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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	8	5.8949	0.0070	5.8949	2.4279
No log	2.0	16	2.5954	0.0046	2.5954	1.6110
No log	3.0	24	1.1638	0.0316	1.1638	1.0788
No log	4.0	32	1.9457	0.1689	1.9457	1.3949
No log	5.0	40	2.1170	0.1592	2.1170	1.4550
No log	6.0	48	1.6340	0.1477	1.6340	1.2783
No log	7.0	56	1.6427	0.0926	1.6427	1.2817
No log	8.0	64	2.2801	0.0323	2.2801	1.5100
No log	9.0	72	2.3703	0.0635	2.3703	1.5396
No log	10.0	80	2.1234	0.0828	2.1234	1.4572
No log	11.0	88	2.3111	0.0493	2.3111	1.5202
No log	12.0	96	2.0426	0.0531	2.0426	1.4292
No log	13.0	104	1.7923	0.0571	1.7923	1.3387
No log	14.0	112	1.7973	0.0787	1.7973	1.3406
No log	15.0	120	1.5612	0.1069	1.5612	1.2495
No log	16.0	128	1.2255	0.1688	1.2255	1.1070
No log	17.0	136	1.1983	0.1358	1.1983	1.0947
No log	18.0	144	1.1537	0.2446	1.1537	1.0741
No log	19.0	152	1.3896	0.1533	1.3896	1.1788
No log	20.0	160	1.2846	0.2184	1.2846	1.1334
No log	21.0	168	1.0334	0.2902	1.0334	1.0166
No log	22.0	176	1.0127	0.3175	1.0127	1.0063
No log	23.0	184	1.3020	0.2153	1.3020	1.1411
No log	24.0	192	1.1690	0.2379	1.1690	1.0812
No log	25.0	200	1.0032	0.3330	1.0032	1.0016
No log	26.0	208	1.1421	0.2852	1.1421	1.0687
No log	27.0	216	1.1353	0.2693	1.1353	1.0655
No log	28.0	224	1.1410	0.2525	1.1410	1.0682
No log	29.0	232	0.9681	0.3216	0.9681	0.9839
No log	30.0	240	0.9309	0.3423	0.9309	0.9648
No log	31.0	248	0.9582	0.3146	0.9582	0.9789
No log	32.0	256	1.1308	0.2760	1.1308	1.0634
No log	33.0	264	1.0456	0.2967	1.0456	1.0226
No log	34.0	272	1.1701	0.2833	1.1701	1.0817
No log	35.0	280	1.1634	0.2793	1.1634	1.0786
No log	36.0	288	0.9792	0.3251	0.9792	0.9896
No log	37.0	296	1.1208	0.3053	1.1208	1.0587
No log	38.0	304	1.0093	0.3204	1.0093	1.0046
No log	39.0	312	1.0026	0.3152	1.0026	1.0013
No log	40.0	320	1.0502	0.2824	1.0502	1.0248
No log	41.0	328	0.9520	0.3407	0.9520	0.9757
No log	42.0	336	0.9705	0.3330	0.9705	0.9852
No log	43.0	344	1.0521	0.2977	1.0521	1.0257
No log	44.0	352	1.0347	0.2932	1.0347	1.0172
No log	45.0	360	0.9778	0.2762	0.9778	0.9889
No log	46.0	368	0.9545	0.3073	0.9545	0.9770
No log	47.0	376	0.9324	0.3184	0.9324	0.9656
No log	48.0	384	0.8842	0.3409	0.8842	0.9403
No log	49.0	392	0.9168	0.3265	0.9168	0.9575
No log	50.0	400	1.0023	0.3083	1.0023	1.0011
No log	51.0	408	1.0805	0.2896	1.0805	1.0395
No log	52.0	416	0.9830	0.2964	0.9830	0.9915
No log	53.0	424	0.9526	0.2941	0.9526	0.9760
No log	54.0	432	1.1512	0.2809	1.1512	1.0729
No log	55.0	440	1.1085	0.2774	1.1085	1.0529
No log	56.0	448	0.9407	0.2996	0.9407	0.9699
No log	57.0	456	0.9997	0.3047	0.9997	0.9998
No log	58.0	464	1.1044	0.2538	1.1044	1.0509
No log	59.0	472	1.0024	0.3128	1.0024	1.0012
No log	60.0	480	1.1532	0.2907	1.1532	1.0739

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k15_task1_organization_k15_k15_fold0

Version24NewTestASAP_FineTuningBERT_AugV24_k15_task1_organization_k15_k15_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k15_task1_organization_k15_k15_fold0