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	9.9936	0.0036	9.9936	3.1613
No log	2.0	2	8.8166	0.0	8.8166	2.9693
No log	3.0	3	8.0913	0.0	8.0913	2.8445
No log	4.0	4	7.5076	0.0	7.5076	2.7400
No log	5.0	5	7.1988	0.0	7.1988	2.6831
No log	6.0	6	6.7547	0.0	6.7547	2.5990
No log	7.0	7	6.3006	-0.0012	6.3006	2.5101
No log	8.0	8	5.9237	0.0222	5.9237	2.4339
No log	9.0	9	5.4952	0.0112	5.4952	2.3442
No log	10.0	10	5.0728	0.0115	5.0728	2.2523
No log	11.0	11	4.6824	0.0077	4.6824	2.1639
No log	12.0	12	4.3124	0.0039	4.3124	2.0766
No log	13.0	13	3.9333	0.0039	3.9333	1.9833
No log	14.0	14	3.5458	0.0	3.5458	1.8830
No log	15.0	15	3.1824	0.0	3.1824	1.7839
No log	16.0	16	2.8427	0.0	2.8427	1.6860
No log	17.0	17	2.5217	0.0413	2.5217	1.5880
No log	18.0	18	2.2294	0.1102	2.2294	1.4931
No log	19.0	19	1.9860	0.0689	1.9860	1.4093
No log	20.0	20	1.7514	0.0382	1.7514	1.3234
No log	21.0	21	1.5539	0.0316	1.5539	1.2466
No log	22.0	22	1.4062	0.0316	1.4062	1.1859
No log	23.0	23	1.2692	0.0316	1.2692	1.1266
No log	24.0	24	1.1504	0.0316	1.1504	1.0726
No log	25.0	25	1.0549	0.0316	1.0549	1.0271
No log	26.0	26	0.9517	0.0316	0.9517	0.9756
No log	27.0	27	0.8809	0.0362	0.8809	0.9385
No log	28.0	28	0.8238	0.4250	0.8238	0.9077
No log	29.0	29	0.7762	0.3928	0.7762	0.8810
No log	30.0	30	0.7400	0.3927	0.7400	0.8602
No log	31.0	31	0.6984	0.3322	0.6984	0.8357
No log	32.0	32	0.6624	0.3079	0.6624	0.8139
No log	33.0	33	0.6305	0.2954	0.6305	0.7940
No log	34.0	34	0.5932	0.3410	0.5932	0.7702
No log	35.0	35	0.5785	0.3250	0.5785	0.7606
No log	36.0	36	0.5772	0.3362	0.5772	0.7597
No log	37.0	37	0.5301	0.3754	0.5301	0.7281
No log	38.0	38	0.5116	0.4065	0.5116	0.7153
No log	39.0	39	0.5160	0.3642	0.5160	0.7183
No log	40.0	40	0.5053	0.4061	0.5053	0.7109
No log	41.0	41	0.4690	0.4797	0.4690	0.6849
No log	42.0	42	0.4721	0.4483	0.4721	0.6871
No log	43.0	43	0.4671	0.5018	0.4671	0.6835
No log	44.0	44	0.4841	0.5671	0.4841	0.6958
No log	45.0	45	0.4684	0.6115	0.4684	0.6844
No log	46.0	46	0.4677	0.5865	0.4677	0.6839
No log	47.0	47	0.4598	0.6381	0.4598	0.6781
No log	48.0	48	0.5767	0.5899	0.5767	0.7594
No log	49.0	49	0.5830	0.5949	0.5830	0.7635
No log	50.0	50	0.4886	0.6407	0.4886	0.6990
No log	51.0	51	0.4894	0.6422	0.4894	0.6996
No log	52.0	52	0.4879	0.6482	0.4879	0.6985
No log	53.0	53	0.5017	0.6633	0.5017	0.7083
No log	54.0	54	0.6426	0.5911	0.6426	0.8016
No log	55.0	55	0.6836	0.5748	0.6836	0.8268
No log	56.0	56	0.5849	0.6450	0.5849	0.7648
No log	57.0	57	0.4984	0.6614	0.4984	0.7059
No log	58.0	58	0.4971	0.6646	0.4971	0.7050
No log	59.0	59	0.5085	0.6761	0.5085	0.7131
No log	60.0	60	0.6312	0.6268	0.6312	0.7945
No log	61.0	61	0.7490	0.5743	0.7490	0.8655
No log	62.0	62	0.7137	0.5940	0.7137	0.8448
No log	63.0	63	0.5751	0.6643	0.5751	0.7584
No log	64.0	64	0.5151	0.6858	0.5151	0.7177
No log	65.0	65	0.5192	0.6877	0.5192	0.7206
No log	66.0	66	0.5517	0.6729	0.5517	0.7428
No log	67.0	67	0.5357	0.6795	0.5357	0.7319
No log	68.0	68	0.5084	0.6875	0.5084	0.7130
No log	69.0	69	0.4979	0.6807	0.4979	0.7056
No log	70.0	70	0.5150	0.6915	0.5150	0.7176
No log	71.0	71	0.5375	0.6758	0.5375	0.7331
No log	72.0	72	0.5018	0.6875	0.5018	0.7084
No log	73.0	73	0.4605	0.6905	0.4605	0.6786
No log	74.0	74	0.4560	0.6754	0.4560	0.6753
No log	75.0	75	0.4646	0.6718	0.4646	0.6816
No log	76.0	76	0.5163	0.6618	0.5163	0.7186
No log	77.0	77	0.5901	0.6373	0.5901	0.7682
No log	78.0	78	0.5882	0.6321	0.5882	0.7669
No log	79.0	79	0.5247	0.6528	0.5247	0.7244
No log	80.0	80	0.4645	0.6833	0.4645	0.6816
No log	81.0	81	0.4584	0.6700	0.4584	0.6771
No log	82.0	82	0.4603	0.6783	0.4603	0.6785
No log	83.0	83	0.4694	0.6806	0.4694	0.6851
No log	84.0	84	0.5071	0.6708	0.5071	0.7121
No log	85.0	85	0.5444	0.6527	0.5444	0.7378
No log	86.0	86	0.5462	0.6527	0.5462	0.7391
No log	87.0	87	0.5195	0.6516	0.5195	0.7207
No log	88.0	88	0.4941	0.6746	0.4941	0.7029
No log	89.0	89	0.4824	0.6716	0.4824	0.6945
No log	90.0	90	0.4837	0.6716	0.4837	0.6955

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version5ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0