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While modern masked language models (LMs) are trained on ever larger corpora,we here explore the effects of down-scaling training to a modestly-sized butrepresentative, well-balanced, and publicly available English text source --the British National Corpus. We show that pre-training on this carefullycurated corpus can reach better performance than the original BERT model. Weargue that this type of corpora has great potential as a language modelingbenchmark. To showcase this potential, we present fair, reproducible anddata-efficient comparative studies of LMs, in which we evaluate severaltraining objectives and model architectures and replicate previous empiricalresults in a systematic way. We propose an optimized LM architecture calledLTG-BERT.

Trained on 100 million words and still in shape: BERT meets British National Corpus