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
- Xet hash:
- a2c5340af2cf408c6dc00c0ca58934304f0c63f2f90050d10e9ba2fc04a5bc06
- Size of remote file:
- 436 MB
- SHA256:
- 17aded9c609c7c97b9601e5c642a5f7ad2e5df46a7db3cba3880f1d6b2ee8e26
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