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	3	10.5467	0.0053	10.5469	3.2476
No log	2.0	6	7.7728	0.0	7.7732	2.7880
No log	3.0	9	4.9785	0.0203	4.9789	2.2313
No log	4.0	12	3.4615	0.0	3.4619	1.8606
No log	5.0	15	2.0220	0.0970	2.0226	1.4222
No log	6.0	18	1.2905	0.0107	1.2910	1.1362
No log	7.0	21	0.9148	0.0050	0.9153	0.9567
No log	8.0	24	0.8185	0.0636	0.8190	0.9050
No log	9.0	27	0.9105	0.0164	0.9110	0.9545
No log	10.0	30	1.1588	0.0164	1.1593	1.0767
No log	11.0	33	1.4449	0.1243	1.4452	1.2022
No log	12.0	36	1.6548	0.0996	1.6549	1.2864
No log	13.0	39	1.9382	0.1089	1.9380	1.3921
No log	14.0	42	0.9837	0.4196	0.9830	0.9915
No log	15.0	45	0.6354	0.6317	0.6348	0.7967
No log	16.0	48	1.2395	0.5094	1.2382	1.1128
No log	17.0	51	0.7460	0.6219	0.7453	0.8633
No log	18.0	54	0.6872	0.6117	0.6868	0.8287
No log	19.0	57	1.0745	0.4947	1.0741	1.0364
No log	20.0	60	1.8607	0.3777	1.8600	1.3638
No log	21.0	63	0.6564	0.5814	0.6560	0.8099
No log	22.0	66	0.9037	0.5181	0.9030	0.9503
No log	23.0	69	0.5308	0.6243	0.5303	0.7282
No log	24.0	72	0.7250	0.5916	0.7243	0.8510
No log	25.0	75	0.6110	0.6325	0.6104	0.7813
No log	26.0	78	0.6528	0.6170	0.6522	0.8076
No log	27.0	81	0.8847	0.5476	0.8842	0.9403
No log	28.0	84	0.6520	0.6197	0.6516	0.8072
No log	29.0	87	0.8437	0.5938	0.8432	0.9182
No log	30.0	90	1.0776	0.4959	1.0772	1.0379
No log	31.0	93	0.9987	0.5221	0.9984	0.9992
No log	32.0	96	0.7371	0.5877	0.7367	0.8583
No log	33.0	99	0.9979	0.5559	0.9973	0.9986
No log	34.0	102	0.5979	0.6145	0.5974	0.7729
No log	35.0	105	0.7261	0.6044	0.7256	0.8518
No log	36.0	108	0.8159	0.5524	0.8154	0.9030
No log	37.0	111	0.6260	0.6203	0.6256	0.7909
No log	38.0	114	0.9855	0.5332	0.9848	0.9924
No log	39.0	117	0.6875	0.6033	0.6870	0.8289
No log	40.0	120	0.7634	0.5942	0.7629	0.8734
No log	41.0	123	0.8544	0.5826	0.8537	0.9240
No log	42.0	126	0.7747	0.5811	0.7742	0.8799
No log	43.0	129	0.9712	0.5094	0.9708	0.9853
No log	44.0	132	1.1544	0.4668	1.1540	1.0742
No log	45.0	135	0.6521	0.6183	0.6517	0.8073

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold2

Version12AGAINNNASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold2