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	3	9.8961	0.0	9.8945	3.1455
No log	2.0	6	8.1919	0.0	8.1904	2.8619
No log	3.0	9	5.7284	0.0813	5.7272	2.3931
No log	4.0	12	4.1989	0.0076	4.1980	2.0489
No log	5.0	15	2.8303	0.0205	2.8294	1.6821
No log	6.0	18	1.7309	0.0811	1.7302	1.3154
No log	7.0	21	1.5230	0.0488	1.5224	1.2339
No log	8.0	24	1.5652	0.0670	1.5646	1.2508
No log	9.0	27	1.1320	0.0549	1.1317	1.0638
No log	10.0	30	1.7515	0.0914	1.7513	1.3234
No log	11.0	33	1.1048	0.1569	1.1049	1.0511
No log	12.0	36	1.5320	0.1251	1.5323	1.2379
No log	13.0	39	2.0726	0.0967	2.0729	1.4398
No log	14.0	42	0.9284	0.2623	0.9289	0.9638
No log	15.0	45	1.7892	0.1046	1.7895	1.3377
No log	16.0	48	1.6597	0.1380	1.6598	1.2883
No log	17.0	51	1.3667	0.1831	1.3672	1.1693
No log	18.0	54	1.6907	0.1681	1.6912	1.3005
No log	19.0	57	1.4883	0.1910	1.4892	1.2203
No log	20.0	60	1.6933	0.1530	1.6943	1.3016
No log	21.0	63	1.3995	0.1996	1.4005	1.1834
No log	22.0	66	1.6114	0.2164	1.6127	1.2699
No log	23.0	69	1.4086	0.2502	1.4099	1.1874
No log	24.0	72	1.3619	0.2673	1.3634	1.1676
No log	25.0	75	1.1581	0.3071	1.1593	1.0767
No log	26.0	78	1.2737	0.2423	1.2748	1.1291
No log	27.0	81	1.2673	0.2750	1.2684	1.1262
No log	28.0	84	1.3796	0.2582	1.3808	1.1751
No log	29.0	87	1.2891	0.2660	1.2903	1.1359
No log	30.0	90	1.2497	0.2986	1.2508	1.1184
No log	31.0	93	1.1444	0.2998	1.1454	1.0702
No log	32.0	96	1.1962	0.2949	1.1973	1.0942
No log	33.0	99	1.3025	0.2868	1.3038	1.1418
No log	34.0	102	1.2182	0.2928	1.2193	1.1042
No log	35.0	105	1.1678	0.3099	1.1688	1.0811
No log	36.0	108	1.1685	0.3071	1.1696	1.0815
No log	37.0	111	1.0682	0.3828	1.0691	1.0340
No log	38.0	114	1.1818	0.3169	1.1829	1.0876
No log	39.0	117	1.0979	0.3407	1.0990	1.0483
No log	40.0	120	1.0593	0.3343	1.0603	1.0297
No log	41.0	123	1.1061	0.3257	1.1072	1.0522
No log	42.0	126	1.1068	0.3272	1.1080	1.0526
No log	43.0	129	1.1321	0.3673	1.1330	1.0644
No log	44.0	132	1.1396	0.3622	1.1410	1.0682
No log	45.0	135	1.0838	0.3908	1.0850	1.0416
No log	46.0	138	1.0150	0.3805	1.0162	1.0080
No log	47.0	141	1.0404	0.3706	1.0416	1.0206
No log	48.0	144	1.0270	0.4087	1.0282	1.0140
No log	49.0	147	1.2691	0.2795	1.2705	1.1272
No log	50.0	150	1.0412	0.3782	1.0423	1.0209
No log	51.0	153	0.9943	0.3744	0.9953	0.9976
No log	52.0	156	1.0464	0.3336	1.0476	1.0235
No log	53.0	159	1.0316	0.3728	1.0327	1.0162
No log	54.0	162	1.1027	0.3669	1.1038	1.0506
No log	55.0	165	1.2073	0.3271	1.2086	1.0994
No log	56.0	168	1.1061	0.3680	1.1072	1.0523
No log	57.0	171	1.1596	0.3560	1.1609	1.0775
No log	58.0	174	1.0062	0.3980	1.0072	1.0036
No log	59.0	177	1.0242	0.4081	1.0253	1.0126
No log	60.0	180	1.0419	0.4027	1.0430	1.0213
No log	61.0	183	0.9826	0.4041	0.9835	0.9917
No log	62.0	186	1.0369	0.3899	1.0380	1.0188
No log	63.0	189	1.1087	0.4016	1.1099	1.0535
No log	64.0	192	1.0821	0.3828	1.0830	1.0407
No log	65.0	195	1.1705	0.3533	1.1716	1.0824
No log	66.0	198	1.0265	0.3814	1.0274	1.0136
No log	67.0	201	1.0287	0.3679	1.0296	1.0147
No log	68.0	204	1.0411	0.3775	1.0420	1.0208
No log	69.0	207	1.0226	0.4131	1.0235	1.0117
No log	70.0	210	1.0136	0.3962	1.0146	1.0073
No log	71.0	213	1.0689	0.3799	1.0699	1.0344
No log	72.0	216	1.0947	0.3942	1.0957	1.0468
No log	73.0	219	1.0895	0.4025	1.0905	1.0443
No log	74.0	222	1.0951	0.3897	1.0962	1.0470
No log	75.0	225	1.0774	0.3999	1.0784	1.0384
No log	76.0	228	1.0726	0.3910	1.0736	1.0361
No log	77.0	231	1.0844	0.3997	1.0854	1.0418
No log	78.0	234	1.0784	0.3639	1.0794	1.0390
No log	79.0	237	1.0788	0.3737	1.0798	1.0391
No log	80.0	240	1.0352	0.4007	1.0361	1.0179
No log	81.0	243	1.0893	0.3956	1.0903	1.0442
No log	82.0	246	1.1149	0.3849	1.1159	1.0564
No log	83.0	249	1.1083	0.3780	1.1093	1.0532
No log	84.0	252	1.0751	0.3858	1.0761	1.0374
No log	85.0	255	1.0452	0.4047	1.0462	1.0228
No log	86.0	258	1.0579	0.3920	1.0588	1.0290
No log	87.0	261	1.1369	0.3828	1.1380	1.0668
No log	88.0	264	1.0869	0.3789	1.0878	1.0430
No log	89.0	267	1.0161	0.3873	1.0169	1.0084
No log	90.0	270	1.0424	0.3713	1.0433	1.0214
No log	91.0	273	1.0734	0.3889	1.0743	1.0365
No log	92.0	276	1.1192	0.3820	1.1202	1.0584
No log	93.0	279	1.0756	0.3874	1.0765	1.0375
No log	94.0	282	1.0901	0.3817	1.0911	1.0446
No log	95.0	285	1.0957	0.3776	1.0968	1.0473
No log	96.0	288	1.0638	0.3893	1.0646	1.0318
No log	97.0	291	1.0619	0.3757	1.0629	1.0310
No log	98.0	294	1.0811	0.3762	1.0821	1.0402
No log	99.0	297	1.0095	0.4000	1.0104	1.0052

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k5_task1_organization_k5_k5_fold3

Version24NewTestASAP_FineTuningBERT_AugV24_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k5_task1_organization_k5_k5_fold3