microsoft-deberta-large-mnli
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 is the DeBERTa large model fine-tuned with MNLI task.
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-Large 1 | 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-XLarge 1 | -/- | -/- | 91.5/91.2 | 97.0 | - | - | 93.1 | 92.1/94.3 | - | 92.9/92.7 |
| DeBERTa-V2-XLarge 1 | 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-XXLarge 1,2 | 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 |
| 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 |
