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	8	4.6648	0.0140	4.6636	2.1595
No log	2.0	16	1.7703	0.0391	1.7695	1.3302
No log	3.0	24	2.4343	-0.0379	2.4332	1.5599
No log	4.0	32	2.2307	0.0075	2.2296	1.4932
No log	5.0	40	1.2742	0.0775	1.2736	1.1285
No log	6.0	48	0.9245	0.0984	0.9241	0.9613
No log	7.0	56	1.2343	0.0708	1.2331	1.1105
No log	8.0	64	0.9933	0.1462	0.9924	0.9962
No log	9.0	72	1.5005	0.0927	1.4994	1.2245
No log	10.0	80	1.2134	0.1519	1.2125	1.1011
No log	11.0	88	1.1311	0.1726	1.1304	1.0632
No log	12.0	96	1.2421	0.1909	1.2414	1.1142
No log	13.0	104	1.0009	0.2076	1.0002	1.0001
No log	14.0	112	1.0330	0.2394	1.0325	1.0161
No log	15.0	120	1.0699	0.2181	1.0694	1.0341
No log	16.0	128	1.6092	0.1078	1.6083	1.2682
No log	17.0	136	1.1510	0.1844	1.1502	1.0725
No log	18.0	144	1.4632	0.1657	1.4625	1.2093
No log	19.0	152	1.0207	0.2539	1.0204	1.0101
No log	20.0	160	0.9684	0.2998	0.9683	0.9840
No log	21.0	168	1.4016	0.1847	1.4010	1.1836
No log	22.0	176	1.4150	0.1849	1.4143	1.1892
No log	23.0	184	1.3881	0.1523	1.3875	1.1779
No log	24.0	192	1.1835	0.1868	1.1831	1.0877
No log	25.0	200	1.1728	0.2038	1.1725	1.0828
No log	26.0	208	1.1936	0.2456	1.1934	1.0924
No log	27.0	216	1.2242	0.1982	1.2240	1.1063
No log	28.0	224	1.1624	0.2040	1.1621	1.0780
No log	29.0	232	1.2946	0.1813	1.2942	1.1376
No log	30.0	240	1.1490	0.1889	1.1486	1.0717
No log	31.0	248	1.0729	0.2225	1.0726	1.0357
No log	32.0	256	1.4035	0.1871	1.4030	1.1845
No log	33.0	264	1.2215	0.2096	1.2212	1.1051
No log	34.0	272	1.0712	0.2231	1.0710	1.0349
No log	35.0	280	1.0911	0.2490	1.0909	1.0444
No log	36.0	288	0.9844	0.3019	0.9844	0.9922
No log	37.0	296	0.9943	0.3106	0.9944	0.9972
No log	38.0	304	0.9998	0.3208	0.9999	0.9999
No log	39.0	312	1.0293	0.2644	1.0294	1.0146
No log	40.0	320	1.0587	0.2547	1.0587	1.0289
No log	41.0	328	1.0526	0.2511	1.0525	1.0259
No log	42.0	336	1.0417	0.2269	1.0416	1.0206
No log	43.0	344	1.0718	0.2442	1.0717	1.0352
No log	44.0	352	1.0384	0.2731	1.0383	1.0190
No log	45.0	360	0.9804	0.2828	0.9804	0.9902
No log	46.0	368	1.0330	0.2404	1.0329	1.0163
No log	47.0	376	0.9603	0.3227	0.9604	0.9800
No log	48.0	384	0.9461	0.2885	0.9463	0.9728
No log	49.0	392	0.9972	0.2715	0.9972	0.9986
No log	50.0	400	1.0358	0.2889	1.0358	1.0177
No log	51.0	408	1.0795	0.2360	1.0793	1.0389
No log	52.0	416	1.1003	0.2302	1.1001	1.0488
No log	53.0	424	1.2106	0.2221	1.2102	1.1001
No log	54.0	432	1.0411	0.2375	1.0410	1.0203
No log	55.0	440	1.0297	0.2528	1.0296	1.0147
No log	56.0	448	1.0863	0.2336	1.0861	1.0422
No log	57.0	456	0.9893	0.2790	0.9893	0.9946
No log	58.0	464	1.0251	0.2492	1.0250	1.0124
No log	59.0	472	0.9825	0.2551	0.9826	0.9913
No log	60.0	480	1.0146	0.2353	1.0146	1.0073
No log	61.0	488	0.9850	0.2565	0.9851	0.9925
No log	62.0	496	0.9977	0.2700	0.9977	0.9989
0.2979	63.0	504	0.9673	0.3093	0.9674	0.9836
0.2979	64.0	512	0.9682	0.2901	0.9682	0.9840
0.2979	65.0	520	1.0689	0.2694	1.0688	1.0339
0.2979	66.0	528	1.0665	0.2567	1.0664	1.0327
0.2979	67.0	536	1.0327	0.2669	1.0326	1.0161

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold3

ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold3