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	13.2165	0.0	13.2142	3.6351
No log	2.0	4	11.0123	0.0031	11.0103	3.3182
No log	3.0	6	9.1930	0.0	9.1915	3.0317
No log	4.0	8	7.7680	0.0	7.7665	2.7868
No log	5.0	10	6.7254	0.0	6.7240	2.5931
No log	6.0	12	5.0548	0.0124	5.0536	2.2480
No log	7.0	14	3.8790	0.0	3.8779	1.9692
No log	8.0	16	3.0988	0.0	3.0978	1.7601
No log	9.0	18	2.0992	0.0514	2.0984	1.4486
No log	10.0	20	1.7778	0.0202	1.7770	1.3331
No log	11.0	22	1.3609	0.0102	1.3602	1.1663
No log	12.0	24	1.2686	0.0202	1.2680	1.1260
No log	13.0	26	1.0703	0.0202	1.0698	1.0343
No log	14.0	28	1.0454	0.0202	1.0449	1.0222
No log	15.0	30	1.1708	0.0691	1.1704	1.0819
No log	16.0	32	1.2506	0.1286	1.2504	1.1182
No log	17.0	34	1.5001	0.1804	1.5001	1.2248
No log	18.0	36	1.1893	0.2115	1.1894	1.0906
No log	19.0	38	0.9256	0.3982	0.9258	0.9622
No log	20.0	40	1.4279	0.2521	1.4284	1.1952
No log	21.0	42	1.4605	0.2366	1.4612	1.2088
No log	22.0	44	0.8311	0.4743	0.8319	0.9121
No log	23.0	46	0.7738	0.4605	0.7748	0.8802
No log	24.0	48	1.1198	0.3617	1.1209	1.0587
No log	25.0	50	1.1856	0.3411	1.1867	1.0894
No log	26.0	52	0.7510	0.4467	0.7521	0.8673
No log	27.0	54	0.7257	0.4351	0.7268	0.8525
No log	28.0	56	0.9073	0.4388	0.9084	0.9531
No log	29.0	58	0.9966	0.4088	0.9979	0.9989
No log	30.0	60	0.8364	0.4701	0.8377	0.9152
No log	31.0	62	0.9050	0.4586	0.9064	0.9520
No log	32.0	64	1.2732	0.3388	1.2744	1.1289
No log	33.0	66	0.9444	0.4258	0.9457	0.9725
No log	34.0	68	0.9235	0.4787	0.9248	0.9616
No log	35.0	70	1.1143	0.4229	1.1155	1.0562
No log	36.0	72	0.9919	0.4463	0.9932	0.9966
No log	37.0	74	0.9019	0.4870	0.9032	0.9504
No log	38.0	76	0.9207	0.4647	0.9220	0.9602
No log	39.0	78	1.0801	0.4370	1.0814	1.0399
No log	40.0	80	0.9952	0.4655	0.9965	0.9982
No log	41.0	82	1.0290	0.4717	1.0302	1.0150
No log	42.0	84	1.3677	0.3423	1.3689	1.1700
No log	43.0	86	1.3793	0.3279	1.3805	1.1749
No log	44.0	88	0.9764	0.4712	0.9775	0.9887
No log	45.0	90	0.9642	0.4936	0.9653	0.9825
No log	46.0	92	1.1259	0.4455	1.1271	1.0616
No log	47.0	94	1.4775	0.3157	1.4788	1.2160
No log	48.0	96	1.2621	0.3795	1.2634	1.1240
No log	49.0	98	1.0020	0.4889	1.0031	1.0015
No log	50.0	100	0.9991	0.4828	1.0001	1.0000
No log	51.0	102	0.9420	0.4765	0.9431	0.9712
No log	52.0	104	1.1518	0.3898	1.1530	1.0738
No log	53.0	106	1.0689	0.4239	1.0700	1.0344
No log	54.0	108	0.8836	0.4996	0.8847	0.9406
No log	55.0	110	0.8788	0.4854	0.8799	0.9380
No log	56.0	112	0.8865	0.4812	0.8877	0.9422
No log	57.0	114	0.8530	0.4874	0.8541	0.9242
No log	58.0	116	0.8827	0.4900	0.8839	0.9401
No log	59.0	118	0.9000	0.4911	0.9011	0.9493
No log	60.0	120	0.9621	0.4665	0.9632	0.9814
No log	61.0	122	1.0961	0.4142	1.0973	1.0475
No log	62.0	124	0.9851	0.4697	0.9863	0.9931
No log	63.0	126	0.8852	0.4898	0.8863	0.9414
No log	64.0	128	0.8785	0.4904	0.8796	0.9379
No log	65.0	130	0.9426	0.4649	0.9437	0.9714
No log	66.0	132	0.8977	0.4832	0.8988	0.9480
No log	67.0	134	0.8883	0.4814	0.8893	0.9431
No log	68.0	136	0.9276	0.4833	0.9287	0.9637
No log	69.0	138	0.9780	0.4613	0.9791	0.9895
No log	70.0	140	0.9592	0.4678	0.9604	0.9800
No log	71.0	142	0.9342	0.4706	0.9353	0.9671
No log	72.0	144	0.9435	0.4651	0.9447	0.9719
No log	73.0	146	0.9606	0.4591	0.9617	0.9807
No log	74.0	148	0.9355	0.4623	0.9367	0.9678

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold3

ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k1_task1_organization_k1_k1_fold3