Update README.md

README.md

@@ -3,10 +3,36 @@ language:
 - en
 tags:
 - sentiment
-- analysis
-license: apache-2.0
-metrics:
-- bleu
-- sacrebleu
-library_name: transformers
----
+- bert
+- sentiment-analysis
+- transformers
+pipeline_tag: text-classification
+---
+ 
+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.