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: 2,311 Bytes
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"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Primer v2 model config",
"type": "object",
"required": [
"schema_version",
"features",
"ranges",
"labels",
"decision_threshold",
"acceptance_accuracy",
"metrics",
"input",
"output",
"model_sha256",
"status"
],
"properties": {
"schema_version": {
"const": 1
},
"features": {
"const": [
"seviye",
"karar_suresi_sn",
"onceki_yanlis_sayisi",
"ipucu_sayisi",
"sahne_no"
]
},
"decision_threshold": {
"const": 0.6
},
"acceptance_accuracy": {
"const": 0.75
},
"tolerance": {
"const": 0.0001
},
"status": {
"enum": [
"synthetic-demo",
"real-data-candidate"
]
},
"model_sha256": {
"type": "string",
"pattern": "^[a-f0-9]{64}$"
},
"input": {
"type": "object",
"required": [
"name",
"dtype",
"shape"
],
"properties": {
"name": {
"const": "features"
},
"dtype": {
"const": "float32"
},
"shape": {
"const": [
null,
5
]
}
}
},
"output": {
"type": "object",
"required": [
"name",
"positive_class_index"
],
"properties": {
"name": {
"type": "string"
},
"positive_class_index": {
"const": 1
}
}
},
"metrics": {
"type": "object",
"required": [
"accuracy",
"onnx_max_abs_error",
"real_sessions"
],
"properties": {
"accuracy": {
"type": "number",
"minimum": 0.75,
"maximum": 1
},
"majority_baseline": {
"type": "number",
"minimum": 0,
"maximum": 1
},
"onnx_max_abs_error": {
"type": "number",
"minimum": 0,
"exclusiveMaximum": 0.0001
},
"real_sessions": {
"type": "integer",
"minimum": 0
}
}
}
}
}
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