Update README.md

README.md

@@ -13,9 +13,10 @@ license: mit
 
 Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
 
-In DeBERTa V3 we replaced MLM objective with RTD(Replaced Token Detection) objective which was first introduced by ELECTRA for pre-training. The new objective significantly improves the model performance. Please check appendix A11 in our paper [DeBERTa](https://arxiv.org/abs/2006.03654) for more details.
+In DeBERTa V3, we replaced the MLM objective with the RTD(Replaced Token Detection) objective introduced by ELECTRA for pre-training, as well as some innovations to be introduced in our upcoming paper. Compared to DeBERTa-V2,  our V3 version significantly improves the model performance in downstream tasks.  You can find a simple introduction about the model from the appendix A11 in our original [paper](https://arxiv.org/abs/2006.03654),  but we will provide more details in a separate write-up.
+
+The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024 . Its total parameter number is 418M since we use a vocabulary containing 128K tokens which introduce 131M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
 
-This is the DeBERTa V3 large model with 24 layers, 1024 hidden size. Total parameters is 418M while Embedding layer takes about 131M due to the usage of 128k vocabulary. It's trained with 160GB data.
 
 #### Fine-tuning on NLU tasks
 
@@ -26,7 +27,7 @@ We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
 | RoBERTa-large     | 94.6/88.9 | 89.4/86.5 | 90.2   |
 | XLNet-large       | 95.1/89.7 | 90.6/87.9 | 90.8   |
 | DeBERTa-large     | -/-       | 90.7/88.0 | 91.3   |
-| **DeBERTa-v3-large**  | -/-   | 91.5/89.0 | **91.9**   |
+| **DeBERTa-v3-large**  | -/-   | 91.5/89.0 | **92.0**   |
 | DeBERTa-v2-xxlarge|96.1/91.4	|**92.2/89.7**	|  91.7  |
 
 #### Fine-tuning with HF transformers