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	11.0062	0.0006	11.0062	3.3176
No log	2.0	4	8.9400	0.0	8.9400	2.9900
No log	3.0	6	7.6160	0.0002	7.6163	2.7598
No log	4.0	8	5.2407	0.0364	5.2409	2.2893
No log	5.0	10	4.5984	0.0052	4.5985	2.1444
No log	6.0	12	3.3831	0.0078	3.3835	1.8394
No log	7.0	14	3.0044	0.0090	3.0047	1.7334
No log	8.0	16	2.1892	0.0718	2.1897	1.4798
No log	9.0	18	1.8401	0.0314	1.8407	1.3567
No log	10.0	20	1.4833	0.0487	1.4836	1.2180
No log	11.0	22	1.4084	0.0384	1.4087	1.1869
No log	12.0	24	1.1721	0.0280	1.1723	1.0827
No log	13.0	26	0.9599	0.0342	0.9600	0.9798
No log	14.0	28	0.8591	0.2584	0.8591	0.9269
No log	15.0	30	0.7833	0.3204	0.7833	0.8851
No log	16.0	32	0.8114	0.2858	0.8113	0.9007
No log	17.0	34	0.7271	0.3434	0.7270	0.8527
No log	18.0	36	0.7675	0.2619	0.7678	0.8763
No log	19.0	38	0.7639	0.3860	0.7635	0.8738
No log	20.0	40	1.2650	0.2181	1.2647	1.1246
No log	21.0	42	0.9655	0.3502	0.9649	0.9823
No log	22.0	44	0.7063	0.3784	0.7063	0.8404
No log	23.0	46	0.6593	0.4057	0.6588	0.8117
No log	24.0	48	0.6930	0.4227	0.6924	0.8321
No log	25.0	50	0.6232	0.4196	0.6227	0.7891
No log	26.0	52	0.7235	0.3446	0.7238	0.8508
No log	27.0	54	0.7393	0.3412	0.7397	0.8601
No log	28.0	56	0.6102	0.4366	0.6099	0.7810
No log	29.0	58	0.6247	0.4952	0.6240	0.7900
No log	30.0	60	0.6559	0.4813	0.6553	0.8095
No log	31.0	62	0.8446	0.3284	0.8450	0.9192
No log	32.0	64	0.9054	0.3065	0.9059	0.9518
No log	33.0	66	0.6975	0.4829	0.6970	0.8349
No log	34.0	68	0.6839	0.4986	0.6831	0.8265
No log	35.0	70	0.9092	0.3608	0.9096	0.9537
No log	36.0	72	0.9381	0.3247	0.9388	0.9689
No log	37.0	74	0.7574	0.4875	0.7570	0.8701
No log	38.0	76	0.7793	0.4973	0.7790	0.8826
No log	39.0	78	0.9233	0.4116	0.9237	0.9611
No log	40.0	80	0.9716	0.4035	0.9719	0.9858
No log	41.0	82	0.8882	0.4790	0.8878	0.9422
No log	42.0	84	0.9197	0.4453	0.9193	0.9588
No log	43.0	86	1.1477	0.3012	1.1486	1.0717
No log	44.0	88	1.1788	0.2806	1.1798	1.0862
No log	45.0	90	1.0508	0.4114	1.0511	1.0252
No log	46.0	92	0.9514	0.4598	0.9511	0.9752
No log	47.0	94	1.0323	0.4203	1.0327	1.0162
No log	48.0	96	1.0434	0.3892	1.0442	1.0218
No log	49.0	98	0.9154	0.5076	0.9154	0.9568
No log	50.0	100	0.8709	0.4922	0.8703	0.9329
No log	51.0	102	0.9822	0.4519	0.9823	0.9911
No log	52.0	104	1.1092	0.3724	1.1096	1.0534
No log	53.0	106	1.0423	0.4380	1.0424	1.0210
No log	54.0	108	0.9049	0.5029	0.9043	0.9509
No log	55.0	110	0.9164	0.4904	0.9159	0.9570
No log	56.0	112	1.0662	0.4177	1.0664	1.0327
No log	57.0	114	1.1251	0.4036	1.1255	1.0609
No log	58.0	116	1.0142	0.4611	1.0138	1.0069
No log	59.0	118	0.9235	0.5008	0.9222	0.9603
No log	60.0	120	0.9451	0.4983	0.9437	0.9715
No log	61.0	122	1.1310	0.4656	1.1305	1.0632
No log	62.0	124	1.3381	0.4013	1.3387	1.1570
No log	63.0	126	1.3691	0.3891	1.3698	1.1704
No log	64.0	128	1.3463	0.3972	1.3467	1.1605
No log	65.0	130	1.2733	0.4127	1.2734	1.1284
No log	66.0	132	1.1351	0.4635	1.1348	1.0653
No log	67.0	134	1.1016	0.4907	1.1015	1.0495
No log	68.0	136	1.1549	0.4157	1.1556	1.0750
No log	69.0	138	1.2447	0.3727	1.2459	1.1162

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV15_k1_task1_organization_k1_k1_fold2

ASAP_FineTuningBERT_AugV15_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_AugV15_k1_task1_organization_k1_k1_fold2