README.md

---
license: mit
language:
- en
metrics:
- f1
- accuracy
base_model:
- bilalzafar/CentralBank-BERT
pipeline_tag: text-classification
tags:
- CBDC
- Central Bank Digital Currencies
- Central Bank Digital Currency
- Classification
- Wholesale CBDC
- Retail CBDC
- Central Bank
- Tone
- Finance
- NLP
- Finance NLP
- BERT
- Transformers
- Digital Currency
library_name: transformers
---

# CBDC-Type-BERT: Classifying Retail vs Wholesale vs General CBDC Sentences
**A domain-specialized BERT classifier that labels central-bank text about CBDCs into three categories:**
* **Retail CBDC** – statements about a **general-purpose** CBDC for the public (households, merchants, wallets, offline use, legal-tender for everyday payments, holding limits, tiered remuneration, “digital euro/pound/rupee” for citizens, etc.).
* **Wholesale CBDC** – statements about a **financial-institution** CBDC (RTGS/settlement, DLT platforms, PvP/DvP, tokenised assets/markets, interbank use, central-bank reserves on ledger, etc.).
* **General/Unspecified** – CBDC mentions that **don’t clearly indicate retail or wholesale** scope, or discuss CBDCs at a conceptual/policy level without specifying the type.

**Training data:** 1,417 manually annotated CBDC sentences from BIS central-bank speeches — **Retail CBDC** (543), **Wholesale CBDC** (329), and **General/Unspecified** (545) — split **80/10/10** (train/validation/test) with stratification. 

**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.

## 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.

## 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
from transformers import pipeline

# Load pipeline
classifier = pipeline("text-classification", model="bilalzafar/CBDC-Type")

# Example sentences
sentences = [
    "The digital euro will be available to citizens and merchants for daily payments.",         # Retail
    "DLT-based interbank settlement with a central bank liability will lower PvP risk.",       # Wholesale
    "Several central banks are assessing CBDCs to modernise payments and policy transmission." # General
]

# Predict
for s in sentences:
    result = classifier(s, return_all_scores=False)[0]
    print(f"{s}\n → {result['label']} (score={result['score']:.4f})\n")

# Example output
# [{The digital euro will be available to citizens and merchants for daily payments.  → Retail CBDC (score=0.9985)}]
# [{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. *Journal of Economics and Business.* [https://doi.org/10.1016/j.jeconbus.2026.106300](https://doi.org/10.1016/j.jeconbus.2026.106300)**

```bibtex
@article{zafar2025centralbankbert,
  title={CentralBank-BERT: Machine learning evidence on central bank digital currency discourse},
  author={Zafar, Muhammad Bilal},
  year={2026},
  journal={Journal of Economics and Business},
  url={https://doi.org/10.1016/j.jeconbus.2026.106300}
}