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	9.0268	0.0	9.0268	3.0045
No log	2.0	4	7.6307	0.0	7.6307	2.7624
No log	3.0	6	6.7600	0.0	6.7600	2.6000
No log	4.0	8	5.9513	0.0099	5.9513	2.4395
No log	5.0	10	5.0877	0.0115	5.0877	2.2556
No log	6.0	12	4.2557	0.0039	4.2557	2.0629
No log	7.0	14	3.4717	0.0	3.4717	1.8633
No log	8.0	16	2.7109	0.0	2.7109	1.6465
No log	9.0	18	2.0991	0.0689	2.0991	1.4488
No log	10.0	20	1.6469	0.0316	1.6469	1.2833
No log	11.0	22	1.3488	0.0316	1.3488	1.1614
No log	12.0	24	1.3312	0.0484	1.3312	1.1538
No log	13.0	26	1.0645	0.0316	1.0645	1.0318
No log	14.0	28	1.0848	0.0484	1.0848	1.0416
No log	15.0	30	1.5934	0.1594	1.5934	1.2623
No log	16.0	32	1.3335	0.1486	1.3335	1.1548
No log	17.0	34	0.9110	0.0800	0.9110	0.9544
No log	18.0	36	0.9525	0.2343	0.9525	0.9760
No log	19.0	38	1.4559	0.1965	1.4559	1.2066
No log	20.0	40	0.8756	0.3952	0.8756	0.9357
No log	21.0	42	0.7097	0.4029	0.7097	0.8425
No log	22.0	44	0.7467	0.3706	0.7467	0.8641
No log	23.0	46	0.8981	0.3570	0.8981	0.9477
No log	24.0	48	0.6847	0.4172	0.6847	0.8275
No log	25.0	50	0.6396	0.4115	0.6396	0.7998
No log	26.0	52	0.7954	0.3943	0.7954	0.8919
No log	27.0	54	0.7448	0.4194	0.7448	0.8630
No log	28.0	56	0.6262	0.4406	0.6262	0.7913
No log	29.0	58	0.6670	0.4502	0.6670	0.8167
No log	30.0	60	0.6401	0.4998	0.6401	0.8000
No log	31.0	62	0.6648	0.5194	0.6648	0.8153
No log	32.0	64	0.7380	0.5049	0.7380	0.8591
No log	33.0	66	0.6958	0.5348	0.6958	0.8341
No log	34.0	68	0.7542	0.5231	0.7542	0.8684
No log	35.0	70	0.6706	0.5697	0.6706	0.8189
No log	36.0	72	0.6570	0.5511	0.6570	0.8106
No log	37.0	74	0.6499	0.5478	0.6499	0.8062
No log	38.0	76	0.6382	0.5441	0.6382	0.7989
No log	39.0	78	0.6178	0.5647	0.6178	0.7860
No log	40.0	80	0.6279	0.5299	0.6279	0.7924
No log	41.0	82	0.6441	0.5436	0.6441	0.8025
No log	42.0	84	0.7117	0.5418	0.7117	0.8436
No log	43.0	86	0.6217	0.5384	0.6217	0.7885
No log	44.0	88	0.6143	0.5539	0.6143	0.7837
No log	45.0	90	0.5961	0.5921	0.5961	0.7721
No log	46.0	92	0.6257	0.5401	0.6257	0.7910
No log	47.0	94	0.6206	0.5596	0.6206	0.7878
No log	48.0	96	0.6524	0.5686	0.6524	0.8077
No log	49.0	98	0.6436	0.5562	0.6436	0.8022
No log	50.0	100	0.6401	0.5570	0.6401	0.8001
No log	51.0	102	0.6235	0.5727	0.6235	0.7896
No log	52.0	104	0.6755	0.5142	0.6755	0.8219
No log	53.0	106	0.6613	0.5394	0.6613	0.8132
No log	54.0	108	0.6714	0.5439	0.6714	0.8194
No log	55.0	110	0.7150	0.5327	0.7150	0.8456

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV7_k2_task1_organization_k2_k2_fold0

ASAP_FineTuningBERT_AugV7_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_AugV7_k2_task1_organization_k2_k2_fold0