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	7.2814	0.0	7.2820	2.6985
No log	2.0	6	4.7121	0.0050	4.7126	2.1708
No log	3.0	9	3.3894	0.0	3.3897	1.8411
No log	4.0	12	2.4764	0.0128	2.4768	1.5738
No log	5.0	15	1.7241	0.0107	1.7246	1.3132
No log	6.0	18	1.2555	0.0	1.2560	1.1207
No log	7.0	21	0.9616	0.0	0.9621	0.9808
No log	8.0	24	0.8405	0.1043	0.8409	0.9170
No log	9.0	27	0.8187	0.0533	0.8191	0.9051
No log	10.0	30	0.9427	0.0164	0.9431	0.9711
No log	11.0	33	1.2357	0.0045	1.2360	1.1118
No log	12.0	36	1.1528	0.3131	1.1527	1.0737
No log	13.0	39	1.5705	0.1729	1.5701	1.2530
No log	14.0	42	0.9110	0.4247	0.9102	0.9540
No log	15.0	45	0.7416	0.5579	0.7406	0.8606
No log	16.0	48	1.1245	0.4980	1.1231	1.0598
No log	17.0	51	1.0681	0.5020	1.0665	1.0327
No log	18.0	54	0.6686	0.5919	0.6677	0.8171
No log	19.0	57	1.1446	0.4592	1.1432	1.0692
No log	20.0	60	0.6816	0.5711	0.6806	0.8250
No log	21.0	63	0.7534	0.5450	0.7524	0.8674
No log	22.0	66	0.6291	0.6230	0.6283	0.7926
No log	23.0	69	0.8071	0.5741	0.8062	0.8979
No log	24.0	72	1.1404	0.4797	1.1395	1.0675
No log	25.0	75	0.5943	0.6054	0.5938	0.7706
No log	26.0	78	0.6701	0.5663	0.6694	0.8182
No log	27.0	81	1.7763	0.3928	1.7751	1.3323
No log	28.0	84	0.8272	0.5768	0.8263	0.9090
No log	29.0	87	0.5757	0.6379	0.5752	0.7584
No log	30.0	90	1.0225	0.5096	1.0215	1.0107
No log	31.0	93	0.6451	0.6095	0.6444	0.8028
No log	32.0	96	0.7859	0.5590	0.7851	0.8861
No log	33.0	99	1.2838	0.4214	1.2827	1.1326
No log	34.0	102	0.7288	0.5842	0.7280	0.8532
No log	35.0	105	0.8121	0.5644	0.8112	0.9007
No log	36.0	108	0.8119	0.5714	0.8110	0.9005
No log	37.0	111	0.6627	0.5866	0.6620	0.8136
No log	38.0	114	0.8105	0.5321	0.8097	0.8999
No log	39.0	117	0.7333	0.5530	0.7326	0.8559
No log	40.0	120	0.7724	0.5577	0.7716	0.8784
No log	41.0	123	0.8015	0.5529	0.8006	0.8948
No log	42.0	126	0.6496	0.5984	0.6489	0.8056
No log	43.0	129	0.6248	0.6065	0.6241	0.7900
No log	44.0	132	0.7603	0.5795	0.7593	0.8714
No log	45.0	135	0.7396	0.5816	0.7387	0.8595
No log	46.0	138	0.6211	0.6212	0.6205	0.7877
No log	47.0	141	0.8058	0.5443	0.8048	0.8971
No log	48.0	144	0.9698	0.4922	0.9688	0.9843
No log	49.0	147	0.9783	0.4974	0.9773	0.9886

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_fold2

Version2ASAP_FineTuningBERT_AugV12_k10_task1_organization_k10_k10_fold2

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_fold2