Update README.md

README.md

@@ -36,18 +36,37 @@ library_name: transformers
 
 **Base model:** [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT) - **CentralBank-BERT** is a domain-adapted BERT trained on \~2M sentences (66M tokens) of **central bank speeches** (BIS, 1996–2024). It captures monetary-policy and payments vocabulary far better than generic BERT, which materially helps downstream CBDC classification.
 
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-
 ## Preprocessing, Class Weights & Training
 Performed light **manual cleaning** (trimming whitespace, normalizing quotes/dashes, de-duplication, dropping nulls) and tokenized with [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT)’s WordPiece (max length **192**). Because **Wholesale** had fewer examples, we applied **inverse-frequency class weights** in `CrossEntropyLoss` to balance learning (train-split weights ≈ General **0.866**, Retail **0.870**, Wholesale **1.436**). The model was fine-tuned with AdamW (lr **2e-5**, weight decay **0.01**, warmup ratio **0.1**), batch sizes **8/16** (train/eval), for **5 epochs** with **fp16** mixed precision. Early stopping monitored validation **macro-F1** (patience = 2), and the best checkpoint was restored at the end. Training ran on a single Colab GPU.
 
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-
 ## Performance & Evaluation
 On a 10% held-out test set, the model achieved **88.7% accuracy**, **0.898 macro-F1**, and **0.887 weighted-F1**. Class-wise, performance was strong across categories, with **Retail ≈ 0.86 F1**, **Wholesale ≈ 0.97 F1**, and **General ≈ 0.86 F1**, indicating particularly high precision/recall on Wholesale, and balanced, reliable performance on Retail and General.
 
 ---
 
+## 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)   |
+
+
+## Repository and Replication Package
+
+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)**
+
+---
+
 ## Usage
 
 ```python
@@ -73,4 +92,20 @@ for s in sentences:
 # [{DLT-based interbank settlement with a central bank liability will lower PvP risk. → Wholesale CBDC (score=0.9974)}]
 # [{Several central banks are assessing CBDCs to modernise payments and policy transmission. → General/Unspecified (score=0.9979)}]
 
-```
+```
+---
+
+## 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}
+}