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:
- 3b43710a32f280fb207cc0bd25c2032693c51c58b11ea9a4709d6cee3af5e3ce
- Size of remote file:
- 4.66 MB
- SHA256:
- 0c23f0278beb6c9a8a7dc2a0681363dde1c2b81edbcf72b126b319bd4a497078
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