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	1	12.1510	-0.0068	12.1510	3.4858
No log	2.0	2	10.8276	-0.0080	10.8276	3.2905
No log	3.0	3	9.5285	0.0018	9.5285	3.0868
No log	4.0	4	8.5569	0.0018	8.5569	2.9252
No log	5.0	5	7.5901	0.0018	7.5901	2.7550
No log	6.0	6	6.7832	0.0018	6.7832	2.6045
No log	7.0	7	6.1633	0.0029	6.1633	2.4826
No log	8.0	8	5.4591	0.0226	5.4591	2.3365
No log	9.0	9	4.8024	0.0166	4.8024	2.1914
No log	10.0	10	4.2612	0.0156	4.2612	2.0643
No log	11.0	11	3.8097	0.0079	3.8097	1.9519
No log	12.0	12	3.3457	0.0040	3.3457	1.8291
No log	13.0	13	2.9243	0.0040	2.9243	1.7100
No log	14.0	14	2.5921	0.0040	2.5921	1.6100
No log	15.0	15	2.3121	0.1466	2.3121	1.5206
No log	16.0	16	2.0706	0.0654	2.0706	1.4389
No log	17.0	17	1.8382	0.0445	1.8382	1.3558
No log	18.0	18	1.6552	0.0420	1.6552	1.2865
No log	19.0	19	1.4981	0.0420	1.4981	1.2240
No log	20.0	20	1.3572	0.0212	1.3572	1.1650
No log	21.0	21	1.2308	0.0212	1.2308	1.1094
No log	22.0	22	1.1272	0.0212	1.1272	1.0617
No log	23.0	23	1.0433	0.0212	1.0433	1.0214
No log	24.0	24	0.9666	0.0212	0.9666	0.9832
No log	25.0	25	0.8997	0.0212	0.8997	0.9485
No log	26.0	26	0.8569	0.2638	0.8569	0.9257
No log	27.0	27	0.8082	0.4008	0.8082	0.8990
No log	28.0	28	0.7573	0.3995	0.7573	0.8702
No log	29.0	29	0.7168	0.4132	0.7168	0.8466
No log	30.0	30	0.6851	0.4057	0.6851	0.8277
No log	31.0	31	0.6771	0.3338	0.6771	0.8229
No log	32.0	32	0.6362	0.3685	0.6362	0.7977
No log	33.0	33	0.5829	0.4842	0.5829	0.7635
No log	34.0	34	0.5626	0.4755	0.5626	0.7500
No log	35.0	35	0.5717	0.4348	0.5717	0.7561
No log	36.0	36	0.5486	0.4499	0.5486	0.7407
No log	37.0	37	0.5132	0.4962	0.5132	0.7163
No log	38.0	38	0.4953	0.5213	0.4953	0.7038
No log	39.0	39	0.5070	0.5070	0.5070	0.7120
No log	40.0	40	0.5460	0.5055	0.5460	0.7389
No log	41.0	41	0.5112	0.5989	0.5112	0.7150
No log	42.0	42	0.5025	0.6459	0.5025	0.7089
No log	43.0	43	0.5466	0.6028	0.5466	0.7393
No log	44.0	44	0.6851	0.5538	0.6851	0.8277
No log	45.0	45	0.6808	0.5629	0.6808	0.8251
No log	46.0	46	0.5733	0.6218	0.5733	0.7571
No log	47.0	47	0.5760	0.6340	0.5760	0.7590
No log	48.0	48	0.6928	0.5719	0.6928	0.8323
No log	49.0	49	0.7536	0.5545	0.7536	0.8681
No log	50.0	50	0.7182	0.5811	0.7182	0.8475
No log	51.0	51	0.6533	0.6278	0.6533	0.8082
No log	52.0	52	0.6886	0.6171	0.6886	0.8298
No log	53.0	53	0.7833	0.5694	0.7833	0.8850
No log	54.0	54	0.8490	0.5531	0.8490	0.9214
No log	55.0	55	0.8055	0.5740	0.8055	0.8975
No log	56.0	56	0.7811	0.5950	0.7811	0.8838
No log	57.0	57	0.8689	0.5588	0.8689	0.9322
No log	58.0	58	1.0411	0.5291	1.0411	1.0203
No log	59.0	59	0.9813	0.5379	0.9813	0.9906
No log	60.0	60	0.9433	0.5551	0.9433	0.9712
No log	61.0	61	0.9600	0.5551	0.9600	0.9798
No log	62.0	62	0.8291	0.5878	0.8291	0.9105

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4