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--- |
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language: en |
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tags: |
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- deberta-v1 |
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- deberta-mnli |
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tasks: mnli |
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thumbnail: https://huggingface.co./front/thumbnails/microsoft.png |
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license: mit |
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widget: |
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- text: "[CLS] I love you. [SEP] I like you. [SEP]" |
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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 xlarge model(750M) fine-tuned with mnli task. |
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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 | QQP |STS-B | |
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|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------| |
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| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S | |
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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](https://huggingface.co./microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 | |
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| [DeBERTa-XLarge](https://huggingface.co./microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7| |
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| [DeBERTa-V2-XLarge](https://huggingface.co./microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9| |
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|**[DeBERTa-V2-XXLarge](https://huggingface.co./microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** | |
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-------- |
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#### Notes. |
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- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co./microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co./microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co./microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co./microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks. |
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- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co./transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp** |
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```bash |
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cd transformers/examples/text-classification/ |
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export TASK_NAME=mrpc |
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python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \ |
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--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \ |
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--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16 |
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``` |
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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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@inproceedings{ |
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he2021deberta, |
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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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booktitle={International Conference on Learning Representations}, |
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year={2021}, |
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url={https://openreview.net/forum?id=XPZIaotutsD} |
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} |
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``` |
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