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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	3	10.4101	0.0	10.4081	3.2262
No log	2.0	6	9.5785	0.0	9.5766	3.0946
No log	3.0	9	7.0739	0.0	7.0724	2.6594
No log	4.0	12	5.2172	0.0096	5.2161	2.2839
No log	5.0	15	3.6616	0.0	3.6608	1.9133
No log	6.0	18	2.1094	0.0765	2.1087	1.4521
No log	7.0	21	1.4860	0.0202	1.4854	1.2188
No log	8.0	24	1.3245	0.0102	1.3241	1.1507
No log	9.0	27	1.4498	0.0011	1.4491	1.2038
No log	10.0	30	1.0944	0.0129	1.0944	1.0461
No log	11.0	33	1.1205	0.0152	1.1210	1.0588
No log	12.0	36	0.9182	0.2212	0.9188	0.9586
No log	13.0	39	0.8516	0.1830	0.8523	0.9232
No log	14.0	42	0.9702	0.1173	0.9711	0.9854
No log	15.0	45	0.8975	0.1296	0.8983	0.9478
No log	16.0	48	0.9478	0.1162	0.9485	0.9739
No log	17.0	51	1.0698	0.0196	1.0706	1.0347
No log	18.0	54	0.8945	0.1585	0.8952	0.9462
No log	19.0	57	0.9514	0.1140	0.9519	0.9756
No log	20.0	60	1.2321	-0.0063	1.2317	1.1098
No log	21.0	63	0.8169	0.2995	0.8175	0.9042
No log	22.0	66	0.8942	0.3263	0.8950	0.9460
No log	23.0	69	1.5731	0.0270	1.5731	1.2542
No log	24.0	72	1.0038	0.1694	1.0040	1.0020
No log	25.0	75	1.1850	0.0858	1.1850	1.0886
No log	26.0	78	0.9047	0.2525	0.9052	0.9514
No log	27.0	81	1.6052	0.0240	1.6052	1.2670
No log	28.0	84	1.2246	0.1486	1.2253	1.1069
No log	29.0	87	1.0986	0.1566	1.0992	1.0484
No log	30.0	90	1.1296	0.2010	1.1302	1.0631
No log	31.0	93	1.1333	0.2355	1.1338	1.0648
No log	32.0	96	1.1233	0.2811	1.1239	1.0602
No log	33.0	99	1.0800	0.2660	1.0807	1.0396
No log	34.0	102	0.9501	0.3586	0.9510	0.9752
No log	35.0	105	1.0970	0.3138	1.0978	1.0478
No log	36.0	108	1.0787	0.3446	1.0795	1.0390
No log	37.0	111	1.1876	0.3017	1.1882	1.0900
No log	38.0	114	1.1657	0.3054	1.1665	1.0800
No log	39.0	117	1.0686	0.3526	1.0698	1.0343
No log	40.0	120	1.3202	0.2168	1.3211	1.1494
No log	41.0	123	1.0306	0.3087	1.0317	1.0157
No log	42.0	126	1.4233	0.2064	1.4237	1.1932
No log	43.0	129	1.0248	0.3819	1.0259	1.0129
No log	44.0	132	1.5430	0.1976	1.5436	1.2424
No log	45.0	135	0.9433	0.4072	0.9446	0.9719
No log	46.0	138	1.1418	0.2768	1.1427	1.0690
No log	47.0	141	1.1453	0.2786	1.1463	1.0707
No log	48.0	144	1.2249	0.2689	1.2259	1.1072
No log	49.0	147	1.1898	0.2737	1.1908	1.0912
No log	50.0	150	1.2175	0.2613	1.2183	1.1038
No log	51.0	153	1.1756	0.2526	1.1762	1.0845
No log	52.0	156	0.8937	0.3762	0.8947	0.9459
No log	53.0	159	1.5299	0.0666	1.5297	1.2368
No log	54.0	162	1.0961	0.2945	1.0968	1.0473
No log	55.0	165	1.4103	0.1941	1.4105	1.1877
No log	56.0	168	0.9952	0.3144	0.9961	0.9980
No log	57.0	171	1.1002	0.2704	1.1008	1.0492
No log	58.0	174	1.3508	0.1515	1.3510	1.1623
No log	59.0	177	0.9345	0.3422	0.9354	0.9671
No log	60.0	180	1.2818	0.2020	1.2822	1.1323

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/BERT_AugV8_k3_task1_organization_sp060_lw030_fold3

BERT_AugV8_k3_task1_organization_sp060_lw030_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
BERT_AugV8_k3_task1_organization_sp060_lw030_fold3