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	10.5642	0.0037	10.5642	3.2503
No log	2.0	6	8.6116	0.0018	8.6116	2.9345
No log	3.0	9	6.4628	0.0	6.4628	2.5422
No log	4.0	12	3.9522	0.0079	3.9522	1.9880
No log	5.0	15	2.6376	0.0117	2.6376	1.6241
No log	6.0	18	1.5873	0.0484	1.5873	1.2599
No log	7.0	21	1.0383	0.0213	1.0383	1.0190
No log	8.0	24	0.8479	0.2253	0.8479	0.9208
No log	9.0	27	0.8691	0.0738	0.8691	0.9322
No log	10.0	30	0.9803	0.0605	0.9803	0.9901
No log	11.0	33	1.2063	0.0454	1.2063	1.0983
No log	12.0	36	1.2072	0.2639	1.2072	1.0987
No log	13.0	39	1.4495	0.2076	1.4495	1.2039
No log	14.0	42	1.2534	0.3234	1.2534	1.1196
No log	15.0	45	1.5792	0.3026	1.5792	1.2567
No log	16.0	48	0.6374	0.5965	0.6374	0.7984
No log	17.0	51	0.7381	0.6139	0.7381	0.8591
No log	18.0	54	0.7143	0.6254	0.7143	0.8452
No log	19.0	57	0.6790	0.6206	0.6790	0.8240
No log	20.0	60	1.1922	0.5337	1.1922	1.0919
No log	21.0	63	1.0081	0.5768	1.0081	1.0040
No log	22.0	66	1.4770	0.4888	1.4770	1.2153
No log	23.0	69	0.7842	0.6193	0.7842	0.8855
No log	24.0	72	1.0556	0.5618	1.0556	1.0274
No log	25.0	75	1.2464	0.5225	1.2464	1.1164
No log	26.0	78	0.8323	0.6051	0.8323	0.9123
No log	27.0	81	0.8708	0.5989	0.8708	0.9332
No log	28.0	84	0.7313	0.6293	0.7313	0.8552
No log	29.0	87	1.2625	0.5468	1.2625	1.1236
No log	30.0	90	0.9079	0.6118	0.9079	0.9528
No log	31.0	93	1.3186	0.5169	1.3186	1.1483
No log	32.0	96	0.8961	0.5870	0.8961	0.9466
No log	33.0	99	0.6909	0.6460	0.6909	0.8312
No log	34.0	102	1.1374	0.5434	1.1374	1.0665
No log	35.0	105	0.7621	0.6382	0.7621	0.8730
No log	36.0	108	0.7763	0.6362	0.7763	0.8811
No log	37.0	111	1.4744	0.4850	1.4744	1.2143
No log	38.0	114	1.2728	0.5310	1.2728	1.1282
No log	39.0	117	0.6572	0.6472	0.6572	0.8107
No log	40.0	120	0.6314	0.6520	0.6314	0.7946
No log	41.0	123	1.4547	0.4696	1.4547	1.2061
No log	42.0	126	1.5915	0.4644	1.5915	1.2616
No log	43.0	129	0.7694	0.6367	0.7694	0.8772
No log	44.0	132	0.8348	0.6148	0.8348	0.9137
No log	45.0	135	0.8676	0.5754	0.8676	0.9314
No log	46.0	138	1.2788	0.4920	1.2788	1.1308
No log	47.0	141	1.2876	0.4930	1.2876	1.1347
No log	48.0	144	0.7472	0.6286	0.7472	0.8644
No log	49.0	147	0.7756	0.6319	0.7756	0.8807
No log	50.0	150	1.1836	0.5475	1.1836	1.0879
No log	51.0	153	1.1934	0.5484	1.1934	1.0924
No log	52.0	156	0.8628	0.6152	0.8628	0.9289
No log	53.0	159	0.8609	0.5897	0.8609	0.9279
No log	54.0	162	1.0921	0.5294	1.0921	1.0450
No log	55.0	165	0.7322	0.6292	0.7322	0.8557
No log	56.0	168	0.6868	0.6482	0.6868	0.8287
No log	57.0	171	0.9155	0.5919	0.9155	0.9568
No log	58.0	174	0.7859	0.6446	0.7859	0.8865
No log	59.0	177	0.9327	0.6007	0.9327	0.9657
No log	60.0	180	0.9344	0.5891	0.9344	0.9667

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4

Version2ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold4