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	15.3533	0.0	15.3534	3.9183
No log	2.0	2	13.2222	0.0	13.2225	3.6363
No log	3.0	3	11.4967	0.0038	11.4970	3.3907
No log	4.0	4	10.1657	0.0018	10.1659	3.1884
No log	5.0	5	9.6497	0.0	9.6499	3.1064
No log	6.0	6	8.6937	0.0	8.6942	2.9486
No log	7.0	7	7.0135	0.0007	7.0135	2.6483
No log	8.0	8	6.4408	-0.0058	6.4410	2.5379
No log	9.0	9	5.4855	0.0175	5.4859	2.3422
No log	10.0	10	4.6835	0.0095	4.6841	2.1643
No log	11.0	11	4.2020	0.0078	4.2027	2.0500
No log	12.0	12	3.4947	0.0078	3.4953	1.8696
No log	13.0	13	3.4661	0.0078	3.4665	1.8619
No log	14.0	14	2.8460	0.0039	2.8465	1.6871
No log	15.0	15	2.4984	0.0543	2.4989	1.5808
No log	16.0	16	2.2715	0.0499	2.2720	1.5073
No log	17.0	17	1.8453	0.0501	1.8458	1.3586
No log	18.0	18	1.9316	0.1115	1.9321	1.3900
No log	19.0	19	1.7194	0.0797	1.7199	1.3115
No log	20.0	20	1.2765	0.0280	1.2768	1.1300
No log	21.0	21	1.1979	0.0280	1.1982	1.0946
No log	22.0	22	1.0790	0.0280	1.0793	1.0389
No log	23.0	23	0.9154	0.0280	0.9157	0.9569
No log	24.0	24	0.9241	0.0280	0.9244	0.9615
No log	25.0	25	0.8578	0.1512	0.8582	0.9264
No log	26.0	26	0.7224	0.4297	0.7227	0.8501
No log	27.0	27	0.7191	0.3603	0.7192	0.8481
No log	28.0	28	0.6836	0.4162	0.6836	0.8268
No log	29.0	29	0.5986	0.4413	0.5986	0.7737
No log	30.0	30	0.5839	0.4618	0.5840	0.7642
No log	31.0	31	0.5633	0.4657	0.5633	0.7505
No log	32.0	32	0.5471	0.4256	0.5470	0.7396
No log	33.0	33	0.5815	0.4066	0.5814	0.7625
No log	34.0	34	0.5806	0.4060	0.5803	0.7618
No log	35.0	35	0.5044	0.4851	0.5040	0.7100
No log	36.0	36	0.4873	0.5597	0.4871	0.6979
No log	37.0	37	0.4909	0.5600	0.4907	0.7005
No log	38.0	38	0.4828	0.5435	0.4824	0.6945
No log	39.0	39	0.6074	0.5071	0.6068	0.7790
No log	40.0	40	0.6828	0.4421	0.6823	0.8260
No log	41.0	41	0.6604	0.5001	0.6597	0.8122
No log	42.0	42	0.5538	0.6047	0.5531	0.7437
No log	43.0	43	0.4841	0.6532	0.4837	0.6955
No log	44.0	44	0.4974	0.6376	0.4972	0.7051
No log	45.0	45	0.4936	0.6585	0.4933	0.7023
No log	46.0	46	0.5613	0.6306	0.5607	0.7488
No log	47.0	47	0.7607	0.5562	0.7598	0.8717
No log	48.0	48	0.8676	0.4979	0.8667	0.9310
No log	49.0	49	0.8815	0.5094	0.8806	0.9384
No log	50.0	50	0.7891	0.5735	0.7882	0.8878
No log	51.0	51	0.5914	0.6443	0.5907	0.7686
No log	52.0	52	0.5190	0.6481	0.5185	0.7201
No log	53.0	53	0.5261	0.6551	0.5257	0.7250
No log	54.0	54	0.5936	0.6336	0.5930	0.7701
No log	55.0	55	0.7501	0.6000	0.7493	0.8656
No log	56.0	56	0.7995	0.5978	0.7986	0.8936
No log	57.0	57	0.7507	0.6117	0.7498	0.8659
No log	58.0	58	0.6709	0.6409	0.6702	0.8187
No log	59.0	59	0.6697	0.6437	0.6690	0.8179
No log	60.0	60	0.7674	0.6241	0.7666	0.8756
No log	61.0	61	0.7791	0.6253	0.7783	0.8822
No log	62.0	62	0.6930	0.6479	0.6924	0.8321
No log	63.0	63	0.5991	0.6741	0.5987	0.7738
No log	64.0	64	0.5947	0.6700	0.5943	0.7709
No log	65.0	65	0.6609	0.6470	0.6604	0.8126
No log	66.0	66	0.6708	0.6513	0.6703	0.8187
No log	67.0	67	0.7319	0.6486	0.7314	0.8552
No log	68.0	68	0.7585	0.6432	0.7579	0.8706
No log	69.0	69	0.7451	0.6358	0.7445	0.8629
No log	70.0	70	0.6680	0.6497	0.6676	0.8170
No log	71.0	71	0.6870	0.6355	0.6866	0.8286
No log	72.0	72	0.7177	0.6279	0.7173	0.8469
No log	73.0	73	0.7518	0.6254	0.7513	0.8668
No log	74.0	74	0.8850	0.5813	0.8843	0.9404
No log	75.0	75	0.8797	0.5804	0.8790	0.9376
No log	76.0	76	0.7638	0.6182	0.7633	0.8737
No log	77.0	77	0.6171	0.6638	0.6168	0.7854
No log	78.0	78	0.5814	0.6643	0.5812	0.7624
No log	79.0	79	0.6042	0.6689	0.6039	0.7771
No log	80.0	80	0.6736	0.6443	0.6732	0.8205
No log	81.0	81	0.7938	0.6194	0.7933	0.8907
No log	82.0	82	0.8347	0.6133	0.8342	0.9133
No log	83.0	83	0.7897	0.6211	0.7892	0.8884

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold2

Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold2