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	12.7862	0.0	12.7862	3.5758
No log	2.0	4	10.4331	0.0087	10.4331	3.2300
No log	3.0	6	8.7085	0.0018	8.7085	2.9510
No log	4.0	8	7.2555	0.0018	7.2555	2.6936
No log	5.0	10	6.3259	0.0048	6.3259	2.5151
No log	6.0	12	4.8707	0.0195	4.8707	2.2070
No log	7.0	14	3.9041	0.0079	3.9041	1.9759
No log	8.0	16	3.1900	0.0079	3.1900	1.7861
No log	9.0	18	2.6819	0.0295	2.6819	1.6376
No log	10.0	20	2.0674	0.0597	2.0674	1.4378
No log	11.0	22	1.6669	0.0381	1.6669	1.2911
No log	12.0	24	1.3009	0.0394	1.3009	1.1406
No log	13.0	26	1.1286	0.0381	1.1286	1.0623
No log	14.0	28	1.0069	0.0484	1.0069	1.0034
No log	15.0	30	0.8569	0.2089	0.8569	0.9257
No log	16.0	32	0.8491	0.1700	0.8491	0.9215
No log	17.0	34	0.8137	0.2911	0.8137	0.9021
No log	18.0	36	0.7696	0.3749	0.7696	0.8773
No log	19.0	38	0.8020	0.4947	0.8020	0.8956
No log	20.0	40	0.6613	0.5091	0.6613	0.8132
No log	21.0	42	0.6135	0.5048	0.6135	0.7832
No log	22.0	44	0.6480	0.4711	0.6480	0.8050
No log	23.0	46	0.6994	0.4052	0.6994	0.8363
No log	24.0	48	0.6935	0.4336	0.6935	0.8328
No log	25.0	50	0.6710	0.5290	0.6710	0.8191
No log	26.0	52	0.6907	0.5456	0.6907	0.8311
No log	27.0	54	0.5924	0.5566	0.5924	0.7697
No log	28.0	56	0.5825	0.5413	0.5825	0.7632
No log	29.0	58	0.6063	0.5171	0.6063	0.7786
No log	30.0	60	0.6793	0.4381	0.6793	0.8242
No log	31.0	62	0.5536	0.5577	0.5536	0.7441
No log	32.0	64	0.5541	0.5827	0.5541	0.7444
No log	33.0	66	0.7025	0.4525	0.7025	0.8381
No log	34.0	68	0.7177	0.4926	0.7177	0.8471
No log	35.0	70	0.7309	0.4970	0.7309	0.8549
No log	36.0	72	0.6905	0.5797	0.6905	0.8310
No log	37.0	74	0.6423	0.6398	0.6423	0.8015
No log	38.0	76	0.6790	0.6221	0.6790	0.8240
No log	39.0	78	0.7099	0.5872	0.7099	0.8426
No log	40.0	80	0.7190	0.6028	0.7190	0.8479
No log	41.0	82	0.7269	0.5740	0.7269	0.8526
No log	42.0	84	0.9872	0.3433	0.9872	0.9936
No log	43.0	86	0.9883	0.3590	0.9883	0.9941
No log	44.0	88	0.8059	0.5233	0.8059	0.8977
No log	45.0	90	0.8908	0.4990	0.8908	0.9438
No log	46.0	92	1.1153	0.3272	1.1153	1.0561
No log	47.0	94	1.0858	0.3382	1.0858	1.0420
No log	48.0	96	0.7712	0.5808	0.7712	0.8782
No log	49.0	98	0.6264	0.6481	0.6264	0.7915
No log	50.0	100	0.6113	0.5884	0.6113	0.7819
No log	51.0	102	0.7799	0.4759	0.7799	0.8831
No log	52.0	104	0.9074	0.4000	0.9074	0.9526
No log	53.0	106	0.9251	0.4830	0.9251	0.9618
No log	54.0	108	0.7392	0.5915	0.7392	0.8598
No log	55.0	110	0.6746	0.6329	0.6746	0.8213
No log	56.0	112	0.6352	0.6511	0.6352	0.7970
No log	57.0	114	0.6641	0.6055	0.6641	0.8149
No log	58.0	116	0.7150	0.5799	0.7150	0.8456
No log	59.0	118	0.7675	0.5696	0.7675	0.8761
No log	60.0	120	0.6702	0.6304	0.6702	0.8187
No log	61.0	122	0.6267	0.6464	0.6267	0.7917
No log	62.0	124	0.6394	0.6350	0.6394	0.7996
No log	63.0	126	0.8746	0.5056	0.8746	0.9352
No log	64.0	128	1.0765	0.3641	1.0765	1.0375
No log	65.0	130	1.1413	0.3448	1.1413	1.0683
No log	66.0	132	1.0893	0.3895	1.0893	1.0437
No log	67.0	134	0.9558	0.4935	0.9558	0.9777
No log	68.0	136	0.7866	0.5962	0.7866	0.8869
No log	69.0	138	0.7784	0.6020	0.7784	0.8823
No log	70.0	140	0.8400	0.5264	0.8400	0.9165
No log	71.0	142	0.8338	0.5204	0.8338	0.9131

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Trial3BERT_AugV8_k1_task1_organization_sp020_lw030_fold4

Trial3BERT_AugV8_k1_task1_organization_sp020_lw030_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Trial3BERT_AugV8_k1_task1_organization_sp020_lw030_fold4