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
| from setuptools import setup, find_packages | |
| setup( | |
| name="fuson_plm", | |
| version="1.0", | |
| packages=find_packages(), # Automatically find and include all packages | |
| install_requires=[ | |
| # List your dependencies here | |
| # 'numpy', | |
| # 'torch', | |
| ], | |
| author="Sophia Vincoff", | |
| author_email="sophia.vincoff@duke.edu", | |
| ) | |