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	10.0797	0.0036	10.0797	3.1749
No log	2.0	2	8.9519	0.0	8.9519	2.9920
No log	3.0	3	8.1581	0.0	8.1581	2.8562
No log	4.0	4	7.5892	0.0	7.5892	2.7549
No log	5.0	5	7.2559	0.0	7.2559	2.6937
No log	6.0	6	6.8785	0.0	6.8785	2.6227
No log	7.0	7	6.4041	0.0	6.4041	2.5306
No log	8.0	8	5.9783	0.0185	5.9783	2.4451
No log	9.0	9	5.5782	0.0112	5.5782	2.3618
No log	10.0	10	5.1661	0.0115	5.1661	2.2729
No log	11.0	11	4.7599	0.0077	4.7599	2.1817
No log	12.0	12	4.3913	0.0039	4.3913	2.0955
No log	13.0	13	4.0492	0.0039	4.0492	2.0123
No log	14.0	14	3.6728	0.0	3.6728	1.9164
No log	15.0	15	3.2975	0.0	3.2975	1.8159
No log	16.0	16	2.9543	0.0	2.9543	1.7188
No log	17.0	17	2.6358	0.0	2.6358	1.6235
No log	18.0	18	2.3320	0.1040	2.3320	1.5271
No log	19.0	19	2.0678	0.0689	2.0678	1.4380
No log	20.0	20	1.8358	0.0484	1.8358	1.3549
No log	21.0	21	1.6076	0.0316	1.6076	1.2679
No log	22.0	22	1.4398	0.0316	1.4398	1.1999
No log	23.0	23	1.3090	0.0316	1.3090	1.1441
No log	24.0	24	1.1905	0.0316	1.1905	1.0911
No log	25.0	25	1.0847	0.0316	1.0847	1.0415
No log	26.0	26	0.9856	0.0316	0.9856	0.9928
No log	27.0	27	0.9051	0.0316	0.9051	0.9514
No log	28.0	28	0.8410	0.2764	0.8410	0.9170
No log	29.0	29	0.7882	0.4192	0.7882	0.8878
No log	30.0	30	0.7463	0.4123	0.7463	0.8639
No log	31.0	31	0.7007	0.3106	0.7007	0.8371
No log	32.0	32	0.6712	0.2822	0.6712	0.8193
No log	33.0	33	0.6346	0.3056	0.6346	0.7966
No log	34.0	34	0.6049	0.3791	0.6049	0.7778
No log	35.0	35	0.5857	0.3253	0.5857	0.7653
No log	36.0	36	0.5916	0.3091	0.5916	0.7691
No log	37.0	37	0.5420	0.3604	0.5420	0.7362
No log	38.0	38	0.5217	0.4362	0.5217	0.7223
No log	39.0	39	0.5118	0.4061	0.5118	0.7154
No log	40.0	40	0.5417	0.3542	0.5417	0.7360
No log	41.0	41	0.4972	0.4313	0.4972	0.7052
No log	42.0	42	0.4797	0.4682	0.4797	0.6926
No log	43.0	43	0.4730	0.4989	0.4730	0.6877
No log	44.0	44	0.4972	0.5510	0.4972	0.7051
No log	45.0	45	0.5104	0.5948	0.5104	0.7144
No log	46.0	46	0.4754	0.6011	0.4754	0.6895
No log	47.0	47	0.4787	0.6358	0.4787	0.6919
No log	48.0	48	0.5067	0.6243	0.5067	0.7118
No log	49.0	49	0.5066	0.6296	0.5066	0.7118
No log	50.0	50	0.4819	0.6661	0.4819	0.6942
No log	51.0	51	0.4953	0.6528	0.4953	0.7038
No log	52.0	52	0.5526	0.6380	0.5526	0.7433
No log	53.0	53	0.5685	0.6384	0.5685	0.7540
No log	54.0	54	0.5021	0.6580	0.5021	0.7086
No log	55.0	55	0.5124	0.6751	0.5124	0.7158
No log	56.0	56	0.5892	0.6387	0.5892	0.7676
No log	57.0	57	0.6667	0.6129	0.6667	0.8165
No log	58.0	58	0.5982	0.6485	0.5982	0.7735
No log	59.0	59	0.4955	0.6807	0.4955	0.7039
No log	60.0	60	0.4981	0.6785	0.4981	0.7058
No log	61.0	61	0.6076	0.6341	0.6076	0.7795
No log	62.0	62	0.6752	0.6192	0.6752	0.8217
No log	63.0	63	0.5824	0.6549	0.5824	0.7631
No log	64.0	64	0.5185	0.6703	0.5185	0.7201
No log	65.0	65	0.5131	0.6807	0.5131	0.7163
No log	66.0	66	0.5038	0.6729	0.5038	0.7098
No log	67.0	67	0.5352	0.6715	0.5352	0.7316
No log	68.0	68	0.5734	0.6762	0.5734	0.7572
No log	69.0	69	0.5185	0.6726	0.5185	0.7201
No log	70.0	70	0.5232	0.6625	0.5232	0.7233
No log	71.0	71	0.5348	0.6534	0.5348	0.7313
No log	72.0	72	0.4795	0.6666	0.4795	0.6924
No log	73.0	73	0.4749	0.6657	0.4749	0.6891
No log	74.0	74	0.5256	0.6490	0.5256	0.7250
No log	75.0	75	0.5107	0.6622	0.5107	0.7146
No log	76.0	76	0.5166	0.6592	0.5166	0.7187
No log	77.0	77	0.5503	0.6463	0.5503	0.7418
No log	78.0	78	0.5172	0.6585	0.5172	0.7191
No log	79.0	79	0.4693	0.6747	0.4693	0.6851
No log	80.0	80	0.4672	0.6635	0.4672	0.6835
No log	81.0	81	0.4822	0.6812	0.4822	0.6944
No log	82.0	82	0.5309	0.6592	0.5309	0.7286
No log	83.0	83	0.5765	0.6434	0.5765	0.7592
No log	84.0	84	0.5825	0.6429	0.5825	0.7632
No log	85.0	85	0.5412	0.6559	0.5412	0.7357
No log	86.0	86	0.4961	0.6877	0.4961	0.7043
No log	87.0	87	0.4848	0.6782	0.4848	0.6963
No log	88.0	88	0.4896	0.6808	0.4896	0.6997
No log	89.0	89	0.5134	0.6555	0.5134	0.7165
No log	90.0	90	0.5595	0.6371	0.5595	0.7480
No log	91.0	91	0.5798	0.6344	0.5798	0.7614
No log	92.0	92	0.5712	0.6373	0.5712	0.7558
No log	93.0	93	0.5574	0.6342	0.5574	0.7466
No log	94.0	94	0.5551	0.6337	0.5551	0.7451
No log	95.0	95	0.5411	0.6386	0.5411	0.7356
No log	96.0	96	0.5314	0.6459	0.5314	0.7290
No log	97.0	97	0.5308	0.6478	0.5308	0.7286
No log	98.0	98	0.5339	0.6483	0.5339	0.7307
No log	99.0	99	0.5395	0.6490	0.5395	0.7345
No log	100.0	100	0.5416	0.6422	0.5416	0.7359

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold0

Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version19ASAP_FineTuningBERT_AugV19_k3_task1_organization_k3_k3_fold0