Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/microsoft/deberta-base/README.md

README.md

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+---
+thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
+license: mit
+---
+
+## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
+
+[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. 
+
+Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
+
+
+#### Fine-tuning on NLU tasks
+
+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   |
+
+### Citation
+
+If you find DeBERTa useful for your work, please cite the following paper:
+
+``` latex
+@misc{he2020deberta,
+    title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention},
+    author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
+    year={2020},
+    eprint={2006.03654},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+		}
+```