Update README.md

README.md

@@ -33,7 +33,6 @@ The model performs **binary classification**:
 
 **Intended use:** **CBDC-BERT** is intended for research on CBDC discourse across time and jurisdictions, for pre-filtering or flagging CBDC-related sentences in large central-bank speech corpora, and as an input to dashboards, indices, or downstream NLP pipelines used in central banking and finance.
 
----
 ## Training Details
 - **Base checkpoint:** [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT) 
 - **Architecture:** `BertForSequenceClassification` (binary head randomly initialized)
@@ -47,23 +46,31 @@ The model performs **binary classification**:
 - **Evaluation:** per epoch; best model by F1
 - **Hardware:** Google Colab GPU
 
---- 
-
 ## Performance & Robustness
 On the full test split (n=2,200), the model achieves **Accuracy = 0.9950** and **F1 (binary) = 0.9949**. In a separate confusion-matrix run on valid rows (n=2,175), it records **TP=1,065**, **FP=4**, **FN=1**, **TN=1,105**, yielding **Accuracy = 0.9977**, **Precision (CBDC) = 0.9963**, **Recall (CBDC) = 0.9991**, **ROC-AUC = 1.0000**, and a **Brier score = 0.0024**; the class balance is **Non-CBDC = 1,109** and **CBDC = 1,066**. Compared to TF-IDF baselines—**Logistic Regression (0.97)**, **Naive Bayes (0.92)**, **Random Forest (0.98)**, and **XGBoost (0.99)**, CBDC-BERT **matches or exceeds** these results while delivering **near-perfect ROC-AUC** with **well-calibrated probabilities** (low Brier). Robustness checks across **edge cases**, **noise-injected**, **syntactically altered**, and **paraphrased (“translated-like”)** inputs each show **8/10 correct (80%)**, and sentence-length bias is low (**ρ ≈ 0.1222**).
 
 --- 
 
-## Citation
-If you use this model in your research or application, please cite it as:
+## Other CBDC Models
+
+This model is part of the **CentralBank-BERT / CBDC model family**, a suite of domain-adapted classifiers for analyzing central-bank communication.
+
+| **Model**                       | **Purpose**                                                         | **Intended Use**                                                    | **Link**                                                               |
+| ------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | ---------------------------------------------------------------------- |
+| **bilalzafar/CentralBank-BERT** | Domain-adaptive masked LM trained on BIS speeches (1996–2024).      | Base encoder for CBDC downstream tasks; fill-mask tasks.            | [CentralBank-BERT](https://huggingface.co/bilalzafar/CentralBank-BERT) |
+| **bilalzafar/CBDC-BERT**        | Binary classifier: CBDC vs. Non-CBDC.                               | Flagging CBDC-related discourse in large corpora.                   | [CBDC-BERT](https://huggingface.co/bilalzafar/CBDC-BERT)               |
+| **bilalzafar/CBDC-Stance**      | 3-class stance model (Pro, Wait-and-See, Anti).                     | Research on policy stances and discourse monitoring.                | [CBDC-Stance](https://huggingface.co/bilalzafar/CBDC-Stance)           |
+| **bilalzafar/CBDC-Sentiment**   | 3-class sentiment model (Positive, Neutral, Negative).              | Tone analysis in central bank communications.                       | [CBDC-Sentiment](https://huggingface.co/bilalzafar/CBDC-Sentiment)     |
+| **bilalzafar/CBDC-Type**        | Classifies Retail, Wholesale, General CBDC mentions.                | Distinguishing policy focus (retail vs wholesale).                  | [CBDC-Type](https://huggingface.co/bilalzafar/CBDC-Type)               |
+| **bilalzafar/CBDC-Discourse**   | 3-class discourse classifier (Feature, Process, Risk-Benefit).      | Structured categorization of CBDC communications.                   | [CBDC-Discourse](https://huggingface.co/bilalzafar/CBDC-Discourse)     |
+| **bilalzafar/CentralBank-NER**  | Named Entity Recognition (NER) model for central banking discourse. | Identifying institutions, persons, and policy entities in speeches. | [CentralBank-NER](https://huggingface.co/bilalzafar/CentralBank-NER)   |
+
 
-> **CBDC-BERT: A Transformer-Based Model for Identifying Central Bank Digital Currency Discourse in Policy Speeches**  
-> *Paper under write-up*  
->  
+## Repository and Replication Package
 
-For academic or technical inquiries, contact:  
-**Dr. Muhammad Bilal Zafar**  
-📧 bilalezafar@gmail.com
+All **training pipelines, preprocessing scripts, evaluation notebooks, and result outputs** are available in the companion GitHub repository:
+
+🔗 **[https://github.com/bilalezafar/CentralBank-BERT](https://github.com/bilalezafar/CentralBank-BERT)**
 
 ---
 
@@ -86,4 +93,21 @@ result = classifier(text)[0]
 print(f"Prediction: {label_map[result['label']]} | Confidence: {result['score']:.4f}")
 
 # Output example:
-# [{Prediction: CBDC | Confidence: 0.9993}]
+# [{Prediction: CBDC | Confidence: 0.9993}]
+```
+---
+
+## Citation
+
+If you use this model, please cite as:
+
+**Zafar, M. B. (2025). *CentralBank-BERT: Machine Learning Evidence on Central Bank Digital Currency Discourse*. SSRN. [https://papers.ssrn.com/abstract=5404456](https://papers.ssrn.com/abstract=5404456)**
+
+```bibtex
+@article{zafar2025centralbankbert,
+  title={CentralBank-BERT: Machine Learning Evidence on Central Bank Digital Currency Discourse},
+  author={Zafar, Muhammad Bilal},
+  year={2025},
+  journal={SSRN Electronic Journal},
+  url={https://papers.ssrn.com/abstract=5404456}
+}