Update README.md

README.md

@@ -21,8 +21,7 @@ Please refer to  [project page](https://sabirdvd.github.io/project_page/Dataset_
           (3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong 
           relation. In particular, we use Sentence-RoBERTa via cosine similarity to give a soft score, and then 
           we use a threshold to annotate the final label (if th > 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage 
-          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>)
+          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)