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	8.5537	0.0	8.5538	2.9247
No log	2.0	4	7.7945	0.0	7.7944	2.7918
No log	3.0	6	6.0063	0.0210	6.0064	2.4508
No log	4.0	8	4.5021	0.0039	4.5024	2.1219
No log	5.0	10	3.7737	0.0	3.7740	1.9427
No log	6.0	12	2.9828	0.0	2.9832	1.7272
No log	7.0	14	2.4058	0.1105	2.4063	1.5512
No log	8.0	16	1.8431	0.0449	1.8436	1.3578
No log	9.0	18	1.4011	0.0107	1.4016	1.1839
No log	10.0	20	1.1172	0.0	1.1176	1.0572
No log	11.0	22	0.9351	0.0	0.9355	0.9672
No log	12.0	24	0.8096	0.3518	0.8100	0.9000
No log	13.0	26	0.7786	0.1043	0.7789	0.8826
No log	14.0	28	0.7627	0.0781	0.7630	0.8735
No log	15.0	30	0.6795	0.1966	0.6797	0.8244
No log	16.0	32	0.6882	0.1734	0.6882	0.8296
No log	17.0	34	0.7498	0.1734	0.7497	0.8658
No log	18.0	36	0.6520	0.4766	0.6517	0.8073
No log	19.0	38	0.5479	0.5779	0.5474	0.7399
No log	20.0	40	0.6301	0.5618	0.6293	0.7933
No log	21.0	42	0.7429	0.5716	0.7418	0.8613
No log	22.0	44	0.7855	0.5745	0.7843	0.8856
No log	23.0	46	0.7125	0.5853	0.7115	0.8435
No log	24.0	48	0.6254	0.6021	0.6249	0.7905
No log	25.0	50	0.6008	0.5980	0.6003	0.7748
No log	26.0	52	0.8806	0.5211	0.8795	0.9378
No log	27.0	54	0.6752	0.5830	0.6745	0.8213
No log	28.0	56	0.5866	0.5834	0.5863	0.7657
No log	29.0	58	0.7621	0.5550	0.7612	0.8725
No log	30.0	60	0.6083	0.5872	0.6078	0.7796
No log	31.0	62	0.6350	0.6053	0.6345	0.7966
No log	32.0	64	1.0685	0.5155	1.0672	1.0330
No log	33.0	66	1.0694	0.5127	1.0682	1.0335
No log	34.0	68	0.6721	0.5933	0.6714	0.8194
No log	35.0	70	0.8813	0.5602	0.8803	0.9383
No log	36.0	72	0.9324	0.5579	0.9313	0.9650
No log	37.0	74	0.7795	0.5993	0.7788	0.8825
No log	38.0	76	0.8795	0.5677	0.8785	0.9373
No log	39.0	78	0.8763	0.5537	0.8753	0.9356
No log	40.0	80	0.8146	0.5585	0.8138	0.9021
No log	41.0	82	1.0550	0.5169	1.0539	1.0266
No log	42.0	84	0.8687	0.5564	0.8678	0.9315
No log	43.0	86	0.6148	0.6103	0.6146	0.7840
No log	44.0	88	0.5653	0.6125	0.5652	0.7518
No log	45.0	90	0.6577	0.5782	0.6572	0.8107
No log	46.0	92	1.0721	0.4618	1.0711	1.0349
No log	47.0	94	0.9815	0.4824	0.9806	0.9902
No log	48.0	96	0.6227	0.5656	0.6222	0.7888
No log	49.0	98	0.5679	0.5937	0.5675	0.7533
No log	50.0	100	0.6534	0.5935	0.6529	0.8080
No log	51.0	102	0.7193	0.5627	0.7186	0.8477
No log	52.0	104	0.6702	0.5641	0.6696	0.8183
No log	53.0	106	0.6508	0.5443	0.6502	0.8063
No log	54.0	108	0.7080	0.5581	0.7073	0.8410
No log	55.0	110	0.6726	0.5737	0.6720	0.8197
No log	56.0	112	0.6131	0.5859	0.6129	0.7829
No log	57.0	114	0.6422	0.5903	0.6421	0.8013
No log	58.0	116	0.6443	0.5973	0.6439	0.8024
No log	59.0	118	0.8713	0.5316	0.8704	0.9329
No log	60.0	120	1.0552	0.4974	1.0541	1.0267
No log	61.0	122	0.9226	0.5180	0.9217	0.9601
No log	62.0	124	0.7366	0.5540	0.7359	0.8578
No log	63.0	126	0.7452	0.5672	0.7445	0.8628
No log	64.0	128	0.8198	0.5514	0.8190	0.9050

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2