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--- |
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thumbnail: https://huggingface.co/front/thumbnails/microsoft.png |
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license: mit |
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--- |
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## DeBERTa: Decoding-enhanced BERT with Disentangled Attention |
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[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. |
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Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. |
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This the DeBERTa V2 xlarge model fine-tuned with MNLI task, 24 layers, 1536 hidden size. Total parameters 900M. |
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#### Fine-tuning on NLU tasks |
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We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. |
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| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC(acc/f1) | QQP |STS-B| |
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|---------------------------|-----------|-----------|-------------|-------|------|------|--------|--------------|------|-----| |
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| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3 |90.0 | |
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| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2 |92.4 | |
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| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3 |92.5 | |
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| DeBERTa-Large | 95.5/90.1 | 90.7/88.0 | 91.3/91.1 | 96.5 | 95.3 | 69.5 | 86.6 | 92.6/94.6 | 92.3 |92.5 | |
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| DeBERTa-XLarge | -/- | -/- | 91.5/91.0 | - | - | - | 89.5 | 92.1/94.3 | - |- | |
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| DeBERTa-XLarge-V2 | - | - | 91.7/91.6 | - | - | - | - | - | - |- | |
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|**DeBERTa-XXLarge-V2(60%)**| 96.1/91.4 | 92.2/89.7 |**91.7/91.9**| - | - | - | - | - | - |- | |
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| DeBERTa-XLarge-V2-mnli | - | - | 91.7/91.6 | - | - | - | 93.9 | - | - |- | |
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|**DeBERTa-XXLarge-V2-mnli**| - | - |**91.7/91.9**| - | - | - | 93.5 | - | - |- | |
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### Citation |
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If you find DeBERTa useful for your work, please cite the following paper: |
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``` latex |
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@misc{he2020deberta, |
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title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention}, |
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author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, |
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year={2020}, |
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eprint={2006.03654}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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