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 - 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’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 |
| bilalzafar/CBDC-BERT | Binary classifier: CBDC vs. Non-CBDC. | Flagging CBDC-related discourse in large corpora. | CBDC-BERT |
| bilalzafar/CBDC-Stance | 3-class stance model (Pro, Wait-and-See, Anti). | Research on policy stances and discourse monitoring. | CBDC-Stance |
| bilalzafar/CBDC-Sentiment | 3-class sentiment model (Positive, Neutral, Negative). | Tone analysis in central bank communications. | CBDC-Sentiment |
| bilalzafar/CBDC-Type | Classifies Retail, Wholesale, General CBDC mentions. | Distinguishing policy focus (retail vs wholesale). | CBDC-Type |
| bilalzafar/CBDC-Discourse | 3-class discourse classifier (Feature, Process, Risk-Benefit). | Structured categorization of CBDC communications. | CBDC-Discourse |
| bilalzafar/CentralBank-NER | Named Entity Recognition (NER) model for central banking discourse. | Identifying institutions, persons, and policy entities in speeches. | 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
Usage
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. SSRN. https://papers.ssrn.com/abstract=5404456
@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}
}