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: 1,176 Bytes
e679786 | 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 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"beemo": {
"accuracy_at_zero": 0.7172907998150717,
"auroc": 0.7688818952974723,
"auroc_ci95": [
0.7555810120573192,
0.7820446754125032
],
"mean_ai_score": 8.300825430565116,
"mean_human_score": -1.0891033262221061,
"pairs": 2163,
"texts": 4326
},
"full_steps": 75,
"full_train_pairs": 5000,
"full_train_sources": 4071,
"raid_standard_test": {
"accuracy_at_zero": 0.834,
"auroc": 0.9599298888888887,
"auroc_ci95": [
0.9549222138888889,
0.9647777
],
"mean_ai_score": 6.002089725395043,
"mean_human_score": -13.72776710220178,
"pairs": 3000,
"texts": 6000
},
"selected_pilot": {
"accuracy_at_zero": 0.702,
"auroc": 0.790656,
"checkpoint": "/workspace/expanded_raid_ai_20260726/outputs/gemma_locked_pairwise_ce_seed707/pilot/lr_0.001/final",
"final_rolling_loss": 0.10128707309773745,
"lr": 0.001,
"mean_ai_score": 6.3129822878837585,
"mean_human_score": -0.7812474756240845,
"pairs": 250,
"steps": 125,
"texts": 500
},
"tokens": "/workspace/expanded_raid_ai_20260726/outputs/gemma_locked_pairwise_ce_seed707/full/final/tokens"
}
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