DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the official repository for more details and updates.
This the DeBERTa xlarge model(750M) fine-tuned with mnli task.

Fine-tuning on NLU tasks

We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.

Model	SQuAD 1.1	SQuAD 2.0	MNLI-m/mm	SST-2	QNLI	CoLA	RTE	MRPC	QQP	STS-B
F1/EM	F1/EM	Acc	Acc	Acc	MCC	Acc	Acc/F1	Acc/F1	P/S
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/-
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/-
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/-
DeBERTa-Large1	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
DeBERTa-XLarge1	-/-	-/-	91.5/91.2	97.0	–	–	93.1	92.1/94.3	–	92.9/92.7
DeBERTa-V2-XLarge1	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
DeBERTa-V2-XXLarge1,2	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

microsoft/deberta-xlarge-mnli官网入口：https://huggingface.co/microsoft/deberta-xlarge-mnli