Instructions to use pborchert/bert-ic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pborchert/bert-ic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pborchert/bert-ic")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pborchert/bert-ic") model = AutoModelForMaskedLM.from_pretrained("pborchert/bert-ic") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a30bf41f831f65ddddc9bf8c40fea6526bdfa40c5b3bc91606eed2d0199823c8
- Size of remote file:
- 441 MB
- SHA256:
- 1e1bfb5093e51c0a6cd0baababca33c8413d75ae9ea54ef3bca83dc5d492852b
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