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Modern Natural Language Processing (NLP) models based on Transformerstructures represent the state of the art in terms of performance on verydiverse tasks. However, these models are complex and represent several hundredmillion parameters for the smallest of them. This may hinder their adoption atthe industrial level, making it difficult to scale up to a reasonableinfrastructure and/or to comply with societal and environmentalresponsibilities. To this end, we present in this paper a model thatdrastically reduces the computational cost of a well-known French model(CamemBERT), while preserving good performance.

DistilCamemBERT: a distillation of the French model CamemBERT