Instructions to use adarshcod30/margadrishti-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use adarshcod30/margadrishti-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("adarshcod30/margadrishti-models", "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
| { | |
| "model": "b2_road_closure", | |
| "n_features": 36, | |
| "features": [ | |
| "latitude", | |
| "longitude", | |
| "age_of_truck", | |
| "at_junction", | |
| "hour", | |
| "dayofweek", | |
| "is_weekend", | |
| "is_high_priority", | |
| "day", | |
| "month", | |
| "weekofyear", | |
| "hour_of_week", | |
| "is_morning_peak", | |
| "is_evening_peak", | |
| "is_night", | |
| "hour_sin", | |
| "hour_cos", | |
| "dayofweek_sin", | |
| "dayofweek_cos", | |
| "month_sin", | |
| "month_cos", | |
| "dist_centre_km", | |
| "event_type_freq", | |
| "event_cause_freq", | |
| "veh_type_freq", | |
| "corridor_freq", | |
| "zone_freq", | |
| "police_station_freq", | |
| "priority_freq", | |
| "gba_identifier_freq", | |
| "direction_freq", | |
| "concurrent_events", | |
| "is_planned", | |
| "has_vehicle", | |
| "desc_len", | |
| "desc_has_kannada" | |
| ], | |
| "note": "Input must be a float32 array in exactly this order. ONNX has no column names; a different order returns wrong numbers silently." | |
| } |