Instructions to use danielfein/raid-ce-gemma4-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielfein/raid-ce-gemma4-e4b with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("danielfein/raid-ce-gemma4-e4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,196 Bytes
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"beemo": {
"mean_auroc": 0.7600903182148216,
"sample_std_auroc": 0.007658877538481666,
"seeds": {
"42": {
"auroc": 0.754864147563069,
"bootstrap_ci95": [
0.7407155051042317,
0.7685814134621077
]
},
"101": {
"auroc": 0.7565249117839237,
"bootstrap_ci95": [
0.7426192602909137,
0.7701325982367685
]
},
"707": {
"auroc": 0.7688818952974723,
"bootstrap_ci95": [
0.7555810120573192,
0.7820446754125032
]
}
}
},
"raid_standard_test": {
"mean_auroc": 0.9581444074074074,
"sample_std_auroc": 0.007093797574557951,
"seeds": {
"42": {
"auroc": 0.9641748888888888,
"bootstrap_ci95": [
0.9597294527777779,
0.9685374472222222
]
},
"101": {
"auroc": 0.9503284444444446,
"bootstrap_ci95": [
0.9451532750000001,
0.9554070277777778
]
},
"707": {
"auroc": 0.9599298888888887,
"bootstrap_ci95": [
0.9549222138888889,
0.9647777
]
}
}
}
}
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