Update README.md

README.md

@@ -9,7 +9,7 @@ such as text similarity or semantic relation methods, into captioning systems, e
 
 Please refer to  [project page](https://sabirdvd.github.io/project_page/Dataset_2022/index.html) and [Github](https://github.com/ahmedssabir/Visual-Semantic-Relatedness-Dataset-for-Image-Captioning) for more information. [![arXiv](https://img.shields.io/badge/arXiv-2301.08784-b31b1b.svg)](https://arxiv.org/abs/2301.08784)  [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://ahmed.jp/project_page/Dataset_2022/index.html)
 
-
+For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic)  
 
  
 
@@ -25,7 +25,7 @@ Please refer to  [project page](https://sabirdvd.github.io/project_page/Dataset_
           of the visual overlap  between caption and visual context, and to extract global information, we use BERT followed by a shallow CNN (<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
           to estimate the visual relatedness score. 
  
- For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic)  
+ 
  
 <!-- 
 ## Dataset