  Recent advances in NLP have significantly improved the performance of
language models on a variety of tasks. While these advances are largely driven
by the availability of large amounts of data and computational power, they also
benefit from the development of better training methods and architectures. In
this paper, we introduce CamemBERTa, a French DeBERTa model that builds upon
the DeBERTaV3 architecture and training objective. We evaluate our model's
performance on a variety of French downstream tasks and datasets, including
question answering, part-of-speech tagging, dependency parsing, named entity
recognition, and the FLUE benchmark, and compare against CamemBERT, the
state-of-the-art monolingual model for French. Our results show that, given the
same amount of training tokens, our model outperforms BERT-based models trained
with MLM on most tasks. Furthermore, our new model reaches similar or superior
performance on downstream tasks compared to CamemBERT, despite being trained on
only 30% of its total number of input tokens. In addition to our experimental
results, we also publicly release the weights and code implementation of
CamemBERTa, making it the first publicly available DeBERTaV3 model outside of
the original paper and the first openly available implementation of a DeBERTaV3
training objective. https://gitlab.inria.fr/almanach/CamemBERTa
