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	9.1625	0.0	9.1625	3.0270
No log	2.0	4	7.7809	0.0	7.7809	2.7894
No log	3.0	6	6.9099	0.0	6.9099	2.6287
No log	4.0	8	6.1732	-0.0072	6.1732	2.4846
5.9483	5.0	10	5.3514	0.0115	5.3514	2.3133
5.9483	6.0	12	4.5324	0.0039	4.5324	2.1289
5.9483	7.0	14	3.8697	0.0039	3.8697	1.9672
5.9483	8.0	16	3.0459	0.0	3.0459	1.7453
5.9483	9.0	18	2.3885	0.1313	2.3885	1.5455
3.1158	10.0	20	1.9021	0.0601	1.9021	1.3792
3.1158	11.0	22	1.4431	0.0316	1.4431	1.2013
3.1158	12.0	24	1.1866	0.0316	1.1866	1.0893
3.1158	13.0	26	1.2218	0.0484	1.2218	1.1054
3.1158	14.0	28	0.9643	0.0316	0.9643	0.9820
2.0265	15.0	30	1.0644	0.0484	1.0644	1.0317
2.0265	16.0	32	1.0606	0.1151	1.0606	1.0299
2.0265	17.0	34	1.2483	0.1708	1.2483	1.1173
2.0265	18.0	36	0.7999	0.3558	0.7999	0.8944
2.0265	19.0	38	0.7263	0.3720	0.7263	0.8522
1.6279	20.0	40	0.6799	0.4884	0.6799	0.8246
1.6279	21.0	42	0.8028	0.3656	0.8028	0.8960
1.6279	22.0	44	0.5921	0.4609	0.5921	0.7695
1.6279	23.0	46	0.6576	0.3207	0.6576	0.8109
1.6279	24.0	48	0.6375	0.3437	0.6375	0.7984
1.0095	25.0	50	0.5428	0.4462	0.5428	0.7367
1.0095	26.0	52	0.5538	0.4165	0.5538	0.7442
1.0095	27.0	54	0.6072	0.4420	0.6072	0.7793
1.0095	28.0	56	0.5988	0.4254	0.5988	0.7738
1.0095	29.0	58	0.5631	0.5061	0.5631	0.7504
0.4993	30.0	60	0.5970	0.5177	0.5970	0.7727
0.4993	31.0	62	0.6516	0.4809	0.6516	0.8072
0.4993	32.0	64	0.6090	0.5295	0.6090	0.7804
0.4993	33.0	66	0.5760	0.5727	0.5760	0.7590
0.4993	34.0	68	0.6543	0.4978	0.6543	0.8089
0.2852	35.0	70	0.7412	0.4490	0.7412	0.8609
0.2852	36.0	72	0.7386	0.4690	0.7386	0.8594
0.2852	37.0	74	0.6920	0.5261	0.6920	0.8319
0.2852	38.0	76	0.6365	0.5378	0.6365	0.7978
0.2852	39.0	78	0.7297	0.5032	0.7297	0.8542
0.2268	40.0	80	0.7388	0.4736	0.7388	0.8596
0.2268	41.0	82	0.6983	0.5111	0.6983	0.8356
0.2268	42.0	84	0.7665	0.4844	0.7665	0.8755
0.2268	43.0	86	0.6840	0.5170	0.6840	0.8270

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV8_k2_task1_organization_fold0

ASAP_FineTuningBERT_AugV8_k2_task1_organization_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV8_k2_task1_organization_fold0