Instructions to use cvnberk/bert-base-uncased-issues-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/bert-base-uncased-issues-128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cvnberk/bert-base-uncased-issues-128")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cvnberk/bert-base-uncased-issues-128") model = AutoModelForMaskedLM.from_pretrained("cvnberk/bert-base-uncased-issues-128") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 420c817b8b94ae7c9f22e3243d53f5dc11a8e2bd42dbd1cfe9ff58eab1725008
- Size of remote file:
- 438 MB
- SHA256:
- 5b11d285e521e85ae21154dee62e1eb62449d0c938c26c41420efa6d0e80a045
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