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	1	11.8331	-0.0086	11.8304	3.4395
No log	2.0	2	10.4161	-0.0010	10.4134	3.2270
No log	3.0	3	9.0631	0.0	9.0606	3.0101
No log	4.0	4	8.0209	0.0	8.0185	2.8317
No log	5.0	5	7.1010	0.0	7.0985	2.6643
No log	6.0	6	6.5018	0.0	6.4993	2.5494
No log	7.0	7	5.8109	0.0034	5.8084	2.4101
No log	8.0	8	5.0308	0.0262	5.0285	2.2424
No log	9.0	9	4.4170	0.0118	4.4149	2.1012
No log	10.0	10	3.8909	0.0	3.8889	1.9720
No log	11.0	11	3.5313	0.0	3.5293	1.8786
No log	12.0	12	3.2437	0.0	3.2418	1.8005
No log	13.0	13	2.7510	0.0	2.7492	1.6581
No log	14.0	14	2.4068	0.0192	2.4050	1.5508
No log	15.0	15	2.1596	0.1168	2.1579	1.4690
No log	16.0	16	2.0016	0.0607	1.9999	1.4142
No log	17.0	17	1.8161	0.0315	1.8145	1.3470
No log	18.0	18	1.6164	0.0211	1.6148	1.2707
No log	19.0	19	1.4483	0.0106	1.4468	1.2028
No log	20.0	20	1.3215	0.0106	1.3200	1.1489
No log	21.0	21	1.2156	0.0	1.2141	1.1019
No log	22.0	22	1.1434	0.0	1.1420	1.0686
No log	23.0	23	1.0570	0.0	1.0556	1.0274
No log	24.0	24	0.9596	0.0	0.9583	0.9789
No log	25.0	25	0.9019	0.0209	0.9006	0.9490
No log	26.0	26	0.8945	0.2416	0.8932	0.9451
No log	27.0	27	0.8869	0.2068	0.8856	0.9411
No log	28.0	28	0.8370	0.2209	0.8358	0.9142
No log	29.0	29	0.7625	0.2876	0.7613	0.8725
No log	30.0	30	0.7147	0.3117	0.7136	0.8447
No log	31.0	31	0.7152	0.2867	0.7141	0.8450
No log	32.0	32	0.7369	0.2536	0.7358	0.8578
No log	33.0	33	0.6874	0.2867	0.6864	0.8285
No log	34.0	34	0.6528	0.3389	0.6518	0.8074
No log	35.0	35	0.5773	0.3909	0.5763	0.7591
No log	36.0	36	0.5880	0.3777	0.5871	0.7662
No log	37.0	37	0.5810	0.3987	0.5801	0.7617
No log	38.0	38	0.4926	0.4669	0.4918	0.7013
No log	39.0	39	0.4689	0.4917	0.4682	0.6842
No log	40.0	40	0.5601	0.4383	0.5594	0.7479
No log	41.0	41	0.5843	0.4961	0.5836	0.7639
No log	42.0	42	0.4682	0.6647	0.4675	0.6838
No log	43.0	43	0.4747	0.6921	0.4741	0.6885
No log	44.0	44	0.6068	0.6341	0.6063	0.7786
No log	45.0	45	0.6954	0.5682	0.6948	0.8336
No log	46.0	46	0.6072	0.6514	0.6067	0.7789
No log	47.0	47	0.5020	0.7056	0.5015	0.7082
No log	48.0	48	0.5368	0.6777	0.5363	0.7323
No log	49.0	49	0.6934	0.6042	0.6929	0.8324
No log	50.0	50	0.8228	0.5393	0.8224	0.9069
No log	51.0	51	0.7402	0.6056	0.7399	0.8602
No log	52.0	52	0.6294	0.6604	0.6291	0.7932
No log	53.0	53	0.6612	0.6546	0.6609	0.8130
No log	54.0	54	0.8496	0.5744	0.8494	0.9216
No log	55.0	55	0.9241	0.5508	0.9238	0.9612
No log	56.0	56	0.9010	0.5688	0.9008	0.9491
No log	57.0	57	0.7958	0.6115	0.7957	0.8920
No log	58.0	58	0.8805	0.5823	0.8804	0.9383
No log	59.0	59	1.0051	0.5582	1.0050	1.0025
No log	60.0	60	1.0659	0.5472	1.0658	1.0324
No log	61.0	61	0.9243	0.5889	0.9243	0.9614
No log	62.0	62	0.7367	0.6496	0.7368	0.8583
No log	63.0	63	0.7744	0.6480	0.7744	0.8800
No log	64.0	64	1.0236	0.5667	1.0237	1.0118
No log	65.0	65	1.4099	0.4535	1.4099	1.1874
No log	66.0	66	1.4900	0.4443	1.4899	1.2206
No log	67.0	67	1.3257	0.4756	1.3257	1.1514

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1