Update README.md

README.md

@@ -46,7 +46,7 @@ The model may not perform well on highly specialized or domain-specific text dat
 
 ## Bias, Risks, and Limitations
 
-The SentimentTensor model, like any LSTM-based model, may have biases and limitations inherent in its training data and architecture. It might sometimes struggle with capturing long-range dependencies or understanding context in complex sentences.
+The SentimentTensor model, like any LSTM-based model, may have biases and limitations inherent in its training data and architecture. It might sometimes struggle with capturing long-range dependencies or understanding context in complex sentences, also it emphasizes less on neutral sentiment
 
 ### Recommendations
 
@@ -77,6 +77,7 @@ outputs = model(**tokenized_input)
 predicted_label = outputs.logits.argmax().item()
 
 # Example Usage
+```python
 
 #Load the model and tokenizer
 
@@ -97,7 +98,7 @@ predicted_label = outputs.logits.argmax().item()
 sentiment_labels = ["negative", "neutral", "positive"]
 print(f"Predicted Sentiment: {sentiment_labels[predicted_label]}")
 
-
+```
 # Model Architecture and Objective
 
 The SentimentTensor model is based on LSTM architecture, which is well-suited for sequence classification tasks like sentiment analysis. It uses long short-term memory cells to capture dependencies in sequential data.