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	0.25	2	10.3423	0.0092	10.3423	3.2159
No log	0.5	4	9.0145	0.0037	9.0145	3.0024
No log	0.75	6	7.7197	0.0018	7.7197	2.7784
No log	1.0	8	6.8532	0.0018	6.8532	2.6179
6.4018	1.25	10	6.0474	0.0018	6.0474	2.4591
6.4018	1.5	12	5.1275	0.0128	5.1275	2.2644
6.4018	1.75	14	4.1454	0.0040	4.1454	2.0360
6.4018	2.0	16	2.5905	0.0052	2.5905	1.6095
6.4018	2.25	18	2.1296	0.1216	2.1296	1.4593
2.7798	2.5	20	1.6199	0.0544	1.6199	1.2728
2.7798	2.75	22	1.5879	0.0238	1.5879	1.2601
2.7798	3.0	24	1.2737	0.0238	1.2737	1.1286
2.7798	3.25	26	1.1221	0.0212	1.1221	1.0593
2.7798	3.5	28	1.2226	0.0316	1.2226	1.1057
1.7553	3.75	30	1.5063	0.0420	1.5063	1.2273
1.7553	4.0	32	2.0288	0.1601	2.0288	1.4244
1.7553	4.25	34	1.8433	0.1109	1.8433	1.3577
1.7553	4.5	36	1.5115	0.0757	1.5115	1.2294
1.7553	4.75	38	1.3201	0.0445	1.3201	1.1490
1.7333	5.0	40	1.2861	0.0558	1.2861	1.1341
1.7333	5.25	42	1.1777	0.0509	1.1777	1.0852
1.7333	5.5	44	1.1542	0.0445	1.1542	1.0743
1.7333	5.75	46	1.0644	0.0509	1.0644	1.0317
1.7333	6.0	48	1.8744	0.1643	1.8744	1.3691
1.7201	6.25	50	2.1763	0.1570	2.1763	1.4752
1.7201	6.5	52	1.7457	0.1783	1.7457	1.3213
1.7201	6.75	54	1.1182	0.0610	1.1182	1.0574
1.7201	7.0	56	1.2662	0.0445	1.2662	1.1253
1.7201	7.25	58	0.9480	0.0666	0.9480	0.9737
1.5405	7.5	60	1.0434	0.1008	1.0434	1.0215
1.5405	7.75	62	1.1104	0.0958	1.1104	1.0537
1.5405	8.0	64	0.9350	0.1352	0.9350	0.9669
1.5405	8.25	66	0.8286	0.3014	0.8286	0.9103
1.5405	8.5	68	0.8852	0.2636	0.8852	0.9409
1.4268	8.75	70	0.9632	0.2423	0.9632	0.9814
1.4268	9.0	72	0.8796	0.3042	0.8796	0.9379
1.4268	9.25	74	0.7551	0.4221	0.7551	0.8690
1.4268	9.5	76	0.8424	0.3961	0.8424	0.9178
1.4268	9.75	78	1.2577	0.2541	1.2577	1.1215
1.0054	10.0	80	0.8085	0.3728	0.8085	0.8992
1.0054	10.25	82	0.5684	0.4533	0.5684	0.7540
1.0054	10.5	84	0.5676	0.5215	0.5676	0.7534
1.0054	10.75	86	0.6962	0.4452	0.6962	0.8344
1.0054	11.0	88	0.5826	0.4438	0.5826	0.7633
0.6941	11.25	90	0.6648	0.3708	0.6648	0.8154
0.6941	11.5	92	0.5580	0.4754	0.5580	0.7470
0.6941	11.75	94	0.6176	0.5360	0.6176	0.7859
0.6941	12.0	96	0.5918	0.5448	0.5918	0.7693
0.6941	12.25	98	0.5433	0.5664	0.5433	0.7371
0.4638	12.5	100	0.5908	0.5526	0.5908	0.7686
0.4638	12.75	102	0.5956	0.5379	0.5956	0.7718
0.4638	13.0	104	0.6086	0.5680	0.6086	0.7801
0.4638	13.25	106	0.5973	0.5890	0.5973	0.7728
0.4638	13.5	108	0.5626	0.5852	0.5626	0.7501
0.2885	13.75	110	0.6225	0.5863	0.6225	0.7890
0.2885	14.0	112	0.6296	0.5823	0.6296	0.7934
0.2885	14.25	114	0.5746	0.6220	0.5746	0.7580
0.2885	14.5	116	0.5443	0.6016	0.5443	0.7378
0.2885	14.75	118	0.5433	0.6266	0.5433	0.7371
0.2075	15.0	120	0.5344	0.6296	0.5344	0.7310
0.2075	15.25	122	0.5613	0.6253	0.5613	0.7492
0.2075	15.5	124	0.5944	0.6347	0.5944	0.7710
0.2075	15.75	126	0.6619	0.5735	0.6619	0.8136
0.2075	16.0	128	0.6595	0.5727	0.6595	0.8121
0.176	16.25	130	0.6753	0.5760	0.6753	0.8218
0.176	16.5	132	0.6061	0.5965	0.6061	0.7785
0.176	16.75	134	0.6268	0.5990	0.6268	0.7917
0.176	17.0	136	0.5943	0.5971	0.5943	0.7709
0.176	17.25	138	0.5691	0.6009	0.5691	0.7544
0.17	17.5	140	0.6318	0.6054	0.6318	0.7949
0.17	17.75	142	0.7145	0.5573	0.7145	0.8453
0.17	18.0	144	0.6089	0.5812	0.6089	0.7803

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV8_k10_task1_organization_fold4

ASAP_FineTuningBERT_AugV8_k10_task1_organization_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV8_k10_task1_organization_fold4