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	9.8743	0.0018	9.8743	3.1423
No log	2.0	4	9.2911	0.0018	9.2911	3.0481
No log	3.0	6	8.4086	0.0	8.4086	2.8998
No log	4.0	8	7.0711	0.0	7.0711	2.6592
No log	5.0	10	5.5549	0.0196	5.5549	2.3569
No log	6.0	12	4.3625	0.0079	4.3625	2.0887
No log	7.0	14	3.4958	0.0079	3.4958	1.8697
No log	8.0	16	2.9264	0.0040	2.9264	1.7107
No log	9.0	18	2.2685	0.1710	2.2685	1.5062
No log	10.0	20	1.7698	0.0547	1.7698	1.3303
No log	11.0	22	1.4340	0.0316	1.4340	1.1975
No log	12.0	24	1.1040	0.0316	1.1040	1.0507
No log	13.0	26	0.9434	0.0213	0.9434	0.9713
No log	14.0	28	0.8456	0.1454	0.8456	0.9196
No log	15.0	30	0.8270	0.0793	0.8270	0.9094
No log	16.0	32	0.8135	0.1037	0.8135	0.9020
No log	17.0	34	0.9374	0.0793	0.9374	0.9682
No log	18.0	36	0.9738	0.1086	0.9738	0.9868
No log	19.0	38	0.8766	0.2995	0.8766	0.9363
No log	20.0	40	0.9036	0.3695	0.9036	0.9506
No log	21.0	42	0.7900	0.4439	0.7900	0.8888
No log	22.0	44	0.5745	0.5587	0.5745	0.7579
No log	23.0	46	0.8076	0.5310	0.8076	0.8987
No log	24.0	48	0.6189	0.6333	0.6189	0.7867
No log	25.0	50	0.7449	0.6158	0.7449	0.8631
No log	26.0	52	1.0024	0.5928	1.0024	1.0012
No log	27.0	54	0.9963	0.5898	0.9963	0.9982
No log	28.0	56	0.7768	0.6396	0.7768	0.8814
No log	29.0	58	1.2634	0.5185	1.2634	1.1240
No log	30.0	60	0.9517	0.5746	0.9517	0.9755
No log	31.0	62	0.6235	0.6620	0.6235	0.7896
No log	32.0	64	0.8601	0.5898	0.8601	0.9274
No log	33.0	66	0.7962	0.6083	0.7962	0.8923
No log	34.0	68	0.7550	0.6164	0.7550	0.8689
No log	35.0	70	1.1439	0.5145	1.1439	1.0695
No log	36.0	72	1.0766	0.5333	1.0766	1.0376
No log	37.0	74	0.7548	0.6119	0.7548	0.8688
No log	38.0	76	1.1990	0.5360	1.1990	1.0950
No log	39.0	78	1.1326	0.5501	1.1326	1.0643
No log	40.0	80	0.7796	0.6305	0.7796	0.8829
No log	41.0	82	1.1612	0.5402	1.1612	1.0776
No log	42.0	84	1.1390	0.5420	1.1390	1.0672
No log	43.0	86	0.6764	0.6617	0.6764	0.8225
No log	44.0	88	0.6618	0.6560	0.6618	0.8135
No log	45.0	90	0.9699	0.5860	0.9699	0.9849
No log	46.0	92	1.1064	0.5575	1.1064	1.0518
No log	47.0	94	0.7555	0.6351	0.7555	0.8692
No log	48.0	96	0.7310	0.6319	0.7310	0.8550
No log	49.0	98	1.1261	0.5385	1.1261	1.0612
No log	50.0	100	1.1998	0.5091	1.1998	1.0954
No log	51.0	102	0.7889	0.6016	0.7889	0.8882

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_fold4

Version12AGAINNNASAP_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/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold4