Instructions to use hjkim811/SLIM-C_boolean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hjkim811/SLIM-C_boolean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hjkim811/SLIM-C_boolean")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hjkim811/SLIM-C_boolean") model = AutoModelForMaskedLM.from_pretrained("hjkim811/SLIM-C_boolean") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5c3c180aea000fe270c55895915e9f1ae01da7e0d439dc5622e58e7c2d185d2
- Size of remote file:
- 433 MB
- SHA256:
- d8bf2642b08914f1a138e79a3fa02991d12c49a12c0d535f85de427736289ce7
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