---
license: mit
language:
- en
base_model:
- bilalzafar/CentralBank-BERT
pipeline_tag: text-classification
library_name: transformers
metrics:
- accuracy
- f1
tags:
- AI
- Sentiment
- Finance
- Central Bank
- BIS
- Transformers
- Domain Adaptation
---

# CentralBank-AI-Classifier: Detecting AI vs. Non-AI Sentences in Central-Bank Discourse

**CentralBank-AI-Classifier** is a binary sentence-level classifier (`AI`, `Non-AI`) trained on BIS central-bank speeches. The model identifies whether a sentence is *about AI* (e.g., AI/ML/LLM/GenAI/NLP/vision topics) or not. It is built on the domain-adapted encoder [`CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT) , which was pretrained on ~66M tokens from 2M+ sentences of BIS speeches (1996–2024).


## Dataset

- **Total labeled sentences:** **3,245**
- **Class balance:** **AI = 1,619** | **Non-AI = 1,626**
- **Grouping:** **URL-grouped** to avoid speech leakage.
- **Split (80/10/10 by URL):** Train **2,603** · Dev **322** · Test **320**

Labels were curated via rule-based retrieval (domain dictionary) followed by manual audit.


## Training

- **Base model:** [`CentralBank-BERT`](https://huggingface.co/bilalzafar/CentralBank-BERT)  
- **Head:** `BertForSequenceClassification(num_labels=2)`
- **Max length:** 128
- **Optimizer:** AdamW
- **LR:** 2e-5 · **Weight decay:** 0.01 · **Warmup:** 10%
- **Batch:** 16 (train) / 32 (eval)
- **Epochs:** up to 4 (early stopping on dev macro-F1)
- **Precision:** fp16 when available
- **Threshold tuning:** decision threshold selected on dev by macro-F1 sweep → **τ = 0.05**
- **Loss:** standard cross-entropy (dataset is balanced)

## Evaluation (Held-out Test)

| Metric | Value |
|---|---|
| **Accuracy** | **0.9812** |
| **Macro-F1** | **0.9812** |
| F1 (AI) | 0.9810 |
| F1 (Non-AI) | 0.9815 |
| **ROC-AUC** | **0.9932** |
| **PR-AUC (AI as positive)** | **0.9959** |

**Notes.** Threshold **τ = 0.05** was tuned on the dev set for macro-F1 and then fixed for test.

## Reliability Check on Keyword-Retrieved Sentences

Scoring the keyword-retrieved corpus with the trained classifier (τ = 0.05):

- **Mean P(AI)** = **0.9877**; **Median** = **0.9995**
- **Q1–Q3** = **0.9994–0.9995** (IQR = 0.0001)
- **Predicted AI share** = **0.9893**
- **High-confidence share (≥ 0.90)** = **0.9872**
- **Borderline (±0.10 around τ, i.e., [0.00, 0.15])** = **0.0112**

These statistics indicate the rule-based retrieval is **highly reliable**; only a small tail merits manual spot-checks.

## Intended Use

- Filtering and measuring **AI-related discourse** in central-bank communications (speeches, testimonies, reports).
- Pre-filtering before downstream tasks (stance, sentiment, topic modeling).
- Corpus construction and time-series indicators of AI attention.

**Out of scope:** social media, consumer product reviews, or informal text.

---

### Project GitHub Repository

The complete reproducible workflow, including the FinAI dictionary, dictionary-based tagging, AI sentence classification, sentiment analysis, and structural topic modeling, is available on GitHub:

**CentralBank-AI:** [https://github.com/bilalezafar/CentralBank-AI](https://github.com/bilalezafar/CentralBank-AI)

---
## Usage

### Simple pipeline
```python
from transformers import pipeline

clf = pipeline("text-classification",
               model="bilalzafar/CentralBank-AI-Classifier",
               return_all_scores=False)

s = "We are piloting large language models to streamline supervisory analytics."
print(clf(s)[0])   # -> {'label': '1', 'score': 0.999...}
#Note Label_1=AI, Label_0=Non-AI
```

---

### Citation

> Please cite as: **Zafar, M. B., Ali, H., & Aysan, A. F. (2026). *Signals from the Noise: Decoding Global AI Discourse in Central Bank Communications*. Central Bank Review, Article 100268.** [https://doi.org/10.1016/j.cbrev.2026.100268](https://doi.org/10.1016/j.cbrev.2026.100268) 

```bibtex
@article{zafar2026signals,
  title   = {Signals from the Noise: Decoding Global AI Discourse in Central Bank Communications},
  author  = {Zafar, Muhammad Bilal and Ali, Hassnian and Aysan, Ahmet Faruk},
  year    = {2026},
  journal = {Central Bank Review},
  pages   = {100268},
  doi     = {10.1016/j.cbrev.2026.100268},
  url     = {https://doi.org/10.1016/j.cbrev.2026.100268}
}
```
---