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README.md

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 # Visual semantic with BERT-CNN
- To take advantage of the overlapping between the visual context and the caption, and to extract global information from each visual, we use BERT  as an embedding layer followed by a shallow CNN (tri-gram kernel) (Kim, 2014).
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+This model can be used to assign an object-to-caption relatedness score, which is valuable for
+(1) caption diverse re-ranking, and (2) generate soft labels for caption filtering when scraping text-to-captions from the internet.
+
+To take advantage of the overlapping between the visual context and the caption, and to extract global information from each visual (i.e., object, scene, etc) we use BERT  as an embedding layer followed by a shallow CNN (tri-gram kernel) (Kim, 2014).
  
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  For datasets that are less than 100K please have look at our [shallow model](https://github.com/ahmedssabir/Semantic-Relatedness-Based-Reranker-for-Text-Spotting) 
 
 
-This model can be used to assign an object-to-caption relatedness score, which is valuable for
-(1) caption diverse re-ranking, and (2) generate soft labels for caption filtering when scraping text-to-captions from the internet.
+
 
 The model is trained with a strict filter of 0.4 similarity distance thresholds between the object and its related caption.