Instructions to use Zipeng365/WISP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Zipeng365/WISP with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Zipeng365/WISP", "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
Download pyproject.toml from Zipeng365/WISP: direct link, hf CLI and curl.
- Browser
- Download file 659 Bytes
-
https://huggingface.co/Zipeng365/WISP/resolve/main/pyproject.toml
- Command line
-
hf download hf://Zipeng365/WISP/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/Zipeng365/WISP/resolve/main/pyproject.toml
659 Bytes
| [build-system] | |
| requires = ["setuptools==80.9.0", "wheel==0.45.1"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "wisp-har" | |
| version = "0.1.0" | |
| description = "WISP representations, search strategies and trained wrist activity recognition pipelines" | |
| readme = "README.md" | |
| requires-python = ">=3.12.13,<3.13" | |
| license = {file = "LICENSE"} | |
| authors = [{name = "ZipengWu"}] | |
| dependencies = [ | |
| "numpy==2.3.5", "scipy==1.17.1", "scikit-learn==1.7.2", | |
| "joblib==1.5.3", "threadpoolctl==3.6.0" | |
| ] | |
| [project.scripts] | |
| wisp = "wisp_release.cli:main" | |
| [tool.setuptools.packages.find] | |
| where = ["src"] | |
| [tool.pytest.ini_options] | |
| testpaths = ["tests"] | |
| addopts = "-ra" | |