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	1	10.7670	0.0049	10.7673	3.2813
No log	2.0	2	9.6211	0.0	9.6213	3.1018
No log	3.0	3	8.6187	0.0	8.6190	2.9358
No log	4.0	4	7.7525	0.0	7.7527	2.7844
No log	5.0	5	6.9626	0.0	6.9628	2.6387
No log	6.0	6	6.2205	0.0009	6.2207	2.4941
No log	7.0	7	5.5524	0.0260	5.5527	2.3564
No log	8.0	8	5.0053	0.0074	5.0056	2.2373
No log	9.0	9	4.5170	0.0	4.5173	2.1254
No log	10.0	10	4.0529	0.0	4.0532	2.0133
No log	11.0	11	3.6131	0.0	3.6134	1.9009
No log	12.0	12	3.2035	0.0	3.2039	1.7899
No log	13.0	13	2.8816	0.0	2.8820	1.6976
No log	14.0	14	2.5090	0.0081	2.5095	1.5841
No log	15.0	15	2.2199	0.1224	2.2204	1.4901
No log	16.0	16	1.9608	0.0615	1.9612	1.4004
No log	17.0	17	1.7304	0.0513	1.7308	1.3156
No log	18.0	18	1.5728	0.0411	1.5732	1.2543
No log	19.0	19	1.3766	0.0241	1.3770	1.1735
No log	20.0	20	1.2516	0.0	1.2520	1.1189
No log	21.0	21	1.1021	0.0	1.1025	1.0500
No log	22.0	22	1.0100	0.0136	1.0104	1.0052
No log	23.0	23	0.9716	0.0202	0.9720	0.9859
No log	24.0	24	0.8350	0.1832	0.8353	0.9139
No log	25.0	25	0.7815	0.4341	0.7818	0.8842
No log	26.0	26	0.7364	0.4480	0.7367	0.8583
No log	27.0	27	0.6692	0.4801	0.6694	0.8182
No log	28.0	28	0.6998	0.4876	0.7000	0.8367
No log	29.0	29	0.7864	0.4905	0.7866	0.8869
No log	30.0	30	0.7283	0.5268	0.7285	0.8535
No log	31.0	31	0.6431	0.5239	0.6433	0.8021
No log	32.0	32	0.7114	0.5082	0.7117	0.8436
No log	33.0	33	0.5992	0.5269	0.5995	0.7743
No log	34.0	34	0.5315	0.5266	0.5317	0.7292
No log	35.0	35	0.5366	0.5121	0.5369	0.7327
No log	36.0	36	0.5464	0.5004	0.5469	0.7395
No log	37.0	37	0.5593	0.4881	0.5599	0.7483
No log	38.0	38	0.5751	0.4981	0.5755	0.7586
No log	39.0	39	0.6092	0.4840	0.6092	0.7805
No log	40.0	40	0.5835	0.4930	0.5835	0.7639
No log	41.0	41	0.5348	0.5659	0.5352	0.7316
No log	42.0	42	0.5271	0.5998	0.5275	0.7263
No log	43.0	43	0.5648	0.6044	0.5649	0.7516
No log	44.0	44	0.6858	0.5193	0.6857	0.8280
No log	45.0	45	0.7486	0.4637	0.7486	0.8652
No log	46.0	46	0.6928	0.5278	0.6931	0.8325
No log	47.0	47	0.5727	0.6164	0.5733	0.7572
No log	48.0	48	0.5642	0.6070	0.5649	0.7516
No log	49.0	49	0.5410	0.6233	0.5416	0.7359
No log	50.0	50	0.5536	0.6323	0.5540	0.7443
No log	51.0	51	0.6505	0.5946	0.6507	0.8066
No log	52.0	52	0.6938	0.5774	0.6940	0.8331
No log	53.0	53	0.6570	0.5916	0.6574	0.8108
No log	54.0	54	0.5989	0.5935	0.5995	0.7743
No log	55.0	55	0.6168	0.5723	0.6174	0.7857
No log	56.0	56	0.6189	0.5692	0.6195	0.7871
No log	57.0	57	0.5977	0.5918	0.5983	0.7735
No log	58.0	58	0.6543	0.5973	0.6547	0.8091
No log	59.0	59	0.7290	0.5371	0.7292	0.8539
No log	60.0	60	0.7324	0.5454	0.7326	0.8559
No log	61.0	61	0.6709	0.5726	0.6712	0.8193
No log	62.0	62	0.5962	0.6084	0.5966	0.7724
No log	63.0	63	0.5866	0.6072	0.5870	0.7662
No log	64.0	64	0.6079	0.6159	0.6083	0.7800
No log	65.0	65	0.6768	0.5887	0.6771	0.8229
No log	66.0	66	0.7562	0.5338	0.7564	0.8697
No log	67.0	67	0.7561	0.5264	0.7564	0.8697
No log	68.0	68	0.6839	0.5797	0.6843	0.8272
No log	69.0	69	0.6113	0.6047	0.6117	0.7821
No log	70.0	70	0.5896	0.6160	0.5900	0.7681

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemantic_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2