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	4	9.7763	0.0	9.7765	3.1267
No log	2.0	8	5.8641	0.0577	5.8644	2.4217
No log	3.0	12	3.6461	0.0039	3.6466	1.9096
No log	4.0	16	2.3426	0.1162	2.3430	1.5307
No log	5.0	20	1.5063	0.0107	1.5068	1.2275
No log	6.0	24	1.5138	0.0345	1.5143	1.2306
No log	7.0	28	1.1603	0.0345	1.1607	1.0773
No log	8.0	32	1.0353	0.1132	1.0356	1.0176
No log	9.0	36	0.7914	0.3438	0.7917	0.8898
No log	10.0	40	1.2059	0.2217	1.2061	1.0982
No log	11.0	44	0.7631	0.3408	0.7630	0.8735
No log	12.0	48	0.9357	0.3072	0.9352	0.9671
No log	13.0	52	0.9678	0.2881	0.9679	0.9838
No log	14.0	56	0.7673	0.3769	0.7673	0.8760
No log	15.0	60	0.8573	0.3482	0.8573	0.9259
No log	16.0	64	1.0289	0.3136	1.0289	1.0143
No log	17.0	68	0.8527	0.3759	0.8526	0.9234
No log	18.0	72	1.0326	0.3269	1.0324	1.0161
No log	19.0	76	1.1275	0.3110	1.1272	1.0617
No log	20.0	80	0.8969	0.3799	0.8967	0.9469
No log	21.0	84	0.8600	0.3607	0.8600	0.9273
No log	22.0	88	0.9957	0.3530	0.9958	0.9979
No log	23.0	92	0.8103	0.3890	0.8101	0.9001
No log	24.0	96	0.7987	0.3965	0.7985	0.8936
No log	25.0	100	0.8520	0.3649	0.8517	0.9229
No log	26.0	104	0.8870	0.3742	0.8867	0.9417
No log	27.0	108	0.9721	0.3276	0.9718	0.9858
No log	28.0	112	0.8613	0.3585	0.8609	0.9279
No log	29.0	116	0.7607	0.4012	0.7604	0.8720
No log	30.0	120	1.1523	0.2303	1.1521	1.0734
No log	31.0	124	0.8220	0.3644	0.8218	0.9065
No log	32.0	128	0.9111	0.3478	0.9108	0.9544
No log	33.0	132	0.7736	0.4	0.7733	0.8794
No log	34.0	136	1.0711	0.2912	1.0708	1.0348
No log	35.0	140	0.8177	0.4065	0.8172	0.9040
No log	36.0	144	1.1448	0.3042	1.1443	1.0697
No log	37.0	148	0.7618	0.4245	0.7614	0.8726
No log	38.0	152	0.9949	0.3457	0.9946	0.9973
No log	39.0	156	0.7915	0.4031	0.7913	0.8895
No log	40.0	160	1.0058	0.3214	1.0057	1.0029
No log	41.0	164	0.8286	0.3765	0.8284	0.9102
No log	42.0	168	0.9278	0.3426	0.9275	0.9631
No log	43.0	172	0.8254	0.3742	0.8251	0.9083
No log	44.0	176	0.8026	0.3607	0.8023	0.8957
No log	45.0	180	0.8623	0.3559	0.8619	0.9284
No log	46.0	184	0.9136	0.3655	0.9132	0.9556
No log	47.0	188	0.7474	0.4226	0.7470	0.8643
No log	48.0	192	0.9091	0.3647	0.9088	0.9533
No log	49.0	196	0.8408	0.3951	0.8404	0.9167
No log	50.0	200	0.8886	0.3756	0.8882	0.9425
No log	51.0	204	0.8212	0.3984	0.8209	0.9060
No log	52.0	208	0.7924	0.3870	0.7920	0.8900
No log	53.0	212	0.8152	0.3793	0.8148	0.9026
No log	54.0	216	0.7733	0.3764	0.7729	0.8792
No log	55.0	220	0.7828	0.3900	0.7824	0.8846
No log	56.0	224	0.7717	0.3907	0.7713	0.8783
No log	57.0	228	0.7655	0.3931	0.7652	0.8747

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV8_k5_task1_organization_k5_k5_fold2

Version2ASAP_FineTuningBERT_AugV8_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV8_k5_task1_organization_k5_k5_fold2