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	3	10.8626	0.0012	10.8607	3.2956
No log	2.0	6	9.6850	0.0	9.6832	3.1118
No log	3.0	9	7.7524	0.0	7.7508	2.7840
No log	4.0	12	6.8030	0.0	6.8014	2.6080
No log	5.0	15	5.3217	0.0	5.3204	2.3066
No log	6.0	18	3.7362	0.0	3.7351	1.9326
No log	7.0	21	2.7531	0.0	2.7521	1.6590
No log	8.0	24	2.0226	0.1188	2.0219	1.4219
No log	9.0	27	1.4960	0.0166	1.4954	1.2229
No log	10.0	30	1.1014	0.0463	1.1009	1.0492
No log	11.0	33	0.8726	0.1288	0.8722	0.9339
No log	12.0	36	1.2636	0.2000	1.2636	1.1241
No log	13.0	39	0.7574	0.5381	0.7574	0.8703
No log	14.0	42	1.0103	0.3566	1.0106	1.0053
No log	15.0	45	0.7492	0.5131	0.7497	0.8658
No log	16.0	48	0.7300	0.5002	0.7307	0.8548
No log	17.0	51	0.8183	0.4946	0.8193	0.9051
No log	18.0	54	0.6439	0.5103	0.6443	0.8027
No log	19.0	57	0.5490	0.5580	0.5495	0.7413
No log	20.0	60	0.5495	0.5631	0.5500	0.7416
No log	21.0	63	0.5658	0.5803	0.5661	0.7524
No log	22.0	66	0.6740	0.5687	0.6747	0.8214
No log	23.0	69	0.5741	0.6191	0.5745	0.7579
No log	24.0	72	0.6233	0.6271	0.6236	0.7897
No log	25.0	75	0.5777	0.6297	0.5781	0.7603
No log	26.0	78	0.6002	0.6279	0.6004	0.7749
No log	27.0	81	0.6540	0.5803	0.6543	0.8089
No log	28.0	84	0.6732	0.6300	0.6732	0.8205
No log	29.0	87	0.6104	0.6340	0.6104	0.7813
No log	30.0	90	0.6451	0.6382	0.6451	0.8032
No log	31.0	93	0.6615	0.6539	0.6614	0.8133
No log	32.0	96	0.6843	0.6299	0.6842	0.8272
No log	33.0	99	0.7323	0.6374	0.7322	0.8557
No log	34.0	102	0.5778	0.6193	0.5779	0.7602
No log	35.0	105	0.6814	0.6524	0.6813	0.8254
No log	36.0	108	0.5812	0.6409	0.5814	0.7625
No log	37.0	111	0.6267	0.6547	0.6267	0.7916
No log	38.0	114	0.5481	0.6411	0.5481	0.7404
No log	39.0	117	0.5587	0.6424	0.5587	0.7475
No log	40.0	120	0.8097	0.5841	0.8095	0.8997
No log	41.0	123	0.5980	0.6281	0.5980	0.7733
No log	42.0	126	0.6361	0.6261	0.6361	0.7975
No log	43.0	129	0.5595	0.6502	0.5596	0.7481
No log	44.0	132	0.8161	0.5993	0.8160	0.9033
No log	45.0	135	0.7169	0.6425	0.7168	0.8466
No log	46.0	138	0.5724	0.6142	0.5726	0.7567
No log	47.0	141	0.5928	0.6576	0.5928	0.7699
No log	48.0	144	0.6232	0.6583	0.6231	0.7894
No log	49.0	147	0.5945	0.6523	0.5945	0.7711
No log	50.0	150	0.5678	0.6397	0.5678	0.7535
No log	51.0	153	0.5373	0.6326	0.5373	0.7330
No log	52.0	156	0.5453	0.6294	0.5452	0.7384
No log	53.0	159	0.7201	0.5944	0.7199	0.8485
No log	54.0	162	0.5502	0.6455	0.5502	0.7418
No log	55.0	165	0.5480	0.6167	0.5481	0.7403
No log	56.0	168	0.6023	0.6548	0.6022	0.7760
No log	57.0	171	0.6575	0.6303	0.6575	0.8108
No log	58.0	174	0.6444	0.6299	0.6444	0.8027
No log	59.0	177	0.6531	0.6118	0.6530	0.8081
No log	60.0	180	0.6645	0.6193	0.6644	0.8151
No log	61.0	183	0.6279	0.6388	0.6279	0.7924
No log	62.0	186	0.6000	0.6422	0.6000	0.7746
No log	63.0	189	0.5805	0.6472	0.5806	0.7620
No log	64.0	192	0.5465	0.6484	0.5466	0.7393
No log	65.0	195	0.5863	0.6483	0.5864	0.7658
No log	66.0	198	0.7261	0.6206	0.7260	0.8521
No log	67.0	201	0.6160	0.6327	0.6161	0.7849
No log	68.0	204	0.5969	0.6425	0.5969	0.7726

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV6_k1_task1_organization_k1_k1_fold3

Version2ASAP_FineTuningBERT_AugV6_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV6_k1_task1_organization_k1_k1_fold3