Update README.md

README.md

@@ -31,18 +31,6 @@ This model is a second fine-tuned version of [cardiffnlp/twitter-roberta-base-se
 
 **It is specifically fine-tuned to analyze user-generated content such as opinions, reviews, comments, and general customer feedback. It is designed for sentiment analysis in the context of understanding public perception, trend analysis, and gathering insights into consumer satisfaction.**
 
-### Intended Use
-- **Sentiment Analysis**: Classifies the sentiment of user reviews, comments, and opinions into categories like "Negative", "Neutral", or "Positive".
-- **Trend Analysis**: Helps analyze large sets of customer feedback to identify trends, patterns, and overall sentiment in the content.
-- **Customer Insights**: Provides actionable insights for businesses and organizations seeking to understand customer satisfaction and areas for improvement.
-
-### Example Use Cases
-- **E-commerce**: Evaluate product reviews to gauge customer satisfaction.
-- **Social Media**: Analyze user comments on social media posts to monitor brand perception.
-- **Surveys and Polls**: Automatically classify responses to sentiment categories for easier data processing.
-
-This model can be applied to a variety of text sources, such as product reviews, user feedback, social media posts, and general opinions.
-
 ## Full classification example:
 
 ```python