learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 4
seed: 200898
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Roc Auc	Best Threshold	Tpr At Best Threshold	Fpr At Best Threshold	Accuracy At Best Threshold	Avg Score True Positives	Avg Score True Negatives
No log	0	0	5.5312	0.5	inf	0.0	0.0	0.2041	0.5	0.5
5.4727	0.25	5	4.7003	0.6838	0.6095	0.5556	0.2333	0.5986	0.6095	0.6080
5.5	0.5	10	5.2530	0.6873	0.5292	0.4786	0.1333	0.5578	0.5306	0.5246
5.5938	0.75	15	5.3603	0.6386	0.5008	0.8547	0.5333	0.7755	0.5169	0.5063
5.2617	1.0	20	5.9705	0.6081	0.3930	0.8889	0.6333	0.7823	0.4559	0.4257
4.9512	1.25	25	3.8397	0.6333	0.7556	0.8889	0.5333	0.8027	0.8253	0.7773
6.7666	1.5	30	11.7061	0.6138	0.0546	0.9231	0.6333	0.8095	0.2475	0.1789
2.9727	1.75	35	3.6947	0.6548	0.7578	0.8547	0.5333	0.7755	0.8338	0.7156
3.6006	2.0	40	6.9422	0.6554	0.1836	0.8547	0.5333	0.7755	0.4366	0.3189
3.3496	2.25	45	3.2957	0.8148	0.8948	0.6923	0.2	0.7143	0.8950	0.7168
3.8867	2.5	50	4.1705	0.8285	0.7314	0.5556	0.0667	0.6327	0.6692	0.3283
3.0161	2.75	55	3.1849	0.8507	0.7813	0.7436	0.2	0.7551	0.8109	0.3752
1.8958	3.0	60	3.5462	0.8214	0.2971	0.9402	0.4	0.8707	0.7882	0.3446
2.3134	3.25	65	2.8283	0.8796	0.9263	0.8547	0.1667	0.8503	0.9340	0.4731
1.8599	3.5	70	3.2062	0.8635	0.8500	0.8205	0.1667	0.8231	0.8748	0.4034
1.2537	3.75	75	3.6557	0.8776	0.9917	0.7949	0.1333	0.8095	0.9510	0.4720
1.3635	4.0	80	3.8748	0.8755	0.9485	0.9231	0.3	0.8776	0.9518	0.4135
2.6852	4.25	85	4.3741	0.8946	0.6984	0.8889	0.2333	0.8639	0.8849	0.3122
1.145	4.5	90	3.5180	0.9167	0.9924	0.8718	0.1333	0.8707	0.9786	0.4643
0.0497	4.75	95	3.6028	0.8969	0.9826	0.8120	0.1333	0.8231	0.9431	0.4148
1.336	5.0	100	3.5466	0.8967	0.8333	0.9402	0.3	0.8912	0.9483	0.3850
0.0242	5.25	105	4.5654	0.8858	0.9943	0.9231	0.2667	0.8844	0.9843	0.4969
1.0793	5.5	110	4.6831	0.8775	0.9850	0.8974	0.2667	0.8639	0.9490	0.4210
0.0025	5.75	115	5.6413	0.8702	0.9988	0.8718	0.2333	0.8503	0.9626	0.4442
0.0061	6.0	120	7.2413	0.8734	0.9999	0.8462	0.2333	0.8299	0.9926	0.4978
0.0002	6.25	125	7.4424	0.8868	0.9999	0.9060	0.2333	0.8776	0.9625	0.4376
0.0107	6.5	130	8.0227	0.8821	0.9996	0.9402	0.2333	0.9048	0.9573	0.4107
0.5392	6.75	135	8.3781	0.8694	0.9988	0.9487	0.3	0.8980	0.9575	0.4234
2.4695	7.0	140	8.0251	0.8617	0.9990	0.9316	0.2667	0.8912	0.9568	0.3640
0.0005	7.25	145	9.2777	0.8526	0.9999	0.9487	0.3	0.8980	0.9626	0.4531
0.5692	7.5	150	10.4255	0.8521	1.0000	0.9145	0.3	0.8707	0.9854	0.5044
0.0008	7.75	155	10.1286	0.8557	1.0000	0.9402	0.3	0.8912	0.9771	0.4840
0.0025	8.0	160	9.4931	0.8601	1.0000	0.9402	0.3	0.8912	0.9601	0.4510
0.0002	8.25	165	9.3696	0.8670	0.9999	0.9402	0.3	0.8912	0.9579	0.4350
0.0017	8.5	170	9.4050	0.8648	0.9998	0.9402	0.3	0.8912	0.9574	0.4326
0.0059	8.75	175	9.6044	0.8687	0.9999	0.9402	0.3	0.8912	0.9575	0.4334
0.0001	9.0	180	9.6425	0.8675	0.9999	0.9402	0.3	0.8912	0.9575	0.4336
0.0014	9.25	185	9.6665	0.8677	0.9999	0.9402	0.3	0.8912	0.9576	0.4337
0.0004	9.5	190	9.6825	0.8694	1.0000	0.9145	0.2667	0.8776	0.9576	0.4334
0.0001	9.75	195	9.7272	0.8692	0.9999	0.9402	0.3	0.8912	0.9575	0.4340
0.0001	10.0	200	9.6793	0.8704	1.0000	0.9145	0.2667	0.8776	0.9575	0.4333

Framework versions

PEFT 0.18.1
Transformers 4.57.6
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

leobianco/npov_RM_google_S200898_LLM_false_STRUCT_true_epo10_lr2e-4_r8_2602251550

npov_RM_google_S200898_LLM_false_STRUCT_true_epo10_lr2e-4_r8_2602251550

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leobianco/
npov_RM_google_S200898_LLM_false_STRUCT_true_epo10_lr2e-4_r8_2602251550