CBDC-Sentiment: A Domain-Specific BERT for CBDC-Related Sentiment Analysis
CBDC-Sentiment is a 3-class (negative / neutral / positive) sentence-level BERT-based classifier built for Central Bank Digital Currency (CBDC) communications. It is trained to identify overall sentiment in central-bank style text such as consultations, speeches, reports, and reputable news.
Base Model: bilalzafar/CentralBank-BERT —
CentralBank-BERT is a domain-adapted
BERT base (uncased), pretrained on
66M+ tokens across
2M+ sentences from central-bank speeches published via the
Bank for International Settlements (1996–2024). It is optimized for
masked-token prediction within the specialized domains of
monetary policy, financial regulation, and macroeconomic communication, enabling better contextual understanding of central-bank discourse and financial narratives.
Training data: The dataset consists of 2,405 custom, manually annotated sentences related to Central Bank Digital Currencies (CBDCs), extracted from BIS speeches. The class distribution is neutral: 1,068 (44.41%), positive: 1,026 (42.66%), and negative: 311 (12.93%). The data is split row-wise, stratified by label, into train: 1,924, validation: 240, and test: 241 examples.
Intended usage: Use this model to classify sentence-level sentiment in CBDC texts (reports, consultations, speeches, research notes, reputable news). It is domain-specific and not intended for generic or informal sentiment tasks.
Preprocessing & class imbalance
Sentences were
lowercased (no stemming/lemmatization) and tokenized with the base tokenizer from
bilalzafar/CentralBank-BERT using
max_length=320 with truncation and
dynamic padding via
DataCollatorWithPadding. To address imbalance, training used
Focal Loss (γ=1.0) with
class weights computed from the
train split (
class_weight="balanced") applied in the loss, plus a
WeightedRandomSampler with √(inverse-frequency)
per-sample weights.
Training procedure
Training used
bilalzafar/CentralBank-BERT as the base, with a 3-label
AutoModelForSequenceClassification head. Optimization was
AdamW (HF Trainer) with
learning rate 2e-5,
batch size 16 (train/eval), and up to
8 epochs with early stopping (patience=2)
—best epoch ~6*. A
warmup ratio of 0.06,
weight decay 0.01, and
fp16 precision were applied. Runs were seeded (
42) and executed on
Google Colab (T4).
Evaluation
On the validation split (~10% of data), the model achieved accuracy 0.8458, macro-F1 0.8270, and weighted-F1 0.8453.
On the held-out test split (~10%), performance was accuracy 0.8216, macro-F1 0.8121, and weighted-F1 0.8216.
Per-class (test):
| Class | Precision | Recall | F1 | Support |
|---|
| negative | 0.8214 | 0.7419 | 0.7797 | 31 |
| neutral | 0.7857 | 0.8224 | 0.8037 | 107 |
| positive | 0.8614 | 0.8447 | 0.8529 | 103 |
Note: On the entire annotated dataset (in-domain evaluation, no hold-out), the model reaches ~0.95 accuracy / weighted-F1. These should be considered upper bounds; the test split above is the main reference for generalization.
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:
Usage
1from transformers import pipeline
2
3# Load pipeline
4classifier = pipeline("text-classification", model="bilalzafar/CBDC-Sentiment")
5
6# Example sentences
7sentences = [
8"CBDCs will revolutionize payment systems and improve financial inclusion."
9]
10
11# Predict
12for s in sentences:
13 result = classifier(s, return_all_scores=False)[0]
14 print(f"{s}\n → {result['label']} (score={result['score']:.4f})\n")
15
16# Example output:
17# [{CBDCs will revolutionize payment systems and improve financial inclusion. → positive (score=0.9789)}]
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
1@article{zafar2025centralbankbert,
2 title={CentralBank-BERT: Machine learning evidence on central bank digital currency discourse},
3 author={Zafar, Muhammad Bilal},
4 year={2026},
5 journal={Journal of Economics and Business},
6 url={https://doi.org/10.1016/j.jeconbus.2026.106300}
7}
