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	10.2458	0.0245	10.2429	3.2004
No log	2.0	4	6.7461	0.0	6.7437	2.5969
No log	3.0	6	6.1333	-0.0016	6.1310	2.4761
No log	4.0	8	5.4563	0.0141	5.4541	2.3354
No log	5.0	10	4.7278	0.0025	4.7258	2.1739
No log	6.0	12	3.8758	0.0	3.8738	1.9682
No log	7.0	14	3.2822	0.0	3.2803	1.8112
No log	8.0	16	2.8825	0.0	2.8807	1.6973
No log	9.0	18	2.1580	0.0698	2.1563	1.4684
No log	10.0	20	1.9670	-0.0098	1.9654	1.4019
No log	11.0	22	1.4212	0.0106	1.4196	1.1915
No log	12.0	24	1.3171	0.0	1.3157	1.1470
No log	13.0	26	0.9898	0.0	0.9885	0.9942
No log	14.0	28	0.8690	0.3000	0.8678	0.9316
No log	15.0	30	0.9386	0.0466	0.9375	0.9682
No log	16.0	32	0.9009	0.0837	0.8999	0.9487
No log	17.0	34	0.8602	0.1137	0.8593	0.9270
No log	18.0	36	1.1579	0.0660	1.1571	1.0757
No log	19.0	38	1.0415	0.1518	1.0408	1.0202
No log	20.0	40	0.9884	0.3805	0.9877	0.9938
No log	21.0	42	1.1045	0.3668	1.1040	1.0507
No log	22.0	44	0.8908	0.4860	0.8903	0.9436
No log	23.0	46	0.7595	0.5464	0.7591	0.8713
No log	24.0	48	0.8273	0.5715	0.8269	0.9093
No log	25.0	50	0.6863	0.6432	0.6860	0.8282
No log	26.0	52	1.3154	0.4797	1.3151	1.1468
No log	27.0	54	0.6760	0.6506	0.6757	0.8220
No log	28.0	56	1.5520	0.4369	1.5517	1.2457
No log	29.0	58	1.3454	0.5306	1.3453	1.1599
No log	30.0	60	0.5553	0.7415	0.5551	0.7450
No log	31.0	62	0.8635	0.6362	0.8634	0.9292
No log	32.0	64	1.2548	0.5284	1.2547	1.1201
No log	33.0	66	0.7980	0.6019	0.7977	0.8932
No log	34.0	68	1.4066	0.4789	1.4065	1.1860
No log	35.0	70	0.8834	0.5881	0.8832	0.9398
No log	36.0	72	0.9146	0.5977	0.9145	0.9563
No log	37.0	74	1.3470	0.5089	1.3469	1.1606
No log	38.0	76	0.7909	0.6357	0.7907	0.8892
No log	39.0	78	1.2778	0.4845	1.2776	1.1303
No log	40.0	80	1.0958	0.5337	1.0955	1.0467
No log	41.0	82	0.6420	0.6781	0.6417	0.8011
No log	42.0	84	1.1702	0.5427	1.1699	1.0816
No log	43.0	86	2.0496	0.3823	2.0494	1.4316
No log	44.0	88	1.3580	0.4661	1.3577	1.1652
No log	45.0	90	1.0554	0.5427	1.0551	1.0272
No log	46.0	92	1.6622	0.4669	1.6621	1.2892
No log	47.0	94	1.3792	0.5299	1.3792	1.1744
No log	48.0	96	0.7876	0.6581	0.7877	0.8875
No log	49.0	98	1.1831	0.5616	1.1830	1.0877
No log	50.0	100	1.8780	0.4091	1.8778	1.3703

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1

Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1