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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	10.8187	0.0055	10.8187	3.2892
No log	2.0	4	8.6803	0.0018	8.6803	2.9462
No log	3.0	6	6.8097	0.0018	6.8097	2.6095
No log	4.0	8	5.2015	0.0274	5.2015	2.2807
No log	5.0	10	4.0244	0.0079	4.0244	2.0061
No log	6.0	12	3.1309	0.0040	3.1309	1.7694
No log	7.0	14	2.3776	0.0496	2.3776	1.5419
No log	8.0	16	1.8351	0.0761	1.8351	1.3547
No log	9.0	18	1.4724	0.0484	1.4724	1.2134
No log	10.0	20	1.1707	0.0316	1.1707	1.0820
No log	11.0	22	1.0242	0.0316	1.0242	1.0120
No log	12.0	24	0.9920	0.0316	0.9920	0.9960
No log	13.0	26	1.2274	0.0610	1.2274	1.1079
No log	14.0	28	0.9542	0.0582	0.9542	0.9768
No log	15.0	30	0.8496	0.1778	0.8496	0.9217
No log	16.0	32	0.9204	-0.0030	0.9204	0.9594
No log	17.0	34	0.7777	0.3641	0.7777	0.8819
No log	18.0	36	1.0754	0.1626	1.0754	1.0370
No log	19.0	38	1.2658	0.2042	1.2658	1.1251
No log	20.0	40	0.6897	0.5061	0.6897	0.8305
No log	21.0	42	0.6095	0.4647	0.6095	0.7807
No log	22.0	44	0.6005	0.4871	0.6005	0.7749
No log	23.0	46	0.9257	0.3322	0.9257	0.9621
No log	24.0	48	1.3382	0.2232	1.3382	1.1568
No log	25.0	50	1.1044	0.2487	1.1044	1.0509
No log	26.0	52	0.6144	0.5278	0.6144	0.7838
No log	27.0	54	0.5658	0.4854	0.5658	0.7522
No log	28.0	56	0.5819	0.5458	0.5819	0.7628
No log	29.0	58	0.8745	0.4486	0.8745	0.9351
No log	30.0	60	1.0507	0.3884	1.0507	1.0250
No log	31.0	62	0.8011	0.4955	0.8011	0.8950
No log	32.0	64	0.5213	0.5094	0.5213	0.7220
No log	33.0	66	0.6214	0.4523	0.6214	0.7883
No log	34.0	68	0.6118	0.4671	0.6118	0.7822
No log	35.0	70	0.5585	0.5326	0.5585	0.7473
No log	36.0	72	1.1875	0.3864	1.1875	1.0897
No log	37.0	74	1.8364	0.2487	1.8364	1.3551
No log	38.0	76	1.4141	0.3548	1.4141	1.1892
No log	39.0	78	0.6446	0.5634	0.6446	0.8028
No log	40.0	80	0.5876	0.5104	0.5876	0.7666
No log	41.0	82	0.6427	0.5080	0.6427	0.8017
No log	42.0	84	0.5718	0.5338	0.5718	0.7562
No log	43.0	86	0.6315	0.5718	0.6315	0.7947
No log	44.0	88	0.7406	0.5126	0.7406	0.8606
No log	45.0	90	0.6659	0.5336	0.6659	0.8160
No log	46.0	92	0.5602	0.5473	0.5602	0.7485
No log	47.0	94	0.6754	0.5186	0.6754	0.8218
No log	48.0	96	0.6942	0.4949	0.6942	0.8332
No log	49.0	98	0.7643	0.4589	0.7643	0.8742
No log	50.0	100	0.8292	0.4437	0.8292	0.9106
No log	51.0	102	0.7418	0.5425	0.7418	0.8613
No log	52.0	104	0.6971	0.5828	0.6971	0.8349
No log	53.0	106	0.7282	0.5774	0.7282	0.8533
No log	54.0	108	0.6684	0.6026	0.6684	0.8175
No log	55.0	110	0.8566	0.4914	0.8566	0.9255
No log	56.0	112	0.8185	0.5137	0.8185	0.9047
No log	57.0	114	0.6349	0.5870	0.6349	0.7968
No log	58.0	116	0.6733	0.5775	0.6733	0.8205
No log	59.0	118	0.6752	0.5597	0.6752	0.8217
No log	60.0	120	0.6241	0.5594	0.6241	0.7900
No log	61.0	122	0.6344	0.5364	0.6344	0.7965
No log	62.0	124	0.6599	0.5048	0.6599	0.8123
No log	63.0	126	0.6220	0.5519	0.6220	0.7887
No log	64.0	128	0.6781	0.5828	0.6781	0.8235
No log	65.0	130	0.7946	0.5560	0.7946	0.8914
No log	66.0	132	0.8086	0.5431	0.8086	0.8992
No log	67.0	134	0.6798	0.5763	0.6798	0.8245
No log	68.0	136	0.8063	0.4995	0.8063	0.8979
No log	69.0	138	0.9282	0.4641	0.9282	0.9634

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold4

Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Trial2BERT_AugV8_k1_task1_organization_sp010_lw030_fold4