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README.md

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 # Visual Semantic with BERT-CNN
 
- This model can be used to assign an object-to-caption semantic relatedness score, which is valuable for (1) caption diverse re-ranking (this work), 
- and (2) (as an application) generating soft labels for filtering image-to-caption when scraping text-to-captions from the internet (e,g., Instagram). 
+This model can be used to assign an object-to-caption semantic relatedness score, which is valuable for (1) caption diverse re-ranking (this work), 
+and (2) (as an application) 
+generating soft labels for filtering out the related/non-related image-to-post when scraping images from the internet (e.g. Instagram).
 
 
 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).