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: 916 Bytes
8e88500 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"ai_token": "/workspace/expanded_raid_ai_20260726/outputs/gemma_ai_pairwise_finecurve5k_lr1e4_seed42/full/step_00075/tokens/ai_token.pt",
"beemo": {
"accuracy_at_zero": 0.7059639389736477,
"auroc": 0.754864147563069,
"auroc_ci95": [
0.7407155051042317,
0.7685814134621077
],
"mean_ai_score": 8.748883680532197,
"mean_human_score": 0.3008289260551216,
"pairs": 2163,
"texts": 4326
},
"eval_only": true,
"human_token": "/workspace/rebuttal_apo_20260724/outputs/rebuttal_text_multiseed_20260726/host_b_complete/gemma4_human_steps900_20260726/tokens/human_token.pt",
"raid_standard_test": {
"accuracy_at_zero": 0.848,
"auroc": 0.9641748888888888,
"auroc_ci95": [
0.9597294527777779,
0.9685374472222222
],
"mean_ai_score": 6.8369182267834745,
"mean_human_score": -12.562866653303304,
"pairs": 3000,
"texts": 6000
}
}
|