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
| { | |
| "beemo": { | |
| "accuracy_at_zero": 0.7068885806749884, | |
| "auroc": 0.7565249117839237, | |
| "auroc_ci95": [ | |
| 0.7426192602909137, | |
| 0.7701325982367685 | |
| ], | |
| "mean_ai_score": 8.459569506846641, | |
| "mean_human_score": -0.7130339165776624, | |
| "pairs": 2163, | |
| "texts": 4326 | |
| }, | |
| "full_steps": 75, | |
| "full_train_pairs": 5000, | |
| "full_train_sources": 4081, | |
| "raid_standard_test": { | |
| "accuracy_at_zero": 0.8465, | |
| "auroc": 0.9503284444444446, | |
| "auroc_ci95": [ | |
| 0.9451532750000001, | |
| 0.9554070277777778 | |
| ], | |
| "mean_ai_score": 6.605718453546365, | |
| "mean_human_score": -13.570049094657103, | |
| "pairs": 3000, | |
| "texts": 6000 | |
| }, | |
| "selected_pilot": { | |
| "accuracy_at_zero": 0.746, | |
| "auroc": 0.790496, | |
| "checkpoint": "/workspace/expanded_raid_ai_20260726/outputs/gemma_locked_pairwise_ce_seed101/pilot/lr_0.001/final", | |
| "final_rolling_loss": 0.09688073217368583, | |
| "lr": 0.001, | |
| "mean_ai_score": 5.69093910741806, | |
| "mean_human_score": -2.0359047451019285, | |
| "pairs": 250, | |
| "steps": 125, | |
| "texts": 500 | |
| }, | |
| "tokens": "/workspace/expanded_raid_ai_20260726/outputs/gemma_locked_pairwise_ce_seed101/full/final/tokens" | |
| } | |