Update README.md

README.md

@@ -16,7 +16,7 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# deberta-v3-ft-news-sentiment-analisys
+# DeBERTa-v3-small-ft-news-sentiment-analisys
 
 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
 It achieves the following results on the evaluation set:
@@ -28,7 +28,13 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+[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. 
+
+In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa,  our V3 version significantly improves the model performance on downstream tasks.  You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
+
+Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
+
+The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. It has **44M** backbone parameters  with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
 
 ## Intended uses & limitations