Instructions to use ChatterjeeLab/FusOn-pLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/FusOn-pLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/FusOn-pLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/FusOn-pLM") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/FusOn-pLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55b1fc14f90973c1b665f1bb6f23802a122c3a3680533b67580255f958b8b732
- Size of remote file:
- 2.61 GB
- SHA256:
- 32da531e0eb79f0722c298c1ae20a4752221225d949388fe5a8b77ead730fee7
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