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")# pip install -U transformers accelerate # 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
Commit History
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Model Files
Verified signatureAll essential model and tokenizer files for cb-bert-mlm, including configuration, vocabulary, training arguments, and weights. These files ensure full compatibility with transformers for model loading, inference, and further fine-tuning. The directory includes: * model.safetensors – trained model weights * config.json – model architecture * tokenizer.json, vocab.txt, tokenizer_config.json, special_tokens_map.json – tokenizer and vocabulary * training_args.bin – hyperparameters used during training All files are required to fully reproduce or deploy the pretrained domain-specific masked language model.8390c85