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	9.0744	0.0	9.0744	3.0124
No log	2.0	4	7.6899	0.0	7.6899	2.7731
No log	3.0	6	6.8767	0.0	6.8767	2.6224
No log	4.0	8	6.1241	0.0107	6.1241	2.4747
No log	5.0	10	5.3069	0.0115	5.3069	2.3037
No log	6.0	12	4.5430	0.0039	4.5430	2.1314
No log	7.0	14	3.8006	0.0	3.8006	1.9495
No log	8.0	16	3.0288	0.0	3.0288	1.7403
No log	9.0	18	2.3782	0.1210	2.3782	1.5422
No log	10.0	20	1.8516	0.0484	1.8516	1.3607
No log	11.0	22	1.4310	0.0316	1.4310	1.1963
No log	12.0	24	1.1403	0.0316	1.1403	1.0679
No log	13.0	26	0.9332	0.0316	0.9332	0.9660
No log	14.0	28	0.7952	0.2761	0.7952	0.8917
No log	15.0	30	0.7259	0.2436	0.7259	0.8520
No log	16.0	32	0.6944	0.1975	0.6944	0.8333
No log	17.0	34	0.6517	0.2394	0.6517	0.8073
No log	18.0	36	0.6976	0.2351	0.6976	0.8352
No log	19.0	38	0.6392	0.2826	0.6392	0.7995
No log	20.0	40	0.5146	0.4186	0.5146	0.7174
No log	21.0	42	0.8112	0.4322	0.8112	0.9006
No log	22.0	44	0.5991	0.5241	0.5991	0.7740
No log	23.0	46	0.5178	0.5698	0.5178	0.7196
No log	24.0	48	0.7901	0.5312	0.7901	0.8889
No log	25.0	50	0.6259	0.6124	0.6259	0.7911
No log	26.0	52	0.6282	0.6126	0.6282	0.7926
No log	27.0	54	0.8430	0.5731	0.8430	0.9181
No log	28.0	56	0.6196	0.6485	0.6196	0.7872
No log	29.0	58	0.5452	0.6670	0.5452	0.7384
No log	30.0	60	0.7795	0.5647	0.7795	0.8829
No log	31.0	62	0.5376	0.6425	0.5376	0.7332
No log	32.0	64	0.4401	0.6505	0.4401	0.6634
No log	33.0	66	0.5345	0.6501	0.5345	0.7311
No log	34.0	68	0.5004	0.6648	0.5004	0.7074
No log	35.0	70	0.5413	0.6765	0.5413	0.7357
No log	36.0	72	0.4823	0.6452	0.4823	0.6945
No log	37.0	74	0.8244	0.5688	0.8244	0.9079
No log	38.0	76	0.7386	0.5842	0.7386	0.8594
No log	39.0	78	0.4612	0.6286	0.4612	0.6791
No log	40.0	80	0.5329	0.6423	0.5329	0.7300
No log	41.0	82	0.6594	0.6476	0.6594	0.8120
No log	42.0	84	0.6454	0.6070	0.6454	0.8034
No log	43.0	86	0.6534	0.6107	0.6534	0.8083
No log	44.0	88	0.6701	0.6310	0.6701	0.8186
No log	45.0	90	0.7592	0.5860	0.7592	0.8713
No log	46.0	92	0.4634	0.6200	0.4634	0.6808
No log	47.0	94	0.4482	0.5860	0.4482	0.6695
No log	48.0	96	0.5214	0.6284	0.5214	0.7221
No log	49.0	98	0.5763	0.6299	0.5763	0.7591
No log	50.0	100	0.5132	0.6344	0.5132	0.7164
No log	51.0	102	0.6387	0.6422	0.6387	0.7992
No log	52.0	104	0.5668	0.6535	0.5668	0.7529
No log	53.0	106	0.4690	0.6300	0.4690	0.6848
No log	54.0	108	0.4651	0.6310	0.4651	0.6819
No log	55.0	110	0.7897	0.5633	0.7897	0.8887

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0