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	13.4548	0.0	13.4551	3.6681
No log	2.0	2	11.4711	0.0036	11.4715	3.3870
No log	3.0	3	9.8559	0.0055	9.8565	3.1395
No log	4.0	4	8.9353	0.0	8.9359	2.9893
No log	5.0	5	7.7395	0.0	7.7398	2.7821
No log	6.0	6	7.7572	0.0	7.7574	2.7852
No log	7.0	7	6.7501	0.0023	6.7504	2.5982
No log	8.0	8	5.3922	0.0465	5.3924	2.3222
No log	9.0	9	4.6728	0.0172	4.6732	2.1617
No log	10.0	10	4.0814	0.0113	4.0819	2.0204
No log	11.0	11	3.9429	0.0074	3.9432	1.9858
No log	12.0	12	3.3302	0.0039	3.3306	1.8250
No log	13.0	13	2.6749	0.0091	2.6753	1.6356
No log	14.0	14	2.4280	0.0704	2.4283	1.5583
No log	15.0	15	2.0024	0.1256	2.0028	1.4152
No log	16.0	16	1.7725	0.0867	1.7730	1.3315
No log	17.0	17	1.5449	0.0489	1.5453	1.2431
No log	18.0	18	1.3685	0.0213	1.3689	1.1700
No log	19.0	19	1.2151	0.0213	1.2155	1.1025
No log	20.0	20	1.0385	0.0213	1.0389	1.0193
No log	21.0	21	0.9367	0.0107	0.9370	0.9680
No log	22.0	22	0.9182	0.0820	0.9186	0.9584
No log	23.0	23	0.8519	0.1897	0.8523	0.9232
No log	24.0	24	0.7880	0.2341	0.7883	0.8879
No log	25.0	25	0.7729	0.2452	0.7732	0.8793
No log	26.0	26	0.7340	0.2005	0.7343	0.8569
No log	27.0	27	0.7612	0.1471	0.7615	0.8727
No log	28.0	28	0.7607	0.1304	0.7610	0.8724
No log	29.0	29	0.7487	0.1287	0.7489	0.8654
No log	30.0	30	0.7161	0.2142	0.7163	0.8464
No log	31.0	31	0.7034	0.2373	0.7035	0.8388
No log	32.0	32	0.7513	0.1786	0.7513	0.8668
No log	33.0	33	0.8053	0.1488	0.8053	0.8974
No log	34.0	34	0.7677	0.2274	0.7676	0.8761
No log	35.0	35	0.6781	0.3407	0.6778	0.8233
No log	36.0	36	0.6400	0.3642	0.6397	0.7998
No log	37.0	37	0.6635	0.3861	0.6632	0.8144
No log	38.0	38	0.7471	0.4111	0.7467	0.8641
No log	39.0	39	0.7453	0.4354	0.7448	0.8630
No log	40.0	40	0.6373	0.4999	0.6367	0.7979
No log	41.0	41	0.6210	0.5204	0.6203	0.7876
No log	42.0	42	0.6787	0.5102	0.6779	0.8234
No log	43.0	43	0.6524	0.5342	0.6516	0.8072
No log	44.0	44	0.5992	0.5802	0.5984	0.7736
No log	45.0	45	0.5930	0.6051	0.5921	0.7695
No log	46.0	46	0.6355	0.5767	0.6345	0.7966
No log	47.0	47	0.6590	0.5847	0.6580	0.8112
No log	48.0	48	0.6621	0.5774	0.6611	0.8131
No log	49.0	49	0.6928	0.5829	0.6918	0.8317
No log	50.0	50	0.7218	0.5776	0.7207	0.8489
No log	51.0	51	0.6575	0.5820	0.6565	0.8103
No log	52.0	52	0.5935	0.5863	0.5927	0.7699
No log	53.0	53	0.5994	0.5871	0.5987	0.7737
No log	54.0	54	0.7069	0.5784	0.7059	0.8402
No log	55.0	55	0.7822	0.5533	0.7811	0.8838
No log	56.0	56	0.7229	0.5671	0.7220	0.8497
No log	57.0	57	0.6653	0.5811	0.6645	0.8152
No log	58.0	58	0.6646	0.5735	0.6639	0.8148
No log	59.0	59	0.5984	0.5851	0.5978	0.7732
No log	60.0	60	0.6136	0.5868	0.6130	0.7829
No log	61.0	61	0.6519	0.5747	0.6512	0.8070
No log	62.0	62	0.6415	0.5722	0.6409	0.8005
No log	63.0	63	0.5993	0.5831	0.5988	0.7738
No log	64.0	64	0.5850	0.5922	0.5845	0.7645
No log	65.0	65	0.6164	0.5812	0.6158	0.7847

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Version3ASAP_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/
Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2