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	10.3618	0.0071	10.3592	3.2186
No log	2.0	6	9.2517	0.0	9.2492	3.0413
No log	3.0	9	7.3314	0.0	7.3291	2.7072
No log	4.0	12	6.3898	0.0	6.3875	2.5274
No log	5.0	15	4.9011	0.0	4.8990	2.2134
No log	6.0	18	3.4311	0.0	3.4292	1.8518
No log	7.0	21	2.4554	0.0	2.4537	1.5664
No log	8.0	24	1.8731	0.0790	1.8715	1.3680
No log	9.0	27	1.4858	0.0545	1.4842	1.2183
No log	10.0	30	1.2308	0.1006	1.2293	1.1088
No log	11.0	33	1.9021	0.1973	1.9005	1.3786
No log	12.0	36	1.0049	0.1295	1.0037	1.0018
No log	13.0	39	1.4772	0.1951	1.4760	1.2149
No log	14.0	42	1.3577	0.2140	1.3569	1.1648
No log	15.0	45	1.2898	0.2384	1.2891	1.1354
No log	16.0	48	1.3419	0.2625	1.3413	1.1581
No log	17.0	51	0.7572	0.5100	0.7563	0.8697
No log	18.0	54	1.2881	0.3599	1.2875	1.1347
No log	19.0	57	0.9853	0.4442	0.9846	0.9923
No log	20.0	60	1.4804	0.3447	1.4798	1.2165
No log	21.0	63	1.0990	0.4382	1.0981	1.0479
No log	22.0	66	0.9747	0.4749	0.9737	0.9868
No log	23.0	69	1.1732	0.4470	1.1722	1.0827
No log	24.0	72	1.0038	0.4837	1.0028	1.0014
No log	25.0	75	0.9402	0.4921	0.9392	0.9691
No log	26.0	78	1.4487	0.3557	1.4477	1.2032
No log	27.0	81	0.9611	0.4872	0.9602	0.9799
No log	28.0	84	1.5708	0.3370	1.5700	1.2530
No log	29.0	87	1.3359	0.3957	1.3352	1.1555
No log	30.0	90	0.9814	0.4581	0.9805	0.9902
No log	31.0	93	1.0012	0.4718	1.0004	1.0002
No log	32.0	96	1.0745	0.4288	1.0736	1.0362
No log	33.0	99	0.7332	0.5834	0.7322	0.8557
No log	34.0	102	1.1652	0.3938	1.1641	1.0789
No log	35.0	105	0.7473	0.5641	0.7463	0.8639
No log	36.0	108	1.1373	0.3767	1.1361	1.0659
No log	37.0	111	0.7047	0.5556	0.7038	0.8389
No log	38.0	114	1.1578	0.3958	1.1569	1.0756
No log	39.0	117	1.0022	0.4805	1.0015	1.0007
No log	40.0	120	0.6919	0.5884	0.6912	0.8314
No log	41.0	123	1.2032	0.3932	1.2024	1.0965
No log	42.0	126	0.7838	0.5465	0.7831	0.8849
No log	43.0	129	0.8219	0.5134	0.8211	0.9061
No log	44.0	132	1.0506	0.4088	1.0498	1.0246
No log	45.0	135	0.9274	0.4759	0.9268	0.9627
No log	46.0	138	0.9494	0.4597	0.9488	0.9741
No log	47.0	141	0.8196	0.5450	0.8191	0.9050
No log	48.0	144	1.1087	0.4096	1.1080	1.0526
No log	49.0	147	0.8782	0.4881	0.8775	0.9368
No log	50.0	150	0.7482	0.5515	0.7475	0.8646
No log	51.0	153	0.9616	0.4438	0.9609	0.9803
No log	52.0	156	0.8359	0.5355	0.8353	0.9139
No log	53.0	159	0.8652	0.5390	0.8647	0.9299
No log	54.0	162	0.8613	0.5314	0.8607	0.9277
No log	55.0	165	0.6847	0.5703	0.6840	0.8270
No log	56.0	168	0.8687	0.5203	0.8680	0.9317
No log	57.0	171	0.7911	0.5505	0.7905	0.8891
No log	58.0	174	0.6817	0.5797	0.6811	0.8253
No log	59.0	177	0.8098	0.5306	0.8091	0.8995
No log	60.0	180	0.6970	0.5687	0.6963	0.8344

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV6_k3_task1_organization_k3_k3_fold1

ASAP_FineTuningBERT_AugV6_k3_task1_organization_k3_k3_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV6_k3_task1_organization_k3_k3_fold1