ModernBERT or DeBERTaV3? Examining Architecture and Data Influence on
  Transformer Encoder Models Performance
Authors: name: Wissam Antoun, name: Benoît Sagot, name: Djamé Seddah

Pretrained transformer-encoder models like DeBERTaV3 and ModernBERT introduce
architectural advancements aimed at improving efficiency and performance.
Although the authors of ModernBERT report improved performance over DeBERTaV3
on several benchmarks, the lack of disclosed training data and the absence of
comparisons using a shared dataset make it difficult to determine whether these
gains are due to architectural improvements or differences in training data. In
this work, we conduct a controlled study by pretraining ModernBERT on the same
dataset as CamemBERTaV2, a DeBERTaV3 French model, isolating the effect of
model design. Our results show that the previous model generation remains
superior in sample efficiency and overall benchmark performance, with
ModernBERT's primary advantage being faster training and inference speed.
However, the new proposed model still provides meaningful architectural
improvements compared to earlier models such as BERT and RoBERTa. Additionally,
we observe that high-quality pre-training data accelerates convergence but does
not significantly improve final performance, suggesting potential benchmark
saturation. These findings show the importance of disentangling pretraining
data from architectural innovations when evaluating transformer models.