Instructions to use khyeom/SVSTR-Score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use khyeom/SVSTR-Score with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("khyeom/SVSTR-Score", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| from setuptools import setup, find_packages | |
| setup( | |
| name='svspr', | |
| version='0.1.0', | |
| description='SV-SPR: Caller-agnostic short-read SV confidence scoring (reference-only).', | |
| author='Woohun Kim', | |
| author_email='alex990713@gmail.com', | |
| license='CC-BY-4.0', | |
| package_dir={'svspr': 'src'}, | |
| packages=['svspr'], | |
| package_data={'svspr': ['../model/*.pkl']}, | |
| include_package_data=True, | |
| install_requires=[ | |
| 'numpy>=1.21', | |
| 'pandas>=1.3', | |
| 'scikit-learn>=1.0', | |
| 'pysam>=0.20', | |
| ], | |
| python_requires='>=3.8', | |
| entry_points={ | |
| 'console_scripts': ['svspr=svspr.cli:main'], | |
| }, | |
| classifiers=[ | |
| 'Intended Audience :: Science/Research', | |
| 'License :: OSI Approved :: Creative Commons Attribution 4.0 International', | |
| 'Programming Language :: Python :: 3', | |
| 'Topic :: Scientific/Engineering :: Bio-Informatics', | |
| ], | |
| ) | |