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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	1	11.9642	-0.0003	11.9617	3.4586
No log	2.0	2	10.5692	0.0064	10.5666	3.2506
No log	3.0	3	9.8354	0.0	9.8327	3.1357
No log	4.0	4	8.9376	0.0	8.9349	2.9891
No log	5.0	5	7.9914	0.0	7.9890	2.8265
No log	6.0	6	7.2906	0.0	7.2881	2.6997
No log	7.0	7	6.6224	0.0	6.6200	2.5729
No log	8.0	8	5.9597	-0.0003	5.9575	2.4408
No log	9.0	9	5.3574	0.0231	5.3552	2.3141
No log	10.0	10	4.8212	0.0040	4.8191	2.1952
No log	11.0	11	4.3091	0.0	4.3070	2.0753
No log	12.0	12	3.8066	0.0	3.8046	1.9505
No log	13.0	13	3.3759	0.0	3.3739	1.8368
No log	14.0	14	2.9721	0.0	2.9703	1.7234
No log	15.0	15	2.5658	0.0	2.5641	1.6013
No log	16.0	16	2.3385	0.0520	2.3367	1.5286
No log	17.0	17	2.0051	0.0676	2.0034	1.4154
No log	18.0	18	1.9010	0.0443	1.8993	1.3782
No log	19.0	19	1.6153	0.0106	1.6138	1.2703
No log	20.0	20	1.4849	0.0198	1.4834	1.2179
No log	21.0	21	1.3100	0.0	1.3085	1.1439
No log	22.0	22	1.1637	0.0	1.1622	1.0780
No log	23.0	23	1.0787	0.0	1.0773	1.0379
No log	24.0	24	0.9944	0.0	0.9930	0.9965
No log	25.0	25	0.9503	0.0	0.9491	0.9742
No log	26.0	26	0.9003	0.1830	0.8992	0.9482
No log	27.0	27	0.8423	0.3084	0.8412	0.9172
No log	28.0	28	0.7704	0.3745	0.7694	0.8771
No log	29.0	29	0.7071	0.4343	0.7061	0.8403
No log	30.0	30	0.7070	0.4636	0.7062	0.8403
No log	31.0	31	0.6752	0.5289	0.6744	0.8212
No log	32.0	32	0.6645	0.5440	0.6638	0.8147
No log	33.0	33	0.6252	0.5145	0.6244	0.7902
No log	34.0	34	0.6259	0.5303	0.6253	0.7907
No log	35.0	35	0.6689	0.5502	0.6684	0.8176
No log	36.0	36	0.6927	0.5494	0.6922	0.8320
No log	37.0	37	0.6331	0.5485	0.6326	0.7954
No log	38.0	38	0.7007	0.5322	0.7003	0.8368
No log	39.0	39	0.6385	0.5268	0.6380	0.7988
No log	40.0	40	0.6833	0.4837	0.6827	0.8263
No log	41.0	41	0.7006	0.4767	0.7001	0.8367
No log	42.0	42	0.7075	0.4855	0.7071	0.8409
No log	43.0	43	0.7466	0.4855	0.7462	0.8638
No log	44.0	44	0.7242	0.4919	0.7238	0.8508
No log	45.0	45	0.7367	0.4831	0.7364	0.8581
No log	46.0	46	0.7774	0.4775	0.7772	0.8816
No log	47.0	47	0.7651	0.4899	0.7649	0.8746
No log	48.0	48	0.7515	0.4699	0.7514	0.8668
No log	49.0	49	0.7581	0.4770	0.7578	0.8705
No log	50.0	50	0.7308	0.4884	0.7306	0.8547
No log	51.0	51	0.7365	0.4874	0.7363	0.8581
No log	52.0	52	0.7545	0.4848	0.7543	0.8685
No log	53.0	53	0.8159	0.4929	0.8157	0.9032
No log	54.0	54	0.8029	0.4866	0.8027	0.8959
No log	55.0	55	0.7767	0.4855	0.7764	0.8812
No log	56.0	56	0.7938	0.4774	0.7936	0.8908
No log	57.0	57	0.8150	0.4869	0.8148	0.9026
No log	58.0	58	0.8418	0.4848	0.8415	0.9173
No log	59.0	59	0.8679	0.4729	0.8676	0.9315
No log	60.0	60	0.8477	0.4714	0.8474	0.9205
No log	61.0	61	0.8304	0.4981	0.8300	0.9110
No log	62.0	62	0.8354	0.4818	0.8350	0.9138
No log	63.0	63	0.8468	0.4735	0.8465	0.9200
No log	64.0	64	0.9008	0.4824	0.9004	0.9489
No log	65.0	65	0.9095	0.4814	0.9092	0.9535

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold1

Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k1_task1_organization_k1_k1_fold1