learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	4	9.5741	0.0	9.5741	3.0942
No log	2.0	8	8.2843	0.0	8.2843	2.8782
No log	3.0	12	5.9063	0.0044	5.9063	2.4303
No log	4.0	16	4.1762	0.0	4.1762	2.0436
No log	5.0	20	3.0686	0.0	3.0686	1.7517
No log	6.0	24	2.3391	0.0	2.3391	1.5294
No log	7.0	28	1.8115	0.0	1.8115	1.3459
No log	8.0	32	2.1588	0.0246	2.1588	1.4693
No log	9.0	36	1.6038	0.0	1.6038	1.2664
No log	10.0	40	2.2873	0.0560	2.2873	1.5124
No log	11.0	44	1.7592	0.0545	1.7592	1.3264
No log	12.0	48	2.1592	0.1256	2.1592	1.4694
No log	13.0	52	1.7692	0.1609	1.7692	1.3301
No log	14.0	56	1.6762	0.1429	1.6762	1.2947
No log	15.0	60	2.1325	0.1471	2.1325	1.4603
No log	16.0	64	1.7670	0.1791	1.7670	1.3293
No log	17.0	68	1.5982	0.1926	1.5982	1.2642
No log	18.0	72	2.3402	0.1714	2.3402	1.5298
No log	19.0	76	1.6507	0.2118	1.6507	1.2848
No log	20.0	80	1.7451	0.2300	1.7451	1.3210
No log	21.0	84	1.9494	0.1919	1.9494	1.3962
No log	22.0	88	1.4335	0.2308	1.4335	1.1973
No log	23.0	92	2.2135	0.1935	2.2135	1.4878
No log	24.0	96	1.5814	0.2626	1.5814	1.2575
No log	25.0	100	1.4248	0.2060	1.4248	1.1937
No log	26.0	104	1.9174	0.2360	1.9174	1.3847
No log	27.0	108	1.4530	0.2874	1.4530	1.2054
No log	28.0	112	1.3098	0.3253	1.3098	1.1444
No log	29.0	116	1.8359	0.2695	1.8359	1.3549
No log	30.0	120	1.4696	0.2742	1.4696	1.2123
No log	31.0	124	1.5364	0.2452	1.5364	1.2395
No log	32.0	128	1.2685	0.3373	1.2685	1.1263
No log	33.0	132	1.2748	0.3257	1.2748	1.1291
No log	34.0	136	1.6492	0.2853	1.6492	1.2842
No log	35.0	140	1.2554	0.3449	1.2554	1.1205
No log	36.0	144	1.5293	0.2918	1.5293	1.2367
No log	37.0	148	1.3811	0.2717	1.3811	1.1752
No log	38.0	152	1.2358	0.3251	1.2358	1.1116
No log	39.0	156	1.3243	0.3829	1.3243	1.1508
No log	40.0	160	1.2249	0.4209	1.2249	1.1068
No log	41.0	164	1.2822	0.3810	1.2822	1.1323
No log	42.0	168	1.3867	0.3134	1.3867	1.1776
No log	43.0	172	1.1211	0.3994	1.1211	1.0588
No log	44.0	176	1.5550	0.2459	1.5550	1.2470
No log	45.0	180	1.2844	0.2743	1.2844	1.1333
No log	46.0	184	1.4000	0.2982	1.4000	1.1832
No log	47.0	188	1.2728	0.3333	1.2728	1.1282
No log	48.0	192	1.7193	0.2642	1.7193	1.3112
No log	49.0	196	1.1691	0.3766	1.1691	1.0812
No log	50.0	200	1.5878	0.2691	1.5878	1.2601
No log	51.0	204	1.2974	0.2931	1.2974	1.1390
No log	52.0	208	1.3736	0.2907	1.3736	1.1720
No log	53.0	212	1.3368	0.2888	1.3368	1.1562
No log	54.0	216	1.5010	0.2457	1.5010	1.2252
No log	55.0	220	1.3796	0.2493	1.3796	1.1746
No log	56.0	224	1.4476	0.2529	1.4476	1.2031
No log	57.0	228	1.3524	0.2766	1.3524	1.1629
No log	58.0	232	1.3745	0.2686	1.3745	1.1724
No log	59.0	236	1.6755	0.2973	1.6755	1.2944
No log	60.0	240	1.2507	0.3046	1.2507	1.1184

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12NewTestASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Version12NewTestASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12NewTestASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2