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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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base_model: |
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- bilalzafar/CentralBank-BERT |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- finance |
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- cbdc |
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- central-bank |
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- financial-nlp |
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- economic-policy |
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- monetary-policy |
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- sentence-classification |
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- text-classification |
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- transformers |
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- bert |
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- discourse-analysis |
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- policy-analysis |
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- centralbank-bert |
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- bis-speeches |
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--- |
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# CBDC-Discourse |
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`CBDC-Discourse` is a **BERT-based sentence classifier** fine-tuned to categorize central bank digital currency (CBDC) discourse into three conceptually distinct classes: **Feature, Risk-Benefit, and Process**. |
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This model enables structured analysis of CBDC-related policy and research texts by separating **design attributes**, **evaluative outcomes**, and **procedural activities**. |
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| Class | Description | |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | |
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| **Feature** | A sentence that specifies a **concrete design element or operational mechanism** of CBDC. Examples include: wallet/card modality; programmability/smart contracts; privacy model; interoperability requirements; legal tender status; distribution via intermediaries; holding limits/caps; interest-bearing/remuneration (incl. negative rates); rulebook/scheme rules; settlement architecture (DLT/RPS/RTGS links). | |
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| **Risk-Benefit** | A sentence that asserts or implies **outcomes, effects, or trade-offs** (positive or negative) from a CBDC feature or its introduction, including policy/equilibrium impacts. Examples include: faster/cheaper/more transparent cross-border payments; financial inclusion; regional cooperation; competition/innovation; sovereignty/autonomy; efficiency/productivity gains. Also, negative concerns such as bank disintermediation; cyber/operational risk; crisis flight from deposits; privacy harms; monetary/fiscal dominance concerns; “too successful” crowd-out; legal/regulatory fragility. | |
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| **Process** | A sentence about **research, consultations, pilots, governance, timeline, or agenda-setting**, without specifying a concrete feature or claiming effects/trade-offs. Examples include: public consultations; surveys/focus groups; task forces; phases (investigation/preparation/pilot); rulebook drafting as an activity (absent specifics); reports/citations; statements of interest/attention; open questions; goal/timeline setting (e.g., “medium-term goal”). | |
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## Base Model |
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This classifier is built on top of [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT), a **domain-adapted BERT model** pretrained on over **2 million sentences (\~66M tokens)** from **BIS central bank speeches (1996–2024)**. |
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CentralBank-BERT provides deep contextual understanding of **monetary policy, financial regulation, and central banking discourse**, making it an optimal foundation for downstream CBDC-related text classification. |
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## Dataset |
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The model was fine-tuned on a **manually annotated dataset of CBDC-related sentences** extracted from **Bank for International Settlements (BIS) central bank speeches (1996–2024)**. |
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The dataset was balanced across three discourse classes with a total of **2,886 sentences (962 per class)**: |
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## Intended Use |
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This model is designed for the **automatic classification of CBDC discourse** in policy, research, and financial communications. It enables researchers, analysts, and practitioners to distinguish whether a sentence describes **procedural aspects**, **design features**, or **evaluative outcomes** of central bank digital currencies. |
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Such categorization supports **policy analysis, thematic mapping of central bank communication, and structured NLP-based research** in the fields of **finance, monetary economics, and economic policy**. |
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## Training Details |
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* Tokenization: WordPiece (CentralBank-BERT tokenizer) |
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* Maximum sequence length: 256 tokens |
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* Dynamic padding (`DataCollatorWithPadding`) |
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* Train/Val/Test split: 80/10/10 stratified by label |
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| Parameter | Value | |
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| ----------------------------- | --------------------------- | |
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| Base model | [`bilalzafar/CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT) | |
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| Epochs | 6 | |
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| Train batch size (per device) | 8 | |
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| Eval batch size (per device) | 16 | |
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| Gradient accumulation | 2 | |
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| Effective batch size | 16 | |
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| Learning rate | 2e-5 | |
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| Weight decay | 0.01 | |
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| Warmup ratio | 0.06 | |
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| Scheduler | Cosine | |
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| Mixed precision (fp16) | Enabled | |
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* Environment: Google Colab |
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* GPU: Tesla T4 (16GB) |
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* Framework: PyTorch 2.8.0 + Hugging Face Transformers |
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## Evaluation Results |
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| Split | Accuracy | Macro-F1 | Weighted-F1 | Class | Precision | Recall | F1 | |
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| ---------- | --------- | --------- | ----------- | ---------------- | --------- | ------ | ----- | |
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| Validation | **0.851** | **0.839** | **0.852** | – | – | – | – | |
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| Test | **0.823** | **0.803** | **0.825** | **Feature** | 0.759 | 0.782 | 0.770 | |
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| | | | | **Process** | 0.927 | 0.845 | 0.884 | |
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| | | | | **Risk-Benefit** | 0.700 | 0.817 | 0.754 | |
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--- |
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## Other CBDC Models |
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This model is part of the **CentralBank-BERT / CBDC model family**, a suite of domain-adapted classifiers for analyzing central-bank communication. |
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| **Model** | **Purpose** | **Intended Use** | **Link** | |
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| ------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | ---------------------------------------------------------------------- | |
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| **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) | |
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| **bilalzafar/CBDC-BERT** | Binary classifier: CBDC vs. Non-CBDC. | Flagging CBDC-related discourse in large corpora. | [CBDC-BERT](https://huggingface.co/bilalzafar/CBDC-BERT) | |
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| **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) | |
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| **bilalzafar/CBDC-Sentiment** | 3-class sentiment model (Positive, Neutral, Negative). | Tone analysis in central bank communications. | [CBDC-Sentiment](https://huggingface.co/bilalzafar/CBDC-Sentiment) | |
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| **bilalzafar/CBDC-Type** | Classifies Retail, Wholesale, General CBDC mentions. | Distinguishing policy focus (retail vs wholesale). | [CBDC-Type](https://huggingface.co/bilalzafar/CBDC-Type) | |
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| **bilalzafar/CBDC-Discourse** | 3-class discourse classifier (Feature, Process, Risk-Benefit). | Structured categorization of CBDC communications. | [CBDC-Discourse](https://huggingface.co/bilalzafar/CBDC-Discourse) | |
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| **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) | |
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## Repository and Replication Package |
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All **training pipelines, preprocessing scripts, evaluation notebooks, and result outputs** are available in the companion GitHub repository: |
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🔗 **[https://github.com/bilalezafar/CentralBank-BERT](https://github.com/bilalezafar/CentralBank-BERT)** |
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--- |
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## How to Use |
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```python |
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from transformers import pipeline |
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# Load pipeline |
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classifier = pipeline("text-classification", model="bilalzafar/CBDC-Discourse") |
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# Example sentences |
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sentences = [ |
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"The central bank launched a pilot project for CBDC cross-border settlement.", # Process |
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"Programmability in CBDC allows conditional payments.", # Feature |
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"CBDC may increase risks of bank disintermediation." # Risk-Benefit |
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] |
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# Predict |
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for s in sentences: |
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result = classifier(s, return_all_scores=False)[0] |
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print(f"{s}\n → {result['label']} (score={result['score']:.4f})\n") |
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# Example output |
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# [{The central bank launched a pilot project for CBDC cross-border settlement. → Process (score=0.9989)}] |
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# [{Programmability in CBDC allows conditional payments. → Feature (score=0.9991)}] |
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# [{CBDC may increase risks of bank disintermediation. → Risk-Benefit (score=0.9986)}] |
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``` |
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--- |
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## Citation |
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If you use this model, please cite as: |
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**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)** |
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```bibtex |
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@article{zafar2025centralbankbert, |
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title={CentralBank-BERT: Machine Learning Evidence on Central Bank Digital Currency Discourse}, |
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author={Zafar, Muhammad Bilal}, |
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year={2025}, |
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journal={SSRN Electronic Journal}, |
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url={https://papers.ssrn.com/abstract=5404456} |
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} |
