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	8.9793	0.0	8.9793	2.9966
No log	2.0	8	6.5024	0.0328	6.5024	2.5500
No log	3.0	12	4.5063	0.0	4.5063	2.1228
No log	4.0	16	2.9783	0.0	2.9783	1.7258
No log	5.0	20	1.8775	0.0	1.8775	1.3702
No log	6.0	24	1.2459	0.0	1.2459	1.1162
No log	7.0	28	0.8928	0.1262	0.8928	0.9449
No log	8.0	32	0.8100	0.0	0.8100	0.9000
No log	9.0	36	0.8514	0.0	0.8514	0.9227
No log	10.0	40	1.1420	0.1899	1.1420	1.0686
No log	11.0	44	0.9069	0.4353	0.9069	0.9523
No log	12.0	48	0.6115	0.6538	0.6115	0.7820
No log	13.0	52	0.7585	0.5943	0.7585	0.8709
No log	14.0	56	1.3179	0.4845	1.3179	1.1480
No log	15.0	60	0.9823	0.4621	0.9823	0.9911
No log	16.0	64	1.1286	0.5005	1.1286	1.0623
No log	17.0	68	0.4982	0.6206	0.4982	0.7058
No log	18.0	72	0.9732	0.5900	0.9732	0.9865
No log	19.0	76	0.9005	0.5390	0.9005	0.9490
No log	20.0	80	0.4723	0.6308	0.4723	0.6872
No log	21.0	84	0.8565	0.5435	0.8565	0.9255
No log	22.0	88	0.5232	0.6797	0.5232	0.7233
No log	23.0	92	0.7327	0.6145	0.7327	0.8560
No log	24.0	96	1.0678	0.4844	1.0678	1.0333
No log	25.0	100	0.6868	0.6178	0.6868	0.8288
No log	26.0	104	0.5579	0.6883	0.5579	0.7469
No log	27.0	108	0.5646	0.6841	0.5646	0.7514
No log	28.0	112	0.5299	0.6765	0.5299	0.7279
No log	29.0	116	0.6571	0.6543	0.6571	0.8106
No log	30.0	120	0.4962	0.6634	0.4962	0.7044
No log	31.0	124	0.6341	0.6681	0.6341	0.7963
No log	32.0	128	0.6241	0.6774	0.6241	0.7900
No log	33.0	132	0.5085	0.6995	0.5085	0.7131
No log	34.0	136	0.6287	0.6686	0.6287	0.7929
No log	35.0	140	0.5838	0.7042	0.5838	0.7641
No log	36.0	144	0.6446	0.6618	0.6446	0.8029
No log	37.0	148	0.5238	0.7162	0.5238	0.7237
No log	38.0	152	0.6450	0.6552	0.6450	0.8031
No log	39.0	156	0.6591	0.6292	0.6591	0.8118
No log	40.0	160	0.5495	0.7143	0.5495	0.7413
No log	41.0	164	0.5799	0.7104	0.5799	0.7615
No log	42.0	168	0.7732	0.5423	0.7732	0.8793
No log	43.0	172	0.5380	0.7116	0.5380	0.7335
No log	44.0	176	0.5188	0.6917	0.5188	0.7203
No log	45.0	180	0.6171	0.6820	0.6171	0.7856
No log	46.0	184	0.5276	0.6835	0.5276	0.7263
No log	47.0	188	0.6841	0.6165	0.6841	0.8271
No log	48.0	192	0.5600	0.6799	0.5600	0.7483
No log	49.0	196	0.5598	0.6807	0.5598	0.7482
No log	50.0	200	0.6954	0.5940	0.6954	0.8339
No log	51.0	204	0.5821	0.6658	0.5821	0.7629
No log	52.0	208	0.6048	0.6569	0.6048	0.7777
No log	53.0	212	0.5349	0.6623	0.5349	0.7314
No log	54.0	216	0.6191	0.6921	0.6191	0.7868
No log	55.0	220	0.5228	0.6716	0.5228	0.7230
No log	56.0	224	0.5092	0.6591	0.5092	0.7136
No log	57.0	228	0.7187	0.5773	0.7187	0.8478

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12LowDevASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold3

Version12LowDevASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12LowDevASAP_FineTuningBERT_AugV12_k15_task1_organization_k15_k15_fold3