Reproduce “Adaptive Fine-Tuning of Transformer-Based Language Models for Named Entity Recognition” on OpenResearch

About this paper

Felix Stollenwerk

This paper proposes an adaptive fine-tuning method for transformer-based language models in Named Entity Recognition, which dynamically adjusts the number of training epochs to the dataset size. The approach achieves superior performance on small datasets while maintaining competitive results on larger datasets with significantly reduced computational effort, and it enhances the stability of the fine-tuning process.

Key insights

• Dynamically adjusting the number of training epochs eliminates a critical hyperparameter, streamlining the fine-tuning process for transformer-based language models.
• A hybrid learning rate schedule combined with a dynamic early stopping mechanism significantly improves fine-tuning performance and stability, particularly for small datasets.
• The adaptive fine-tuning approach maintains robustness across varying validation dataset sizes and remains effective when the validation set is merged into the training data for final model deployment.

Summary by alphaXiv.