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	10.4184	-0.0010	10.4184	3.2277
No log	2.0	4	7.1146	0.0	7.1150	2.6674
No log	3.0	6	5.0651	0.0141	5.0655	2.2507
No log	4.0	8	3.8793	0.0	3.8797	1.9697
No log	5.0	10	2.9269	0.0	2.9274	1.7110
No log	6.0	12	2.1042	0.0986	2.1047	1.4508
No log	7.0	14	2.3690	-0.0176	2.3693	1.5393
No log	8.0	16	1.3844	0.0107	1.3849	1.1768
No log	9.0	18	1.1910	0.0	1.1915	1.0916
No log	10.0	20	1.0719	0.0	1.0723	1.0355
No log	11.0	22	0.9832	0.0	0.9835	0.9917
No log	12.0	24	1.0399	0.0443	1.0402	1.0199
No log	13.0	26	1.0398	0.1209	1.0401	1.0198
No log	14.0	28	0.7124	0.4429	0.7126	0.8441
No log	15.0	30	0.6687	0.4684	0.6689	0.8178
No log	16.0	32	0.7175	0.4576	0.7177	0.8472
No log	17.0	34	0.8962	0.3935	0.8965	0.9469
No log	18.0	36	0.8153	0.3904	0.8156	0.9031
No log	19.0	38	0.6100	0.4875	0.6100	0.7811
No log	20.0	40	0.5946	0.4332	0.5945	0.7711
No log	21.0	42	0.5248	0.5122	0.5250	0.7245
No log	22.0	44	0.8747	0.3934	0.8751	0.9355
No log	23.0	46	0.7086	0.4649	0.7090	0.8420
No log	24.0	48	0.5205	0.4621	0.5206	0.7215
No log	25.0	50	0.5931	0.4065	0.5931	0.7701
No log	26.0	52	0.5118	0.4940	0.5119	0.7155
No log	27.0	54	0.6318	0.5157	0.6322	0.7951
No log	28.0	56	0.6487	0.5123	0.6491	0.8057
No log	29.0	58	0.5347	0.4847	0.5349	0.7313
No log	30.0	60	0.6481	0.3948	0.6480	0.8050
No log	31.0	62	0.6608	0.3930	0.6606	0.8128
No log	32.0	64	0.5622	0.5216	0.5622	0.7498
No log	33.0	66	0.5518	0.5704	0.5519	0.7429
No log	34.0	68	0.6437	0.5343	0.6436	0.8023
No log	35.0	70	0.7426	0.4782	0.7424	0.8616
No log	36.0	72	0.5892	0.5706	0.5891	0.7675
No log	37.0	74	0.5387	0.5936	0.5388	0.7340
No log	38.0	76	0.5356	0.5935	0.5356	0.7319
No log	39.0	78	0.6744	0.5568	0.6741	0.8210
No log	40.0	80	0.6813	0.5758	0.6810	0.8252
No log	41.0	82	0.5811	0.6041	0.5812	0.7624
No log	42.0	84	0.5998	0.5933	0.6000	0.7746
No log	43.0	86	0.5314	0.6053	0.5314	0.7290
No log	44.0	88	0.6511	0.5685	0.6509	0.8068
No log	45.0	90	0.5998	0.5788	0.5997	0.7744
No log	46.0	92	0.5066	0.5819	0.5067	0.7118
No log	47.0	94	0.5226	0.5894	0.5227	0.7230
No log	48.0	96	0.5725	0.5861	0.5726	0.7567
No log	49.0	98	0.8772	0.5080	0.8770	0.9365
No log	50.0	100	0.9091	0.4884	0.9089	0.9534
No log	51.0	102	0.6348	0.5534	0.6349	0.7968
No log	52.0	104	0.5896	0.5672	0.5896	0.7679
No log	53.0	106	0.5811	0.5613	0.5811	0.7623
No log	54.0	108	0.5264	0.5584	0.5265	0.7256
No log	55.0	110	0.5516	0.5635	0.5516	0.7427
No log	56.0	112	0.7149	0.5632	0.7148	0.8455
No log	57.0	114	0.6725	0.5591	0.6725	0.8201
No log	58.0	116	0.5953	0.5884	0.5955	0.7717
No log	59.0	118	0.6280	0.5669	0.6281	0.7925
No log	60.0	120	0.7257	0.5356	0.7256	0.8518
No log	61.0	122	0.5765	0.5669	0.5764	0.7592
No log	62.0	124	0.5267	0.5602	0.5268	0.7258
No log	63.0	126	0.5586	0.5727	0.5585	0.7473

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_fold2

ASAP_FineTuningBERT_AugV9_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_FineTuningBERT_AugV9_k1_task1_organization_k1_k1_fold2