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	12.2931	-0.0049	12.2931	3.5062
No log	2.0	2	11.0973	-0.0181	11.0973	3.3313
No log	3.0	3	9.8898	0.0033	9.8898	3.1448
No log	4.0	4	8.9061	0.0018	8.9061	2.9843
No log	5.0	5	8.0275	0.0018	8.0275	2.8333
No log	6.0	6	7.1204	0.0018	7.1204	2.6684
No log	7.0	7	6.3945	0.0018	6.3945	2.5287
No log	8.0	8	5.9896	0.0058	5.9896	2.4474
No log	9.0	9	5.2978	0.0145	5.2978	2.3017
No log	10.0	10	4.5741	0.0118	4.5741	2.1387
No log	11.0	11	4.1519	0.0118	4.1519	2.0376
No log	12.0	12	3.7358	0.0040	3.7358	1.9328
No log	13.0	13	3.2957	0.0040	3.2957	1.8154
No log	14.0	14	2.9880	0.0040	2.9880	1.7286
No log	15.0	15	2.6359	0.0040	2.6359	1.6236
No log	16.0	16	2.3230	0.1487	2.3230	1.5241
No log	17.0	17	2.0984	0.1174	2.0984	1.4486
No log	18.0	18	1.9228	0.0509	1.9228	1.3866
No log	19.0	19	1.7333	0.0445	1.7333	1.3165
No log	20.0	20	1.5457	0.0445	1.5457	1.2433
No log	21.0	21	1.4062	0.0316	1.4062	1.1858
No log	22.0	22	1.3016	0.0212	1.3016	1.1409
No log	23.0	23	1.1728	0.0212	1.1728	1.0829
No log	24.0	24	1.0752	0.0316	1.0752	1.0369
No log	25.0	25	0.9990	0.0316	0.9990	0.9995
No log	26.0	26	0.9239	0.0316	0.9239	0.9612
No log	27.0	27	0.8738	0.1157	0.8738	0.9348
No log	28.0	28	0.8215	0.3857	0.8215	0.9064
No log	29.0	29	0.7681	0.4440	0.7681	0.8764
No log	30.0	30	0.7096	0.4726	0.7096	0.8424
No log	31.0	31	0.6737	0.5140	0.6737	0.8208
No log	32.0	32	0.6353	0.5167	0.6353	0.7971
No log	33.0	33	0.6025	0.4771	0.6025	0.7762
No log	34.0	34	0.6071	0.4336	0.6071	0.7791
No log	35.0	35	0.5745	0.4588	0.5745	0.7580
No log	36.0	36	0.5314	0.5468	0.5314	0.7290
No log	37.0	37	0.5266	0.5646	0.5266	0.7257
No log	38.0	38	0.5058	0.5228	0.5058	0.7112
No log	39.0	39	0.5330	0.4980	0.5330	0.7301
No log	40.0	40	0.5590	0.4951	0.5590	0.7477
No log	41.0	41	0.5153	0.5178	0.5153	0.7178
No log	42.0	42	0.4916	0.5589	0.4916	0.7012
No log	43.0	43	0.4991	0.5877	0.4991	0.7065
No log	44.0	44	0.5714	0.5824	0.5714	0.7559
No log	45.0	45	0.6714	0.5371	0.6714	0.8194
No log	46.0	46	0.6502	0.5711	0.6502	0.8063
No log	47.0	47	0.5755	0.6062	0.5755	0.7586
No log	48.0	48	0.5557	0.6294	0.5557	0.7455
No log	49.0	49	0.6017	0.6090	0.6017	0.7757
No log	50.0	50	0.7265	0.5627	0.7265	0.8524
No log	51.0	51	0.7706	0.5523	0.7706	0.8778
No log	52.0	52	0.7022	0.5858	0.7022	0.8380
No log	53.0	53	0.6960	0.5887	0.6960	0.8342
No log	54.0	54	0.7098	0.5910	0.7098	0.8425
No log	55.0	55	0.7998	0.5572	0.7998	0.8943
No log	56.0	56	0.8131	0.5550	0.8131	0.9017
No log	57.0	57	0.7888	0.5669	0.7888	0.8882
No log	58.0	58	0.7358	0.5942	0.7358	0.8578
No log	59.0	59	0.8231	0.5690	0.8231	0.9073
No log	60.0	60	0.9822	0.5256	0.9822	0.9910
No log	61.0	61	0.9708	0.5389	0.9708	0.9853
No log	62.0	62	0.8964	0.5600	0.8964	0.9468
No log	63.0	63	0.9331	0.5561	0.9331	0.9660
No log	64.0	64	0.9978	0.5465	0.9978	0.9989
No log	65.0	65	0.9238	0.5569	0.9238	0.9611
No log	66.0	66	0.8167	0.5854	0.8167	0.9037
No log	67.0	67	0.8655	0.5803	0.8655	0.9303
No log	68.0	68	1.0350	0.5406	1.0350	1.0174

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold4