Update README.md

README.md

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 ---
 license: bsd-3-clause
+  
+language:
+- en
+  
+tags:
+- sentiment
+- bert
+- sentiment-analysis
+- transformers
+
+pipeline_tag: text-classification
 ---
+> Authors : GRP209
+
+# User Comment Sentiment Analysis
+
+This model aims to analyze user comments on products and extracting the expressed sentiments. 
+
+User ratings on the internet do not always provide detailed qualitative information about their experience. 
+
+Therefore, it is important to go beyond these ratings and extract more insightful information that can help a brand improve their product or service.
+
+# Objective
+
+The model utilizes the BERT architecture and is trained on a dataset of user comments with sentiment labels.
+
+The model is capable of analyzing comments and extracting sentiments such as **positive**, **negative**, or **neutral**.
+
+# Features
+
+**Sentiment Classification**: The model can classify user comments into positive, negative, or neutral sentiments, providing an overall indication of the expressed opinion.
+
+**Improvement Suggestions**: In cases where a comment expresses a negative or neutral sentiment, the model suggests an improved version of the text with a more positive sentiment. 
+This can help businesses understand consumer reactions and identify areas for product or service improvement.
+
+# Usage
+
+To use this sentiment analysis system, follow these steps:
+
+- Install the required dependencies by running the command pip install -r requirements.txt.
+- Once the training is complete, the best-trained model will be saved in the best_model_state.bin file.
+- To make predictions on new comments, use the analyze_sentiment(comment_text) function, replacing comment_text with the actual comment text to analyze. 
+- The model will return the sentiment expressed in the comment.
+- To suggest an improved version of a comment, use the suggest_improved_text(comment_text) function. 
+- If the comment expresses a negative or neutral sentiment, the function will generate an improved version of the text with a more positive sentiment. Otherwise, the original text will be returned without modification.