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	8.5640	0.0	8.5642	2.9265
No log	2.0	2	7.6874	0.0	7.6876	2.7726
No log	3.0	3	7.0401	0.0	7.0404	2.6534
No log	4.0	4	6.5911	0.0	6.5914	2.5674
No log	5.0	5	6.1059	0.0004	6.1062	2.4711
No log	6.0	6	5.6805	0.0073	5.6808	2.3834
No log	7.0	7	5.2484	0.0035	5.2487	2.2910
No log	8.0	8	4.7976	0.0039	4.7980	2.1904
No log	9.0	9	4.3576	0.0	4.3579	2.0876
No log	10.0	10	3.9434	0.0	3.9438	1.9859
No log	11.0	11	3.5062	0.0	3.5066	1.8726
No log	12.0	12	3.0768	0.0	3.0772	1.7542
No log	13.0	13	2.6472	0.0	2.6476	1.6272
No log	14.0	14	2.2950	0.1795	2.2955	1.5151
No log	15.0	15	1.9980	0.0539	1.9984	1.4137
No log	16.0	16	1.7767	0.0372	1.7771	1.3331
No log	17.0	17	1.5511	0.0280	1.5515	1.2456
No log	18.0	18	1.3918	0.0280	1.3922	1.1799
No log	19.0	19	1.2632	0.0280	1.2636	1.1241
No log	20.0	20	1.1496	0.0280	1.1499	1.0724
No log	21.0	21	1.0403	0.0174	1.0407	1.0201
No log	22.0	22	0.9532	0.0174	0.9535	0.9765
No log	23.0	23	0.8839	0.0280	0.8842	0.9403
No log	24.0	24	0.8219	0.2456	0.8222	0.9067
No log	25.0	25	0.7413	0.4498	0.7414	0.8611
No log	26.0	26	0.6892	0.4113	0.6894	0.8303
No log	27.0	27	0.6403	0.4501	0.6404	0.8003
No log	28.0	28	0.6106	0.4900	0.6106	0.7814
No log	29.0	29	0.5650	0.4857	0.5649	0.7516
No log	30.0	30	0.5401	0.4876	0.5400	0.7348
No log	31.0	31	0.5296	0.4555	0.5294	0.7276
No log	32.0	32	0.5226	0.4632	0.5224	0.7228
No log	33.0	33	0.4959	0.5229	0.4956	0.7040
No log	34.0	34	0.4945	0.5324	0.4942	0.7030
No log	35.0	35	0.4798	0.5342	0.4795	0.6924
No log	36.0	36	0.5313	0.4917	0.5309	0.7287
No log	37.0	37	0.5154	0.4906	0.5149	0.7176
No log	38.0	38	0.4729	0.5808	0.4725	0.6874
No log	39.0	39	0.4780	0.5838	0.4777	0.6912
No log	40.0	40	0.5039	0.6351	0.5034	0.7095
No log	41.0	41	0.6739	0.5455	0.6733	0.8205
No log	42.0	42	0.6961	0.5513	0.6954	0.8339
No log	43.0	43	0.5885	0.6102	0.5879	0.7667
No log	44.0	44	0.5010	0.6438	0.5007	0.7076
No log	45.0	45	0.5118	0.6520	0.5115	0.7152
No log	46.0	46	0.5596	0.6425	0.5590	0.7477
No log	47.0	47	0.8166	0.5316	0.8157	0.9032
No log	48.0	48	0.9304	0.5032	0.9294	0.9641
No log	49.0	49	0.8471	0.5343	0.8462	0.9199
No log	50.0	50	0.6126	0.6352	0.6120	0.7823
No log	51.0	51	0.5493	0.6358	0.5489	0.7409
No log	52.0	52	0.5654	0.6360	0.5650	0.7516
No log	53.0	53	0.6921	0.6114	0.6915	0.8315
No log	54.0	54	0.8025	0.5740	0.8017	0.8954
No log	55.0	55	0.7518	0.6017	0.7512	0.8667
No log	56.0	56	0.6371	0.6451	0.6366	0.7979
No log	57.0	57	0.6018	0.6362	0.6014	0.7755
No log	58.0	58	0.6609	0.6252	0.6605	0.8127
No log	59.0	59	0.8704	0.5831	0.8697	0.9326
No log	60.0	60	0.9087	0.5657	0.9080	0.9529
No log	61.0	61	0.7652	0.6100	0.7647	0.8745
No log	62.0	62	0.5832	0.6299	0.5829	0.7634
No log	63.0	63	0.5550	0.6167	0.5547	0.7448
No log	64.0	64	0.5869	0.6152	0.5865	0.7659
No log	65.0	65	0.7210	0.6214	0.7204	0.8487

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version_weird_ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

Version_weird_ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version_weird_ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold2