Instructions to use saifhmb/Credit-Card-Risk-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use saifhmb/Credit-Card-Risk-Model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("saifhmb/Credit-Card-Risk-Model", "skops-akmmropo.pkl") ) # 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 config.json from saifhmb/Credit-Card-Risk-Model: direct link, hf CLI and curl.
- Browser
- Download file 1.57 kB
-
https://huggingface.co/saifhmb/Credit-Card-Risk-Model/resolve/main/config.json
- Command line
-
hf download hf://saifhmb/Credit-Card-Risk-Model/config.json
-
curl -L -o config.json https://huggingface.co/saifhmb/Credit-Card-Risk-Model/resolve/main/config.json
1.57 kB
| { | |
| "sklearn": { | |
| "columns": [ | |
| "AGE", | |
| "INCOME", | |
| "GENDER", | |
| "MARITAL", | |
| "NUMKIDS", | |
| "NUMCARDS", | |
| "HOWPAID", | |
| "MORTGAGE", | |
| "STORECAR", | |
| "LOANS" | |
| ], | |
| "environment": [ | |
| "scikit-learn=1.2.2" | |
| ], | |
| "example_input": { | |
| "AGE": [ | |
| 32, | |
| 45, | |
| 25 | |
| ], | |
| "GENDER": [ | |
| "m", | |
| "f", | |
| "f" | |
| ], | |
| "HOWPAID": [ | |
| "weekly ", | |
| "weekly ", | |
| "weekly " | |
| ], | |
| "INCOME": [ | |
| 21772, | |
| 27553, | |
| 23477 | |
| ], | |
| "LOANS": [ | |
| 1, | |
| 2, | |
| 1 | |
| ], | |
| "MARITAL": [ | |
| "married ", | |
| "divsepwid", | |
| "single " | |
| ], | |
| "MORTGAGE": [ | |
| "y", | |
| "y", | |
| "n" | |
| ], | |
| "NUMCARDS": [ | |
| 2, | |
| 6, | |
| 1 | |
| ], | |
| "NUMKIDS": [ | |
| 1, | |
| 4, | |
| 1 | |
| ], | |
| "STORECAR": [ | |
| 3, | |
| 5, | |
| 2 | |
| ] | |
| }, | |
| "model": { | |
| "file": "skops-akmmropo.pkl" | |
| }, | |
| "model_format": "pickle", | |
| "task": "tabular-classification" | |
| } | |
| } |