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	6.0823	0.0352	6.0823	2.4662
No log	2.0	16	1.7917	0.0759	1.7917	1.3386
No log	3.0	24	1.3859	0.0418	1.3859	1.1772
No log	4.0	32	1.3677	0.0408	1.3677	1.1695
No log	5.0	40	1.0872	0.0518	1.0872	1.0427
No log	6.0	48	1.0062	0.1179	1.0062	1.0031
No log	7.0	56	1.3001	0.0963	1.3001	1.1402
No log	8.0	64	0.9065	0.3001	0.9065	0.9521
No log	9.0	72	1.8545	0.0665	1.8545	1.3618
No log	10.0	80	1.0179	0.2337	1.0179	1.0089
No log	11.0	88	0.8270	0.3063	0.8270	0.9094
No log	12.0	96	1.0842	0.2961	1.0842	1.0412
No log	13.0	104	1.2423	0.2395	1.2423	1.1146
No log	14.0	112	1.1986	0.2872	1.1986	1.0948
No log	15.0	120	0.9067	0.3874	0.9067	0.9522
No log	16.0	128	0.8362	0.3929	0.8362	0.9145
No log	17.0	136	1.0150	0.3555	1.0150	1.0075
No log	18.0	144	0.8917	0.3818	0.8917	0.9443
No log	19.0	152	0.8489	0.4280	0.8489	0.9213
No log	20.0	160	0.8632	0.4526	0.8632	0.9291
No log	21.0	168	0.9890	0.3554	0.9890	0.9945
No log	22.0	176	0.9240	0.3918	0.9240	0.9612
No log	23.0	184	0.8426	0.4149	0.8426	0.9179
No log	24.0	192	0.8565	0.4283	0.8565	0.9255
No log	25.0	200	0.7579	0.4593	0.7579	0.8706
No log	26.0	208	0.8484	0.4087	0.8484	0.9211
No log	27.0	216	0.9020	0.4186	0.9020	0.9497
No log	28.0	224	0.7861	0.4336	0.7861	0.8866
No log	29.0	232	0.7636	0.4521	0.7636	0.8738
No log	30.0	240	0.9003	0.4164	0.9003	0.9488
No log	31.0	248	0.8225	0.4295	0.8225	0.9069
No log	32.0	256	0.8154	0.4288	0.8154	0.9030
No log	33.0	264	0.7493	0.4507	0.7493	0.8656
No log	34.0	272	0.9034	0.4088	0.9034	0.9505
No log	35.0	280	0.7800	0.4231	0.7800	0.8832
No log	36.0	288	0.6878	0.4944	0.6878	0.8293
No log	37.0	296	0.7026	0.4604	0.7026	0.8382
No log	38.0	304	0.6908	0.4732	0.6908	0.8311
No log	39.0	312	0.6666	0.4944	0.6666	0.8165
No log	40.0	320	0.6597	0.5201	0.6597	0.8122
No log	41.0	328	0.6943	0.5027	0.6943	0.8332
No log	42.0	336	0.7192	0.4667	0.7192	0.8480
No log	43.0	344	0.6735	0.4850	0.6735	0.8207
No log	44.0	352	0.6911	0.4755	0.6911	0.8313
No log	45.0	360	0.6912	0.4901	0.6912	0.8314
No log	46.0	368	0.7287	0.4400	0.7287	0.8537
No log	47.0	376	0.7725	0.4277	0.7725	0.8789
No log	48.0	384	0.7723	0.4375	0.7723	0.8788
No log	49.0	392	0.7289	0.4435	0.7289	0.8538
No log	50.0	400	0.7262	0.4764	0.7262	0.8521
No log	51.0	408	0.6947	0.4495	0.6947	0.8335
No log	52.0	416	0.7264	0.4881	0.7264	0.8523
No log	53.0	424	0.6914	0.4877	0.6914	0.8315
No log	54.0	432	0.8301	0.4034	0.8301	0.9111
No log	55.0	440	0.7635	0.4456	0.7635	0.8738
No log	56.0	448	0.6824	0.4644	0.6824	0.8261
No log	57.0	456	0.6936	0.4718	0.6936	0.8328
No log	58.0	464	0.6721	0.4957	0.6721	0.8198
No log	59.0	472	0.7149	0.4994	0.7149	0.8455
No log	60.0	480	0.6495	0.5211	0.6495	0.8059
No log	61.0	488	0.6599	0.4989	0.6599	0.8124
No log	62.0	496	0.6838	0.4740	0.6838	0.8269
0.5455	63.0	504	0.6520	0.5054	0.6520	0.8075
0.5455	64.0	512	0.6730	0.4983	0.6730	0.8204
0.5455	65.0	520	0.6827	0.4980	0.6827	0.8263
0.5455	66.0	528	0.7034	0.4952	0.7034	0.8387
0.5455	67.0	536	0.7001	0.4766	0.7001	0.8367
0.5455	68.0	544	0.6672	0.5117	0.6672	0.8168
0.5455	69.0	552	0.7090	0.4700	0.7090	0.8420
0.5455	70.0	560	0.6765	0.5295	0.6765	0.8225
0.5455	71.0	568	0.6605	0.5311	0.6605	0.8127
0.5455	72.0	576	0.6544	0.5014	0.6544	0.8090
0.5455	73.0	584	0.6994	0.4843	0.6994	0.8363
0.5455	74.0	592	0.6668	0.4852	0.6668	0.8166
0.5455	75.0	600	0.6582	0.5135	0.6582	0.8113
0.5455	76.0	608	0.6516	0.5002	0.6516	0.8072
0.5455	77.0	616	0.6575	0.5321	0.6575	0.8109
0.5455	78.0	624	0.6603	0.5062	0.6603	0.8126
0.5455	79.0	632	0.6690	0.5361	0.6690	0.8179
0.5455	80.0	640	0.6542	0.5023	0.6542	0.8088
0.5455	81.0	648	0.6498	0.5217	0.6498	0.8061
0.5455	82.0	656	0.6413	0.5027	0.6413	0.8008
0.5455	83.0	664	0.6849	0.4893	0.6849	0.8276
0.5455	84.0	672	0.6686	0.5111	0.6686	0.8177
0.5455	85.0	680	0.6501	0.5240	0.6501	0.8063
0.5455	86.0	688	0.6470	0.5050	0.6470	0.8043
0.5455	87.0	696	0.6572	0.4994	0.6572	0.8107
0.5455	88.0	704	0.6585	0.5122	0.6585	0.8115
0.5455	89.0	712	0.6405	0.5181	0.6405	0.8003
0.5455	90.0	720	0.6819	0.4934	0.6819	0.8258
0.5455	91.0	728	0.6505	0.5238	0.6505	0.8065
0.5455	92.0	736	0.6595	0.5063	0.6595	0.8121
0.5455	93.0	744	0.6490	0.5286	0.6490	0.8056
0.5455	94.0	752	0.6540	0.5408	0.6540	0.8087
0.5455	95.0	760	0.6567	0.5147	0.6567	0.8104
0.5455	96.0	768	0.6513	0.5257	0.6513	0.8070
0.5455	97.0	776	0.6537	0.5380	0.6537	0.8085
0.5455	98.0	784	0.6523	0.5339	0.6523	0.8076
0.5455	99.0	792	0.6582	0.5225	0.6582	0.8113
0.5455	100.0	800	0.6547	0.5352	0.6547	0.8091

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold0

Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV5_k10_task1_organization_k10_k10_fold0