Instructions to use kurtulus7182/SEPrimer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use kurtulus7182/SEPrimer with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("kurtulus7182/SEPrimer", "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: 1,615 Bytes
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"schema_version": 1,
"features": [
"seviye",
"karar_suresi_sn",
"onceki_yanlis_sayisi",
"ipucu_sayisi",
"sahne_no"
],
"ranges": [
[
0,
1
],
[
0,
600
],
[
0,
2
],
[
0,
30
],
[
1,
3
]
],
"integer_features": [
0,
2,
3,
4
],
"labels": {
"0": "normal_akis",
"1": "kriz_freni"
},
"decision_threshold": 0.6,
"acceptance_accuracy": 0.75,
"tolerance": 0.0001,
"seed": 42,
"model_version": "2.0.0",
"status": "synthetic-demo",
"input": {
"name": "features",
"dtype": "float32",
"shape": [
null,
5
]
},
"output": {
"name": "probabilities",
"positive_class_index": 1
},
"model_sha256": "c4f7734a68d93a40d114960c44b47111e950fcaef02c1db5a28a71e5ceb46289",
"data_sha256": "d1dcd344e2f7815763143fd40d984577312bed3bc04a140f9bad6caf7697df6b",
"metrics": {
"accuracy": 0.9055555555555556,
"onnx_max_abs_error": 1.3763748296646838e-07,
"confusion_matrix": [
[
99,
3
],
[
14,
64
]
],
"train_sessions": 180,
"test_sessions": 60,
"train_rows": 540,
"test_rows": 180,
"real_sessions": 0
},
"split": "GroupShuffleSplit: oturum_id, holdout=0.25; test oturumlari egitimde yok",
"dependencies": {
"numpy": "2.2.6",
"scikit-learn": "1.7.2",
"skl2onnx": "1.19.1",
"onnx": "1.19.1",
"onnxruntime": "1.23.2"
}
}
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