Update README.md

README.md

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 ### Overview
 
-This model is a fine-tuned version of `distilbert-base-uncased` on a social media dataset for the purpose of sentiment analysis. It can classify text into positive, negative, and neutral sentiments.
+This model is a fine-tuned version of `distilbert-base-uncased` on a social media dataset for the purpose of sentiment analysis. It can classify text into non-negative and negative sentiments.
 
 ### Intended Use
 
-This model is intended for sentiment analysis tasks, particularly for analyzing social media texts. It supports multiple languages, making it versatile for international applications.
+This model is intended for sentiment analysis tasks, particularly for analyzing social media texts.
 
 ### Model Architecture
 
@@ -29,7 +29,7 @@ This model is based on the `DistilBertForSequenceClassification` architecture, a
 
 ### Training Data
 
-The model was trained on a dataset consisting of social media posts, labeled for sentiment (positive, negative, neutral). The dataset includes multiple languages, enhancing the model's multilingual capabilities.
+The model was trained on a dataset consisting of social media posts, surveys and interviews, labeled for sentiment (non-negative and negative). The dataset includes texts from a variety of sources and demographics.
 
 ### Training Procedure