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	10.5427	0.0	10.5429	3.2470
No log	2.0	8	8.1680	0.0	8.1682	2.8580
No log	3.0	12	7.4644	0.0	7.4646	2.7321
No log	4.0	16	5.3880	0.0159	5.3883	2.3213
No log	5.0	20	3.9086	0.0	3.9089	1.9771
No log	6.0	24	2.2141	0.1257	2.2144	1.4881
No log	7.0	28	1.2378	0.0107	1.2382	1.1127
No log	8.0	32	1.0386	0.0107	1.0390	1.0193
No log	9.0	36	1.3368	0.0170	1.3373	1.1564
No log	10.0	40	1.0514	0.0684	1.0518	1.0256
No log	11.0	44	1.0765	0.1742	1.0765	1.0376
No log	12.0	48	1.2125	0.1632	1.2122	1.1010
No log	13.0	52	1.8490	0.1341	1.8482	1.3595
No log	14.0	56	1.7091	0.1065	1.7081	1.3070
No log	15.0	60	1.5419	0.1341	1.5412	1.2414
No log	16.0	64	1.0475	0.2983	1.0469	1.0232
No log	17.0	68	1.2970	0.3059	1.2961	1.1385
No log	18.0	72	1.3196	0.2460	1.3188	1.1484
No log	19.0	76	1.3536	0.2314	1.3531	1.1632
No log	20.0	80	1.4131	0.2019	1.4128	1.1886
No log	21.0	84	1.3991	0.1795	1.3991	1.1828
No log	22.0	88	1.1933	0.2128	1.1934	1.0924
No log	23.0	92	1.5817	0.1874	1.5816	1.2576
No log	24.0	96	1.6524	0.2047	1.6521	1.2853
No log	25.0	100	1.6378	0.1617	1.6376	1.2797
No log	26.0	104	1.3845	0.1718	1.3845	1.1766
No log	27.0	108	1.0973	0.2380	1.0974	1.0476
No log	28.0	112	0.9722	0.3297	0.9721	0.9860
No log	29.0	116	1.0938	0.3107	1.0936	1.0458
No log	30.0	120	1.2257	0.2464	1.2256	1.1071
No log	31.0	124	1.1139	0.1993	1.1140	1.0555
No log	32.0	128	0.9445	0.2652	0.9446	0.9719
No log	33.0	132	0.8916	0.2898	0.8916	0.9443
No log	34.0	136	1.3965	0.2208	1.3963	1.1817
No log	35.0	140	0.8295	0.3520	0.8294	0.9107
No log	36.0	144	1.4412	0.2172	1.4410	1.2004
No log	37.0	148	0.9707	0.2432	0.9707	0.9852
No log	38.0	152	1.1726	0.2370	1.1726	1.0829
No log	39.0	156	1.0869	0.2558	1.0870	1.0426
No log	40.0	160	1.2615	0.2235	1.2615	1.1231
No log	41.0	164	1.3210	0.2005	1.3210	1.1493
No log	42.0	168	0.9911	0.2570	0.9912	0.9956
No log	43.0	172	1.0128	0.2445	1.0130	1.0065
No log	44.0	176	1.0005	0.2702	1.0006	1.0003
No log	45.0	180	0.9185	0.2814	0.9185	0.9584
No log	46.0	184	1.0185	0.2637	1.0186	1.0093
No log	47.0	188	0.7940	0.3404	0.7941	0.8911
No log	48.0	192	1.4283	0.1893	1.4284	1.1951
No log	49.0	196	0.9037	0.3299	0.9037	0.9506
No log	50.0	200	1.5556	0.1798	1.5556	1.2472
No log	51.0	204	0.9326	0.3185	0.9326	0.9657
No log	52.0	208	1.3432	0.2265	1.3432	1.1590
No log	53.0	212	1.0697	0.2658	1.0697	1.0343
No log	54.0	216	0.9758	0.2512	0.9758	0.9878
No log	55.0	220	1.1700	0.2143	1.1701	1.0817

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_fold2

Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV14_k5_task1_organization_k5_k5_fold2