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	1	9.8329	0.0	9.8332	3.1358
No log	2.0	2	8.6378	0.0	8.6382	2.9391
No log	3.0	3	7.8686	0.0	7.8690	2.8052
No log	4.0	4	7.3781	0.0	7.3783	2.7163
No log	5.0	5	7.0798	0.0	7.0798	2.6608
No log	6.0	6	6.5907	0.0	6.5908	2.5673
No log	7.0	7	6.1294	-0.0028	6.1296	2.4758
No log	8.0	8	5.7058	0.0131	5.7061	2.3887
No log	9.0	9	5.2515	0.0039	5.2518	2.2917
No log	10.0	10	4.8337	0.0039	4.8340	2.1986
No log	11.0	11	4.4321	0.0039	4.4325	2.1054
No log	12.0	12	4.0149	0.0	4.0153	2.0038
No log	13.0	13	3.6023	0.0	3.6027	1.8981
No log	14.0	14	3.2146	0.0	3.2150	1.7930
No log	15.0	15	2.8618	0.0	2.8622	1.6918
No log	16.0	16	2.5448	0.0	2.5452	1.5954
No log	17.0	17	2.2943	0.1558	2.2947	1.5148
No log	18.0	18	2.0345	0.0860	2.0350	1.4265
No log	19.0	19	1.8021	0.0372	1.8026	1.3426
No log	20.0	20	1.6244	0.0213	1.6249	1.2747
No log	21.0	21	1.4730	0.0107	1.4734	1.2139
No log	22.0	22	1.3314	0.0107	1.3318	1.1540
No log	23.0	23	1.2108	0.0107	1.2112	1.1005
No log	24.0	24	1.0922	0.0107	1.0926	1.0453
No log	25.0	25	0.9920	0.0107	0.9924	0.9962
No log	26.0	26	0.9186	0.0107	0.9189	0.9586
No log	27.0	27	0.8655	0.1111	0.8658	0.9305
No log	28.0	28	0.8186	0.3572	0.8189	0.9049
No log	29.0	29	0.7736	0.4012	0.7738	0.8797
No log	30.0	30	0.7341	0.3851	0.7343	0.8569
No log	31.0	31	0.6900	0.4104	0.6902	0.8308
No log	32.0	32	0.6480	0.4069	0.6480	0.8050
No log	33.0	33	0.6114	0.4053	0.6114	0.7819
No log	34.0	34	0.5860	0.4233	0.5859	0.7654
No log	35.0	35	0.5603	0.4053	0.5601	0.7484
No log	36.0	36	0.5506	0.4333	0.5504	0.7419
No log	37.0	37	0.5868	0.4843	0.5866	0.7659
No log	38.0	38	0.5763	0.4937	0.5759	0.7589
No log	39.0	39	0.5590	0.4716	0.5585	0.7473
No log	40.0	40	0.5703	0.5169	0.5697	0.7548
No log	41.0	41	0.6468	0.4856	0.6463	0.8039
No log	42.0	42	0.6255	0.4905	0.6249	0.7905
No log	43.0	43	0.5964	0.5804	0.5956	0.7717
No log	44.0	44	0.6106	0.5696	0.6097	0.7808
No log	45.0	45	0.6287	0.5934	0.6278	0.7923
No log	46.0	46	0.6899	0.5828	0.6892	0.8302
No log	47.0	47	0.7163	0.5738	0.7155	0.8459
No log	48.0	48	0.6885	0.5907	0.6875	0.8292
No log	49.0	49	0.7089	0.5750	0.7077	0.8412
No log	50.0	50	0.7362	0.5748	0.7350	0.8573
No log	51.0	51	0.7890	0.5579	0.7880	0.8877
No log	52.0	52	0.8831	0.5145	0.8824	0.9393
No log	53.0	53	0.9020	0.5112	0.9013	0.9494
No log	54.0	54	0.8012	0.5548	0.8002	0.8945
No log	55.0	55	0.7625	0.5586	0.7612	0.8725
No log	56.0	56	0.7555	0.5660	0.7543	0.8685
No log	57.0	57	0.7338	0.5660	0.7327	0.8560
No log	58.0	58	0.7367	0.5576	0.7358	0.8578
No log	59.0	59	0.7244	0.5636	0.7235	0.8506
No log	60.0	60	0.7171	0.5756	0.7160	0.8462
No log	61.0	61	0.7317	0.5691	0.7306	0.8547
No log	62.0	62	0.7590	0.5673	0.7580	0.8707
No log	63.0	63	0.8422	0.5450	0.8416	0.9174
No log	64.0	64	0.8802	0.5265	0.8796	0.9379
No log	65.0	65	0.8596	0.5348	0.8589	0.9268

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2