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	2	9.7380	0.0	9.7356	3.1202
No log	2.0	4	9.2394	0.0	9.2370	3.0392
No log	3.0	6	8.4395	0.0	8.4371	2.9047
No log	4.0	8	7.1375	0.0	7.1352	2.6712
No log	5.0	10	5.6136	0.0217	5.6114	2.3688
No log	6.0	12	4.3637	0.0079	4.3616	2.0884
No log	7.0	14	3.5078	0.0	3.5057	1.8724
No log	8.0	16	2.8392	0.0	2.8374	1.6844
No log	9.0	18	2.2756	0.1683	2.2738	1.5079
No log	10.0	20	1.8005	0.0545	1.7988	1.3412
No log	11.0	22	1.5144	0.0106	1.5129	1.2300
No log	12.0	24	1.1730	0.0	1.1715	1.0824
No log	13.0	26	0.9745	0.0	0.9732	0.9865
No log	14.0	28	0.9368	0.1023	0.9356	0.9673
No log	15.0	30	0.8545	0.0749	0.8534	0.9238
No log	16.0	32	0.8718	0.0749	0.8708	0.9332
No log	17.0	34	1.1096	0.0429	1.1087	1.0530
No log	18.0	36	1.1858	0.0571	1.1851	1.0886
No log	19.0	38	0.9900	0.2031	0.9893	0.9946
No log	20.0	40	1.0810	0.3517	1.0803	1.0394
No log	21.0	42	0.7704	0.4740	0.7699	0.8774
No log	22.0	44	0.6939	0.5250	0.6934	0.8327
No log	23.0	46	0.7039	0.5662	0.7035	0.8387
No log	24.0	48	0.5002	0.6477	0.4996	0.7068
No log	25.0	50	0.8111	0.5795	0.8109	0.9005
No log	26.0	52	0.5545	0.6622	0.5542	0.7444
No log	27.0	54	0.6661	0.6632	0.6659	0.8160
No log	28.0	56	0.8407	0.6192	0.8405	0.9168
No log	29.0	58	0.4336	0.7054	0.4330	0.6580
No log	30.0	60	0.6843	0.6389	0.6838	0.8269
No log	31.0	62	1.8513	0.4059	1.8512	1.3606
No log	32.0	64	1.1520	0.5260	1.1517	1.0732
No log	33.0	66	0.4535	0.6638	0.4528	0.6729
No log	34.0	68	0.6189	0.6286	0.6183	0.7863
No log	35.0	70	1.4623	0.4718	1.4620	1.2091
No log	36.0	72	1.0039	0.5803	1.0036	1.0018
No log	37.0	74	0.4587	0.7143	0.4581	0.6768
No log	38.0	76	0.6319	0.6668	0.6314	0.7946
No log	39.0	78	1.4170	0.5064	1.4168	1.1903
No log	40.0	80	1.0594	0.5851	1.0591	1.0291
No log	41.0	82	0.5483	0.6996	0.5477	0.7401
No log	42.0	84	0.7376	0.6222	0.7372	0.8586
No log	43.0	86	1.3200	0.4986	1.3198	1.1488
No log	44.0	88	0.9909	0.5860	0.9906	0.9953
No log	45.0	90	0.5710	0.6675	0.5704	0.7553
No log	46.0	92	0.7463	0.6160	0.7459	0.8637
No log	47.0	94	1.0938	0.5708	1.0936	1.0457
No log	48.0	96	0.9967	0.6015	0.9964	0.9982
No log	49.0	98	0.6503	0.6544	0.6499	0.8062
No log	50.0	100	0.6474	0.6481	0.6470	0.8044
No log	51.0	102	1.1831	0.5501	1.1828	1.0876
No log	52.0	104	1.3296	0.5021	1.3293	1.1530
No log	53.0	106	0.9352	0.5885	0.9348	0.9669
No log	54.0	108	0.7692	0.6203	0.7688	0.8768
No log	55.0	110	1.1076	0.5527	1.1073	1.0523
No log	56.0	112	1.1096	0.5571	1.1093	1.0532
No log	57.0	114	0.8542	0.6118	0.8539	0.9240

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version5ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1

Version5ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version5ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold1