Update README.md

README.md

@@ -40,7 +40,7 @@ The model uses a hierarchical approach:
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 # Load model and tokenizer
-model = AutoModelForSequenceClassification.from_pretrained("chungpt2123/test1", trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained("chungpt2123/esg-subfactor-classifier", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
 
 # Example usage
@@ -69,10 +69,6 @@ The model achieves strong performance on ESG classification tasks with hierarchi
 - Performance may vary on domain-specific or technical ESG content
 - Best performance on texts similar to training data distribution
 
-## Citation
-
-If you use this model, please cite:
-
 ```bibtex
 @misc{esg_hierarchical_model,
   title={ESG Hierarchical Multi-Task Learning Model},