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	4.1986	0.0167	4.1964	2.0485
No log	2.0	16	1.4897	0.0418	1.4882	1.2199
No log	3.0	24	1.1127	0.0418	1.1111	1.0541
No log	4.0	32	2.2504	0.1086	2.2482	1.4994
No log	5.0	40	1.6719	0.1223	1.6699	1.2922
No log	6.0	48	2.2993	0.0686	2.2968	1.5155
No log	7.0	56	1.2191	0.2245	1.2174	1.1034
No log	8.0	64	0.7604	0.4134	0.7590	0.8712
No log	9.0	72	1.1446	0.2547	1.1425	1.0689
No log	10.0	80	1.5753	0.2114	1.5729	1.2542
No log	11.0	88	0.8964	0.3263	0.8947	0.9459
No log	12.0	96	0.9845	0.3086	0.9827	0.9913
No log	13.0	104	0.7306	0.4348	0.7292	0.8539
No log	14.0	112	0.9396	0.3222	0.9379	0.9685
No log	15.0	120	0.9334	0.3271	0.9317	0.9653
No log	16.0	128	1.0172	0.3301	1.0155	1.0077
No log	17.0	136	0.7912	0.4093	0.7898	0.8887
No log	18.0	144	0.9886	0.3458	0.9869	0.9934
No log	19.0	152	0.8060	0.4087	0.8046	0.8970
No log	20.0	160	0.6891	0.4841	0.6882	0.8296
No log	21.0	168	0.9010	0.3404	0.8995	0.9484
No log	22.0	176	0.9857	0.3522	0.9841	0.9920
No log	23.0	184	0.9770	0.3659	0.9754	0.9876
No log	24.0	192	0.9150	0.3518	0.9134	0.9557
No log	25.0	200	1.0075	0.3137	1.0058	1.0029
No log	26.0	208	0.9283	0.3647	0.9267	0.9626
No log	27.0	216	0.6857	0.5026	0.6845	0.8274
No log	28.0	224	0.6925	0.4971	0.6915	0.8315
No log	29.0	232	0.7883	0.3777	0.7870	0.8871
No log	30.0	240	1.1014	0.3198	1.0998	1.0487
No log	31.0	248	0.9134	0.3219	0.9119	0.9549
No log	32.0	256	1.0331	0.3240	1.0314	1.0156
No log	33.0	264	1.0979	0.3168	1.0963	1.0470
No log	34.0	272	0.7508	0.3745	0.7495	0.8658
No log	35.0	280	0.6666	0.5076	0.6656	0.8158
No log	36.0	288	0.7335	0.3911	0.7322	0.8557
No log	37.0	296	1.0186	0.3410	1.0171	1.0085
No log	38.0	304	1.0070	0.3430	1.0054	1.0027
No log	39.0	312	0.8600	0.3555	0.8587	0.9266
No log	40.0	320	1.2209	0.2574	1.2192	1.1042
No log	41.0	328	0.9417	0.3373	0.9402	0.9696
No log	42.0	336	1.0165	0.3249	1.0151	1.0075
No log	43.0	344	0.8816	0.3626	0.8802	0.9382
No log	44.0	352	0.8173	0.3903	0.8161	0.9034
No log	45.0	360	0.8654	0.3608	0.8641	0.9296
No log	46.0	368	0.7149	0.4042	0.7137	0.8448
No log	47.0	376	0.8080	0.3875	0.8067	0.8982
No log	48.0	384	0.8551	0.3682	0.8538	0.9240
No log	49.0	392	0.6868	0.4507	0.6857	0.8281
No log	50.0	400	0.8806	0.3993	0.8792	0.9376
No log	51.0	408	0.8096	0.3866	0.8081	0.8990
No log	52.0	416	0.7941	0.3823	0.7927	0.8904
No log	53.0	424	0.8204	0.4292	0.8191	0.9051
No log	54.0	432	0.6904	0.4695	0.6893	0.8302
No log	55.0	440	0.9260	0.3476	0.9246	0.9615

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold1

ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k10_task1_organization_k10_k10_fold1