Fill-Mask
Transformers
Safetensors
English
bert
masked-language-modeling
mlm
finance
central-bank
financial-nlp
economic-policy
monetary-policy
BIS
speeches
BIS-Speeches
pretraining
domain-adaptation
financial-domain-adaptation
Instructions to use bilalzafar/CentralBank-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilalzafar/CentralBank-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bilalzafar/CentralBank-BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bilalzafar/CentralBank-BERT") model = AutoModelForMaskedLM.from_pretrained("bilalzafar/CentralBank-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3b6e2b7eded63f1389f4c918bdca74bd62f53fca9fcd6691e4dbc0b4ec78de8
- Size of remote file:
- 5.24 kB
- SHA256:
- 1af61a4b34e7c93c98790940ab05a97ed4580e77394e705e6eabed5b681f0c4b
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