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	10.0701	0.0	10.0703	3.1734
No log	2.0	4	9.5661	0.0	9.5662	3.0929
No log	3.0	6	8.7280	0.0	8.7282	2.9543
No log	4.0	8	7.2635	0.0	7.2638	2.6952
No log	5.0	10	5.6387	0.0361	5.6392	2.3747
No log	6.0	12	4.3990	0.0039	4.3995	2.0975
No log	7.0	14	3.5803	0.0	3.5807	1.8923
No log	8.0	16	2.8088	0.0	2.8093	1.6761
No log	9.0	18	2.1447	0.1487	2.1451	1.4646
No log	10.0	20	1.5630	0.0345	1.5634	1.2503
No log	11.0	22	1.1967	0.0107	1.1971	1.0941
No log	12.0	24	0.9162	0.0107	0.9166	0.9574
No log	13.0	26	0.7900	0.4148	0.7903	0.8890
No log	14.0	28	0.7808	0.1203	0.7812	0.8838
No log	15.0	30	0.7762	0.0856	0.7765	0.8812
No log	16.0	32	0.8099	0.0695	0.8101	0.9001
No log	17.0	34	0.8917	0.0533	0.8918	0.9443
No log	18.0	36	0.7631	0.3938	0.7628	0.8734
No log	19.0	38	0.7512	0.4302	0.7506	0.8664
No log	20.0	40	0.6572	0.5477	0.6564	0.8102
No log	21.0	42	0.6654	0.5699	0.6645	0.8151
No log	22.0	44	0.8321	0.5552	0.8309	0.9115
No log	23.0	46	0.7971	0.5990	0.7959	0.8921
No log	24.0	48	0.8407	0.5938	0.8394	0.9162
No log	25.0	50	1.0882	0.5117	1.0868	1.0425
No log	26.0	52	0.7564	0.6238	0.7551	0.8690
No log	27.0	54	0.6711	0.6403	0.6699	0.8185
No log	28.0	56	0.5814	0.6471	0.5805	0.7619
No log	29.0	58	0.6164	0.6444	0.6155	0.7845
No log	30.0	60	1.0906	0.4923	1.0895	1.0438
No log	31.0	62	0.8836	0.5711	0.8826	0.9395
No log	32.0	64	0.8143	0.5828	0.8134	0.9019
No log	33.0	66	0.5999	0.6653	0.5993	0.7741
No log	34.0	68	1.1203	0.5242	1.1192	1.0579
No log	35.0	70	1.1199	0.5414	1.1188	1.0577
No log	36.0	72	0.5992	0.6592	0.5987	0.7738
No log	37.0	74	0.5414	0.6642	0.5411	0.7356
No log	38.0	76	0.8639	0.5712	0.8631	0.9290
No log	39.0	78	1.2797	0.4710	1.2786	1.1307
No log	40.0	80	0.8392	0.5835	0.8384	0.9156
No log	41.0	82	0.6214	0.6657	0.6208	0.7879
No log	42.0	84	0.6941	0.6562	0.6933	0.8327
No log	43.0	86	1.0071	0.5437	1.0060	1.0030
No log	44.0	88	0.8211	0.6190	0.8201	0.9056
No log	45.0	90	0.6047	0.6544	0.6041	0.7772
No log	46.0	92	0.6487	0.6525	0.6480	0.8050
No log	47.0	94	0.8919	0.5400	0.8910	0.9439
No log	48.0	96	0.7280	0.6119	0.7272	0.8528
No log	49.0	98	0.7114	0.6334	0.7106	0.8430
No log	50.0	100	1.0982	0.5212	1.0970	1.0474
No log	51.0	102	1.2256	0.5058	1.2243	1.1065
No log	52.0	104	0.8672	0.6014	0.8663	0.9307
No log	53.0	106	0.6483	0.6578	0.6476	0.8047
No log	54.0	108	0.6736	0.6501	0.6729	0.8203
No log	55.0	110	1.0289	0.5298	1.0279	1.0139
No log	56.0	112	1.1773	0.4816	1.1764	1.0846
No log	57.0	114	0.8880	0.5602	0.8872	0.9419
No log	58.0	116	0.7127	0.6207	0.7120	0.8438
No log	59.0	118	0.8057	0.6035	0.8049	0.8971
No log	60.0	120	1.0145	0.5442	1.0134	1.0067
No log	61.0	122	0.8735	0.6024	0.8725	0.9341

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Version3ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version3ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold2