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arXiv · 2010.03813

On the importance of pre-training data volume for compact language models

Vincent Micheli·Martin d'Hoffschmidt·François Fleuret
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Abstract

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.

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Vincent Micheli, Martin d'Hoffschmidt, François Fleuret. 2020-10-08. On the importance of pre-training data volume for compact language models. https://arxiv.org/abs/2010.03813

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