Instructions to use jhu-clsp/bernice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/bernice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/bernice")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/bernice") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/bernice") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c34369d303444eec6239f7325e4e07da006fd66ae25660893a72402b5626776e
- Size of remote file:
- 1.12 GB
- SHA256:
- 3ac97e74961841fbd0556a8c127ea446f8f54fd769afdb19c260671c0c3d8ed0
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