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	10.9771	0.0020	10.9774	3.3132
No log	2.0	2	9.2593	0.0003	9.2595	3.0429
No log	3.0	3	7.9248	0.0	7.9250	2.8151
No log	4.0	4	7.1007	0.0	7.1009	2.6648
No log	5.0	5	6.5650	0.0	6.5652	2.5623
No log	6.0	6	6.1006	0.0	6.1009	2.4700
No log	7.0	7	5.6240	0.0	5.6243	2.3716
No log	8.0	8	5.1186	0.0	5.1189	2.2625
No log	9.0	9	4.6049	0.0	4.6052	2.1460
No log	10.0	10	4.1088	0.0	4.1091	2.0271
No log	11.0	11	3.6648	0.0	3.6651	1.9145
No log	12.0	12	3.2816	0.0	3.2820	1.8116
No log	13.0	13	2.9438	0.0	2.9442	1.7159
No log	14.0	14	2.6418	0.0	2.6422	1.6255
No log	15.0	15	2.3607	0.1407	2.3612	1.5366
No log	16.0	16	2.0829	0.0513	2.0833	1.4434
No log	17.0	17	1.8437	0.0384	1.8442	1.3580
No log	18.0	18	1.6492	0.0213	1.6496	1.2844
No log	19.0	19	1.4821	0.0213	1.4825	1.2176
No log	20.0	20	1.3347	0.0213	1.3351	1.1555
No log	21.0	21	1.2244	0.0213	1.2248	1.1067
No log	22.0	22	1.1169	0.0213	1.1173	1.0570
No log	23.0	23	1.0293	0.0213	1.0297	1.0147
No log	24.0	24	0.9455	0.0213	0.9459	0.9726
No log	25.0	25	0.8776	0.0213	0.8779	0.9370
No log	26.0	26	0.8322	0.3282	0.8325	0.9124
No log	27.0	27	0.7628	0.4469	0.7631	0.8735
No log	28.0	28	0.7033	0.4506	0.7035	0.8387
No log	29.0	29	0.6597	0.4545	0.6599	0.8124
No log	30.0	30	0.6370	0.4234	0.6372	0.7982
No log	31.0	31	0.5829	0.4797	0.5829	0.7635
No log	32.0	32	0.5543	0.4768	0.5543	0.7445
No log	33.0	33	0.5350	0.5009	0.5350	0.7314
No log	34.0	34	0.5088	0.4981	0.5087	0.7132
No log	35.0	35	0.5120	0.4614	0.5118	0.7154
No log	36.0	36	0.4833	0.5215	0.4831	0.6951
No log	37.0	37	0.4814	0.5105	0.4811	0.6936
No log	38.0	38	0.5058	0.4742	0.5054	0.7109
No log	39.0	39	0.4658	0.5436	0.4655	0.6822
No log	40.0	40	0.4738	0.5299	0.4734	0.6881
No log	41.0	41	0.5225	0.4775	0.5220	0.7225
No log	42.0	42	0.5067	0.6048	0.5062	0.7114
No log	43.0	43	0.4637	0.6274	0.4633	0.6807
No log	44.0	44	0.4950	0.6500	0.4945	0.7032
No log	45.0	45	0.6049	0.5699	0.6042	0.7773
No log	46.0	46	0.5850	0.5804	0.5843	0.7644
No log	47.0	47	0.4893	0.6641	0.4887	0.6991
No log	48.0	48	0.4988	0.6526	0.4982	0.7058
No log	49.0	49	0.5869	0.6060	0.5861	0.7656
No log	50.0	50	0.6527	0.5765	0.6519	0.8074
No log	51.0	51	0.5690	0.6298	0.5682	0.7538
No log	52.0	52	0.5080	0.6640	0.5074	0.7123
No log	53.0	53	0.5149	0.6628	0.5143	0.7171
No log	54.0	54	0.5995	0.6158	0.5987	0.7737
No log	55.0	55	0.6755	0.5947	0.6746	0.8213
No log	56.0	56	0.6849	0.5959	0.6840	0.8271
No log	57.0	57	0.5765	0.6246	0.5758	0.7588
No log	58.0	58	0.5783	0.6304	0.5776	0.7600
No log	59.0	59	0.7058	0.5977	0.7050	0.8396
No log	60.0	60	0.7163	0.5977	0.7155	0.8458
No log	61.0	61	0.6587	0.6230	0.6579	0.8111
No log	62.0	62	0.6812	0.6246	0.6804	0.8249
No log	63.0	63	0.6781	0.6307	0.6773	0.8230
No log	64.0	64	0.6348	0.6341	0.6340	0.7963
No log	65.0	65	0.6086	0.6385	0.6079	0.7797
No log	66.0	66	0.5386	0.6398	0.5380	0.7335
No log	67.0	67	0.5390	0.6449	0.5385	0.7338

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Version2ASAP_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/
Version2ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2