Update README.md

README.md

@@ -48,7 +48,7 @@ BankAI-BERT was fine-tuned on a manually annotated dataset comprising sentences
 | Hardware                 | Google Colab GPU (T4) |
 
 ## Evaluation & Robustness
-* Benchmarked against Logistic Regression, Naive Bayes, Random Forest, and XGBoost (TF-IDF features); FinAI-BERT scored highest on F1.  
+* Benchmarked against Logistic Regression, Naive Bayes, Random Forest, and XGBoost (TF-IDF features); BankAI-BERT scored highest on F1.  
 * Calibration checked via Brier Score (0 = perfect).
 * SHAP analysis shows the model focuses on meaningful cues (e.g., machine learning, AI-powered)—not noise—ensuring interpretability and trust. 
 * Robust to:
@@ -60,12 +60,15 @@ BankAI-BERT was fine-tuned on a manually annotated dataset comprising sentences
 - `config.json`, `tokenizer.json`, `vocab.txt`, `model.safetensors`: Model files
 - `tokenizer_config.json`, `special_tokens_map.json`: Tokenizer configuration
 
+## GitHub Repository
+
+For full pipeline, data, and visualizations, see the **[GitHub repository]**(https://github.com/bilalezafar/BankAI-BERT).
+.
+
 ## Citation
 Please cite my paper if you use this model:
-- **Zafar, M. B. (2025). FinAI-BERT: A transformer-based model for sentence-level detection of AI disclosures in financial reports. arXiv. https://arxiv.org/abs/2507.01991**
+- **Zafar, M. B. (2025). AI in Banking Disclosures: A BERT Classifier and Corpus-Level Thematic Mapping**
 
-## Contact
-  For questions or feedback, please contact me at bilalezafar@gmail.com
 
 ## Usage
 ```python