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	3	11.0724	0.0098	11.0723	3.3275
No log	2.0	6	7.1117	0.0	7.1119	2.6668
No log	3.0	9	4.9264	0.0330	4.9269	2.2197
No log	4.0	12	3.1187	0.0039	3.1191	1.7661
No log	5.0	15	2.1997	0.1440	2.2002	1.4833
No log	6.0	18	1.3866	0.0475	1.3871	1.1778
No log	7.0	21	1.2607	0.0307	1.2612	1.1230
No log	8.0	24	0.7746	0.3239	0.7750	0.8803
No log	9.0	27	0.7607	0.4097	0.7611	0.8724
No log	10.0	30	0.7232	0.4542	0.7235	0.8506
No log	11.0	33	0.6186	0.4983	0.6188	0.7867
No log	12.0	36	0.5549	0.4912	0.5551	0.7451
No log	13.0	39	0.7436	0.4611	0.7440	0.8625
No log	14.0	42	0.4766	0.4990	0.4767	0.6904
No log	15.0	45	1.1401	0.3595	1.1406	1.0680
No log	16.0	48	0.8785	0.4163	0.8790	0.9375
No log	17.0	51	0.6079	0.4305	0.6078	0.7796
No log	18.0	54	0.5548	0.4846	0.5550	0.7450
No log	19.0	57	0.5511	0.5027	0.5513	0.7425
No log	20.0	60	0.5088	0.5716	0.5087	0.7132
No log	21.0	63	0.4834	0.5972	0.4834	0.6953
No log	22.0	66	0.4890	0.5802	0.4889	0.6992
No log	23.0	69	0.5057	0.5699	0.5057	0.7111
No log	24.0	72	0.5099	0.5857	0.5099	0.7141
No log	25.0	75	0.5016	0.6069	0.5015	0.7082
No log	26.0	78	0.5568	0.5614	0.5570	0.7463
No log	27.0	81	0.4963	0.5955	0.4964	0.7046
No log	28.0	84	0.5615	0.5454	0.5617	0.7495
No log	29.0	87	0.5073	0.5893	0.5074	0.7123
No log	30.0	90	0.5360	0.5812	0.5361	0.7322
No log	31.0	93	0.5597	0.5828	0.5596	0.7480
No log	32.0	96	0.5096	0.6181	0.5097	0.7139
No log	33.0	99	0.9131	0.4239	0.9135	0.9558
No log	34.0	102	0.5235	0.5815	0.5234	0.7235
No log	35.0	105	0.5327	0.5703	0.5326	0.7298
No log	36.0	108	0.8147	0.4323	0.8150	0.9028
No log	37.0	111	0.5841	0.4915	0.5843	0.7644
No log	38.0	114	0.5272	0.5500	0.5271	0.7260
No log	39.0	117	0.6661	0.4509	0.6663	0.8163
No log	40.0	120	0.6456	0.4576	0.6458	0.8036
No log	41.0	123	0.5212	0.5778	0.5211	0.7219
No log	42.0	126	0.5817	0.5046	0.5818	0.7628
No log	43.0	129	0.6152	0.4834	0.6154	0.7844
No log	44.0	132	0.4998	0.5908	0.4998	0.7069
No log	45.0	135	0.5411	0.5432	0.5411	0.7356
No log	46.0	138	0.5104	0.6176	0.5104	0.7144
No log	47.0	141	0.5325	0.5855	0.5326	0.7298
No log	48.0	144	0.5025	0.5869	0.5026	0.7089
No log	49.0	147	0.4920	0.5794	0.4920	0.7014
No log	50.0	150	0.5303	0.5509	0.5304	0.7283
No log	51.0	153	0.4985	0.5827	0.4986	0.7061
No log	52.0	156	0.6014	0.4926	0.6016	0.7756

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold2

ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV16_k3_task1_organization_k3_k3_fold2