Update README.md

README.md

@@ -78,25 +78,25 @@ predicted_label = outputs.logits.argmax().item()
 # Example Usage
 ```python
 
-#Load the model and tokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("your-model-name")
-tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-#Tokenize text data
+# Load the model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("saishshinde15/SentimentTensor")
+tokenizer = AutoTokenizer.from_pretrained("saishshinde15/SentimentTensor")
 
+# Tokenize text data
 text = "This is a great movie!"
 tokenized_input = tokenizer(text, return_tensors="pt")
 
-#Perform sentiment analysis
-
+# Perform sentiment analysis
 outputs = model(**tokenized_input)
 predicted_label = outputs.logits.argmax().item()
 
-#Print predicted sentiment
+# Print predicted sentiment
 sentiment_labels = ["negative", "neutral", "positive"]
 print(f"Predicted Sentiment: {sentiment_labels[predicted_label]}")
 
+
 ```
 # Model Architecture and Objective