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	1	14.4557	0.0	14.4557	3.8021
No log	2.0	2	13.4658	0.0	13.4658	3.6696
No log	3.0	3	12.1923	0.0024	12.1923	3.4917
No log	4.0	4	10.7376	0.0109	10.7376	3.2768
No log	5.0	5	9.4285	0.0054	9.4285	3.0706
No log	6.0	6	8.1642	0.0054	8.1642	2.8573
No log	7.0	7	7.0301	0.0036	7.0301	2.6514
No log	8.0	8	5.9498	0.0474	5.9498	2.4392
No log	9.0	9	4.9669	0.0234	4.9669	2.2287
No log	10.0	10	4.1752	0.0177	4.1752	2.0433
No log	11.0	11	3.5706	0.0115	3.5706	1.8896
No log	12.0	12	3.0926	0.0132	3.0926	1.7586
No log	13.0	13	2.3672	0.0582	2.3672	1.5386
No log	14.0	14	2.6076	0.0180	2.6076	1.6148
No log	15.0	15	2.3611	0.0350	2.3611	1.5366
No log	16.0	16	1.5372	0.0838	1.5372	1.2398
No log	17.0	17	1.6816	0.0863	1.6816	1.2968
No log	18.0	18	1.6567	0.0838	1.6567	1.2871
No log	19.0	19	1.4880	0.0681	1.4880	1.2198
No log	20.0	20	1.2321	0.0482	1.2321	1.1100
No log	21.0	21	0.9105	0.0634	0.9105	0.9542
No log	22.0	22	0.8629	0.1320	0.8629	0.9289
No log	23.0	23	0.7954	0.1581	0.7954	0.8919
No log	24.0	24	0.7464	0.2103	0.7464	0.8639
No log	25.0	25	0.7197	0.2303	0.7197	0.8483
No log	26.0	26	0.6946	0.1927	0.6946	0.8334
No log	27.0	27	0.7014	0.1636	0.7014	0.8375
No log	28.0	28	0.7250	0.1531	0.7250	0.8515
No log	29.0	29	0.7478	0.1487	0.7478	0.8647
No log	30.0	30	0.7198	0.1844	0.7198	0.8484
No log	31.0	31	0.6696	0.2368	0.6696	0.8183
No log	32.0	32	0.6653	0.2453	0.6653	0.8157
No log	33.0	33	0.6916	0.2833	0.6916	0.8317
No log	34.0	34	0.6856	0.3991	0.6856	0.8280
No log	35.0	35	0.6314	0.4451	0.6314	0.7946
No log	36.0	36	0.6321	0.4944	0.6321	0.7951
No log	37.0	37	0.6729	0.5089	0.6729	0.8203
No log	38.0	38	0.5877	0.5520	0.5877	0.7666
No log	39.0	39	0.5230	0.6026	0.5230	0.7232
No log	40.0	40	0.5407	0.5953	0.5407	0.7354
No log	41.0	41	0.6411	0.5763	0.6411	0.8007
No log	42.0	42	0.6071	0.6060	0.6071	0.7792
No log	43.0	43	0.4867	0.6330	0.4867	0.6976
No log	44.0	44	0.4837	0.6349	0.4837	0.6955
No log	45.0	45	0.5600	0.6504	0.5600	0.7483
No log	46.0	46	0.5506	0.6441	0.5506	0.7420
No log	47.0	47	0.5387	0.6458	0.5387	0.7340
No log	48.0	48	0.5388	0.6610	0.5388	0.7341
No log	49.0	49	0.6249	0.6376	0.6249	0.7905
No log	50.0	50	0.5712	0.6559	0.5712	0.7558
No log	51.0	51	0.4992	0.6657	0.4992	0.7066
No log	52.0	52	0.5056	0.6624	0.5056	0.7111
No log	53.0	53	0.5914	0.6367	0.5914	0.7691
No log	54.0	54	0.5967	0.6340	0.5967	0.7725
No log	55.0	55	0.5164	0.6527	0.5164	0.7186
No log	56.0	56	0.5161	0.6546	0.5161	0.7184
No log	57.0	57	0.5837	0.6419	0.5837	0.7640
No log	58.0	58	0.5916	0.6355	0.5916	0.7691
No log	59.0	59	0.5144	0.6493	0.5144	0.7172
No log	60.0	60	0.4955	0.6556	0.4955	0.7039
No log	61.0	61	0.5088	0.6557	0.5088	0.7133
No log	62.0	62	0.5780	0.6362	0.5780	0.7603
No log	63.0	63	0.5814	0.6400	0.5814	0.7625
No log	64.0	64	0.6372	0.6021	0.6372	0.7983
No log	65.0	65	0.5889	0.6223	0.5889	0.7674
No log	66.0	66	0.5074	0.6539	0.5074	0.7123
No log	67.0	67	0.4995	0.6377	0.4995	0.7067
No log	68.0	68	0.5191	0.6590	0.5191	0.7205
No log	69.0	69	0.5933	0.6296	0.5933	0.7702
No log	70.0	70	0.6429	0.6185	0.6429	0.8018
No log	71.0	71	0.6206	0.6249	0.6206	0.7878

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0