Update README.md

README.md

@@ -23,13 +23,16 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This model takes in text input and outputs a binary classification of 0 if the text is a text-based question or 1 if it is a visual-based question
+The Text-Visual Question Classifier is a Hugging Face model that can classify questions as either text-based or visual-based. 
+It uses a natural language processing and techniques to analyze the question and determine its type. 
+The model has been trained on a large dataset of questions labeled as either text-based or visual-based, and has achieved high accuracy in identifying the correct type of question.
 
 ## Intended uses & limitations
 
-The model can be used to classify questions in natural language processing tasks such as chatbots or virtual assistants
-but may not perform well on questions that are ambiguous or have multiple interpretations. 
-it may also be biased towards certain types of questions based on the training data.
+#### Applications 
+This model can be used in various applications such as chatbots, virtual assistants, search engines, and recommendation systems. For example, it can help chatbots to provide more accurate responses by understanding the type of question being asked. It can also help search engines to retrieve more relevant results by filtering out irrelevant content based on the type of question.
+#### Limitations: 
+The model may not perform well on questions that are ambiguous or have multiple interpretations. It may also be biased towards certain types of questions based on the training data.
 
 ## Training and evaluation data