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	12	2.7223	0.0	2.7206	1.6494
No log	2.0	24	1.4879	0.0568	1.4863	1.2192
No log	3.0	36	1.6969	-0.0236	1.6951	1.3020
No log	4.0	48	1.1379	-0.1394	1.1366	1.0661
No log	5.0	60	0.9048	0.1585	0.9037	0.9507
No log	6.0	72	1.1092	0.1265	1.1084	1.0528
No log	7.0	84	1.6725	-0.0180	1.6712	1.2928
No log	8.0	96	1.1449	0.2080	1.1443	1.0697
No log	9.0	108	1.8063	0.1469	1.8048	1.3434
No log	10.0	120	0.7962	0.3518	0.7952	0.8918
No log	11.0	132	1.2731	0.1270	1.2716	1.1276
No log	12.0	144	1.2400	0.1465	1.2386	1.1129
No log	13.0	156	1.2290	0.1736	1.2277	1.1080
No log	14.0	168	1.0725	0.2182	1.0717	1.0352
No log	15.0	180	0.8322	0.3845	0.8316	0.9119
No log	16.0	192	1.0186	0.2084	1.0179	1.0089
No log	17.0	204	1.2223	0.1872	1.2214	1.1052
No log	18.0	216	1.2842	0.1799	1.2832	1.1328
No log	19.0	228	1.0949	0.1992	1.0940	1.0459
No log	20.0	240	0.8415	0.3224	0.8406	0.9169
No log	21.0	252	0.9295	0.3392	0.9285	0.9636
No log	22.0	264	0.9581	0.3019	0.9573	0.9784
No log	23.0	276	1.0244	0.2050	1.0234	1.0116
No log	24.0	288	1.0896	0.2213	1.0884	1.0433
No log	25.0	300	1.0592	0.2256	1.0581	1.0287
No log	26.0	312	0.8414	0.3498	0.8408	0.9169
No log	27.0	324	1.0203	0.2730	1.0192	1.0096
No log	28.0	336	0.7773	0.4253	0.7768	0.8814
No log	29.0	348	1.0395	0.2665	1.0388	1.0192
No log	30.0	360	0.8765	0.3660	0.8759	0.9359
No log	31.0	372	1.0187	0.3029	1.0180	1.0089
No log	32.0	384	0.9454	0.3203	0.9448	0.9720
No log	33.0	396	0.9500	0.2836	0.9493	0.9743
No log	34.0	408	0.9221	0.3443	0.9214	0.9599
No log	35.0	420	0.9402	0.3297	0.9398	0.9694
No log	36.0	432	0.9932	0.3351	0.9923	0.9962
No log	37.0	444	1.0513	0.2733	1.0505	1.0249
No log	38.0	456	0.9682	0.3183	0.9676	0.9837
No log	39.0	468	0.9907	0.3246	0.9904	0.9952
No log	40.0	480	0.9352	0.3430	0.9349	0.9669
No log	41.0	492	0.9041	0.3333	0.9034	0.9505
0.3593	42.0	504	0.8604	0.3938	0.8600	0.9274
0.3593	43.0	516	0.8572	0.3707	0.8568	0.9256
0.3593	44.0	528	0.8199	0.4117	0.8194	0.9052
0.3593	45.0	540	0.8710	0.4024	0.8706	0.9330
0.3593	46.0	552	0.8373	0.3944	0.8368	0.9148
0.3593	47.0	564	0.8061	0.4436	0.8056	0.8976
0.3593	48.0	576	0.8198	0.3859	0.8193	0.9051
0.3593	49.0	588	0.9519	0.2973	0.9513	0.9753
0.3593	50.0	600	0.9401	0.3382	0.9396	0.9693
0.3593	51.0	612	0.9030	0.3611	0.9024	0.9500
0.3593	52.0	624	0.9149	0.3617	0.9144	0.9562
0.3593	53.0	636	0.8858	0.3813	0.8853	0.9409
0.3593	54.0	648	0.8692	0.3902	0.8687	0.9321
0.3593	55.0	660	0.8604	0.3687	0.8600	0.9273
0.3593	56.0	672	0.8374	0.4029	0.8370	0.9149
0.3593	57.0	684	0.9187	0.3368	0.9183	0.9583
0.3593	58.0	696	0.9497	0.3363	0.9491	0.9742
0.3593	59.0	708	0.7701	0.4191	0.7695	0.8772
0.3593	60.0	720	0.8969	0.3453	0.8965	0.9468
0.3593	61.0	732	0.8345	0.3923	0.8341	0.9133
0.3593	62.0	744	0.8814	0.3923	0.8808	0.9385
0.3593	63.0	756	0.8598	0.4297	0.8593	0.9270
0.3593	64.0	768	0.8163	0.3987	0.8158	0.9032
0.3593	65.0	780	0.8724	0.4067	0.8718	0.9337
0.3593	66.0	792	0.8329	0.4194	0.8323	0.9123
0.3593	67.0	804	0.7877	0.4339	0.7871	0.8872

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV6_k15_task1_organization_k15_k15_fold1

ASAP_FineTuningBERT_AugV6_k15_task1_organization_k15_k15_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV6_k15_task1_organization_k15_k15_fold1