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	9.2479	-0.0002	9.2461	3.0407
No log	2.0	4	6.7778	0.0	6.7763	2.6031
No log	3.0	6	5.3995	0.0130	5.3978	2.3233
No log	4.0	8	4.4006	0.0114	4.3996	2.0975
No log	5.0	10	3.3556	0.0076	3.3546	1.8315
No log	6.0	12	2.7222	0.0	2.7212	1.6496
No log	7.0	14	2.2669	0.1227	2.2661	1.5053
No log	8.0	16	1.7358	0.0524	1.7351	1.3172
No log	9.0	18	1.3893	0.0365	1.3886	1.1784
No log	10.0	20	1.1035	0.0202	1.1029	1.0502
No log	11.0	22	0.9008	0.0828	0.9003	0.9489
No log	12.0	24	0.7744	0.3774	0.7740	0.8798
No log	13.0	26	0.6902	0.3530	0.6900	0.8307
No log	14.0	28	0.6321	0.4771	0.6320	0.7950
No log	15.0	30	0.5820	0.4372	0.5820	0.7629
No log	16.0	32	0.5988	0.4518	0.5989	0.7739
No log	17.0	34	0.5668	0.5597	0.5668	0.7529
No log	18.0	36	0.5828	0.5614	0.5829	0.7635
No log	19.0	38	0.6088	0.6167	0.6089	0.7803
No log	20.0	40	0.6504	0.5964	0.6506	0.8066
No log	21.0	42	0.6594	0.6144	0.6594	0.8120
No log	22.0	44	0.6907	0.6204	0.6905	0.8310
No log	23.0	46	0.6117	0.6325	0.6116	0.7821
No log	24.0	48	0.6618	0.6464	0.6615	0.8133
No log	25.0	50	0.9330	0.5969	0.9326	0.9657
No log	26.0	52	0.6563	0.6301	0.6560	0.8100
No log	27.0	54	0.7105	0.6375	0.7101	0.8427
No log	28.0	56	0.8716	0.5968	0.8711	0.9333
No log	29.0	58	0.8359	0.6007	0.8354	0.9140
No log	30.0	60	0.6876	0.6118	0.6875	0.8291
No log	31.0	62	0.6942	0.6283	0.6940	0.8331
No log	32.0	64	0.8467	0.6071	0.8461	0.9198
No log	33.0	66	0.6639	0.6423	0.6637	0.8147
No log	34.0	68	0.6645	0.6453	0.6642	0.8150
No log	35.0	70	0.8321	0.6037	0.8314	0.9118
No log	36.0	72	0.7090	0.5951	0.7087	0.8418
No log	37.0	74	0.7984	0.5317	0.7984	0.8935
No log	38.0	76	0.6637	0.6260	0.6634	0.8145
No log	39.0	78	1.0644	0.5550	1.0636	1.0313
No log	40.0	80	0.8310	0.5913	0.8305	0.9113
No log	41.0	82	0.5537	0.6650	0.5534	0.7439
No log	42.0	84	0.6221	0.6650	0.6218	0.7885
No log	43.0	86	0.8535	0.6011	0.8529	0.9235
No log	44.0	88	0.8607	0.6005	0.8602	0.9275
No log	45.0	90	0.7238	0.6027	0.7235	0.8506
No log	46.0	92	0.6774	0.6241	0.6772	0.8229
No log	47.0	94	0.7239	0.6358	0.7235	0.8506
No log	48.0	96	0.6671	0.6554	0.6668	0.8166
No log	49.0	98	0.6092	0.6611	0.6089	0.7804
No log	50.0	100	0.6534	0.6593	0.6531	0.8082
No log	51.0	102	0.7068	0.6484	0.7064	0.8405
No log	52.0	104	0.6304	0.6618	0.6302	0.7938
No log	53.0	106	0.6511	0.6518	0.6509	0.8068
No log	54.0	108	0.8042	0.6193	0.8037	0.8965
No log	55.0	110	0.8193	0.6187	0.8189	0.9049
No log	56.0	112	0.6234	0.6399	0.6232	0.7894
No log	57.0	114	0.6098	0.6048	0.6097	0.7809
No log	58.0	116	0.5851	0.6128	0.5850	0.7649
No log	59.0	118	0.6132	0.6596	0.6129	0.7829
No log	60.0	120	0.8943	0.5817	0.8938	0.9454
No log	61.0	122	0.9149	0.5808	0.9143	0.9562

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

Version2ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold3