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 6 layers and a hidden size of 768. Its total parameter number is 143M since we use a vocabulary containing 128K tokens which introduce 98M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
 
-This is the DeBERTa V3 small model with 6 layers, 768 hidden size. Total parameters is 143M while Embedding layer take about 98M 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-base      | 91.5/84.6 | 83.7/80.5 | 87.6   |
 | XLNet-base        | -/-       | -/80.2    | 86.8   |
 |DeBERTa-base	|93.1/87.2|	86.2/83.1|	88.8|
-| **DeBERTa-v3-small**  | -/- | -/- | 88.1   |
+| **DeBERTa-v3-small**  | -/- | -/- | 88.2   |
 | DeBERTa-v3-small+SiFT  | -/- | -/- | 88.8   |