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
File size: 951 Bytes
55151a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"model": "b1_clearance_duration",
"n_features": 37,
"features": [
"latitude",
"longitude",
"requires_road_closure",
"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."
} |