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	7.7320	0.0	7.7322	2.7807
No log	2.0	4	6.5940	0.0	6.5943	2.5679
No log	3.0	6	5.6618	0.0048	5.6620	2.3795
No log	4.0	8	4.7881	0.0039	4.7884	2.1882
No log	5.0	10	3.9171	0.0	3.9175	1.9793
No log	6.0	12	3.1345	0.0	3.1349	1.7706
No log	7.0	14	2.3327	0.1506	2.3331	1.5275
No log	8.0	16	1.7378	0.0280	1.7383	1.3184
No log	9.0	18	1.3818	0.0107	1.3822	1.1757
No log	10.0	20	1.1358	0.0107	1.1362	1.0659
No log	11.0	22	0.9568	0.0107	0.9572	0.9784
No log	12.0	24	0.8220	0.2907	0.8224	0.9069
No log	13.0	26	0.7650	0.1595	0.7654	0.8749
No log	14.0	28	0.7036	0.3979	0.7037	0.8389
No log	15.0	30	0.6367	0.3075	0.6367	0.7980
No log	16.0	32	0.7157	0.1944	0.7157	0.8460
No log	17.0	34	0.5503	0.3912	0.5499	0.7416
No log	18.0	36	0.5160	0.4699	0.5155	0.7180
No log	19.0	38	0.6411	0.5171	0.6404	0.8003
No log	20.0	40	0.5233	0.6016	0.5226	0.7229
No log	21.0	42	0.5272	0.6063	0.5264	0.7256
No log	22.0	44	1.1530	0.4188	1.1518	1.0732
No log	23.0	46	1.0332	0.4663	1.0319	1.0158
No log	24.0	48	0.5759	0.6269	0.5753	0.7585
No log	25.0	50	0.5979	0.6156	0.5973	0.7729
No log	26.0	52	0.8314	0.5867	0.8303	0.9112
No log	27.0	54	0.6063	0.6349	0.6057	0.7783
No log	28.0	56	0.6304	0.6388	0.6301	0.7938
No log	29.0	58	0.6485	0.6289	0.6480	0.8050
No log	30.0	60	0.5825	0.6489	0.5822	0.7630
No log	31.0	62	0.9695	0.5337	0.9687	0.9842
No log	32.0	64	0.9009	0.5280	0.9001	0.9487
No log	33.0	66	0.5234	0.6296	0.5231	0.7232
No log	34.0	68	0.7458	0.5563	0.7452	0.8632
No log	35.0	70	0.6661	0.5899	0.6656	0.8158
No log	36.0	72	0.5227	0.6241	0.5225	0.7228
No log	37.0	74	0.6372	0.6039	0.6366	0.7979
No log	38.0	76	0.6157	0.6201	0.6151	0.7843
No log	39.0	78	0.5219	0.6323	0.5216	0.7222
No log	40.0	80	0.6498	0.5981	0.6492	0.8057
No log	41.0	82	0.6356	0.6026	0.6350	0.7969
No log	42.0	84	0.5145	0.6390	0.5141	0.7170
No log	43.0	86	0.6482	0.5998	0.6476	0.8047
No log	44.0	88	0.6227	0.6147	0.6221	0.7887
No log	45.0	90	0.5227	0.6303	0.5224	0.7227
No log	46.0	92	0.6822	0.5859	0.6816	0.8256
No log	47.0	94	0.6790	0.5849	0.6784	0.8237
No log	48.0	96	0.5446	0.6453	0.5442	0.7377
No log	49.0	98	0.6193	0.6122	0.6188	0.7866
No log	50.0	100	0.6710	0.5987	0.6704	0.8188

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2