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	8	5.2494	0.0358	5.2494	2.2912
No log	2.0	16	1.5823	0.0316	1.5823	1.2579
No log	3.0	24	1.1550	0.0316	1.1550	1.0747
No log	4.0	32	1.1690	0.1199	1.1690	1.0812
No log	5.0	40	1.7625	0.1958	1.7625	1.3276
No log	6.0	48	1.4950	0.2178	1.4950	1.2227
No log	7.0	56	1.0254	0.3856	1.0254	1.0126
No log	8.0	64	0.8162	0.4549	0.8162	0.9034
No log	9.0	72	1.8735	0.2280	1.8735	1.3688
No log	10.0	80	1.2427	0.3132	1.2427	1.1148
No log	11.0	88	1.3033	0.3422	1.3033	1.1416
No log	12.0	96	1.0698	0.3834	1.0698	1.0343
No log	13.0	104	0.7137	0.4904	0.7137	0.8448
No log	14.0	112	0.7094	0.4911	0.7094	0.8423
No log	15.0	120	0.7431	0.4846	0.7431	0.8620
No log	16.0	128	0.7169	0.4925	0.7169	0.8467
No log	17.0	136	0.7258	0.5022	0.7258	0.8519
No log	18.0	144	0.8073	0.4469	0.8073	0.8985
No log	19.0	152	0.7537	0.4751	0.7537	0.8682
No log	20.0	160	0.6882	0.5230	0.6882	0.8296
No log	21.0	168	1.0564	0.3950	1.0564	1.0278
No log	22.0	176	0.7921	0.4481	0.7921	0.8900
No log	23.0	184	0.7054	0.5004	0.7054	0.8399
No log	24.0	192	0.8359	0.4479	0.8359	0.9143
No log	25.0	200	0.8070	0.4604	0.8070	0.8983
No log	26.0	208	0.8508	0.4241	0.8508	0.9224
No log	27.0	216	0.8534	0.4629	0.8534	0.9238
No log	28.0	224	0.9616	0.3986	0.9616	0.9806
No log	29.0	232	0.7147	0.4891	0.7147	0.8454
No log	30.0	240	0.7199	0.5014	0.7199	0.8485
No log	31.0	248	0.7341	0.4734	0.7341	0.8568
No log	32.0	256	0.7680	0.4791	0.7680	0.8763
No log	33.0	264	0.7337	0.4838	0.7337	0.8566
No log	34.0	272	0.7557	0.4866	0.7557	0.8693
No log	35.0	280	0.7544	0.4855	0.7544	0.8685
No log	36.0	288	0.7901	0.5002	0.7901	0.8889
No log	37.0	296	0.7689	0.4927	0.7689	0.8769
No log	38.0	304	0.7109	0.5256	0.7109	0.8431
No log	39.0	312	0.6732	0.5258	0.6732	0.8205
No log	40.0	320	0.6887	0.5130	0.6887	0.8299
No log	41.0	328	0.7314	0.5191	0.7314	0.8552
No log	42.0	336	0.7510	0.4928	0.7510	0.8666
No log	43.0	344	0.7343	0.4843	0.7343	0.8569
No log	44.0	352	0.6784	0.5052	0.6784	0.8237
No log	45.0	360	0.8591	0.4952	0.8591	0.9269
No log	46.0	368	0.7729	0.4829	0.7729	0.8791
No log	47.0	376	0.7284	0.4852	0.7284	0.8535
No log	48.0	384	0.7448	0.4841	0.7448	0.8630
No log	49.0	392	0.7267	0.5234	0.7267	0.8525
No log	50.0	400	0.7407	0.4806	0.7407	0.8606
No log	51.0	408	0.8322	0.5180	0.8322	0.9122
No log	52.0	416	0.7278	0.5015	0.7278	0.8531
No log	53.0	424	0.7100	0.5276	0.7100	0.8426
No log	54.0	432	0.7101	0.5285	0.7101	0.8427
No log	55.0	440	0.7303	0.5038	0.7303	0.8546
No log	56.0	448	0.7537	0.4885	0.7537	0.8682
No log	57.0	456	0.7276	0.4971	0.7276	0.8530
No log	58.0	464	0.7911	0.4772	0.7911	0.8895
No log	59.0	472	0.7746	0.4995	0.7746	0.8801
No log	60.0	480	0.7382	0.5196	0.7382	0.8592
No log	61.0	488	0.7165	0.5222	0.7165	0.8465
No log	62.0	496	0.7242	0.5143	0.7242	0.8510
0.3665	63.0	504	0.7727	0.5055	0.7727	0.8790
0.3665	64.0	512	0.7460	0.5039	0.7460	0.8637
0.3665	65.0	520	0.6986	0.5031	0.6986	0.8359
0.3665	66.0	528	0.6981	0.5123	0.6981	0.8355
0.3665	67.0	536	0.7529	0.5033	0.7529	0.8677
0.3665	68.0	544	0.7459	0.4925	0.7459	0.8637
0.3665	69.0	552	0.6886	0.5205	0.6886	0.8298
0.3665	70.0	560	0.7368	0.5098	0.7368	0.8584
0.3665	71.0	568	0.7358	0.4846	0.7358	0.8578
0.3665	72.0	576	0.7534	0.4963	0.7534	0.8680
0.3665	73.0	584	0.7646	0.5097	0.7646	0.8744
0.3665	74.0	592	0.6942	0.5133	0.6942	0.8332

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_fold4

ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV10_k10_task1_organization_k10_k10_fold4