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	9.3031	0.0	9.3031	3.0501
No log	2.0	4	7.7218	0.0	7.7218	2.7788
No log	3.0	6	6.5495	0.0001	6.5495	2.5592
No log	4.0	8	5.4264	0.0201	5.4264	2.3295
No log	5.0	10	4.4790	0.0115	4.4790	2.1164
No log	6.0	12	3.5725	0.0115	3.5725	1.8901
No log	7.0	14	2.9554	0.0	2.9554	1.7191
No log	8.0	16	2.4055	0.0909	2.4055	1.5510
No log	9.0	18	2.0028	0.0873	2.0028	1.4152
No log	10.0	20	1.5906	0.0419	1.5906	1.2612
No log	11.0	22	1.3085	0.0419	1.3085	1.1439
No log	12.0	24	1.1539	0.0726	1.1539	1.0742
No log	13.0	26	0.9026	0.0881	0.9026	0.9501
No log	14.0	28	1.1279	0.2324	1.1279	1.0620
No log	15.0	30	0.7565	0.4884	0.7565	0.8698
No log	16.0	32	0.7523	0.4834	0.7523	0.8674
No log	17.0	34	1.1261	0.3261	1.1261	1.0612
No log	18.0	36	0.5611	0.4661	0.5611	0.7491
No log	19.0	38	0.5610	0.4794	0.5610	0.7490
No log	20.0	40	0.9051	0.4386	0.9051	0.9514
No log	21.0	42	0.6155	0.5037	0.6155	0.7845
No log	22.0	44	0.6779	0.4897	0.6779	0.8233
No log	23.0	46	0.6583	0.4827	0.6583	0.8114
No log	24.0	48	0.6498	0.5403	0.6498	0.8061
No log	25.0	50	0.6783	0.5347	0.6783	0.8236
No log	26.0	52	0.6567	0.5738	0.6567	0.8104
No log	27.0	54	0.7624	0.5116	0.7624	0.8731
No log	28.0	56	0.6864	0.5688	0.6864	0.8285
No log	29.0	58	0.8845	0.5161	0.8845	0.9405
No log	30.0	60	0.8662	0.5197	0.8662	0.9307
No log	31.0	62	1.1792	0.3853	1.1792	1.0859
No log	32.0	64	0.8519	0.5132	0.8519	0.9230
No log	33.0	66	1.2147	0.4143	1.2147	1.1022
No log	34.0	68	0.9676	0.4920	0.9676	0.9837
No log	35.0	70	0.9323	0.5110	0.9323	0.9656
No log	36.0	72	0.8691	0.5092	0.8691	0.9322
No log	37.0	74	1.1920	0.4674	1.1920	1.0918
No log	38.0	76	0.9089	0.5018	0.9089	0.9534
No log	39.0	78	1.1730	0.3707	1.1730	1.0831
No log	40.0	80	0.8980	0.4873	0.8980	0.9477
No log	41.0	82	0.9752	0.4796	0.9752	0.9875
No log	42.0	84	1.2105	0.4201	1.2105	1.1002
No log	43.0	86	0.7232	0.5271	0.7232	0.8504
No log	44.0	88	0.6858	0.5381	0.6858	0.8282
No log	45.0	90	0.7574	0.5383	0.7574	0.8703
No log	46.0	92	1.0103	0.4863	1.0103	1.0052
No log	47.0	94	0.8343	0.5253	0.8343	0.9134
No log	48.0	96	0.7416	0.5533	0.7416	0.8612
No log	49.0	98	0.7368	0.5423	0.7368	0.8584
No log	50.0	100	0.7789	0.5199	0.7789	0.8825
No log	51.0	102	0.7732	0.5420	0.7732	0.8793
No log	52.0	104	0.8308	0.5445	0.8308	0.9115
No log	53.0	106	0.9180	0.5038	0.9180	0.9581
No log	54.0	108	0.7532	0.5536	0.7532	0.8679
No log	55.0	110	0.7145	0.5386	0.7145	0.8453
No log	56.0	112	0.8529	0.4992	0.8529	0.9235

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold0

ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold0