Update README.md

README.md

@@ -5,26 +5,5 @@ 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. 
+## This model is DEPRECATED, please use [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli)
 
-Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
-
-This the DeBERTa V2 xlarge model fine-tuned with MNLI task, 24 layers, 1536 hidden size. Total parameters 900M.
-
-## This model is deprecated, please use [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli)
-
-
-### Citation
-
-If you find DeBERTa useful for your work, please cite the following paper:
-
-``` latex
-@inproceedings{
-he2021deberta,
-title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
-author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
-booktitle={International Conference on Learning Representations},
-year={2021},
-url={https://openreview.net/forum?id=XPZIaotutsD}
-}
-```