Update README.md

README.md

@@ -21,7 +21,7 @@ For quick start please have a look this [demo](https://github.com/ahmedssabir/Te
           to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier 
           is not confident enough,  and (2) semantic alignment with semantic similarity to remove duplicated objects. 
           (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 
+          relation. In particular, we use Sentence-RoBERTa-sts 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>)
           to estimate the visual relatedness score.