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	9.4347	0.0	9.4328	3.0713
No log	2.0	8	5.4607	0.0354	5.4595	2.3366
No log	3.0	12	3.2577	0.0038	3.2566	1.8046
No log	4.0	16	1.8224	0.0524	1.8216	1.3497
No log	5.0	20	1.3708	0.0202	1.3701	1.1705
No log	6.0	24	1.3690	0.0463	1.3683	1.1698
No log	7.0	28	0.9181	0.1629	0.9177	0.9580
No log	8.0	32	1.8505	0.1959	1.8501	1.3602
No log	9.0	36	1.0465	0.2338	1.0466	1.0230
No log	10.0	40	0.9984	0.3630	0.9988	0.9994
No log	11.0	44	1.4966	0.2329	1.4971	1.2235
No log	12.0	48	0.7603	0.4126	0.7609	0.8723
No log	13.0	52	0.8009	0.4314	0.8018	0.8954
No log	14.0	56	1.3388	0.2774	1.3393	1.1573
No log	15.0	60	0.8893	0.3823	0.8900	0.9434
No log	16.0	64	1.0112	0.4004	1.0121	1.0060
No log	17.0	68	1.6685	0.2894	1.6693	1.2920
No log	18.0	72	0.8993	0.4019	0.9000	0.9487
No log	19.0	76	1.0084	0.3581	1.0092	1.0046
No log	20.0	80	1.7951	0.2230	1.7956	1.3400
No log	21.0	84	0.9387	0.3770	0.9393	0.9692
No log	22.0	88	1.0042	0.3464	1.0050	1.0025
No log	23.0	92	1.1182	0.3220	1.1190	1.0578
No log	24.0	96	0.8227	0.4292	0.8234	0.9074
No log	25.0	100	1.4353	0.2741	1.4361	1.1984
No log	26.0	104	0.7610	0.4624	0.7617	0.8727
No log	27.0	108	1.3651	0.2890	1.3660	1.1688
No log	28.0	112	0.8578	0.4316	0.8586	0.9266
No log	29.0	116	1.7793	0.2146	1.7802	1.3342
No log	30.0	120	0.8282	0.4135	0.8289	0.9104
No log	31.0	124	1.1624	0.2918	1.1632	1.0785
No log	32.0	128	1.1673	0.2982	1.1681	1.0808
No log	33.0	132	1.0202	0.3458	1.0210	1.0104
No log	34.0	136	1.0580	0.3557	1.0588	1.0290
No log	35.0	140	1.0473	0.3528	1.0481	1.0237
No log	36.0	144	0.9277	0.3881	0.9283	0.9635
No log	37.0	148	0.7876	0.4513	0.7881	0.8877
No log	38.0	152	1.1154	0.3423	1.1159	1.0564
No log	39.0	156	0.9077	0.4042	0.9083	0.9530
No log	40.0	160	1.0049	0.3728	1.0054	1.0027
No log	41.0	164	0.9170	0.3850	0.9175	0.9578
No log	42.0	168	1.0124	0.3628	1.0131	1.0065
No log	43.0	172	0.8714	0.4372	0.8722	0.9339
No log	44.0	176	0.8943	0.4096	0.8951	0.9461
No log	45.0	180	0.7924	0.4799	0.7931	0.8906
No log	46.0	184	1.1505	0.3406	1.1511	1.0729
No log	47.0	188	0.9756	0.3757	0.9762	0.9880
No log	48.0	192	0.8347	0.4574	0.8353	0.9140
No log	49.0	196	1.0990	0.3287	1.0998	1.0487
No log	50.0	200	0.8106	0.4621	0.8114	0.9008
No log	51.0	204	0.9415	0.3789	0.9423	0.9707
No log	52.0	208	0.9901	0.3628	0.9908	0.9954
No log	53.0	212	0.8603	0.4075	0.8610	0.9279
No log	54.0	216	1.1055	0.3464	1.1062	1.0518
No log	55.0	220	1.0391	0.3633	1.0399	1.0197
No log	56.0	224	0.9576	0.3989	0.9582	0.9789
No log	57.0	228	0.9629	0.3870	0.9636	0.9816
No log	58.0	232	0.9433	0.3824	0.9439	0.9716
No log	59.0	236	0.8642	0.3923	0.8648	0.9299
No log	60.0	240	0.9458	0.3925	0.9463	0.9728
No log	61.0	244	0.9332	0.3921	0.9337	0.9663
No log	62.0	248	0.9487	0.4045	0.9492	0.9743
No log	63.0	252	0.9780	0.3994	0.9785	0.9892
No log	64.0	256	1.0113	0.3778	1.0119	1.0059
No log	65.0	260	0.8373	0.4170	0.8379	0.9153

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k5_task1_organization_k5_k5_fold3

ASAP_FineTuningBERT_AugV11_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k5_task1_organization_k5_k5_fold3