WhiteLightning offers several advanced configuration parameters to optimize model performance:
- Cue Optimization Loop: By
-r 3Parameters increase the number of optimizations (default 1) to improve synthetic data quality - Edge Case Generation: default on (
--generate-edge-cases True), generating 50 edge cases per class to enhance model robustness - Data Extension: Use
--target-volume-per-class 100Increase the amount of training data per class - LLM Selection: Different large-scale language models such as Grok-3-beta or GPT-4o-mini can be specified in the configuration file to generate data.
It is recommended to monitor accuracy and loss values through logs (e.g. Accuracy: 1.0000), and gradually adjust the parameters. Complex classification tasks may require more training data and optimization loops.
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 to optimize the performance of text classification models generated by WhiteLightning?
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