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	13.6149	0.0	13.6127	3.6895
No log	2.0	4	10.3282	0.0054	10.3263	3.2135
No log	3.0	6	8.1282	0.0	8.1262	2.8506
No log	4.0	8	7.0120	-0.0164	7.0107	2.6478
No log	5.0	10	5.7294	0.0050	5.7275	2.3932
No log	6.0	12	4.1166	0.0076	4.1151	2.0286
No log	7.0	14	3.7946	0.0076	3.7937	1.9478
No log	8.0	16	2.5223	0.0978	2.5217	1.5880
No log	9.0	18	2.1251	0.0465	2.1241	1.4574
No log	10.0	20	1.4883	0.0302	1.4877	1.2197
No log	11.0	22	1.1493	0.0202	1.1489	1.0719
No log	12.0	24	1.0214	0.0320	1.0207	1.0103
No log	13.0	26	0.8558	0.2077	0.8554	0.9249
No log	14.0	28	0.7771	0.2516	0.7769	0.8814
No log	15.0	30	0.7916	0.1706	0.7915	0.8897
No log	16.0	32	0.8867	0.1225	0.8866	0.9416
No log	17.0	34	0.8937	0.1656	0.8936	0.9453
No log	18.0	36	0.9724	0.3146	0.9724	0.9861
No log	19.0	38	1.0913	0.3102	1.0915	1.0448
No log	20.0	40	1.2612	0.3051	1.2614	1.1231
No log	21.0	42	1.1539	0.3511	1.1539	1.0742
No log	22.0	44	1.0636	0.4243	1.0636	1.0313
No log	23.0	46	1.0170	0.5095	1.0169	1.0084
No log	24.0	48	0.7066	0.6175	0.7066	0.8406
No log	25.0	50	0.8685	0.5975	0.8684	0.9319
No log	26.0	52	1.1413	0.5395	1.1413	1.0683
No log	27.0	54	1.0885	0.5716	1.0883	1.0432
No log	28.0	56	0.9536	0.6114	0.9535	0.9765
No log	29.0	58	0.8164	0.6118	0.8163	0.9035
No log	30.0	60	0.9327	0.6050	0.9324	0.9656
No log	31.0	62	0.8441	0.6268	0.8438	0.9186
No log	32.0	64	0.5666	0.6645	0.5664	0.7526
No log	33.0	66	0.7221	0.6338	0.7218	0.8496
No log	34.0	68	0.9485	0.5883	0.9481	0.9737
No log	35.0	70	0.6252	0.6629	0.6250	0.7906
No log	36.0	72	0.6625	0.6363	0.6623	0.8138
No log	37.0	74	0.8997	0.6045	0.8992	0.9483
No log	38.0	76	0.7968	0.6351	0.7964	0.8924
No log	39.0	78	0.6562	0.6283	0.6559	0.8099
No log	40.0	80	0.7114	0.6584	0.7111	0.8432
No log	41.0	82	1.2241	0.5375	1.2235	1.1061
No log	42.0	84	0.9895	0.5721	0.9890	0.9945
No log	43.0	86	0.7021	0.6296	0.7018	0.8377
No log	44.0	88	0.7873	0.6124	0.7869	0.8871
No log	45.0	90	0.8193	0.6091	0.8189	0.9049
No log	46.0	92	0.7933	0.6051	0.7930	0.8905
No log	47.0	94	0.7726	0.6130	0.7723	0.8788
No log	48.0	96	0.8486	0.6258	0.8481	0.9209
No log	49.0	98	0.7960	0.6312	0.7955	0.8919
No log	50.0	100	0.6437	0.6469	0.6433	0.8021
No log	51.0	102	0.6728	0.6434	0.6724	0.8200
No log	52.0	104	0.8448	0.6140	0.8442	0.9188

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_fold3

Version12AGAINNNASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

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_fold3