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	12.7683	0.0	12.7664	3.5730
No log	2.0	4	11.1063	0.0154	11.1045	3.3323
No log	3.0	6	9.2068	0.0018	9.2053	3.0340
No log	4.0	8	6.9316	0.0005	6.9304	2.6326
No log	5.0	10	5.0458	0.0331	5.0448	2.2461
No log	6.0	12	3.6789	0.0076	3.6780	1.9178
No log	7.0	14	2.5246	0.1061	2.5238	1.5887
No log	8.0	16	2.0839	0.0921	2.0833	1.4434
No log	9.0	18	1.5304	0.0365	1.5299	1.2369
No log	10.0	20	1.1862	0.0202	1.1857	1.0889
No log	11.0	22	1.0986	0.0202	1.0982	1.0479
No log	12.0	24	0.8656	0.3278	0.8653	0.9302
No log	13.0	26	0.8185	0.1675	0.8182	0.9046
No log	14.0	28	0.7951	0.1203	0.7949	0.8916
No log	15.0	30	0.8582	0.0876	0.8580	0.9263
No log	16.0	32	0.9073	0.0871	0.9071	0.9524
No log	17.0	34	1.0171	0.1836	1.0170	1.0085
No log	18.0	36	1.0099	0.2703	1.0098	1.0049
No log	19.0	38	0.8289	0.3983	0.8289	0.9105
No log	20.0	40	0.9015	0.4097	0.9016	0.9495
No log	21.0	42	0.8064	0.5091	0.8063	0.8980
No log	22.0	44	0.7661	0.5808	0.7659	0.8752
No log	23.0	46	0.8875	0.5582	0.8872	0.9419
No log	24.0	48	1.1238	0.5379	1.1234	1.0599
No log	25.0	50	1.0608	0.5385	1.0601	1.0296
No log	26.0	52	0.8280	0.5940	0.8275	0.9097
No log	27.0	54	0.7624	0.6127	0.7618	0.8728
No log	28.0	56	0.8870	0.5861	0.8864	0.9415
No log	29.0	58	0.7300	0.6339	0.7293	0.8540
No log	30.0	60	0.7484	0.6209	0.7477	0.8647
No log	31.0	62	0.7761	0.6259	0.7754	0.8805
No log	32.0	64	0.7065	0.6228	0.7058	0.8401
No log	33.0	66	0.7278	0.6401	0.7271	0.8527
No log	34.0	68	0.6740	0.6571	0.6733	0.8206
No log	35.0	70	0.8609	0.5921	0.8601	0.9274
No log	36.0	72	0.7332	0.6413	0.7325	0.8559
No log	37.0	74	0.6410	0.6555	0.6404	0.8003
No log	38.0	76	0.9080	0.5797	0.9073	0.9525
No log	39.0	78	0.9224	0.5646	0.9217	0.9600
No log	40.0	80	0.6782	0.6405	0.6777	0.8232
No log	41.0	82	0.7473	0.6260	0.7467	0.8641
No log	42.0	84	0.9063	0.5788	0.9055	0.9516
No log	43.0	86	0.7611	0.6237	0.7605	0.8720
No log	44.0	88	0.7025	0.6120	0.7020	0.8378
No log	45.0	90	0.7639	0.6386	0.7633	0.8737
No log	46.0	92	0.7556	0.6436	0.7550	0.8689
No log	47.0	94	0.6702	0.6350	0.6697	0.8183
No log	48.0	96	0.7227	0.6476	0.7221	0.8498
No log	49.0	98	0.6812	0.6475	0.6807	0.8251
No log	50.0	100	0.7431	0.6517	0.7425	0.8617
No log	51.0	102	0.9139	0.5918	0.9132	0.9556
No log	52.0	104	0.8309	0.6151	0.8303	0.9112
No log	53.0	106	0.7756	0.6294	0.7751	0.8804
No log	54.0	108	0.8010	0.6171	0.8004	0.8947

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3