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	4	7.2940	0.0	7.2926	2.7005
No log	2.0	8	4.4314	0.0107	4.4302	2.1048
No log	3.0	12	2.6113	0.0582	2.6104	1.6157
No log	4.0	16	1.7246	0.0391	1.7238	1.3129
No log	5.0	20	1.2574	0.0391	1.2568	1.1211
No log	6.0	24	1.6113	0.1212	1.6107	1.2691
No log	7.0	28	1.2986	0.1060	1.2982	1.1394
No log	8.0	32	1.2467	0.1195	1.2464	1.1164
No log	9.0	36	1.1740	0.2432	1.1741	1.0836
No log	10.0	40	0.9813	0.2738	0.9815	0.9907
No log	11.0	44	1.1878	0.2468	1.1879	1.0899
No log	12.0	48	1.6310	0.1953	1.6310	1.2771
No log	13.0	52	0.8130	0.3279	0.8134	0.9019
No log	14.0	56	1.2795	0.2345	1.2797	1.1312
No log	15.0	60	0.9415	0.3626	0.9421	0.9706
No log	16.0	64	1.3866	0.2548	1.3867	1.1776
No log	17.0	68	1.0836	0.2531	1.0838	1.0411
No log	18.0	72	0.9986	0.3368	0.9990	0.9995
No log	19.0	76	1.6068	0.2307	1.6068	1.2676
No log	20.0	80	1.1467	0.3021	1.1469	1.0709
No log	21.0	84	1.0634	0.3186	1.0638	1.0314
No log	22.0	88	1.3762	0.2416	1.3764	1.1732
No log	23.0	92	1.1087	0.2889	1.1090	1.0531
No log	24.0	96	1.0866	0.3378	1.0871	1.0426
No log	25.0	100	1.4492	0.2303	1.4495	1.2039
No log	26.0	104	1.1992	0.2823	1.1995	1.0952
No log	27.0	108	1.1251	0.3220	1.1259	1.0611
No log	28.0	112	1.1607	0.2868	1.1615	1.0777
No log	29.0	116	1.2519	0.2600	1.2526	1.1192
No log	30.0	120	1.1428	0.3610	1.1436	1.0694
No log	31.0	124	1.0327	0.3696	1.0333	1.0165
No log	32.0	128	1.1115	0.3372	1.1121	1.0546
No log	33.0	132	1.1701	0.3293	1.1706	1.0820
No log	34.0	136	1.2259	0.2719	1.2263	1.1074
No log	35.0	140	0.9692	0.3307	0.9698	0.9848
No log	36.0	144	1.2650	0.2834	1.2656	1.1250
No log	37.0	148	1.2061	0.2647	1.2066	1.0984
No log	38.0	152	0.9713	0.3515	0.9719	0.9859
No log	39.0	156	1.1947	0.2991	1.1953	1.0933
No log	40.0	160	1.1325	0.3101	1.1330	1.0644
No log	41.0	164	1.1445	0.3147	1.1451	1.0701
No log	42.0	168	1.0734	0.3083	1.0738	1.0363
No log	43.0	172	1.1254	0.3159	1.1259	1.0611
No log	44.0	176	0.9773	0.3538	0.9779	0.9889
No log	45.0	180	1.1359	0.3115	1.1364	1.0660
No log	46.0	184	1.0825	0.3286	1.0830	1.0407
No log	47.0	188	0.9465	0.3599	0.9471	0.9732
No log	48.0	192	1.1408	0.3204	1.1414	1.0683
No log	49.0	196	0.8719	0.3726	0.8725	0.9341
No log	50.0	200	1.1877	0.3201	1.1883	1.0901
No log	51.0	204	0.9385	0.3811	0.9391	0.9691
No log	52.0	208	1.2926	0.2672	1.2931	1.1372
No log	53.0	212	0.9084	0.3871	0.9090	0.9534
No log	54.0	216	1.2245	0.3037	1.2251	1.1068
No log	55.0	220	0.8923	0.4099	0.8930	0.9450
No log	56.0	224	1.1095	0.3338	1.1101	1.0536
No log	57.0	228	0.9046	0.3429	0.9051	0.9514
No log	58.0	232	1.0394	0.3609	1.0401	1.0198
No log	59.0	236	1.2249	0.2857	1.2254	1.1070
No log	60.0	240	0.8966	0.3915	0.8971	0.9472
No log	61.0	244	1.1530	0.3136	1.1535	1.0740
No log	62.0	248	0.9610	0.3699	0.9616	0.9806
No log	63.0	252	1.1572	0.3139	1.1577	1.0760
No log	64.0	256	0.9599	0.3547	0.9604	0.9800
No log	65.0	260	1.0034	0.3443	1.0040	1.0020
No log	66.0	264	1.1126	0.3478	1.1131	1.0550
No log	67.0	268	1.0181	0.3441	1.0186	1.0093
No log	68.0	272	0.9957	0.3535	0.9962	0.9981
No log	69.0	276	1.1349	0.3322	1.1354	1.0655
No log	70.0	280	0.9542	0.3663	0.9547	0.9771
No log	71.0	284	1.0533	0.3431	1.0538	1.0266
No log	72.0	288	1.1069	0.3493	1.1075	1.0524
No log	73.0	292	1.0821	0.3420	1.0826	1.0405
No log	74.0	296	1.0646	0.3356	1.0651	1.0321
No log	75.0	300	1.0574	0.3527	1.0580	1.0286

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold3

ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV9_k5_task1_organization_k5_k5_fold3