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	5	7.6694	0.0	7.6683	2.7692
No log	2.0	10	4.3569	0.0127	4.3560	2.0871
No log	3.0	15	2.1561	0.1023	2.1556	1.4682
No log	4.0	20	1.3849	0.0302	1.3843	1.1765
No log	5.0	25	0.9311	0.2318	0.9308	0.9648
No log	6.0	30	0.8722	0.2318	0.8721	0.9339
No log	7.0	35	0.9220	0.2447	0.9225	0.9605
No log	8.0	40	0.7831	0.3115	0.7836	0.8852
No log	9.0	45	0.8318	0.2706	0.8324	0.9124
No log	10.0	50	1.0729	0.1612	1.0727	1.0357
No log	11.0	55	0.8996	0.2885	0.8996	0.9484
No log	12.0	60	0.6428	0.4720	0.6431	0.8019
No log	13.0	65	0.7544	0.4362	0.7543	0.8685
No log	14.0	70	0.8344	0.4119	0.8346	0.9135
No log	15.0	75	0.9478	0.3680	0.9479	0.9736
No log	16.0	80	0.7751	0.4951	0.7756	0.8807
No log	17.0	85	0.7776	0.4866	0.7783	0.8822
No log	18.0	90	0.6980	0.5079	0.6982	0.8356
No log	19.0	95	1.0881	0.2886	1.0882	1.0432
No log	20.0	100	1.1601	0.2330	1.1602	1.0771
No log	21.0	105	0.7667	0.4345	0.7669	0.8757
No log	22.0	110	0.8010	0.5264	0.8015	0.8953
No log	23.0	115	0.8507	0.4060	0.8511	0.9225
No log	24.0	120	0.9077	0.3651	0.9079	0.9528
No log	25.0	125	0.7336	0.4665	0.7342	0.8569
No log	26.0	130	0.8353	0.3779	0.8357	0.9141
No log	27.0	135	0.9431	0.3425	0.9431	0.9712
No log	28.0	140	0.8363	0.3831	0.8366	0.9147
No log	29.0	145	0.7355	0.4627	0.7360	0.8579
No log	30.0	150	0.8010	0.4124	0.8013	0.8952
No log	31.0	155	0.8399	0.4094	0.8401	0.9166
No log	32.0	160	0.9293	0.3193	0.9294	0.9641
No log	33.0	165	0.9594	0.3074	0.9598	0.9797
No log	34.0	170	0.7137	0.4723	0.7140	0.8450
No log	35.0	175	0.7459	0.4894	0.7463	0.8639
No log	36.0	180	0.9859	0.3766	0.9859	0.9929
No log	37.0	185	0.8026	0.3963	0.8028	0.8960
No log	38.0	190	0.7786	0.4200	0.7788	0.8825
No log	39.0	195	1.0792	0.2335	1.0794	1.0389
No log	40.0	200	0.7217	0.4718	0.7221	0.8498
No log	41.0	205	0.8089	0.4220	0.8092	0.8996
No log	42.0	210	0.8444	0.4247	0.8446	0.9190
No log	43.0	215	0.7465	0.4919	0.7470	0.8643
No log	44.0	220	0.9452	0.3415	0.9454	0.9723
No log	45.0	225	0.7996	0.4275	0.8002	0.8945
No log	46.0	230	0.8725	0.3760	0.8728	0.9342
No log	47.0	235	0.8463	0.4057	0.8467	0.9201
No log	48.0	240	0.7539	0.4402	0.7542	0.8684
No log	49.0	245	0.8100	0.4079	0.8102	0.9001
No log	50.0	250	0.8369	0.3770	0.8372	0.9150
No log	51.0	255	0.8038	0.4081	0.8042	0.8968
No log	52.0	260	0.9762	0.3343	0.9763	0.9881

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV21_k5_task1_organization_k5_k5_fold3

ASAP_FineTuningBERT_AugV21_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV21_k5_task1_organization_k5_k5_fold3