microsoft-deberta-large
DeBERTa (Decoding-enhanced BERT with Disentangled Attention) 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.
Please check the official repository for more details and updates.
This the DeBERTa XLarge model with 48 layers, 1024 hidden size. Total parameters 750M.
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 |
|---|---|---|---|---|
| Fill Mask | Fill Mask | rcds/wikipedia-for-mask-filling | evaluate-model-fill-mask.ipynb | evaluate-model-fill-mask.yml |