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	6	7.6274	0.0018	7.6274	2.7618
No log	2.0	12	4.6441	0.0118	4.6441	2.1550
No log	3.0	18	2.2924	-0.0033	2.2924	1.5141
No log	4.0	24	1.1193	0.0212	1.1193	1.0580
No log	5.0	30	1.8766	0.0533	1.8766	1.3699
No log	6.0	36	1.1982	0.0445	1.1982	1.0946
No log	7.0	42	1.1230	0.1390	1.1230	1.0597
No log	8.0	48	1.3112	0.2910	1.3112	1.1451
No log	9.0	54	0.6270	0.4904	0.6270	0.7918
No log	10.0	60	0.7113	0.5172	0.7113	0.8434
No log	11.0	66	0.6010	0.4970	0.6010	0.7753
No log	12.0	72	0.7060	0.4774	0.7060	0.8402
No log	13.0	78	0.7061	0.5024	0.7061	0.8403
No log	14.0	84	0.6408	0.5394	0.6408	0.8005
No log	15.0	90	0.6866	0.5455	0.6866	0.8286
No log	16.0	96	0.6359	0.5752	0.6359	0.7974
No log	17.0	102	0.6503	0.5877	0.6503	0.8064
No log	18.0	108	0.6984	0.5575	0.6984	0.8357
No log	19.0	114	0.6473	0.5654	0.6473	0.8045
No log	20.0	120	0.6488	0.5852	0.6488	0.8055
No log	21.0	126	0.6280	0.5642	0.6280	0.7925
No log	22.0	132	0.6175	0.5742	0.6175	0.7858
No log	23.0	138	0.7846	0.5637	0.7846	0.8858
No log	24.0	144	0.6350	0.5824	0.6350	0.7969
No log	25.0	150	0.7704	0.5355	0.7704	0.8777
No log	26.0	156	0.6041	0.5896	0.6041	0.7772
No log	27.0	162	0.6824	0.5764	0.6824	0.8261
No log	28.0	168	0.6219	0.5970	0.6219	0.7886
No log	29.0	174	0.8364	0.5511	0.8364	0.9146
No log	30.0	180	0.6678	0.5919	0.6678	0.8172
No log	31.0	186	0.6402	0.5928	0.6402	0.8001
No log	32.0	192	0.7253	0.5924	0.7253	0.8516
No log	33.0	198	0.6365	0.6049	0.6365	0.7978
No log	34.0	204	0.6110	0.6010	0.6110	0.7817
No log	35.0	210	0.6842	0.5923	0.6842	0.8272
No log	36.0	216	0.9370	0.5411	0.9370	0.9680
No log	37.0	222	0.8917	0.5152	0.8917	0.9443
No log	38.0	228	0.6862	0.5739	0.6862	0.8284
No log	39.0	234	0.6200	0.6098	0.6200	0.7874
No log	40.0	240	0.7344	0.5999	0.7344	0.8570
No log	41.0	246	0.6946	0.5941	0.6946	0.8334
No log	42.0	252	0.5956	0.5816	0.5956	0.7718
No log	43.0	258	0.6384	0.5749	0.6384	0.7990
No log	44.0	264	0.6375	0.5600	0.6375	0.7985
No log	45.0	270	0.6540	0.5849	0.6540	0.8087
No log	46.0	276	0.6625	0.5657	0.6625	0.8140
No log	47.0	282	0.7587	0.5762	0.7587	0.8711
No log	48.0	288	0.9142	0.4895	0.9142	0.9561
No log	49.0	294	0.5955	0.5933	0.5955	0.7717
No log	50.0	300	0.5835	0.5913	0.5835	0.7638
No log	51.0	306	0.6163	0.5945	0.6163	0.7851
No log	52.0	312	0.6610	0.5812	0.6610	0.8130
No log	53.0	318	0.6251	0.5634	0.6251	0.7907
No log	54.0	324	0.6286	0.5761	0.6286	0.7928
No log	55.0	330	0.7288	0.5722	0.7288	0.8537
No log	56.0	336	0.6279	0.5553	0.6279	0.7924
No log	57.0	342	0.7028	0.5440	0.7028	0.8383
No log	58.0	348	0.6105	0.5861	0.6105	0.7813
No log	59.0	354	0.5904	0.5857	0.5904	0.7684
No log	60.0	360	0.6339	0.5712	0.6339	0.7962
No log	61.0	366	0.6246	0.5643	0.6246	0.7903
No log	62.0	372	0.5678	0.5933	0.5678	0.7535
No log	63.0	378	0.6678	0.5981	0.6678	0.8172
No log	64.0	384	0.6360	0.5982	0.6360	0.7975
No log	65.0	390	0.6630	0.5763	0.6630	0.8142
No log	66.0	396	0.7597	0.5540	0.7597	0.8716
No log	67.0	402	0.7176	0.5687	0.7176	0.8471
No log	68.0	408	0.6248	0.5845	0.6248	0.7905
No log	69.0	414	0.6333	0.5801	0.6333	0.7958

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version24NewTestASAP_FineTuningBERT_AugV24_k10_task1_organization_k10_k10_fold4

Version24NewTestASAP_FineTuningBERT_AugV24_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version24NewTestASAP_FineTuningBERT_AugV24_k10_task1_organization_k10_k10_fold4