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
File size: 351 Bytes
6968a40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | 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",
)
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