learning_rate: 5e-05
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
30.0312	0.0820	5	40.5488	0.6062	0.0134	0.4085	0.1364	0.5161	0.1387	0.0490
11.7852	0.1639	10	39.7617	0.6029	0.0077	0.4577	0.2045	0.5376	0.1493	0.0548
24.7657	0.2459	15	38.5001	0.6068	0.0153	0.4437	0.1818	0.5323	0.1595	0.0530
38.4141	0.3279	20	36.6091	0.6066	0.0228	0.4225	0.1591	0.5215	0.1730	0.0593
9.4689	0.4098	25	33.3035	0.6132	0.0286	0.4507	0.1818	0.5376	0.2074	0.0685
3.2359	0.4918	30	26.1523	0.6143	0.1613	0.4366	0.1591	0.5323	0.3086	0.1124
24.0159	0.5738	35	20.3393	0.6229	0.6352	0.3944	0.1136	0.5108	0.4199	0.2044
20.2304	0.6557	40	15.6310	0.6304	0.9202	0.3944	0.0909	0.5161	0.5496	0.3636
29.5782	0.7377	45	12.7325	0.6399	0.9800	0.4296	0.0909	0.5430	0.6501	0.5289
13.3971	0.8197	50	12.6072	0.6375	0.9643	0.4437	0.1136	0.5484	0.6392	0.5191
16.1886	0.9016	55	12.1443	0.6472	0.9399	0.4789	0.1136	0.5753	0.6306	0.4864
22.7931	0.9836	60	11.9929	0.6520	0.9482	0.4225	0.0455	0.5484	0.5978	0.4071
2.8357	1.0656	65	10.4334	0.6500	0.9696	0.3732	0.0455	0.5108	0.6343	0.4702
20.0226	1.1475	70	9.0343	0.6510	0.9828	0.3732	0.1136	0.4946	0.6744	0.5108
29.364	1.2295	75	8.2011	0.6454	0.8741	0.5282	0.2727	0.5753	0.7060	0.5360
10.1014	1.3115	80	8.5136	0.6493	0.3451	0.7676	0.4773	0.7097	0.6659	0.4778
11.2312	1.3934	85	8.3646	0.6621	0.3311	0.7535	0.4773	0.6989	0.6600	0.4571
20.1967	1.4754	90	8.5717	0.6770	0.5889	0.5775	0.2727	0.6129	0.6227	0.3998
20.8906	1.5574	95	9.9459	0.6856	0.4036	0.5986	0.2727	0.6290	0.5405	0.2956
14.0107	1.6393	100	10.3393	0.7007	0.3486	0.6056	0.2727	0.6344	0.5281	0.2648
8.5986	1.7213	105	10.2701	0.7047	0.4017	0.6197	0.25	0.6505	0.5548	0.2833
5.803	1.8033	110	9.0336	0.7139	0.7086	0.5704	0.1818	0.6290	0.6278	0.3530
11.9284	1.8852	115	8.9568	0.7158	0.7260	0.5986	0.1818	0.6505	0.6457	0.3675
3.2432	1.9672	120	10.1721	0.7226	0.5254	0.5986	0.1591	0.6559	0.5795	0.2619
10.2734	2.0492	125	11.9370	0.7240	0.2659	0.6197	0.1818	0.6667	0.5005	0.1743
1.58	2.1311	130	9.6124	0.7374	0.4388	0.6479	0.1818	0.6882	0.6038	0.2567
6.0737	2.2131	135	8.7078	0.7350	0.6584	0.6197	0.1591	0.6720	0.6586	0.3389
15.7963	2.2951	140	7.8064	0.7412	0.7509	0.6761	0.2273	0.6989	0.7239	0.4361
2.9316	2.3770	145	7.9574	0.7462	0.7677	0.6056	0.1364	0.6667	0.6898	0.3709
7.3629	2.4590	150	11.6594	0.7422	0.2613	0.6408	0.1818	0.6828	0.5052	0.1651
12.4144	2.5410	155	11.8311	0.7431	0.2075	0.6620	0.1818	0.6989	0.4877	0.1529
3.8578	2.6230	160	8.2735	0.7518	0.5617	0.6831	0.1818	0.7151	0.6401	0.2903
8.1315	2.7049	165	6.6880	0.7584	0.8228	0.6690	0.1591	0.7097	0.7488	0.4496
1.4131	2.7869	170	6.6641	0.7619	0.7555	0.6761	0.1591	0.7151	0.7269	0.3960
4.7418	2.8689	175	7.4847	0.7669	0.6654	0.6549	0.1591	0.6989	0.6710	0.2913
7.6802	2.9508	180	8.2907	0.7590	0.5938	0.6479	0.1364	0.6989	0.6311	0.2487
1.6871	3.0328	185	9.9082	0.7601	0.2628	0.6620	0.1591	0.7043	0.5586	0.1891
0.2959	3.1148	190	8.4259	0.7636	0.5185	0.6479	0.1591	0.6935	0.6272	0.2435
17.9895	3.1967	195	7.4181	0.7687	0.7856	0.6268	0.1364	0.6828	0.6946	0.3207
4.7273	3.2787	200	7.1780	0.7696	0.6918	0.7042	0.2045	0.7258	0.7237	0.3644
0.1337	3.3607	205	9.0440	0.7682	0.2606	0.7183	0.2273	0.7312	0.6117	0.2222
3.4993	3.4426	210	7.8792	0.7702	0.5794	0.6831	0.1818	0.7151	0.6710	0.2794
1.3619	3.5246	215	6.5586	0.7771	0.7963	0.7394	0.2045	0.7527	0.7784	0.4220
16.3103	3.6066	220	6.2714	0.7853	0.8852	0.7254	0.2045	0.7419	0.8156	0.4918
3.3842	3.6885	225	7.6241	0.7850	0.4639	0.7254	0.2045	0.7419	0.6809	0.2635
5.4085	3.7705	230	10.5478	0.7892	0.1422	0.7042	0.1818	0.7312	0.5256	0.1440
1.7648	3.8525	235	9.5956	0.7839	0.1380	0.7465	0.2273	0.7527	0.5605	0.1709
1.6893	3.9344	240	6.6278	0.7822	0.6935	0.6620	0.1364	0.7097	0.7216	0.3201
11.7487	4.0164	245	6.0717	0.7812	0.8952	0.6690	0.1364	0.7151	0.7995	0.4587
8.3149	4.0984	250	6.3770	0.7847	0.6067	0.7606	0.2273	0.7634	0.7547	0.3681
2.8237	4.1803	255	9.1689	0.7814	0.1399	0.7817	0.2727	0.7688	0.5923	0.1903
2.2771	4.2623	260	10.9474	0.7743	0.1772	0.6901	0.1591	0.7258	0.5225	0.1519
5.4687	4.3443	265	7.5840	0.7843	0.4311	0.7606	0.2273	0.7634	0.6983	0.2822
2.5051	4.4262	270	6.6344	0.7825	0.7102	0.7746	0.2273	0.7742	0.7853	0.4242
2.5152	4.5082	275	6.9670	0.7816	0.6248	0.7887	0.2273	0.7849	0.7598	0.3431
4.0514	4.5902	280	7.9310	0.7865	0.3145	0.7958	0.25	0.7849	0.6877	0.2490
0.9328	4.6721	285	7.1491	0.7889	0.5351	0.7958	0.25	0.7849	0.7428	0.3059
1.1048	4.7541	290	6.8217	0.7858	0.6307	0.7958	0.25	0.7849	0.7691	0.3533
0.9687	4.8361	295	7.2298	0.7836	0.4766	0.7887	0.2273	0.7849	0.7319	0.2986
0.3787	4.9180	300	8.6441	0.7821	0.2591	0.7746	0.25	0.7688	0.6519	0.2284
20.6671	5.0	305	8.2091	0.7813	0.3074	0.7676	0.25	0.7634	0.6766	0.2469
2.1522	5.0820	310	6.5430	0.7791	0.7673	0.7676	0.2273	0.7688	0.7967	0.4164
4.2995	5.1639	315	6.5973	0.7670	0.8539	0.7394	0.2273	0.7473	0.8071	0.4543
0.6759	5.2459	320	6.8292	0.7750	0.5842	0.7746	0.25	0.7688	0.7653	0.3636
0.565	5.3279	325	7.8719	0.7704	0.4688	0.7324	0.2273	0.7419	0.6832	0.2669
10.6954	5.4098	330	7.0601	0.7687	0.5927	0.7535	0.2273	0.7581	0.7402	0.3372
2.328	5.4918	335	6.6413	0.7655	0.9300	0.6761	0.1364	0.7204	0.8014	0.4631
0.5746	5.5738	340	6.8892	0.7617	0.8408	0.7183	0.1818	0.7419	0.7746	0.4166
0.3686	5.6557	345	6.9708	0.7778	0.8182	0.7183	0.1591	0.7473	0.7645	0.3787
0.6103	5.7377	350	7.1687	0.7722	0.8732	0.6338	0.1364	0.6882	0.7468	0.3407
1.4985	5.8197	355	7.0332	0.7732	0.8736	0.6831	0.1591	0.7204	0.7694	0.3724
9.7517	5.9016	360	6.6673	0.7814	0.9281	0.6620	0.1364	0.7097	0.8042	0.4146
0.1501	5.9836	365	6.8510	0.7778	0.8408	0.7817	0.25	0.7742	0.8177	0.4409
7.425	6.0656	370	6.8885	0.7827	0.9118	0.7183	0.2045	0.7366	0.8159	0.4346
0.1734	6.1475	375	6.8558	0.7819	0.9670	0.6901	0.1818	0.7204	0.8368	0.4835
0.5011	6.2295	380	7.1435	0.7783	0.8844	0.7254	0.25	0.7312	0.8050	0.4252
0.9888	6.3115	385	7.0603	0.7778	0.9055	0.6972	0.1818	0.7258	0.8069	0.4215
1.9475	6.3934	390	6.8621	0.7905	0.9540	0.7465	0.2273	0.7527	0.8599	0.5052
0.1386	6.4754	395	6.8341	0.7899	0.9759	0.6972	0.2045	0.7204	0.8622	0.5024
0.5628	6.5574	400	6.7988	0.7923	0.9456	0.6549	0.1591	0.6989	0.8097	0.4022
0.1144	6.6393	405	7.1489	0.7945	0.9281	0.6268	0.1136	0.6882	0.7657	0.3398
0.6131	6.7213	410	7.1699	0.7951	0.9322	0.6338	0.1136	0.6935	0.7746	0.3523
8.2027	6.8033	415	7.2416	0.7892	0.5609	0.7887	0.3182	0.7634	0.7837	0.3669
1.0782	6.8852	420	7.0991	0.7919	0.9771	0.5915	0.1136	0.6613	0.8005	0.3861
0.5271	6.9672	425	7.1077	0.7837	0.9540	0.6761	0.2045	0.7043	0.8169	0.4202
0.1101	7.0492	430	6.8886	0.7851	0.6822	0.8169	0.3182	0.7849	0.8354	0.4377
3.816	7.1311	435	7.1053	0.7841	0.7050	0.8028	0.3182	0.7742	0.8263	0.4300
0.566	7.2131	440	7.1446	0.7777	0.5496	0.8310	0.3636	0.7849	0.8146	0.4143
0.3669	7.2951	445	7.0694	0.7853	0.9752	0.6620	0.1818	0.6989	0.8330	0.4381
0.2328	7.3770	450	7.0203	0.7883	0.7912	0.8310	0.3409	0.7903	0.8565	0.4780
0.0288	7.4590	455	7.3054	0.7746	0.8062	0.8028	0.3182	0.7742	0.8482	0.4803
0.0642	7.5410	460	7.3038	0.7750	0.9663	0.7113	0.2273	0.7258	0.8514	0.4943
0.1382	7.6230	465	7.2857	0.7768	0.8222	0.7958	0.3182	0.7688	0.8543	0.4930
0.1095	7.7049	470	7.0670	0.7849	0.9353	0.7042	0.2045	0.7258	0.8356	0.4574
0.1472	7.7869	475	6.9725	0.7849	0.9518	0.6972	0.2045	0.7204	0.8432	0.4647
4.1629	7.8689	480	7.3202	0.7796	0.9705	0.6901	0.2045	0.7151	0.8481	0.4894
0.041	7.9508	485	7.1955	0.7823	0.7372	0.8028	0.3182	0.7742	0.8396	0.4472
0.0159	8.0328	490	7.1271	0.7792	0.9664	0.6690	0.1818	0.7043	0.8263	0.4358
0.0392	8.1148	495	7.0911	0.7823	0.7458	0.7958	0.3182	0.7688	0.8254	0.4326
0.3075	8.1967	500	7.2183	0.7812	0.7725	0.7958	0.3182	0.7688	0.8293	0.4348
0.0307	8.2787	505	7.0258	0.7846	0.7284	0.8169	0.3182	0.7849	0.8347	0.4357
5.4977	8.3607	510	7.1027	0.7825	0.7142	0.8099	0.3182	0.7796	0.8374	0.4339
5.3974	8.4426	515	7.2084	0.7782	0.9750	0.6549	0.1818	0.6935	0.8407	0.4486
0.0534	8.5246	520	7.4524	0.7769	0.8568	0.7958	0.3182	0.7688	0.8542	0.4853
0.0726	8.6066	525	7.3162	0.7771	0.8500	0.7958	0.3182	0.7688	0.8597	0.5063
0.0396	8.6885	530	7.2762	0.7793	0.8158	0.8099	0.3182	0.7796	0.8559	0.4855
0.2052	8.7705	535	7.1374	0.7835	0.7739	0.8099	0.3182	0.7796	0.8507	0.4626
0.1171	8.8525	540	7.3353	0.7778	0.8317	0.8028	0.3182	0.7742	0.8547	0.4762
0.0243	8.9344	545	7.1955	0.7823	0.8300	0.8169	0.3182	0.7849	0.8611	0.4895
0.0123	9.0164	550	7.2980	0.7823	0.8228	0.8169	0.3182	0.7849	0.8614	0.4971
0.0464	9.0984	555	7.2766	0.7820	0.8670	0.8028	0.3182	0.7742	0.8685	0.4950
0.041	9.1803	560	7.3925	0.7787	0.8597	0.8239	0.3182	0.7903	0.8701	0.5015
0.3789	9.2623	565	7.3078	0.7801	0.8828	0.8169	0.3182	0.7849	0.8666	0.4998
0.0118	9.3443	570	7.3482	0.7804	0.8601	0.8028	0.3182	0.7742	0.8627	0.5008
0.058	9.4262	575	7.3102	0.7819	0.8505	0.8028	0.3182	0.7742	0.8687	0.4946
0.2515	9.5082	580	7.3086	0.7817	0.8568	0.8028	0.3182	0.7742	0.8628	0.4952
0.0212	9.5902	585	7.4179	0.7754	0.9879	0.6620	0.1818	0.6989	0.8616	0.4979
4.938	9.6721	590	7.3090	0.7809	0.7976	0.8310	0.3409	0.7903	0.8662	0.4957
0.1873	9.7541	595	7.1331	0.7876	0.8007	0.8099	0.3182	0.7796	0.8619	0.4889
0.4543	9.8361	600	7.2125	0.7815	0.9860	0.6620	0.1818	0.6989	0.8616	0.4921
0.0149	9.9180	605	7.2915	0.7807	0.8918	0.8028	0.3182	0.7742	0.8662	0.4946
0.0017	10.0	610	7.2548	0.7828	0.9803	0.6690	0.1818	0.7043	0.8625	0.4814

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-5_r8_2602041119

bosch_RM_Qwen_S12345_LLM_false_STRUCT_true_epo10_lr5e-5_r8_2602041119

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-5_r8_2602041119