Update README.md

README.md

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 2. **Dependence on Training Data**: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.  
 3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.  
 4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.  
-5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.  
+5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.
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