Instructions to use Higgs32/tornet-ml-higgins with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Higgs32/tornet-ml-higgins with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Higgs32/tornet-ml-higgins") - Notebooks
- Google Colab
- Kaggle
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# NOTE: **ALL**
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### 📈 Model Comparison Table
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| Model Name | Threshold | Model parameters | AUC | AUCPR | BinaryAccuracy | TruePositives | FalsePositives | TrueNegatives | FalseNegatives | Precision | Recall | FalseAlarmRate | F1 | ThreatScore |
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- recall
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# NOTE: **ALL** ten folds are in the ensemblev8+v9.zip, the five are in the zip for v8 file. A like would be appreciated!
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### 📈 Model Comparison Table
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| Model Name | Threshold | Model parameters | AUC | AUCPR | BinaryAccuracy | TruePositives | FalsePositives | TrueNegatives | FalseNegatives | Precision | Recall | FalseAlarmRate | F1 | ThreatScore |
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