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.

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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

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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)}]

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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}
}