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Reproducibility in scientific work has been becoming increasingly importantin research communities such as machine learning, natural language processing,and computer vision communities due to the rapid development of the researchdomains supported by recent advances in deep learning. In this work, we presenta significantly upgraded version of torchdistill, a modular-driven coding-freedeep learning framework significantly upgraded from the initial release, whichsupports only image classification and object detection tasks for reproducibleknowledge distillation experiments. To demonstrate that the upgraded frameworkcan support more tasks with third-party libraries, we reproduce the GLUEbenchmark results of BERT models using a script based on the upgradedtorchdistill, harmonizing with various Hugging Face libraries. All the 27fine-tuned BERT models and configurations to reproduce the results arepublished at Hugging Face, and the model weights have already been widely usedin research communities. We also reimplement popular small-sized models and newknowledge distillation methods and perform additional experiments for computervision tasks.

torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP