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	9.3762	0.0018	9.3762	3.0621
No log	2.0	4	8.9574	0.0	8.9574	2.9929
No log	3.0	6	8.6544	0.0	8.6544	2.9418
No log	4.0	8	8.3386	0.0	8.3386	2.8877
No log	5.0	10	7.9751	0.0	7.9751	2.8240
No log	6.0	12	7.5973	0.0	7.5973	2.7563
No log	7.0	14	7.2405	0.0	7.2405	2.6908
No log	8.0	16	6.7876	0.0	6.7876	2.6053
No log	9.0	18	5.8647	0.0111	5.8647	2.4217
No log	10.0	20	4.6519	0.0039	4.6519	2.1568
No log	11.0	22	3.7156	0.0	3.7156	1.9276
No log	12.0	24	3.3637	0.0	3.3637	1.8340
No log	13.0	26	2.4646	0.1257	2.4646	1.5699
No log	14.0	28	2.0484	0.0627	2.0484	1.4312
No log	15.0	30	1.6931	0.0601	1.6931	1.3012
No log	16.0	32	1.3521	0.0484	1.3521	1.1628
No log	17.0	34	1.1351	0.0316	1.1351	1.0654
No log	18.0	36	0.9063	0.0419	0.9063	0.9520
No log	19.0	38	1.1250	0.0419	1.1250	1.0606
No log	20.0	40	0.9164	0.1395	0.9164	0.9573
No log	21.0	42	0.7017	0.3950	0.7017	0.8377
No log	22.0	44	0.6914	0.3987	0.6914	0.8315
No log	23.0	46	1.1066	0.1868	1.1066	1.0519
No log	24.0	48	0.9961	0.2906	0.9961	0.9980
No log	25.0	50	0.6026	0.4108	0.6026	0.7763
No log	26.0	52	0.6133	0.3921	0.6133	0.7832
No log	27.0	54	0.7983	0.4200	0.7983	0.8935
No log	28.0	56	0.9119	0.3746	0.9119	0.9549
No log	29.0	58	0.6392	0.4240	0.6392	0.7995
No log	30.0	60	0.6577	0.4756	0.6577	0.8110
No log	31.0	62	0.6714	0.4590	0.6714	0.8194
No log	32.0	64	0.8080	0.4147	0.8080	0.8989
No log	33.0	66	0.6988	0.4577	0.6988	0.8359
No log	34.0	68	0.7070	0.4926	0.7070	0.8408
No log	35.0	70	0.7754	0.4235	0.7754	0.8806
No log	36.0	72	0.7610	0.4632	0.7610	0.8724
No log	37.0	74	0.7793	0.4904	0.7793	0.8828
No log	38.0	76	0.8151	0.4844	0.8151	0.9028
No log	39.0	78	0.8369	0.4480	0.8369	0.9148
No log	40.0	80	0.8190	0.4454	0.8190	0.9050
No log	41.0	82	0.7869	0.5029	0.7869	0.8871
No log	42.0	84	0.7472	0.4965	0.7472	0.8644
No log	43.0	86	0.8162	0.4424	0.8162	0.9034
No log	44.0	88	0.7285	0.4984	0.7285	0.8535
No log	45.0	90	0.7463	0.5184	0.7463	0.8639
No log	46.0	92	0.8228	0.4446	0.8228	0.9071
No log	47.0	94	0.8118	0.4957	0.8118	0.9010
No log	48.0	96	0.8629	0.5196	0.8629	0.9289
No log	49.0	98	0.8502	0.5096	0.8502	0.9221
No log	50.0	100	0.8488	0.5033	0.8488	0.9213
No log	51.0	102	0.9401	0.5136	0.9401	0.9696
No log	52.0	104	0.8770	0.5262	0.8770	0.9365
No log	53.0	106	0.8367	0.4608	0.8367	0.9147
No log	54.0	108	0.8385	0.4553	0.8385	0.9157
No log	55.0	110	0.8027	0.4916	0.8027	0.8959
No log	56.0	112	0.8692	0.5031	0.8692	0.9323
No log	57.0	114	0.7701	0.5227	0.7701	0.8775
No log	58.0	116	0.8111	0.4490	0.8111	0.9006
No log	59.0	118	0.8864	0.4427	0.8864	0.9415
No log	60.0	120	0.7776	0.4975	0.7776	0.8818
No log	61.0	122	0.8034	0.5340	0.8034	0.8963
No log	62.0	124	0.7789	0.5066	0.7789	0.8826
No log	63.0	126	0.8550	0.4566	0.8550	0.9247
No log	64.0	128	0.8781	0.4405	0.8781	0.9371
No log	65.0	130	0.7564	0.4843	0.7564	0.8697
No log	66.0	132	0.7679	0.5396	0.7679	0.8763
No log	67.0	134	0.7368	0.5394	0.7368	0.8583
No log	68.0	136	0.6931	0.5106	0.6931	0.8325
No log	69.0	138	0.7246	0.4959	0.7246	0.8513
No log	70.0	140	0.7192	0.5058	0.7192	0.8480
No log	71.0	142	0.7324	0.5329	0.7324	0.8558
No log	72.0	144	0.7496	0.5441	0.7496	0.8658
No log	73.0	146	0.7423	0.5110	0.7423	0.8616
No log	74.0	148	0.7467	0.4810	0.7467	0.8641
No log	75.0	150	0.7319	0.4832	0.7319	0.8555
No log	76.0	152	0.7416	0.5273	0.7416	0.8611
No log	77.0	154	0.7594	0.5490	0.7594	0.8714
No log	78.0	156	0.7412	0.5086	0.7412	0.8609
No log	79.0	158	0.7517	0.4850	0.7517	0.8670
No log	80.0	160	0.7693	0.4927	0.7693	0.8771
No log	81.0	162	0.7458	0.4936	0.7458	0.8636
No log	82.0	164	0.7454	0.5152	0.7454	0.8634
No log	83.0	166	0.7478	0.5182	0.7478	0.8647
No log	84.0	168	0.7430	0.5181	0.7430	0.8620
No log	85.0	170	0.7514	0.5002	0.7514	0.8668
No log	86.0	172	0.7601	0.4991	0.7601	0.8718
No log	87.0	174	0.7686	0.4987	0.7686	0.8767
No log	88.0	176	0.7783	0.5152	0.7783	0.8822
No log	89.0	178	0.7830	0.5116	0.7830	0.8849
No log	90.0	180	0.7840	0.5005	0.7840	0.8854
No log	91.0	182	0.7855	0.4984	0.7855	0.8863
No log	92.0	184	0.7814	0.5050	0.7814	0.8839
No log	93.0	186	0.7824	0.5076	0.7824	0.8846
No log	94.0	188	0.7840	0.5163	0.7840	0.8854
No log	95.0	190	0.7789	0.5189	0.7789	0.8825
No log	96.0	192	0.7722	0.5197	0.7722	0.8787
No log	97.0	194	0.7676	0.5122	0.7676	0.8761

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV9_k1_task1_organization_k1_k1_fold0

ASAP_FineTuningBERT_AugV9_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_AugV9_k1_task1_organization_k1_k1_fold0