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	2	11.1355	0.0104	11.1356	3.3370
No log	2.0	4	8.7118	0.0	8.7122	2.9516
No log	3.0	6	7.2990	0.0	7.2993	2.7017
No log	4.0	8	5.5164	0.0257	5.5167	2.3488
No log	5.0	10	4.4359	0.0	4.4363	2.1062
No log	6.0	12	3.7781	0.0	3.7787	1.9439
No log	7.0	14	2.9473	0.0	2.9478	1.7169
No log	8.0	16	2.3452	0.1220	2.3456	1.5316
No log	9.0	18	1.9330	0.0323	1.9336	1.3905
No log	10.0	20	1.4110	0.0213	1.4114	1.1880
No log	11.0	22	1.5653	0.0107	1.5656	1.2513
No log	12.0	24	0.9593	0.0	0.9597	0.9796
No log	13.0	26	1.2680	-0.0029	1.2687	1.1264
No log	14.0	28	1.2417	-0.0683	1.2424	1.1146
No log	15.0	30	0.8682	0.0986	0.8687	0.9321
No log	16.0	32	0.8816	0.0949	0.8820	0.9392
No log	17.0	34	1.1197	0.0475	1.1201	1.0583
No log	18.0	36	1.2014	0.0765	1.2018	1.0963
No log	19.0	38	0.9240	0.0684	0.9244	0.9614
No log	20.0	40	0.8033	0.1783	0.8036	0.8964
No log	21.0	42	0.7273	0.3350	0.7275	0.8530
No log	22.0	44	0.7480	0.2804	0.7482	0.8650
No log	23.0	46	0.5961	0.5182	0.5963	0.7722
No log	24.0	48	0.6772	0.5109	0.6776	0.8232
No log	25.0	50	1.0103	0.1806	1.0105	1.0053
No log	26.0	52	0.7760	0.3977	0.7765	0.8812
No log	27.0	54	0.7106	0.4293	0.7110	0.8432
No log	28.0	56	0.8248	0.3326	0.8250	0.9083
No log	29.0	58	0.5878	0.5031	0.5879	0.7667
No log	30.0	60	0.5791	0.4682	0.5792	0.7611
No log	31.0	62	0.7465	0.3363	0.7466	0.8641
No log	32.0	64	0.5468	0.4803	0.5470	0.7396
No log	33.0	66	0.6365	0.4457	0.6369	0.7980
No log	34.0	68	0.8957	0.2746	0.8961	0.9466
No log	35.0	70	0.5797	0.4697	0.5800	0.7616
No log	36.0	72	0.5158	0.4964	0.5158	0.7182
No log	37.0	74	0.7196	0.3691	0.7198	0.8484
No log	38.0	76	0.8087	0.3624	0.8089	0.8994
No log	39.0	78	0.6739	0.4870	0.6738	0.8208
No log	40.0	80	0.8366	0.3580	0.8367	0.9147
No log	41.0	82	0.7375	0.4706	0.7374	0.8587
No log	42.0	84	0.5702	0.5745	0.5700	0.7550
No log	43.0	86	0.6348	0.4976	0.6348	0.7967
No log	44.0	88	0.7666	0.4008	0.7667	0.8756
No log	45.0	90	0.6677	0.5115	0.6676	0.8170
No log	46.0	92	0.7138	0.4987	0.7138	0.8449
No log	47.0	94	0.7000	0.4974	0.6999	0.8366
No log	48.0	96	0.6158	0.5708	0.6153	0.7844
No log	49.0	98	0.6072	0.5771	0.6068	0.7790
No log	50.0	100	0.7177	0.4616	0.7179	0.8473
No log	51.0	102	0.7396	0.4327	0.7398	0.8601
No log	52.0	104	0.6123	0.5765	0.6120	0.7823
No log	53.0	106	0.5896	0.6267	0.5888	0.7673
No log	54.0	108	0.5898	0.6150	0.5891	0.7676
No log	55.0	110	0.6485	0.5198	0.6484	0.8053
No log	56.0	112	0.7413	0.4328	0.7415	0.8611
No log	57.0	114	0.7497	0.5236	0.7495	0.8657
No log	58.0	116	0.7027	0.5344	0.7021	0.8379
No log	59.0	118	0.6102	0.5780	0.6098	0.7809
No log	60.0	120	0.6081	0.4816	0.6080	0.7798
No log	61.0	122	0.6286	0.4755	0.6286	0.7929
No log	62.0	124	0.6172	0.5441	0.6170	0.7855
No log	63.0	126	0.6122	0.5430	0.6118	0.7822
No log	64.0	128	0.6450	0.5183	0.6448	0.8030
No log	65.0	130	0.6848	0.5044	0.6847	0.8275
No log	66.0	132	0.7659	0.5312	0.7655	0.8749
No log	67.0	134	0.8366	0.4613	0.8364	0.9146
No log	68.0	136	0.7738	0.4792	0.7738	0.8797
No log	69.0	138	0.6617	0.5357	0.6616	0.8134
No log	70.0	140	0.5935	0.5784	0.5932	0.7702
No log	71.0	142	0.5947	0.5797	0.5944	0.7710
No log	72.0	144	0.6327	0.5764	0.6323	0.7952
No log	73.0	146	0.7168	0.5557	0.7167	0.8466

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold2

Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV14_k1_task1_organization_k1_k1_fold2