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	6	7.9060	0.0018	7.9060	2.8118
No log	2.0	12	4.5694	0.0079	4.5694	2.1376
No log	3.0	18	2.4297	0.0223	2.4297	1.5588
No log	4.0	24	1.1302	0.0316	1.1302	1.0631
No log	5.0	30	1.1184	0.0420	1.1184	1.0576
No log	6.0	36	1.4140	0.1399	1.4140	1.1891
No log	7.0	42	0.7188	0.3450	0.7188	0.8478
No log	8.0	48	1.2849	0.3267	1.2849	1.1335
No log	9.0	54	1.0695	0.3414	1.0695	1.0342
No log	10.0	60	0.8316	0.3879	0.8316	0.9119
No log	11.0	66	0.7826	0.4202	0.7826	0.8846
No log	12.0	72	0.7788	0.5424	0.7788	0.8825
No log	13.0	78	1.0006	0.4145	1.0006	1.0003
No log	14.0	84	0.9576	0.4414	0.9576	0.9786
No log	15.0	90	0.9322	0.4685	0.9322	0.9655
No log	16.0	96	0.7437	0.5727	0.7437	0.8624
No log	17.0	102	0.7395	0.5675	0.7395	0.8599
No log	18.0	108	0.8327	0.4908	0.8327	0.9125
No log	19.0	114	0.7729	0.5136	0.7729	0.8792
No log	20.0	120	0.9280	0.4462	0.9280	0.9633
No log	21.0	126	0.7123	0.5586	0.7123	0.8440
No log	22.0	132	0.7752	0.5603	0.7752	0.8804
No log	23.0	138	0.7250	0.5702	0.7250	0.8514
No log	24.0	144	0.7865	0.5684	0.7865	0.8869
No log	25.0	150	0.7599	0.5685	0.7599	0.8717
No log	26.0	156	0.6852	0.5695	0.6852	0.8278
No log	27.0	162	0.7859	0.5059	0.7859	0.8865
No log	28.0	168	0.6680	0.5539	0.6680	0.8173
No log	29.0	174	0.8950	0.5170	0.8950	0.9460
No log	30.0	180	0.7429	0.5589	0.7429	0.8619
No log	31.0	186	0.6562	0.5652	0.6562	0.8100
No log	32.0	192	0.7234	0.5704	0.7234	0.8506
No log	33.0	198	0.6546	0.5930	0.6546	0.8091
No log	34.0	204	0.6717	0.5805	0.6717	0.8196
No log	35.0	210	0.6554	0.5506	0.6554	0.8096
No log	36.0	216	0.7103	0.5785	0.7103	0.8428
No log	37.0	222	0.7402	0.5582	0.7402	0.8603
No log	38.0	228	0.6912	0.5640	0.6912	0.8314
No log	39.0	234	0.6465	0.5557	0.6465	0.8040
No log	40.0	240	0.6914	0.5743	0.6914	0.8315
No log	41.0	246	0.7262	0.5621	0.7262	0.8521
No log	42.0	252	0.6671	0.5656	0.6671	0.8167
No log	43.0	258	0.6752	0.5758	0.6752	0.8217
No log	44.0	264	0.6887	0.5781	0.6887	0.8299
No log	45.0	270	0.6487	0.5791	0.6487	0.8054
No log	46.0	276	0.6842	0.5601	0.6842	0.8272
No log	47.0	282	0.6460	0.5837	0.6460	0.8037
No log	48.0	288	0.6535	0.5877	0.6535	0.8084
No log	49.0	294	0.7153	0.5718	0.7153	0.8458
No log	50.0	300	0.7141	0.5765	0.7141	0.8450
No log	51.0	306	0.7259	0.5116	0.7259	0.8520
No log	52.0	312	0.6669	0.5791	0.6669	0.8167
No log	53.0	318	0.7697	0.5489	0.7697	0.8773

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV14_k3_task1_organization_k3_k3_fold4

ASAP_FineTuningBERT_AugV14_k3_task1_organization_k3_k3_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV14_k3_task1_organization_k3_k3_fold4