Update README.md

README.md

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 The sentiment analysis model is trained using a Support Vector Machine (SVM) classifier with a linear kernel. The cleaned text data is transformed into a bag-of-words representation using the CountVectorizer. The trained model is saved as `Sentiment_classifier_model.joblib`, and the corresponding TF-IDF vectorizer is saved as `vectorizer_model.joblib`.
 
-# Model Usage : 
 
-from huggingface_hub import hf_hub_download
-import joblib
-from sklearn.preprocessing import LabelEncoder
+
+
+
+
+- from huggingface_hub import hf_hub_download
+- import joblib
+- from sklearn.preprocessing import LabelEncoder
 
 # Download the sentiment analysis model
-model = joblib.load(
+- model = joblib.load(
     hf_hub_download("DineshKumar1329/Sentiment_Analysis", "sklearn_model.joblib")
 )