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	0.5	2	10.6551	-0.0055	10.6551	3.2642
No log	1.0	4	9.3998	0.0018	9.3998	3.0659
No log	1.5	6	8.5348	0.0018	8.5348	2.9214
No log	2.0	8	7.6490	0.0018	7.6490	2.7657
7.3106	2.5	10	6.9636	0.0	6.9636	2.6389
7.3106	3.0	12	6.0798	0.0	6.0798	2.4657
7.3106	3.5	14	5.3738	-0.0001	5.3738	2.3181
7.3106	4.0	16	4.3776	0.0079	4.3776	2.0923
7.3106	4.5	18	3.6539	0.0040	3.6539	1.9115
3.6722	5.0	20	3.1273	0.0040	3.1273	1.7684
3.6722	5.5	22	2.4647	0.0	2.4647	1.5699
3.6722	6.0	24	2.1820	0.0962	2.1820	1.4772
3.6722	6.5	26	1.7263	0.0239	1.7263	1.3139
3.6722	7.0	28	1.4220	0.0213	1.4220	1.1925
2.1624	7.5	30	1.2235	0.0213	1.2235	1.1061
2.1624	8.0	32	1.1225	0.0213	1.1225	1.0595
2.1624	8.5	34	1.2824	0.0317	1.2824	1.1324
2.1624	9.0	36	1.2670	0.0626	1.2670	1.1256
2.1624	9.5	38	1.4089	0.1007	1.4089	1.1870
1.7529	10.0	40	1.6309	0.1979	1.6309	1.2771
1.7529	10.5	42	1.2237	0.1450	1.2237	1.1062
1.7529	11.0	44	1.1384	0.1663	1.1384	1.0670
1.7529	11.5	46	1.4952	0.2174	1.4952	1.2228
1.7529	12.0	48	1.2157	0.2620	1.2157	1.1026
1.5899	12.5	50	0.8029	0.4588	0.8029	0.8961
1.5899	13.0	52	1.0831	0.3626	1.0831	1.0407
1.5899	13.5	54	0.7906	0.4625	0.7906	0.8892
1.5899	14.0	56	0.7230	0.3870	0.7230	0.8503
1.5899	14.5	58	0.6752	0.4900	0.6752	0.8217
1.1781	15.0	60	1.2732	0.3767	1.2732	1.1284
1.1781	15.5	62	0.8177	0.5053	0.8177	0.9043
1.1781	16.0	64	0.6513	0.4603	0.6513	0.8070
1.1781	16.5	66	0.6487	0.4778	0.6487	0.8054
1.1781	17.0	68	1.0368	0.4193	1.0368	1.0182
0.919	17.5	70	0.6965	0.4902	0.6965	0.8346
0.919	18.0	72	0.7103	0.4844	0.7103	0.8428
0.919	18.5	74	0.6683	0.5540	0.6683	0.8175
0.919	19.0	76	0.7479	0.5456	0.7479	0.8648
0.919	19.5	78	0.6432	0.5743	0.6432	0.8020
0.5635	20.0	80	0.6621	0.5585	0.6621	0.8137
0.5635	20.5	82	0.7227	0.5564	0.7227	0.8501
0.5635	21.0	84	0.6786	0.5778	0.6786	0.8238
0.5635	21.5	86	0.6798	0.5482	0.6798	0.8245
0.5635	22.0	88	0.6770	0.6057	0.6770	0.8228
0.2963	22.5	90	0.6661	0.6067	0.6661	0.8161
0.2963	23.0	92	0.6876	0.5485	0.6876	0.8292
0.2963	23.5	94	0.6650	0.5927	0.6650	0.8155
0.2963	24.0	96	0.6599	0.5889	0.6599	0.8123
0.2963	24.5	98	0.6240	0.6102	0.6240	0.7899
0.256	25.0	100	0.6041	0.6219	0.6041	0.7772
0.256	25.5	102	0.5972	0.6155	0.5972	0.7728
0.256	26.0	104	0.5639	0.6407	0.5639	0.7509
0.256	26.5	106	0.5869	0.6271	0.5869	0.7661
0.256	27.0	108	0.9721	0.4927	0.9721	0.9859
0.198	27.5	110	0.6480	0.5891	0.6480	0.8050
0.198	28.0	112	0.6183	0.6164	0.6183	0.7863
0.198	28.5	114	0.5563	0.6325	0.5563	0.7458
0.198	29.0	116	0.6943	0.5662	0.6943	0.8333
0.198	29.5	118	0.6054	0.6342	0.6054	0.7781
0.215	30.0	120	0.8387	0.5590	0.8387	0.9158
0.215	30.5	122	0.7214	0.5938	0.7214	0.8494
0.215	31.0	124	0.7658	0.5782	0.7658	0.8751

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV8_k4_task1_organization_fold4

ASAP_FineTuningBERT_AugV8_k4_task1_organization_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV8_k4_task1_organization_fold4