learning_rate: 0.0005
train_batch_size: 1
eval_batch_size: 1
seed: 12345
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
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	40.8364	0.6060	0.0100	0.4225	0.1364	0.5269	0.1403	0.0493
28.8285	0.0820	5	38.9552	0.6020	0.0143	0.4155	0.1591	0.5161	0.1546	0.0568
5.5753	0.1639	10	27.6406	0.6119	0.0724	0.4789	0.2045	0.5538	0.2820	0.0980
23.2123	0.2459	15	13.0211	0.6327	0.9711	0.4507	0.1364	0.5484	0.6489	0.5370
7.8475	0.3279	20	11.2093	0.6444	0.9963	0.4225	0.0682	0.5430	0.7439	0.6858
9.473	0.4098	25	11.8551	0.6708	0.8728	0.5352	0.1136	0.6183	0.6254	0.4223
2.9322	0.4918	30	22.4130	0.6683	0.0354	0.5915	0.25	0.6290	0.3040	0.0961
22.6466	0.5738	35	18.0691	0.6847	0.1678	0.5493	0.2045	0.6075	0.3848	0.1289
17.502	0.6557	40	8.9824	0.6925	0.9211	0.5845	0.1591	0.6452	0.7011	0.4893
24.1598	0.7377	45	7.7143	0.7246	0.9929	0.5845	0.1136	0.6559	0.8176	0.6669
8.5775	0.8197	50	13.9473	0.7447	0.4533	0.4789	0.0909	0.5806	0.4415	0.1303
14.7684	0.9016	55	6.3081	0.7666	0.9784	0.5211	0.0227	0.6290	0.7945	0.5265
8.3014	0.9836	60	7.1705	0.7001	0.9931	0.5282	0.1591	0.6022	0.8840	0.7473
3.1932	1.0656	65	13.2264	0.6117	0.0504	0.7676	0.5682	0.6882	0.3395	0.2328
9.1619	1.1475	70	8.0989	0.6467	0.9840	0.6268	0.3409	0.6344	0.8986	0.8013
11.5784	1.2295	75	7.7178	0.6829	0.4017	0.7606	0.3864	0.7258	0.6546	0.3956
4.4414	1.3115	80	7.4705	0.7185	0.2728	0.8310	0.4091	0.7742	0.7294	0.3918
1.5211	1.3934	85	7.2196	0.7348	0.5156	0.7676	0.3409	0.7419	0.7241	0.3844
3.67	1.4754	90	6.7641	0.7710	0.4542	0.8310	0.3182	0.7957	0.7620	0.3659
18.2743	1.5574	95	7.4079	0.7868	0.9794	0.7606	0.2727	0.7527	0.8848	0.6338
16.1565	1.6393	100	28.6324	0.6623	0.0020	0.7113	0.4318	0.6774	0.1175	0.0475
2.8826	1.7213	105	7.9842	0.6837	0.9888	0.6338	0.3409	0.6398	0.8999	0.7219
1.4679	1.8033	110	10.2804	0.6867	0.9966	0.7254	0.4318	0.6882	0.9415	0.7639
2.1539	1.8852	115	14.5194	0.6560	0.9968	0.8521	0.5455	0.7581	0.9762	0.8498
6.6433	1.9672	120	12.9897	0.6690	0.9990	0.7254	0.4318	0.6882	0.9658	0.8124
0.0284	2.0492	125	16.3520	0.6817	0.9999	0.7606	0.4318	0.7151	0.9732	0.8558
0.0	2.1311	130	20.5226	0.6989	1.0000	0.7465	0.3409	0.7258	0.9781	0.8680
6.274	2.2131	135	23.8237	0.7127	1.0000	0.7394	0.3409	0.7204	0.9898	0.9056
0.1631	2.2951	140	17.5642	0.7200	1.0000	0.6197	0.2273	0.6559	0.9519	0.8062
0.0049	2.3770	145	18.0914	0.7214	1.0000	0.7183	0.3182	0.7097	0.9317	0.7793
0.0011	2.4590	150	31.2587	0.6753	1.0000	0.8873	0.5455	0.7849	0.9775	0.8696
0.0	2.5410	155	47.2682	0.5682	1.0000	0.9789	0.8182	0.7903	0.9930	0.9415
0.223	2.6230	160	44.2346	0.5841	1.0	0.8873	0.7273	0.7419	0.9913	0.9291
1.6017	2.7049	165	28.8776	0.6653	1.0	0.7324	0.4318	0.6935	0.9399	0.7701
0.6836	2.7869	170	20.3755	0.7626	1.0000	0.6127	0.1818	0.6613	0.9623	0.7212
0.0	2.8689	175	32.6699	0.6741	1.0	0.8521	0.5227	0.7634	0.9719	0.8740
0.0	2.9508	180	49.0044	0.5799	1.0	0.9789	0.8182	0.7903	0.9927	0.9092
0.0	3.0328	185	43.4996	0.5949	1.0	0.9366	0.75	0.7742	0.9848	0.9088
0.0	3.1148	190	35.2144	0.6480	1.0	0.8592	0.5682	0.7581	0.9691	0.8864
0.0	3.1967	195	46.9583	0.5591	1.0	0.9577	0.8409	0.7688	0.9875	0.9091
0.0574	3.2787	200	53.8803	0.5432	1.0000	0.9789	0.8864	0.7742	0.9930	0.9295
0.0	3.3607	205	52.8863	0.5432	1.0000	0.9789	0.8864	0.7742	0.9930	0.9367
0.0002	3.4426	210	40.4971	0.5964	1.0000	0.9296	0.7273	0.7742	0.9813	0.9085
0.0	3.5246	215	36.3432	0.5989	1.0000	0.8944	0.6818	0.7581	0.9825	0.9090
0.0	3.6066	220	33.7853	0.5998	1.0000	0.9225	0.7045	0.7742	0.9831	0.9094
0.0	3.6885	225	38.2540	0.5754	1.0000	0.8873	0.7273	0.7419	0.9860	0.9168
0.0	3.7705	230	43.9833	0.5690	1.0000	0.9577	0.8182	0.7742	0.9917	0.9309
0.0	3.8525	235	46.7549	0.5563	1.0000	0.9648	0.8409	0.7742	0.9928	0.9317
0.0	3.9344	240	47.3752	0.5594	1.0000	0.9648	0.8409	0.7742	0.9926	0.9306
0.0	4.0164	245	47.7207	0.5623	1.0	0.9648	0.8409	0.7742	0.9893	0.9155
0.0063	4.0984	250	46.3658	0.5535	1.0000	0.9648	0.8409	0.7742	0.9865	0.9090
0.0	4.1803	255	43.1433	0.5546	1.0000	0.9577	0.8409	0.7688	0.9795	0.8923
0.0	4.2623	260	41.2130	0.5619	1.0	0.9155	0.7955	0.7473	0.9769	0.8869
0.0	4.3443	265	42.4682	0.5411	1.0000	0.9507	0.8409	0.7634	0.9790	0.8876
0.0	4.4262	270	48.0518	0.5423	1.0000	0.9789	0.8636	0.7796	0.9911	0.9093
0.0	4.5082	275	50.8757	0.5576	1.0	0.9789	0.8636	0.7796	0.9929	0.9100
0.0	4.5902	280	52.1373	0.5576	1.0	0.9789	0.8636	0.7796	0.9929	0.9105
0.0	4.6721	285	49.9221	0.5656	1.0	0.9718	0.8409	0.7796	0.9926	0.9094
0.0	4.7541	290	48.3527	0.5563	1.0000	0.9789	0.8636	0.7796	0.9905	0.9093
0.0	4.8361	295	44.2731	0.5656	1.0	0.9225	0.7955	0.7527	0.9832	0.9083
0.0	4.9180	300	42.0750	0.5598	1.0000	0.9225	0.7955	0.7527	0.9803	0.9029
0.0	5.0	305	40.9163	0.5565	1.0000	0.9225	0.7955	0.7527	0.9792	0.8950
0.0	5.0820	310	39.9522	0.5532	1.0000	0.9225	0.7955	0.7527	0.9790	0.8917
0.0	5.1639	315	39.6474	0.5607	1.0000	0.9014	0.7727	0.7419	0.9786	0.8902
0.0001	5.2459	320	39.3956	0.5529	1.0000	0.9155	0.7955	0.7473	0.9787	0.8901
0.0	5.3279	325	39.3267	0.5605	1.0000	0.9085	0.7727	0.7473	0.9784	0.8901
0.0	5.4098	330	39.5534	0.5629	1.0	0.8944	0.7727	0.7366	0.9788	0.8916
0.0024	5.4918	335	41.5621	0.5560	1.0000	0.9085	0.7955	0.7419	0.9799	0.9002
0.0	5.5738	340	47.0258	0.5532	1.0000	0.9789	0.8636	0.7796	0.9887	0.9094
0.0	5.6557	345	49.6703	0.5514	1.0000	0.9789	0.8636	0.7796	0.9927	0.9101
0.0	5.7377	350	51.1517	0.5514	1.0000	0.9789	0.8636	0.7796	0.9929	0.9122
0.0	5.8197	355	52.0806	0.5544	1.0000	0.9789	0.8636	0.7796	0.9929	0.9141
0.0	5.9016	360	52.3023	0.5544	1.0000	0.9789	0.8636	0.7796	0.9930	0.9158
0.0	5.9836	365	52.6798	0.5546	1.0000	0.9789	0.8636	0.7796	0.9929	0.9151
0.0	6.0656	370	52.8054	0.5546	1.0000	0.9789	0.8636	0.7796	0.9930	0.9195
0.0	6.1475	375	52.8369	0.5575	1.0	0.9789	0.8636	0.7796	0.9930	0.9185
0.0	6.2295	380	49.4701	0.5514	1.0000	0.9789	0.8636	0.7796	0.9923	0.9096
0.0	6.3115	385	43.3307	0.5577	1.0000	0.9789	0.8636	0.7796	0.9855	0.9090
0.0	6.3934	390	40.0170	0.5803	1.0000	0.9155	0.75	0.7581	0.9811	0.9063
0.0	6.4754	395	37.9468	0.5785	1.0000	0.9014	0.7273	0.7527	0.9792	0.8991
0.0	6.5574	400	36.9711	0.5853	1.0000	0.8732	0.7045	0.7366	0.9789	0.8954
0.0	6.6393	405	36.4814	0.5991	1.0000	0.8732	0.6818	0.7419	0.9786	0.8942
0.0	6.7213	410	36.0732	0.5951	1.0000	0.9085	0.7273	0.7581	0.9784	0.8918
0.0	6.8033	415	35.7390	0.5955	1.0000	0.9155	0.7273	0.7634	0.9783	0.8912
0.001	6.8852	420	35.9047	0.5957	1.0000	0.9155	0.7273	0.7634	0.9784	0.8921
0.0	6.9672	425	37.0032	0.5907	1.0000	0.9085	0.7273	0.7581	0.9790	0.8969
0.0	7.0492	430	37.4474	0.5809	1.0000	0.8803	0.7045	0.7419	0.9795	0.8991
0.0	7.1311	435	37.8826	0.5703	1.0000	0.9225	0.75	0.7634	0.9798	0.9022
0.0	7.2131	440	37.9434	0.5758	1.0000	0.9085	0.7273	0.7581	0.9797	0.9040
0.0	7.2951	445	38.0193	0.5887	1.0000	0.9014	0.7045	0.7581	0.9794	0.9038
0.0	7.3770	450	38.1121	0.5730	1.0000	0.9014	0.7273	0.7527	0.9797	0.9017
0.0	7.4590	455	38.2472	0.5755	1.0000	0.9225	0.75	0.7634	0.9799	0.9042
0.0	7.5410	460	38.2276	0.5674	1.0000	0.9155	0.75	0.7581	0.9797	0.9026
0.0	7.6230	465	38.3880	0.5782	1.0000	0.9085	0.7273	0.7581	0.9794	0.9016
0.0	7.7049	470	38.4094	0.5782	1.0000	0.9085	0.7273	0.7581	0.9799	0.9032
0.0	7.7869	475	38.3583	0.5785	1.0000	0.9085	0.7273	0.7581	0.9798	0.9034
0.0	7.8689	480	38.5069	0.5785	1.0000	0.9085	0.7273	0.7581	0.9800	0.9033
0.0	7.9508	485	38.3627	0.5647	1.0000	0.9155	0.75	0.7581	0.9800	0.9031
0.0	8.0328	490	38.4440	0.5674	1.0000	0.9155	0.75	0.7581	0.9797	0.9025
0.0	8.1148	495	38.5018	0.5677	1.0000	0.9155	0.75	0.7581	0.9797	0.9032
0.0	8.1967	500	38.3083	0.5781	1.0000	0.9014	0.7273	0.7527	0.9800	0.9030
0.0	8.2787	505	38.3668	0.5678	1.0000	0.9155	0.75	0.7581	0.9798	0.9035
0.0	8.3607	510	38.4451	0.5704	1.0000	0.9225	0.75	0.7634	0.9802	0.9029
0.0	8.4426	515	38.4450	0.5782	1.0000	0.9085	0.7273	0.7581	0.9796	0.9044
0.0	8.5246	520	38.5422	0.5783	1.0000	0.9085	0.7273	0.7581	0.9801	0.9040
0.0	8.6066	525	38.5355	0.5753	1.0000	0.9014	0.7273	0.7527	0.9798	0.9017
0.0	8.6885	530	38.5503	0.5674	1.0000	0.9155	0.75	0.7581	0.9804	0.9043
0.0	8.7705	535	38.4562	0.5646	1.0000	0.9155	0.75	0.7581	0.9799	0.9037
0.0	8.8525	540	38.4420	0.5758	1.0000	0.9085	0.7273	0.7581	0.9802	0.9033
0.0	8.9344	545	38.4622	0.5677	1.0000	0.9155	0.75	0.7581	0.9799	0.9032
0.0	9.0164	550	38.4403	0.5730	1.0000	0.9014	0.7273	0.7527	0.9799	0.9036
0.0	9.0984	555	38.4377	0.5646	1.0000	0.9155	0.75	0.7581	0.9797	0.9023
0.0	9.1803	560	38.5681	0.5783	1.0000	0.9085	0.7273	0.7581	0.9802	0.9034
0.0	9.2623	565	38.4404	0.5703	1.0000	0.9155	0.75	0.7581	0.9798	0.9037
0.0	9.3443	570	38.5288	0.5812	1.0000	0.9085	0.7273	0.7581	0.9797	0.9033
0.0	9.4262	575	38.4418	0.5811	1.0000	0.9085	0.7273	0.7581	0.9798	0.9045
0.0	9.5082	580	38.5196	0.5784	1.0000	0.9014	0.7273	0.7527	0.9798	0.9027
0.0	9.5902	585	38.4925	0.5780	1.0000	0.9085	0.7273	0.7581	0.9798	0.9041
0.0	9.6721	590	38.5499	0.5755	1.0000	0.9085	0.7273	0.7581	0.9798	0.9039
0.0	9.7541	595	38.5149	0.5779	1.0000	0.9155	0.7273	0.7634	0.9797	0.9038
0.0	9.8361	600	38.4374	0.5673	1.0000	0.9155	0.75	0.7581	0.9801	0.9028
0.0	9.9180	605	38.5842	0.5702	1.0000	0.9155	0.75	0.7581	0.9799	0.9042
0.0	10.0	610	38.5374	0.5703	1.0000	0.9155	0.75	0.7581	0.9798	0.9042

Framework versions

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

leobianco/bosch_RM_Qwen_S12345_LLM_false_STRUCT_true_epo10_lr5e-4_r8_2602041801

bosch_RM_Qwen_S12345_LLM_false_STRUCT_true_epo10_lr5e-4_r8_2602041801

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leobianco/
bosch_RM_Qwen_S12345_LLM_false_STRUCT_true_epo10_lr5e-4_r8_2602041801