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	10.0781	0.0036	10.0781	3.1746
No log	2.0	2	8.9805	0.0	8.9805	2.9967
No log	3.0	3	8.1333	0.0	8.1333	2.8519
No log	4.0	4	7.5865	0.0	7.5865	2.7544
No log	5.0	5	7.2817	0.0	7.2817	2.6985
No log	6.0	6	6.8921	0.0	6.8921	2.6253
No log	7.0	7	6.4161	0.0	6.4161	2.5330
No log	8.0	8	5.9989	0.0226	5.9989	2.4493
No log	9.0	9	5.5936	0.0112	5.5936	2.3651
No log	10.0	10	5.1722	0.0115	5.1722	2.2742
No log	11.0	11	4.7587	0.0039	4.7587	2.1814
No log	12.0	12	4.3740	0.0039	4.3740	2.0914
No log	13.0	13	4.0031	0.0039	4.0031	2.0008
No log	14.0	14	3.6344	0.0	3.6344	1.9064
No log	15.0	15	3.2773	0.0	3.2773	1.8103
No log	16.0	16	2.9428	0.0	2.9428	1.7155
No log	17.0	17	2.6283	0.0	2.6283	1.6212
No log	18.0	18	2.3279	0.1127	2.3279	1.5257
No log	19.0	19	2.0576	0.0689	2.0576	1.4344
No log	20.0	20	1.8160	0.0484	1.8160	1.3476
No log	21.0	21	1.6036	0.0316	1.6036	1.2663
No log	22.0	22	1.4471	0.0316	1.4471	1.2029
No log	23.0	23	1.3108	0.0316	1.3108	1.1449
No log	24.0	24	1.1986	0.0316	1.1986	1.0948
No log	25.0	25	1.0936	0.0316	1.0936	1.0458
No log	26.0	26	0.9810	0.0316	0.9810	0.9904
No log	27.0	27	0.9031	0.0316	0.9031	0.9503
No log	28.0	28	0.8391	0.3179	0.8391	0.9160
No log	29.0	29	0.7858	0.4088	0.7858	0.8864
No log	30.0	30	0.7412	0.3211	0.7412	0.8609
No log	31.0	31	0.7098	0.2798	0.7098	0.8425
No log	32.0	32	0.6733	0.2815	0.6733	0.8206
No log	33.0	33	0.6347	0.3210	0.6347	0.7967
No log	34.0	34	0.6007	0.3297	0.6007	0.7750
No log	35.0	35	0.5949	0.3323	0.5949	0.7713
No log	36.0	36	0.5809	0.3323	0.5809	0.7622
No log	37.0	37	0.5352	0.4137	0.5352	0.7316
No log	38.0	38	0.5206	0.4222	0.5206	0.7215
No log	39.0	39	0.5291	0.3878	0.5291	0.7274
No log	40.0	40	0.5328	0.3878	0.5328	0.7299
No log	41.0	41	0.4859	0.4413	0.4859	0.6971
No log	42.0	42	0.4826	0.4443	0.4826	0.6947
No log	43.0	43	0.4828	0.4505	0.4828	0.6948
No log	44.0	44	0.5200	0.5290	0.5200	0.7211
No log	45.0	45	0.4903	0.5505	0.4903	0.7002
No log	46.0	46	0.4735	0.5753	0.4735	0.6881
No log	47.0	47	0.4824	0.5940	0.4824	0.6945
No log	48.0	48	0.5692	0.5742	0.5692	0.7545
No log	49.0	49	0.5525	0.5967	0.5525	0.7433
No log	50.0	50	0.4976	0.6488	0.4976	0.7054
No log	51.0	51	0.5098	0.6346	0.5098	0.7140
No log	52.0	52	0.5146	0.6519	0.5146	0.7174
No log	53.0	53	0.5858	0.6357	0.5858	0.7654
No log	54.0	54	0.5672	0.6516	0.5672	0.7532
No log	55.0	55	0.5098	0.6612	0.5098	0.7140
No log	56.0	56	0.5080	0.6630	0.5080	0.7128
No log	57.0	57	0.5583	0.6651	0.5583	0.7472
No log	58.0	58	0.7298	0.5811	0.7298	0.8543
No log	59.0	59	0.7401	0.5758	0.7401	0.8603
No log	60.0	60	0.5864	0.6570	0.5864	0.7658
No log	61.0	61	0.5052	0.6769	0.5052	0.7108
No log	62.0	62	0.5098	0.6736	0.5098	0.7140
No log	63.0	63	0.5834	0.6644	0.5834	0.7638
No log	64.0	64	0.6635	0.6186	0.6635	0.8146
No log	65.0	65	0.6102	0.6506	0.6102	0.7811
No log	66.0	66	0.5195	0.6760	0.5195	0.7208
No log	67.0	67	0.5084	0.6619	0.5084	0.7130
No log	68.0	68	0.5153	0.6688	0.5153	0.7178
No log	69.0	69	0.5424	0.6619	0.5424	0.7365
No log	70.0	70	0.6565	0.6289	0.6565	0.8103
No log	71.0	71	0.6728	0.6209	0.6728	0.8202
No log	72.0	72	0.5810	0.6580	0.5810	0.7622
No log	73.0	73	0.4877	0.6705	0.4877	0.6983
No log	74.0	74	0.4759	0.6603	0.4759	0.6899
No log	75.0	75	0.4702	0.6533	0.4702	0.6857
No log	76.0	76	0.4866	0.6707	0.4866	0.6975
No log	77.0	77	0.5700	0.6373	0.5700	0.7550
No log	78.0	78	0.6074	0.6314	0.6074	0.7793
No log	79.0	79	0.5727	0.6391	0.5727	0.7568
No log	80.0	80	0.5074	0.6601	0.5074	0.7123
No log	81.0	81	0.4873	0.6785	0.4873	0.6981
No log	82.0	82	0.4888	0.6747	0.4888	0.6991
No log	83.0	83	0.4998	0.6702	0.4998	0.7069
No log	84.0	84	0.5300	0.6625	0.5300	0.7280
No log	85.0	85	0.5325	0.6619	0.5325	0.7298
No log	86.0	86	0.5184	0.6608	0.5184	0.7200
No log	87.0	87	0.5025	0.6646	0.5025	0.7089
No log	88.0	88	0.5004	0.6617	0.5004	0.7074
No log	89.0	89	0.5006	0.6654	0.5006	0.7076
No log	90.0	90	0.5080	0.6689	0.5080	0.7128
No log	91.0	91	0.5260	0.6620	0.5260	0.7253
No log	92.0	92	0.5386	0.6608	0.5386	0.7339
No log	93.0	93	0.5508	0.6461	0.5508	0.7421
No log	94.0	94	0.5528	0.6427	0.5528	0.7435
No log	95.0	95	0.5457	0.6454	0.5457	0.7387
No log	96.0	96	0.5316	0.6540	0.5316	0.7291
No log	97.0	97	0.5227	0.6600	0.5227	0.7230
No log	98.0	98	0.5149	0.6639	0.5149	0.7176
No log	99.0	99	0.5123	0.6591	0.5123	0.7158
No log	100.0	100	0.5114	0.6591	0.5114	0.7151

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_fold0

Version3ASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

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_fold0