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	8.9957	0.0	8.9957	2.9993
No log	2.0	4	7.6378	0.0	7.6378	2.7637
No log	3.0	6	6.8358	0.0	6.8358	2.6145
No log	4.0	8	5.9800	-0.0002	5.9800	2.4454
No log	5.0	10	5.1131	0.0115	5.1131	2.2612
No log	6.0	12	4.2947	0.0039	4.2947	2.0724
No log	7.0	14	3.7459	0.0	3.7459	1.9354
No log	8.0	16	2.8837	0.0	2.8837	1.6982
No log	9.0	18	2.2702	0.1001	2.2702	1.5067
No log	10.0	20	1.9563	0.0909	1.9563	1.3987
No log	11.0	22	1.5580	0.0316	1.5580	1.2482
No log	12.0	24	1.2738	0.0316	1.2738	1.1286
No log	13.0	26	1.0965	0.0316	1.0965	1.0472
No log	14.0	28	1.2488	0.0897	1.2488	1.1175
No log	15.0	30	1.6378	0.1656	1.6378	1.2798
No log	16.0	32	1.2659	0.1556	1.2659	1.1251
No log	17.0	34	0.7658	0.4108	0.7658	0.8751
No log	18.0	36	0.7649	0.3977	0.7649	0.8746
No log	19.0	38	1.6212	0.1942	1.6212	1.2732
No log	20.0	40	1.9952	0.1634	1.9952	1.4125
No log	21.0	42	1.0705	0.2656	1.0705	1.0347
No log	22.0	44	0.6853	0.4422	0.6853	0.8278
No log	23.0	46	0.6597	0.4287	0.6597	0.8122
No log	24.0	48	0.7877	0.4342	0.7877	0.8875
No log	25.0	50	0.8330	0.4074	0.8330	0.9127
No log	26.0	52	0.5973	0.4332	0.5973	0.7728
No log	27.0	54	0.6079	0.3719	0.6079	0.7797
No log	28.0	56	0.5852	0.4301	0.5852	0.7650
No log	29.0	58	0.7121	0.4374	0.7121	0.8438
No log	30.0	60	0.6366	0.4244	0.6366	0.7979
No log	31.0	62	0.6125	0.3905	0.6125	0.7826
No log	32.0	64	0.6831	0.3728	0.6831	0.8265
No log	33.0	66	0.6738	0.4277	0.6738	0.8209
No log	34.0	68	0.7226	0.4205	0.7226	0.8501
No log	35.0	70	0.7082	0.4170	0.7082	0.8415
No log	36.0	72	0.5804	0.4966	0.5804	0.7618
No log	37.0	74	0.5845	0.5344	0.5845	0.7645
No log	38.0	76	0.6040	0.5380	0.6040	0.7772
No log	39.0	78	0.6431	0.5279	0.6431	0.8019
No log	40.0	80	0.6188	0.5137	0.6188	0.7867
No log	41.0	82	0.5782	0.5453	0.5782	0.7604
No log	42.0	84	0.6008	0.5275	0.6008	0.7751
No log	43.0	86	0.6096	0.5531	0.6096	0.7808
No log	44.0	88	0.6245	0.5575	0.6245	0.7903
No log	45.0	90	0.5997	0.5747	0.5997	0.7744
No log	46.0	92	0.5882	0.5511	0.5882	0.7670
No log	47.0	94	0.5507	0.5798	0.5507	0.7421
No log	48.0	96	0.6635	0.5697	0.6635	0.8146
No log	49.0	98	0.5823	0.5639	0.5823	0.7631
No log	50.0	100	0.5767	0.5763	0.5767	0.7594
No log	51.0	102	0.5856	0.5720	0.5856	0.7652
No log	52.0	104	0.7623	0.5224	0.7623	0.8731
No log	53.0	106	0.8633	0.4837	0.8633	0.9291
No log	54.0	108	0.6668	0.5603	0.6668	0.8166
No log	55.0	110	0.6427	0.5680	0.6427	0.8017
No log	56.0	112	0.7219	0.5431	0.7219	0.8496
No log	57.0	114	0.6777	0.5628	0.6777	0.8232
No log	58.0	116	0.6579	0.5314	0.6579	0.8111
No log	59.0	118	0.6558	0.5415	0.6558	0.8098
No log	60.0	120	0.7177	0.5602	0.7177	0.8472
No log	61.0	122	0.7565	0.5323	0.7565	0.8698
No log	62.0	124	0.6642	0.5580	0.6642	0.8150
No log	63.0	126	0.6518	0.5533	0.6518	0.8073
No log	64.0	128	0.6854	0.5537	0.6854	0.8279
No log	65.0	130	0.7189	0.5511	0.7189	0.8479
No log	66.0	132	0.7813	0.5249	0.7813	0.8839
No log	67.0	134	0.7167	0.5555	0.7167	0.8466

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold0

ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV10_k1_task1_organization_k1_k1_fold0