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	11.9300	-0.0278	11.9277	3.4536
No log	2.0	4	10.4478	-0.0008	10.4457	3.2320
No log	3.0	6	8.3891	0.0	8.3873	2.8961
No log	4.0	8	7.0342	0.0	7.0325	2.6519
No log	5.0	10	5.7609	0.0301	5.7595	2.3999
No log	6.0	12	4.8754	0.0038	4.8742	2.2078
No log	7.0	14	4.0596	0.0	4.0585	2.0146
No log	8.0	16	3.2564	0.0	3.2554	1.8043
No log	9.0	18	2.6031	0.0061	2.6022	1.6131
No log	10.0	20	2.1299	0.0202	2.1290	1.4591
No log	11.0	22	1.6568	0.0202	1.6561	1.2869
No log	12.0	24	1.3143	0.0102	1.3137	1.1462
No log	13.0	26	1.0644	0.0102	1.0638	1.0314
No log	14.0	28	0.8979	0.3539	0.8974	0.9473
No log	15.0	30	0.8632	0.1589	0.8628	0.9289
No log	16.0	32	0.8055	0.1549	0.8052	0.8973
No log	17.0	34	0.8836	0.0889	0.8834	0.9399
No log	18.0	36	0.9689	0.0848	0.9687	0.9842
No log	19.0	38	0.8906	0.1785	0.8905	0.9436
No log	20.0	40	0.9575	0.3417	0.9575	0.9785
No log	21.0	42	0.7130	0.4565	0.7132	0.8445
No log	22.0	44	0.7381	0.5217	0.7384	0.8593
No log	23.0	46	0.7237	0.5403	0.7239	0.8508
No log	24.0	48	0.7951	0.5159	0.7952	0.8918
No log	25.0	50	0.7617	0.5473	0.7617	0.8728
No log	26.0	52	0.7485	0.5736	0.7485	0.8652
No log	27.0	54	0.7516	0.5624	0.7515	0.8669
No log	28.0	56	0.8090	0.6007	0.8087	0.8993
No log	29.0	58	0.7956	0.5541	0.7955	0.8919
No log	30.0	60	0.6806	0.6425	0.6805	0.8249
No log	31.0	62	0.6717	0.6426	0.6716	0.8195
No log	32.0	64	0.6883	0.6275	0.6883	0.8296
No log	33.0	66	0.8153	0.5167	0.8155	0.9031
No log	34.0	68	0.8327	0.6033	0.8324	0.9124
No log	35.0	70	0.8877	0.5820	0.8874	0.9420
No log	36.0	72	0.6804	0.5674	0.6806	0.8250
No log	37.0	74	0.6683	0.5901	0.6684	0.8176
No log	38.0	76	1.0462	0.5642	1.0457	1.0226
No log	39.0	78	1.3287	0.5106	1.3280	1.1524
No log	40.0	80	0.7335	0.6206	0.7332	0.8562
No log	41.0	82	0.7048	0.6110	0.7046	0.8394
No log	42.0	84	0.7964	0.6204	0.7959	0.8921
No log	43.0	86	0.7128	0.6398	0.7125	0.8441
No log	44.0	88	0.6632	0.6320	0.6629	0.8142
No log	45.0	90	0.8636	0.5680	0.8630	0.9290
No log	46.0	92	0.9322	0.5492	0.9315	0.9652
No log	47.0	94	0.6171	0.6341	0.6168	0.7854
No log	48.0	96	0.6054	0.6676	0.6052	0.7779
No log	49.0	98	0.7380	0.5967	0.7375	0.8588
No log	50.0	100	0.7887	0.5965	0.7882	0.8878
No log	51.0	102	0.6880	0.6303	0.6876	0.8292
No log	52.0	104	0.7459	0.5944	0.7456	0.8635
No log	53.0	106	0.8286	0.5856	0.8282	0.9100
No log	54.0	108	0.8330	0.6006	0.8325	0.9124
No log	55.0	110	0.7337	0.6191	0.7333	0.8563
No log	56.0	112	0.6686	0.6369	0.6683	0.8175
No log	57.0	114	0.7805	0.5893	0.7801	0.8832
No log	58.0	116	0.7975	0.5708	0.7971	0.8928
No log	59.0	118	0.6464	0.6377	0.6461	0.8038
No log	60.0	120	0.6429	0.6528	0.6425	0.8015
No log	61.0	122	0.7065	0.6280	0.7060	0.8402
No log	62.0	124	0.7871	0.6256	0.7865	0.8868
No log	63.0	126	0.7006	0.6348	0.7002	0.8368
No log	64.0	128	0.6744	0.6278	0.6741	0.8211
No log	65.0	130	0.6872	0.6288	0.6868	0.8287
No log	66.0	132	0.7282	0.6099	0.7277	0.8531
No log	67.0	134	0.6627	0.6402	0.6623	0.8138
No log	68.0	136	0.6512	0.6472	0.6509	0.8068

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_fold3

Version3ASAP_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/
Version3ASAP_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3