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DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

18 Nov 2021arXiv:2111.09543archive 2025-07-28

Pengcheng He, Jianfeng Gao, Weizhu Chen

This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing mask language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. This is because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the "tug-of-war" dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multi-lingual model mDeBERTa and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTa Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. We have made our pre-trained models and inference code publicly available at https://github.com/microsoft/DeBERTa.

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microsoft/DeBERTa officialmentioned in papermentioned on GitHubpytorchMIT report
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build_relative_position microsoft/DeBERTa/DeBERTa/deberta/da_utils.py official repository unverified MIT (permissive) · ac8689e2d4eb4c75 · report
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Tasks

Language ModelingLanguage ModellingNatural Language InferenceNatural Language UnderstandingQuestion AnsweringXLM-R

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Natural Language Inference MRPC DeBERTaV3large Acc 92.2 #1 of 1 Archive leaderboard report
Natural Language Inference QNLI DeBERTaV3large Accuracy 96% #8 of 43 Archive leaderboard report
Natural Language Inference RTE DeBERTaV3large Accuracy 92.7% #7 of 90 Archive leaderboard report
Question Answering SWAG DeBERTaV3large Accuracy 93.4 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AdamAttentionAttention DropoutDeBERTaDense ConnectionsDropoutELECTRALayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPieceXLM-R

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