Add model_cards for DynaBERT

model_cards/huawei-noah/DynaBERT_MNLI/README.md

@@ -0,0 +1,20 @@
+## DynaBERT: Dynamic BERT with Adaptive Width and Depth
+
+* DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and 
+the subnetworks of it have competitive performances as other similar-sized compressed models.
+The training process of DynaBERT includes first training a width-adaptive BERT and then 
+allowing both adaptive width and depth using knowledge distillation. 
+
+* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
+
+### Reference
+Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Qun Liu.
+[DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
+```
+@inproceedings{hou2020dynabert,
+  title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
+  author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Qun Liu},  
+  booktitle = {NeurIPS},
+  year = {2020}
+}
+```

model_cards/huawei-noah/DynaBERT_SST-2/README.md

@@ -1,9 +1,20 @@
-# DynaBERT: Dynamic BERT with Adaptive Width and Depth
+## DynaBERT: Dynamic BERT with Adaptive Width and Depth
 
 * DynaBERT can flexibly adjust the size and latency by selecting adaptive width and depth, and 
 the subnetworks of it have competitive performances as other similar-sized compressed models.
 The training process of DynaBERT includes first training a width-adaptive BERT and then 
 allowing both adaptive width and depth using knowledge distillation. 
 
-* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1)
-* The results in the paper are produced by using single V100 GPU.
+* This code is modified based on the repository developed by Hugging Face: [Transformers v2.1.1](https://github.com/huggingface/transformers/tree/v2.1.1), and is released in [GitHub](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT).
+
+### Reference
+Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Qun Liu.
+[DynaBERT: Dynamic BERT with Adaptive Width and Depth](https://arxiv.org/abs/2004.04037).
+```
+@inproceedings{hou2020dynabert,
+  title = {DynaBERT: Dynamic BERT with Adaptive Width and Depth},
+  author = {Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Qun Liu},  
+  booktitle = {NeurIPS},
+  year = {2020}
+}
+```