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/serving_input_example.json from YASHR2002/fraudlens-model: direct link, hf CLI and curl.
- Browser
- Download file 1.61 kB
-
https://huggingface.co/YASHR2002/fraudlens-model/resolve/main/champion/model/serving_input_example.json
- Command line
-
hf download hf://YASHR2002/fraudlens-model/champion/model/serving_input_example.json
-
curl -L -o serving_input_example.json https://huggingface.co/YASHR2002/fraudlens-model/resolve/main/champion/model/serving_input_example.json
1.61 kB
| { | |
| "dataframe_split": { | |
| "columns": [ | |
| "amt", | |
| "log_amt", | |
| "category", | |
| "hour", | |
| "day_of_week", | |
| "is_night", | |
| "age_at_txn", | |
| "log_city_pop", | |
| "distance_km", | |
| "card_txn_count_1h", | |
| "card_txn_count_24h", | |
| "card_txn_count_7d", | |
| "card_amt_sum_24h", | |
| "card_avg_amt_prior", | |
| "amt_to_card_avg_ratio", | |
| "secs_since_last_txn", | |
| "is_new_category_for_card", | |
| "card_txn_number" | |
| ], | |
| "data": [ | |
| [ | |
| 118.81, | |
| 4.785907154651808, | |
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| ] | |
| ] | |
| } | |
| } |