Instructions to use YASHR2002/fraudlens-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use YASHR2002/fraudlens-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("YASHR2002/fraudlens-model", "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 champion/model/MLmodel from YASHR2002/fraudlens-model: direct link, hf CLI and curl.
- Browser
- Download file 2.37 kB
-
https://huggingface.co/YASHR2002/fraudlens-model/resolve/main/champion/model/MLmodel
- Command line
-
hf download hf://YASHR2002/fraudlens-model/champion/model/MLmodel
-
curl -L -o MLmodel https://huggingface.co/YASHR2002/fraudlens-model/resolve/main/champion/model/MLmodel
2.37 kB
| artifact_path: mlflow-artifacts:/2/models/m-084a110621994bfdaadb602f7925496e/artifacts | |
| flavors: | |
| python_function: | |
| env: | |
| conda: conda.yaml | |
| virtualenv: python_env.yaml | |
| loader_module: mlflow.sklearn | |
| model_path: model.skops | |
| predict_fn: predict_proba | |
| python_version: 3.11.2 | |
| sklearn: | |
| code: null | |
| pickled_model: model.skops | |
| serialization_format: skops | |
| sklearn_version: 1.9.1 | |
| skops_trusted_types: | |
| - collections.OrderedDict | |
| - lightgbm.basic.Booster | |
| - lightgbm.sklearn.LGBMClassifier | |
| is_signature_from_type_hint: false | |
| mlflow_version: 3.16.1 | |
| model_id: m-084a110621994bfdaadb602f7925496e | |
| model_size_bytes: 1383415 | |
| model_uuid: m-084a110621994bfdaadb602f7925496e | |
| prompts: null | |
| run_id: 4d080b07b5b54d82b8a96852191067db | |
| saved_input_example_info: | |
| artifact_path: input_example.json | |
| pandas_orient: split | |
| serving_input_path: serving_input_example.json | |
| type: dataframe | |
| signature: | |
| inputs: '[{"type": "double", "name": "amt", "required": true}, {"type": "double", | |
| "name": "log_amt", "required": true}, {"type": "string", "name": "category", "required": | |
| true}, {"type": "double", "name": "hour", "required": true}, {"type": "double", | |
| "name": "day_of_week", "required": true}, {"type": "double", "name": "is_night", | |
| "required": true}, {"type": "double", "name": "age_at_txn", "required": true}, | |
| {"type": "double", "name": "log_city_pop", "required": true}, {"type": "double", | |
| "name": "distance_km", "required": true}, {"type": "double", "name": "card_txn_count_1h", | |
| "required": true}, {"type": "double", "name": "card_txn_count_24h", "required": | |
| true}, {"type": "double", "name": "card_txn_count_7d", "required": true}, {"type": | |
| "double", "name": "card_amt_sum_24h", "required": true}, {"type": "double", "name": | |
| "card_avg_amt_prior", "required": true}, {"type": "double", "name": "amt_to_card_avg_ratio", | |
| "required": true}, {"type": "double", "name": "secs_since_last_txn", "required": | |
| true}, {"type": "double", "name": "is_new_category_for_card", "required": true}, | |
| {"type": "double", "name": "card_txn_number", "required": true}]' | |
| outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, | |
| 2]}}]' | |
| params: null | |
| type_hint_from_example: false | |
| utc_time_created: '2026-10-09 06:18:22.741537' | |