Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models. Multiple BERT-based models are trained on gradually increasing amounts of French text. Through fine-tuning on the French Question Answering Dataset (FQuAD), we observe that well-performing models are obtained with as little as 100 MB of text. In addition, we show that past critically low amounts of pre-training data, an intermediate pre-training step on the task-specific corpus does not yield substantial improvements.
@article{arxiv.2010.03813,
title = {On the importance of pre-training data volume for compact language models},
author = {Vincent Micheli and Martin d'Hoffschmidt and François Fleuret},
journal= {arXiv preprint arXiv:2010.03813},
year = {2020}
}