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	11.4157	0.0247	11.4157	3.3787
No log	2.0	2	9.5824	0.0049	9.5824	3.0955
No log	3.0	3	8.1026	0.0037	8.1026	2.8465
No log	4.0	4	6.9315	0.0018	6.9315	2.6328
No log	5.0	5	5.9876	0.0361	5.9876	2.4470
No log	6.0	6	5.2138	0.0330	5.2138	2.2834
No log	7.0	7	4.5573	0.0094	4.5573	2.1348
No log	8.0	8	3.8732	0.0079	3.8732	1.9680
No log	9.0	9	3.2583	0.0079	3.2583	1.8051
No log	10.0	10	2.7978	0.0079	2.7978	1.6727
No log	11.0	11	2.4275	0.0904	2.4275	1.5580
No log	12.0	12	2.1288	0.1287	2.1288	1.4591
No log	13.0	13	1.8490	0.0803	1.8490	1.3598
No log	14.0	14	1.6007	0.0547	1.6007	1.2652
No log	15.0	15	1.4048	0.0420	1.4048	1.1853
No log	16.0	16	1.2653	0.0420	1.2653	1.1249
No log	17.0	17	1.1024	0.0420	1.1024	1.0500
No log	18.0	18	1.0061	0.0420	1.0061	1.0031
No log	19.0	19	0.9380	0.0420	0.9380	0.9685
No log	20.0	20	0.8552	0.2120	0.8552	0.9248
No log	21.0	21	0.7966	0.3971	0.7966	0.8925
No log	22.0	22	0.7431	0.4012	0.7431	0.8620
No log	23.0	23	0.7040	0.3866	0.7040	0.8391
No log	24.0	24	0.6779	0.3805	0.6779	0.8233
No log	25.0	25	0.6572	0.3630	0.6572	0.8107
No log	26.0	26	0.6220	0.3836	0.6220	0.7887
No log	27.0	27	0.5964	0.4023	0.5964	0.7723
No log	28.0	28	0.5711	0.4441	0.5711	0.7557
No log	29.0	29	0.5530	0.4530	0.5530	0.7437
No log	30.0	30	0.5489	0.4781	0.5489	0.7409
No log	31.0	31	0.5325	0.5243	0.5325	0.7297
No log	32.0	32	0.5263	0.5538	0.5263	0.7255
No log	33.0	33	0.5326	0.5619	0.5326	0.7298
No log	34.0	34	0.5570	0.5759	0.5570	0.7463
No log	35.0	35	0.5670	0.6337	0.5670	0.7530
No log	36.0	36	0.6038	0.6433	0.6038	0.7770
No log	37.0	37	0.6165	0.6391	0.6165	0.7852
No log	38.0	38	0.5956	0.6551	0.5956	0.7718
No log	39.0	39	0.6164	0.6529	0.6164	0.7851
No log	40.0	40	0.6547	0.6259	0.6547	0.8092
No log	41.0	41	0.6402	0.6339	0.6402	0.8002
No log	42.0	42	0.7102	0.6229	0.7102	0.8427
No log	43.0	43	0.7485	0.6015	0.7485	0.8652
No log	44.0	44	0.7321	0.6218	0.7321	0.8557
No log	45.0	45	0.7696	0.6098	0.7696	0.8773
No log	46.0	46	0.7051	0.6264	0.7051	0.8397
No log	47.0	47	0.7334	0.6265	0.7334	0.8564
No log	48.0	48	0.8228	0.6210	0.8228	0.9071
No log	49.0	49	0.7055	0.6309	0.7055	0.8400
No log	50.0	50	0.7683	0.6303	0.7683	0.8765
No log	51.0	51	0.7809	0.6322	0.7809	0.8837
No log	52.0	52	0.8389	0.6234	0.8389	0.9159
No log	53.0	53	0.7849	0.6268	0.7849	0.8860
No log	54.0	54	0.7542	0.6308	0.7542	0.8685
No log	55.0	55	0.9052	0.6038	0.9052	0.9514
No log	56.0	56	1.1149	0.5426	1.1149	1.0559
No log	57.0	57	1.0330	0.5692	1.0330	1.0164
No log	58.0	58	0.8596	0.6051	0.8596	0.9271

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold4