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	6	5.5762	0.0371	5.5762	2.3614
No log	2.0	12	2.4358	-0.0138	2.4358	1.5607
No log	3.0	18	1.1923	0.0316	1.1923	1.0919
No log	4.0	24	1.6465	0.1822	1.6465	1.2832
No log	5.0	30	1.5568	0.1911	1.5568	1.2477
No log	6.0	36	2.0978	0.2215	2.0978	1.4484
No log	7.0	42	2.1375	0.2294	2.1375	1.4620
No log	8.0	48	1.0439	0.2229	1.0439	1.0217
No log	9.0	54	1.1594	0.2565	1.1594	1.0767
No log	10.0	60	1.2223	0.3073	1.2223	1.1056
No log	11.0	66	0.9815	0.3760	0.9815	0.9907
No log	12.0	72	2.1136	0.2001	2.1136	1.4538
No log	13.0	78	1.7487	0.3278	1.7487	1.3224
No log	14.0	84	1.1468	0.3628	1.1468	1.0709
No log	15.0	90	1.2944	0.3280	1.2944	1.1377
No log	16.0	96	1.0432	0.3464	1.0432	1.0214
No log	17.0	102	1.1308	0.3382	1.1308	1.0634
No log	18.0	108	1.1639	0.3616	1.1639	1.0789
No log	19.0	114	1.5922	0.2444	1.5922	1.2618
No log	20.0	120	1.3150	0.2925	1.3150	1.1467
No log	21.0	126	1.4145	0.2989	1.4145	1.1893
No log	22.0	132	1.0716	0.3356	1.0716	1.0352
No log	23.0	138	1.2247	0.3481	1.2247	1.1067
No log	24.0	144	1.0014	0.3971	1.0014	1.0007
No log	25.0	150	0.9284	0.3793	0.9284	0.9635
No log	26.0	156	1.0026	0.3411	1.0026	1.0013
No log	27.0	162	1.1663	0.3602	1.1663	1.0800
No log	28.0	168	1.3042	0.3542	1.3042	1.1420
No log	29.0	174	1.2238	0.3484	1.2238	1.1062
No log	30.0	180	0.9826	0.3410	0.9826	0.9913
No log	31.0	186	0.9433	0.3746	0.9433	0.9713
No log	32.0	192	0.9711	0.4028	0.9711	0.9854
No log	33.0	198	1.0475	0.3903	1.0475	1.0235
No log	34.0	204	1.0891	0.3312	1.0891	1.0436
No log	35.0	210	1.0254	0.3299	1.0254	1.0126
No log	36.0	216	0.8148	0.4762	0.8148	0.9027
No log	37.0	222	1.0391	0.3710	1.0391	1.0194
No log	38.0	228	1.3449	0.2921	1.3449	1.1597
No log	39.0	234	1.1688	0.3011	1.1688	1.0811
No log	40.0	240	1.4175	0.3230	1.4175	1.1906
No log	41.0	246	0.7854	0.4892	0.7854	0.8862
No log	42.0	252	1.4458	0.3173	1.4458	1.2024
No log	43.0	258	0.8435	0.4421	0.8435	0.9184
No log	44.0	264	0.8239	0.4462	0.8239	0.9077
No log	45.0	270	1.1108	0.3251	1.1108	1.0539
No log	46.0	276	1.1714	0.3380	1.1714	1.0823
No log	47.0	282	0.8404	0.4071	0.8404	0.9167
No log	48.0	288	0.9095	0.4091	0.9095	0.9537
No log	49.0	294	1.1464	0.3455	1.1464	1.0707
No log	50.0	300	1.0914	0.3220	1.0914	1.0447
No log	51.0	306	0.9604	0.3751	0.9604	0.9800
No log	52.0	312	0.9648	0.4420	0.9648	0.9822
No log	53.0	318	1.0291	0.3980	1.0291	1.0144
No log	54.0	324	1.0104	0.3751	1.0104	1.0052
No log	55.0	330	0.8655	0.4357	0.8655	0.9303
No log	56.0	336	0.9447	0.3807	0.9447	0.9719
No log	57.0	342	1.0010	0.3247	1.0010	1.0005
No log	58.0	348	1.1121	0.2983	1.1121	1.0545
No log	59.0	354	0.9402	0.3479	0.9402	0.9696
No log	60.0	360	0.8994	0.3541	0.8994	0.9483
No log	61.0	366	0.9462	0.3500	0.9462	0.9727

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold4

ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold4