts a BERT-based text-classification training workflow.
Guide limitations
- The quick start uses project-specific arguments and data fields; it does not specify a universal dataset format or provide a complete standalone command.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- alibaba/EasyNLP · Official homepage title · retained in its source language