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	10.6057	-0.0015	10.6039	3.2564
No log	2.0	6	7.5444	0.0	7.5428	2.7464
No log	3.0	9	5.5817	0.0224	5.5803	2.3623
No log	4.0	12	3.7643	0.0038	3.7631	1.9399
No log	5.0	15	2.6552	0.0314	2.6542	1.6292
No log	6.0	18	1.8596	0.0365	1.8588	1.3634
No log	7.0	21	1.4665	0.0302	1.4657	1.2107
No log	8.0	24	1.4627	0.0365	1.4619	1.2091
No log	9.0	27	1.3055	0.0365	1.3048	1.1423
No log	10.0	30	1.2111	0.0365	1.2105	1.1002
No log	11.0	33	1.4416	0.0766	1.4409	1.2004
No log	12.0	36	0.8344	0.3231	0.8341	0.9133
No log	13.0	39	1.0598	0.2645	1.0595	1.0293
No log	14.0	42	1.2835	0.2073	1.2832	1.1328
No log	15.0	45	0.7955	0.3330	0.7956	0.8920
No log	16.0	48	1.4304	0.1923	1.4300	1.1958
No log	17.0	51	0.9495	0.2655	0.9495	0.9744
No log	18.0	54	1.3973	0.2288	1.3970	1.1820
No log	19.0	57	1.0099	0.2884	1.0098	1.0049
No log	20.0	60	1.4528	0.2110	1.4526	1.2053
No log	21.0	63	1.2871	0.2303	1.2870	1.1345
No log	22.0	66	1.2934	0.2342	1.2933	1.1373
No log	23.0	69	1.1731	0.2799	1.1732	1.0831
No log	24.0	72	1.6216	0.1856	1.6214	1.2733
No log	25.0	75	1.0535	0.3178	1.0535	1.0264
No log	26.0	78	1.3796	0.2422	1.3795	1.1745
No log	27.0	81	0.9194	0.3810	0.9195	0.9589
No log	28.0	84	1.2172	0.2773	1.2173	1.1033
No log	29.0	87	1.2275	0.2694	1.2277	1.1080
No log	30.0	90	1.2288	0.2636	1.2291	1.1086
No log	31.0	93	1.0049	0.2999	1.0052	1.0026
No log	32.0	96	1.2209	0.2840	1.2213	1.1051
No log	33.0	99	1.0944	0.3091	1.0947	1.0463
No log	34.0	102	1.1388	0.3010	1.1391	1.0673
No log	35.0	105	0.9628	0.3336	0.9631	0.9814
No log	36.0	108	0.9838	0.3286	0.9842	0.9921
No log	37.0	111	1.1118	0.3205	1.1122	1.0546
No log	38.0	114	0.9330	0.3542	0.9334	0.9661
No log	39.0	117	0.9929	0.3199	0.9933	0.9966
No log	40.0	120	0.9465	0.3285	0.9470	0.9731
No log	41.0	123	0.9933	0.3142	0.9937	0.9968
No log	42.0	126	0.9846	0.3533	0.9850	0.9925
No log	43.0	129	1.1523	0.3240	1.1527	1.0736
No log	44.0	132	0.8978	0.3655	0.8983	0.9478
No log	45.0	135	0.9389	0.3330	0.9395	0.9693
No log	46.0	138	0.8832	0.4029	0.8837	0.9401
No log	47.0	141	0.9999	0.3080	1.0004	1.0002
No log	48.0	144	0.8320	0.4550	0.8324	0.9124
No log	49.0	147	0.8339	0.4586	0.8343	0.9134
No log	50.0	150	1.1224	0.3248	1.1229	1.0596
No log	51.0	153	1.0110	0.3181	1.0114	1.0057
No log	52.0	156	0.8923	0.4325	0.8927	0.9448
No log	53.0	159	0.9847	0.3194	0.9852	0.9926
No log	54.0	162	0.8952	0.3801	0.8957	0.9464
No log	55.0	165	0.9367	0.3611	0.9372	0.9681
No log	56.0	168	0.8691	0.4335	0.8696	0.9325
No log	57.0	171	0.9042	0.3570	0.9046	0.9511
No log	58.0	174	0.8601	0.3800	0.8604	0.9276
No log	59.0	177	0.8915	0.3574	0.8920	0.9444
No log	60.0	180	0.8484	0.4519	0.8488	0.9213
No log	61.0	183	0.8963	0.3759	0.8968	0.9470
No log	62.0	186	0.8177	0.4432	0.8181	0.9045
No log	63.0	189	0.8058	0.4306	0.8061	0.8978
No log	64.0	192	0.8129	0.4416	0.8133	0.9018
No log	65.0	195	0.8932	0.3926	0.8935	0.9453
No log	66.0	198	0.8590	0.4052	0.8592	0.9269
No log	67.0	201	0.8826	0.3938	0.8828	0.9396
No log	68.0	204	0.9197	0.3579	0.9200	0.9591
No log	69.0	207	0.8965	0.4058	0.8969	0.9470

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold3

ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV10_k3_task1_organization_k3_k3_fold3