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	8.8909	0.0	8.8909	2.9818
No log	2.0	4	7.5614	0.0	7.5614	2.7498
No log	3.0	6	6.6634	0.0	6.6634	2.5814
No log	4.0	8	5.8049	0.0163	5.8049	2.4093
No log	5.0	10	4.9242	0.0115	4.9242	2.2190
No log	6.0	12	4.0923	0.0039	4.0923	2.0229
No log	7.0	14	3.2815	0.0	3.2815	1.8115
No log	8.0	16	2.5454	0.0148	2.5454	1.5954
No log	9.0	18	1.9773	0.0689	1.9773	1.4061
No log	10.0	20	1.5658	0.0316	1.5658	1.2513
No log	11.0	22	1.3276	0.0316	1.3276	1.1522
No log	12.0	24	1.1998	0.0316	1.1998	1.0954
No log	13.0	26	1.0253	0.0316	1.0253	1.0126
No log	14.0	28	1.0414	0.0484	1.0414	1.0205
No log	15.0	30	1.2678	0.1301	1.2678	1.1260
No log	16.0	32	1.3230	0.1646	1.3230	1.1502
No log	17.0	34	0.9795	0.1150	0.9795	0.9897
No log	18.0	36	0.9632	0.1128	0.9632	0.9814
No log	19.0	38	0.8966	0.3022	0.8966	0.9469
No log	20.0	40	0.8495	0.3564	0.8495	0.9217
No log	21.0	42	0.6727	0.4553	0.6727	0.8202
No log	22.0	44	0.7777	0.2616	0.7777	0.8819
No log	23.0	46	0.6292	0.4800	0.6292	0.7932
No log	24.0	48	0.6394	0.4967	0.6394	0.7997
No log	25.0	50	0.5744	0.4324	0.5744	0.7579
No log	26.0	52	0.6060	0.4039	0.6060	0.7785
No log	27.0	54	0.5564	0.4655	0.5564	0.7459
No log	28.0	56	0.5569	0.5055	0.5569	0.7463
No log	29.0	58	0.5659	0.4906	0.5659	0.7523
No log	30.0	60	0.5904	0.4557	0.5904	0.7684
No log	31.0	62	0.6090	0.4407	0.6090	0.7804
No log	32.0	64	0.6108	0.5097	0.6108	0.7816
No log	33.0	66	0.6216	0.5113	0.6216	0.7884
No log	34.0	68	0.6416	0.5094	0.6416	0.8010
No log	35.0	70	0.6437	0.5251	0.6437	0.8023
No log	36.0	72	0.6695	0.4735	0.6695	0.8183
No log	37.0	74	0.6717	0.5193	0.6717	0.8196
No log	38.0	76	0.6698	0.5256	0.6698	0.8184
No log	39.0	78	0.7085	0.5049	0.7085	0.8417
No log	40.0	80	0.6251	0.5217	0.6251	0.7906
No log	41.0	82	0.6222	0.5422	0.6222	0.7888
No log	42.0	84	0.6508	0.5301	0.6508	0.8067
No log	43.0	86	0.7259	0.4942	0.7259	0.8520
No log	44.0	88	0.6840	0.5186	0.6840	0.8270
No log	45.0	90	0.7064	0.5316	0.7064	0.8405
No log	46.0	92	0.8093	0.4752	0.8093	0.8996
No log	47.0	94	0.7670	0.4721	0.7670	0.8758
No log	48.0	96	0.7884	0.4672	0.7884	0.8879
No log	49.0	98	0.9166	0.4553	0.9166	0.9574
No log	50.0	100	0.8007	0.4835	0.8007	0.8948
No log	51.0	102	0.7517	0.5047	0.7517	0.8670

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV6_k2_task1_organization_k2_k2_fold0

ASAP_FineTuningBERT_AugV6_k2_task1_organization_k2_k2_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV6_k2_task1_organization_k2_k2_fold0