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	7.0552	0.0121	7.0552	2.6562
No log	2.0	12	2.9889	0.0149	2.9889	1.7288
No log	3.0	18	1.3013	0.0581	1.3013	1.1407
No log	4.0	24	1.4601	0.0838	1.4601	1.2084
No log	5.0	30	1.1983	0.0923	1.1983	1.0947
No log	6.0	36	1.2133	0.1088	1.2133	1.1015
No log	7.0	42	1.3536	0.1839	1.3536	1.1634
No log	8.0	48	1.2639	0.2482	1.2639	1.1242
No log	9.0	54	0.8169	0.3621	0.8169	0.9038
No log	10.0	60	1.0642	0.2835	1.0642	1.0316
No log	11.0	66	0.9784	0.3037	0.9784	0.9891
No log	12.0	72	1.1766	0.2473	1.1766	1.0847
No log	13.0	78	0.9344	0.2914	0.9344	0.9666
No log	14.0	84	1.0113	0.2841	1.0113	1.0056
No log	15.0	90	1.1488	0.2727	1.1488	1.0718
No log	16.0	96	1.4643	0.2081	1.4643	1.2101
No log	17.0	102	0.9739	0.3045	0.9739	0.9869
No log	18.0	108	0.9624	0.3166	0.9624	0.9810
No log	19.0	114	1.0474	0.2998	1.0474	1.0234
No log	20.0	120	1.0927	0.2817	1.0927	1.0453
No log	21.0	126	1.0144	0.2707	1.0144	1.0072
No log	22.0	132	1.2458	0.2412	1.2458	1.1162
No log	23.0	138	1.1621	0.2889	1.1621	1.0780
No log	24.0	144	0.9198	0.3554	0.9198	0.9591
No log	25.0	150	0.9097	0.3804	0.9097	0.9538
No log	26.0	156	1.3611	0.2606	1.3611	1.1666
No log	27.0	162	1.1838	0.2771	1.1838	1.0880
No log	28.0	168	0.9077	0.3660	0.9077	0.9527
No log	29.0	174	0.8944	0.3589	0.8944	0.9457
No log	30.0	180	0.7932	0.4157	0.7932	0.8906
No log	31.0	186	1.2689	0.2922	1.2689	1.1264
No log	32.0	192	1.0236	0.3133	1.0236	1.0117
No log	33.0	198	0.9688	0.3144	0.9688	0.9843
No log	34.0	204	0.9705	0.3293	0.9705	0.9851
No log	35.0	210	0.8243	0.3811	0.8243	0.9079
No log	36.0	216	0.8999	0.3131	0.8999	0.9486
No log	37.0	222	0.8336	0.4061	0.8336	0.9130
No log	38.0	228	0.9087	0.3651	0.9087	0.9533
No log	39.0	234	0.8542	0.3578	0.8542	0.9242
No log	40.0	240	0.9729	0.3329	0.9729	0.9863
No log	41.0	246	0.7977	0.3592	0.7977	0.8931
No log	42.0	252	0.7861	0.3975	0.7861	0.8866
No log	43.0	258	0.7926	0.4439	0.7926	0.8903
No log	44.0	264	0.9574	0.3239	0.9574	0.9785
No log	45.0	270	1.0482	0.3316	1.0482	1.0238
No log	46.0	276	0.8564	0.4029	0.8564	0.9254
No log	47.0	282	0.7868	0.4154	0.7868	0.8870
No log	48.0	288	0.9096	0.3474	0.9096	0.9537
No log	49.0	294	0.9075	0.3303	0.9075	0.9526
No log	50.0	300	0.8730	0.3605	0.8730	0.9343
No log	51.0	306	0.9468	0.3669	0.9468	0.9730
No log	52.0	312	0.9061	0.3638	0.9061	0.9519
No log	53.0	318	0.8552	0.3480	0.8552	0.9248
No log	54.0	324	0.9598	0.3222	0.9598	0.9797
No log	55.0	330	0.9655	0.3631	0.9655	0.9826
No log	56.0	336	0.9243	0.3510	0.9243	0.9614
No log	57.0	342	0.8781	0.3617	0.8781	0.9371
No log	58.0	348	0.8283	0.3727	0.8283	0.9101
No log	59.0	354	1.0047	0.3380	1.0047	1.0024
No log	60.0	360	0.9475	0.3455	0.9475	0.9734
No log	61.0	366	0.8406	0.3592	0.8406	0.9168
No log	62.0	372	0.9098	0.3520	0.9098	0.9538
No log	63.0	378	0.8685	0.3573	0.8685	0.9320

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold0

ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV9_k7_task1_organization_k7_k7_fold0