DeBERTa (Decoding-enhanced BERT with Disentangled Attention) 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 model is the base DeBERTa model fine-tuned with MNLI task

Evaluation Results

We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.

Model	SQuAD 1.1	SQuAD 2.0	MNLI-m
RoBERTa-base	91.5/84.6	83.7/80.5	87.6
XLNet-Large	-/-	-/80.2	86.8
DeBERTa-base	93.1/87.2	86.2/83.1	88.8

Model Evaluation samples

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Text Classification	Sentiment Classification	SST2	evaluate-model-sentiment-analysis.ipynb	evaluate-model-sentiment-analysis.yml

Inference samples

Inference type	Python sample (Notebook)
Real time	sdk-example.ipynb
Real time	text-classification-online-endpoint.ipynb

Sample inputs and outputs (for real-time inference)

Sample input

{
    "input_data": [
        "Today was an amazing day!",
        "It was an unfortunate series of events."
    ]
}

Sample output

[
  {
    "label": "NEUTRAL",
    "score": 0.9817705750465393
  },
  {
    "label": "NEUTRAL",
    "score": 0.9873806238174438
  }
]