Token Classification
Transformers
Safetensors
English
bert
NER
named-entity-recognition
central-bank
BIS
speeches
finance
economics
monetary policy
Instructions to use bilalzafar/CentralBank-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CentralBank-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bilalzafar/CentralBank-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CentralBank-NER") model = AutoModelForTokenClassification.from_pretrained("bilalzafar/CentralBank-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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If you use this model, please cite as:
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**Zafar, M. B. (2025).
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```bibtex
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@article{zafar2025centralbankbert,
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title={CentralBank-BERT: Machine
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author={Zafar, Muhammad Bilal},
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year={
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journal={
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url={https://
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}
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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. *Journal of Economics and Business.* [https://doi.org/10.1016/j.jeconbus.2026.106300](https://doi.org/10.1016/j.jeconbus.2026.106300)**
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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={2026},
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journal={Journal of Economics and Business},
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url={https://doi.org/10.1016/j.jeconbus.2026.106300}
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}
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