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	10.2464	0.0	10.2464	3.2010
No log	2.0	2	9.1015	0.0	9.1015	3.0169
No log	3.0	3	8.2822	0.0	8.2822	2.8779
No log	4.0	4	7.7559	0.0	7.7559	2.7849
No log	5.0	5	7.3902	0.0	7.3902	2.7185
No log	6.0	6	7.0691	0.0	7.0691	2.6588
No log	7.0	7	6.6169	0.0	6.6169	2.5723
No log	8.0	8	6.1824	-0.0177	6.1824	2.4864
No log	9.0	9	5.7836	0.0171	5.7836	2.4049
No log	10.0	10	5.3699	0.0171	5.3699	2.3173
No log	11.0	11	4.9606	0.0171	4.9606	2.2272
No log	12.0	12	4.5971	0.0	4.5971	2.1441
No log	13.0	13	4.2992	0.0	4.2992	2.0735
No log	14.0	14	3.9280	0.0	3.9280	1.9819
No log	15.0	15	3.5309	0.0	3.5309	1.8791
No log	16.0	16	3.1865	0.0	3.1865	1.7851
No log	17.0	17	2.8597	0.0	2.8597	1.6911
No log	18.0	18	2.5283	0.0568	2.5283	1.5901
No log	19.0	19	2.2412	0.0806	2.2412	1.4971
No log	20.0	20	2.0138	0.0780	2.0138	1.4191
No log	21.0	21	1.7711	0.0484	1.7711	1.3308
No log	22.0	22	1.5438	0.0484	1.5438	1.2425
No log	23.0	23	1.3821	0.0484	1.3821	1.1756
No log	24.0	24	1.2552	0.0484	1.2552	1.1204
No log	25.0	25	1.1834	0.0484	1.1834	1.0878
No log	26.0	26	1.0747	0.0484	1.0747	1.0367
No log	27.0	27	0.9413	0.0484	0.9413	0.9702
No log	28.0	28	0.8347	0.0484	0.8347	0.9136
No log	29.0	29	0.7667	0.3601	0.7667	0.8756
No log	30.0	30	0.7433	0.4133	0.7433	0.8621
No log	31.0	31	0.7770	0.3454	0.7770	0.8815
No log	32.0	32	0.6726	0.3461	0.6726	0.8201
No log	33.0	33	0.5993	0.2685	0.5993	0.7741
No log	34.0	34	0.5873	0.2486	0.5873	0.7664
No log	35.0	35	0.5480	0.2867	0.5480	0.7403
No log	36.0	36	0.5605	0.4177	0.5605	0.7487
No log	37.0	37	0.5440	0.4470	0.5440	0.7376
No log	38.0	38	0.4986	0.3799	0.4986	0.7061
No log	39.0	39	0.4880	0.3873	0.4880	0.6986
No log	40.0	40	0.4850	0.3799	0.4850	0.6964
No log	41.0	41	0.4983	0.4714	0.4983	0.7059
No log	42.0	42	0.4892	0.4466	0.4892	0.6994
No log	43.0	43	0.4862	0.4082	0.4862	0.6973
No log	44.0	44	0.4901	0.4536	0.4901	0.7001
No log	45.0	45	0.4940	0.4583	0.4940	0.7029
No log	46.0	46	0.5039	0.5299	0.5039	0.7098
No log	47.0	47	0.5061	0.5498	0.5061	0.7114
No log	48.0	48	0.4929	0.5946	0.4929	0.7021
No log	49.0	49	0.4940	0.6223	0.4940	0.7028
No log	50.0	50	0.4955	0.6381	0.4955	0.7039
No log	51.0	51	0.4970	0.6444	0.4970	0.7050
No log	52.0	52	0.4977	0.6229	0.4977	0.7054
No log	53.0	53	0.4737	0.6627	0.4737	0.6882
No log	54.0	54	0.4669	0.6627	0.4669	0.6833
No log	55.0	55	0.4948	0.6190	0.4948	0.7034
No log	56.0	56	0.4738	0.6306	0.4738	0.6883
No log	57.0	57	0.4724	0.6602	0.4724	0.6873
No log	58.0	58	0.4692	0.6561	0.4692	0.6850
No log	59.0	59	0.4823	0.6618	0.4823	0.6945
No log	60.0	60	0.5351	0.6386	0.5351	0.7315
No log	61.0	61	0.5220	0.6403	0.5220	0.7225
No log	62.0	62	0.4885	0.6740	0.4885	0.6989
No log	63.0	63	0.4803	0.6740	0.4803	0.6930
No log	64.0	64	0.5137	0.6306	0.5137	0.7167
No log	65.0	65	0.4964	0.6346	0.4964	0.7045
No log	66.0	66	0.5230	0.6367	0.5230	0.7232
No log	67.0	67	0.4900	0.6486	0.4900	0.7000
No log	68.0	68	0.4941	0.6119	0.4941	0.7029
No log	69.0	69	0.5129	0.6060	0.5129	0.7162
No log	70.0	70	0.5177	0.6779	0.5177	0.7195
No log	71.0	71	0.5291	0.6714	0.5291	0.7274
No log	72.0	72	0.5706	0.6407	0.5706	0.7554
No log	73.0	73	0.5648	0.6407	0.5648	0.7515
No log	74.0	74	0.5194	0.6775	0.5194	0.7207
No log	75.0	75	0.5003	0.6751	0.5003	0.7073
No log	76.0	76	0.4947	0.6588	0.4947	0.7034
No log	77.0	77	0.5139	0.6486	0.5139	0.7169
No log	78.0	78	0.5779	0.5903	0.5779	0.7602
No log	79.0	79	0.5868	0.5960	0.5868	0.7660
No log	80.0	80	0.5435	0.6407	0.5435	0.7373
No log	81.0	81	0.5005	0.6325	0.5005	0.7075
No log	82.0	82	0.4958	0.6505	0.4958	0.7041
No log	83.0	83	0.5018	0.6468	0.5018	0.7083
No log	84.0	84	0.5085	0.6325	0.5085	0.7131
No log	85.0	85	0.5222	0.6690	0.5222	0.7226
No log	86.0	86	0.5512	0.6423	0.5512	0.7425
No log	87.0	87	0.5629	0.6423	0.5629	0.7503
No log	88.0	88	0.5469	0.6423	0.5469	0.7395
No log	89.0	89	0.5333	0.6664	0.5333	0.7303
No log	90.0	90	0.5084	0.6753	0.5084	0.7130

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12LowDevASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0

Version12LowDevASAP_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/
Version12LowDevASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold0