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	2	13.1168	0.0	13.1168	3.6217
No log	2.0	4	10.7137	0.0031	10.7137	3.2732
No log	3.0	6	9.0768	0.0	9.0768	3.0128
No log	4.0	8	6.3811	-0.0003	6.3811	2.5261
No log	5.0	10	4.6777	0.0153	4.6777	2.1628
No log	6.0	12	4.1246	0.0037	4.1246	2.0309
No log	7.0	14	2.9939	0.0040	2.9939	1.7303
No log	8.0	16	2.7649	0.0227	2.7649	1.6628
No log	9.0	18	1.9556	0.0816	1.9556	1.3984
No log	10.0	20	1.5173	0.0344	1.5173	1.2318
No log	11.0	22	1.2024	0.0107	1.2024	1.0965
No log	12.0	24	0.9383	0.0107	0.9383	0.9687
No log	13.0	26	0.8212	0.3284	0.8212	0.9062
No log	14.0	28	0.7901	0.2128	0.7901	0.8889
No log	15.0	30	0.7879	0.1096	0.7879	0.8877
No log	16.0	32	0.7502	0.1244	0.7502	0.8662
No log	17.0	34	0.8489	0.1093	0.8489	0.9214
No log	18.0	36	0.8006	0.2401	0.8006	0.8947
No log	19.0	38	0.7301	0.3764	0.7301	0.8545
No log	20.0	40	0.6968	0.4883	0.6968	0.8348
No log	21.0	42	0.5503	0.5583	0.5503	0.7418
No log	22.0	44	0.7028	0.5494	0.7028	0.8383
No log	23.0	46	0.5593	0.6295	0.5593	0.7479
No log	24.0	48	0.6172	0.6284	0.6172	0.7856
No log	25.0	50	0.8510	0.6018	0.8510	0.9225
No log	26.0	52	0.6353	0.6454	0.6353	0.7971
No log	27.0	54	0.7108	0.6338	0.7108	0.8431
No log	28.0	56	1.1181	0.5579	1.1181	1.0574
No log	29.0	58	1.3060	0.5265	1.3060	1.1428
No log	30.0	60	0.7168	0.6486	0.7168	0.8467
No log	31.0	62	0.6709	0.6359	0.6709	0.8191
No log	32.0	64	0.8417	0.6466	0.8417	0.9174
No log	33.0	66	0.8078	0.6467	0.8078	0.8988
No log	34.0	68	0.6569	0.6580	0.6569	0.8105
No log	35.0	70	1.0014	0.5800	1.0014	1.0007
No log	36.0	72	0.9716	0.5924	0.9716	0.9857
No log	37.0	74	0.7292	0.6610	0.7292	0.8539
No log	38.0	76	1.1106	0.5667	1.1106	1.0539
No log	39.0	78	1.0770	0.5817	1.0770	1.0378
No log	40.0	80	0.7940	0.6505	0.7940	0.8910
No log	41.0	82	1.0618	0.5844	1.0618	1.0305
No log	42.0	84	0.9817	0.6085	0.9817	0.9908
No log	43.0	86	0.7788	0.6417	0.7788	0.8825
No log	44.0	88	0.8700	0.6254	0.8700	0.9327
No log	45.0	90	0.9892	0.5991	0.9892	0.9946
No log	46.0	92	0.8382	0.6336	0.8382	0.9155
No log	47.0	94	0.8813	0.6093	0.8813	0.9388
No log	48.0	96	0.9269	0.6105	0.9269	0.9627
No log	49.0	98	0.8002	0.6382	0.8002	0.8945
No log	50.0	100	0.9267	0.6232	0.9267	0.9627
No log	51.0	102	1.2448	0.5497	1.2448	1.1157
No log	52.0	104	1.2131	0.5475	1.2131	1.1014
No log	53.0	106	0.7897	0.6527	0.7897	0.8886
No log	54.0	108	0.6916	0.6727	0.6916	0.8316
No log	55.0	110	0.7713	0.6487	0.7713	0.8783
No log	56.0	112	1.0075	0.5996	1.0075	1.0038
No log	57.0	114	0.8946	0.6261	0.8946	0.9459
No log	58.0	116	0.6976	0.6515	0.6976	0.8352
No log	59.0	118	0.8102	0.6275	0.8102	0.9001
No log	60.0	120	1.0751	0.5575	1.0751	1.0369
No log	61.0	122	1.0501	0.5597	1.0501	1.0248
No log	62.0	124	0.7937	0.6542	0.7937	0.8909
No log	63.0	126	0.7455	0.6576	0.7455	0.8634
No log	64.0	128	0.8645	0.6204	0.8645	0.9298
No log	65.0	130	0.9277	0.6247	0.9277	0.9632
No log	66.0	132	0.9143	0.6099	0.9143	0.9562
No log	67.0	134	0.9127	0.6049	0.9127	0.9553
No log	68.0	136	0.9671	0.5833	0.9671	0.9834
No log	69.0	138	0.9140	0.6108	0.9140	0.9560
No log	70.0	140	0.8537	0.6182	0.8537	0.9239
No log	71.0	142	0.7783	0.6471	0.7783	0.8822
No log	72.0	144	0.8195	0.6422	0.8195	0.9053
No log	73.0	146	0.9000	0.6253	0.9000	0.9487
No log	74.0	148	0.8150	0.6381	0.8150	0.9028

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4

ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4