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	15.3489	0.0	15.3489	3.9178
No log	2.0	2	13.5969	0.0	13.5969	3.6874
No log	3.0	3	12.5071	0.0	12.5071	3.5365
No log	4.0	4	11.9955	0.0045	11.9955	3.4634
No log	5.0	5	11.3091	0.0084	11.3091	3.3629
No log	6.0	6	10.4326	0.0049	10.4326	3.2300
No log	7.0	7	9.6180	0.0036	9.6180	3.1013
No log	8.0	8	8.6944	0.0	8.6944	2.9486
No log	9.0	9	7.7529	0.0	7.7529	2.7844
No log	10.0	10	6.9239	0.0	6.9239	2.6313
No log	11.0	11	6.3526	0.0125	6.3526	2.5204
No log	12.0	12	5.6465	0.0227	5.6465	2.3762
No log	13.0	13	4.9255	0.0078	4.9255	2.2194
No log	14.0	14	4.2390	0.0115	4.2390	2.0589
No log	15.0	15	3.5232	0.0115	3.5232	1.8770
No log	16.0	16	3.0366	0.0115	3.0366	1.7426
No log	17.0	17	2.5181	0.0551	2.5181	1.5868
No log	18.0	18	2.0180	0.1022	2.0180	1.4205
No log	19.0	19	1.7525	0.0836	1.7525	1.3238
No log	20.0	20	1.4991	0.0488	1.4991	1.2244
No log	21.0	21	1.2875	0.0418	1.2875	1.1347
No log	22.0	22	1.1935	0.0316	1.1935	1.0925
No log	23.0	23	1.0656	0.0316	1.0656	1.0323
No log	24.0	24	0.9185	0.0312	0.9185	0.9584
No log	25.0	25	0.8413	0.2903	0.8413	0.9172
No log	26.0	26	0.8020	0.3324	0.8020	0.8956
No log	27.0	27	0.7398	0.3480	0.7398	0.8601
No log	28.0	28	0.6895	0.3099	0.6895	0.8303
No log	29.0	29	0.6912	0.2419	0.6912	0.8314
No log	30.0	30	0.6861	0.2411	0.6861	0.8283
No log	31.0	31	0.6538	0.2560	0.6538	0.8086
No log	32.0	32	0.6009	0.3423	0.6009	0.7752
No log	33.0	33	0.5787	0.3812	0.5787	0.7607
No log	34.0	34	0.5651	0.3791	0.5651	0.7517
No log	35.0	35	0.5747	0.3659	0.5747	0.7581
No log	36.0	36	0.5725	0.4063	0.5725	0.7566
No log	37.0	37	0.5845	0.4810	0.5845	0.7645
No log	38.0	38	0.5322	0.5275	0.5322	0.7295
No log	39.0	39	0.5236	0.5791	0.5236	0.7236
No log	40.0	40	0.5193	0.5861	0.5193	0.7206
No log	41.0	41	0.5508	0.5980	0.5508	0.7422
No log	42.0	42	0.6116	0.5869	0.6116	0.7820
No log	43.0	43	0.6045	0.6036	0.6045	0.7775
No log	44.0	44	0.5448	0.6353	0.5448	0.7381
No log	45.0	45	0.5514	0.6345	0.5514	0.7425
No log	46.0	46	0.6137	0.6150	0.6137	0.7834
No log	47.0	47	0.7368	0.5624	0.7368	0.8584
No log	48.0	48	0.7663	0.5456	0.7663	0.8754
No log	49.0	49	0.7300	0.5766	0.7300	0.8544
No log	50.0	50	0.7320	0.5848	0.7320	0.8556
No log	51.0	51	0.8489	0.5312	0.8489	0.9214
No log	52.0	52	0.7929	0.5573	0.7929	0.8904
No log	53.0	53	0.6993	0.6107	0.6993	0.8363
No log	54.0	54	0.7153	0.6051	0.7153	0.8457
No log	55.0	55	0.8382	0.5600	0.8382	0.9155
No log	56.0	56	0.9692	0.5466	0.9692	0.9845
No log	57.0	57	0.9395	0.5475	0.9395	0.9693
No log	58.0	58	0.7890	0.5616	0.7890	0.8883
No log	59.0	59	0.7765	0.5601	0.7765	0.8812
No log	60.0	60	0.8656	0.5395	0.8656	0.9304
No log	61.0	61	0.8156	0.5431	0.8156	0.9031
No log	62.0	62	0.8514	0.5400	0.8514	0.9227
No log	63.0	63	0.8373	0.5485	0.8373	0.9150
No log	64.0	64	0.8723	0.5473	0.8723	0.9340

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version11ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Version11ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version11ASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0