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	14.5701	0.0	14.5701	3.8171
No log	2.0	2	12.6932	0.0	12.6932	3.5627
No log	3.0	3	11.1051	0.0068	11.1051	3.3324
No log	4.0	4	9.8608	0.0	9.8608	3.1402
No log	5.0	5	8.8556	0.0	8.8556	2.9758
No log	6.0	6	7.6768	0.0	7.6768	2.7707
No log	7.0	7	6.5608	0.0076	6.5608	2.5614
No log	8.0	8	5.8074	0.0372	5.8074	2.4098
No log	9.0	9	5.5018	0.0270	5.5018	2.3456
No log	10.0	10	4.6394	0.0102	4.6394	2.1539
No log	11.0	11	4.0687	0.0	4.0687	2.0171
No log	12.0	12	3.6270	0.0	3.6270	1.9045
No log	13.0	13	3.2490	0.0	3.2490	1.8025
No log	14.0	14	2.8647	0.0129	2.8647	1.6925
No log	15.0	15	2.6864	0.1146	2.6864	1.6390
No log	16.0	16	2.3594	0.1478	2.3594	1.5360
No log	17.0	17	2.2421	0.1273	2.2421	1.4974
No log	18.0	18	1.8773	0.0246	1.8773	1.3702
No log	19.0	19	1.7426	0.0	1.7426	1.3201
No log	20.0	20	1.5890	0.0	1.5890	1.2605
No log	21.0	21	1.5165	0.0	1.5165	1.2315
No log	22.0	22	1.3371	0.0	1.3371	1.1563
No log	23.0	23	1.3187	0.0	1.3187	1.1484
No log	24.0	24	1.2097	0.0376	1.2097	1.0999
No log	25.0	25	1.1813	0.1579	1.1813	1.0869
No log	26.0	26	1.1514	0.2093	1.1514	1.0730
No log	27.0	27	1.0502	0.3032	1.0502	1.0248
No log	28.0	28	1.0712	0.1373	1.0712	1.0350
No log	29.0	29	1.0879	0.1034	1.0879	1.0430
No log	30.0	30	1.0238	0.1034	1.0238	1.0118
No log	31.0	31	0.9944	0.1650	0.9944	0.9972
No log	32.0	32	0.9672	0.1176	0.9672	0.9834
No log	33.0	33	0.9300	0.1981	0.9300	0.9644
No log	34.0	34	0.9119	0.2264	0.9119	0.9549
No log	35.0	35	0.9078	0.2264	0.9078	0.9528
No log	36.0	36	0.9135	0.1943	0.9135	0.9558
No log	37.0	37	0.9410	0.1292	0.9410	0.9700
No log	38.0	38	0.9737	0.1509	0.9737	0.9868
No log	39.0	39	0.9435	0.3607	0.9435	0.9714
No log	40.0	40	0.8317	0.2857	0.8317	0.9120
No log	41.0	41	0.7343	0.4386	0.7343	0.8569
No log	42.0	42	0.7007	0.4530	0.7007	0.8371
No log	43.0	43	0.6787	0.4515	0.6787	0.8239
No log	44.0	44	0.7045	0.5860	0.7045	0.8394
No log	45.0	45	0.7986	0.5492	0.7986	0.8936
No log	46.0	46	0.8433	0.5119	0.8433	0.9183
No log	47.0	47	0.7913	0.5327	0.7913	0.8896
No log	48.0	48	0.6936	0.6393	0.6936	0.8328
No log	49.0	49	0.6613	0.6622	0.6613	0.8132
No log	50.0	50	0.6568	0.6977	0.6568	0.8104
No log	51.0	51	0.6749	0.6615	0.6749	0.8215
No log	52.0	52	0.6716	0.6657	0.6716	0.8195
No log	53.0	53	0.6444	0.6881	0.6444	0.8028
No log	54.0	54	0.6132	0.6943	0.6132	0.7831
No log	55.0	55	0.6180	0.6803	0.6180	0.7861
No log	56.0	56	0.6617	0.6506	0.6617	0.8135
No log	57.0	57	0.7387	0.6207	0.7387	0.8595
No log	58.0	58	0.7520	0.6207	0.7520	0.8672
No log	59.0	59	0.6808	0.6506	0.6808	0.8251
No log	60.0	60	0.6535	0.6647	0.6535	0.8084
No log	61.0	61	0.6217	0.6848	0.6217	0.7885
No log	62.0	62	0.6109	0.6881	0.6109	0.7816
No log	63.0	63	0.6109	0.6855	0.6109	0.7816
No log	64.0	64	0.6216	0.6822	0.6216	0.7884
No log	65.0	65	0.6158	0.6937	0.6158	0.7847
No log	66.0	66	0.5728	0.6766	0.5728	0.7569
No log	67.0	67	0.5529	0.7055	0.5529	0.7436
No log	68.0	68	0.5528	0.7108	0.5528	0.7435
No log	69.0	69	0.5465	0.6875	0.5465	0.7393
No log	70.0	70	0.5458	0.6849	0.5458	0.7388
No log	71.0	71	0.5468	0.7035	0.5468	0.7395
No log	72.0	72	0.5761	0.7092	0.5761	0.7590
No log	73.0	73	0.6416	0.6744	0.6416	0.8010
No log	74.0	74	0.6677	0.6504	0.6677	0.8171
No log	75.0	75	0.6332	0.6736	0.6332	0.7958
No log	76.0	76	0.5730	0.7248	0.5730	0.7570
No log	77.0	77	0.5501	0.7211	0.5501	0.7417
No log	78.0	78	0.5496	0.7194	0.5496	0.7413
No log	79.0	79	0.5543	0.7176	0.5543	0.7445
No log	80.0	80	0.5740	0.7485	0.5740	0.7577
No log	81.0	81	0.6253	0.6696	0.6253	0.7908
No log	82.0	82	0.6510	0.6715	0.6510	0.8068
No log	83.0	83	0.6382	0.6696	0.6382	0.7989
No log	84.0	84	0.6054	0.7241	0.6054	0.7781
No log	85.0	85	0.5708	0.7275	0.5708	0.7555
No log	86.0	86	0.5569	0.6755	0.5569	0.7462
No log	87.0	87	0.5609	0.6786	0.5609	0.7490
No log	88.0	88	0.5626	0.6842	0.5626	0.7500
No log	89.0	89	0.5599	0.6755	0.5599	0.7482
No log	90.0	90	0.5596	0.6977	0.5596	0.7480
No log	91.0	91	0.5698	0.7159	0.5698	0.7549
No log	92.0	92	0.5842	0.7307	0.5842	0.7643
No log	93.0	93	0.5995	0.7430	0.5995	0.7742
No log	94.0	94	0.6052	0.7094	0.6052	0.7779
No log	95.0	95	0.6020	0.7421	0.6020	0.7759
No log	96.0	96	0.5940	0.7522	0.5940	0.7707
No log	97.0	97	0.5851	0.7329	0.5851	0.7649
No log	98.0	98	0.5789	0.7259	0.5789	0.7608
No log	99.0	99	0.5742	0.7310	0.5742	0.7577
No log	100.0	100	0.5720	0.7143	0.5720	0.7563

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12LowDevASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2

Version12LowDevASAP_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/
Version12LowDevASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold2