  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.
