The generated ONNX model can be deployed and run in the following ways:
- Python Examples: Installation
onnxruntimeAfter the library, load the model file and enter the text to get the classification results, such as the sentiment analysis output['positive']The - cross-language support: The model is compatible with Rust, Swift and other languages, you need to refer to the ONNX runtime documentation to implement, for example, use the Swift version of the ONNX runtime for mobile integration.
- Edge device deployment: Embed model files smaller than 1MB directly into devices such as the Raspberry Pi to process real-time text input (e.g., log analysis).
- continuous integration: Automate training and deployment processes with GitHub Actions for team collaboration.
The model runs completely offline without an Internet connection, which is especially suitable for privacy-sensitive scenarios such as medical data processing. The tool also provides an online Playground for quick validation.
This answer comes from the articleWhiteLightning: an open source tool for generating lightweight offline text classification models in one clickThe
May not be reproduced without permission:AI productivity tools " How can WhiteLightning generated models be used in real applications?
