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	10.0625	0.0036	10.0625	3.1721
No log	2.0	2	8.9563	0.0	8.9563	2.9927
No log	3.0	3	8.2313	0.0	8.2313	2.8690
No log	4.0	4	7.6190	0.0	7.6190	2.7603
No log	5.0	5	7.2339	0.0	7.2339	2.6896
No log	6.0	6	6.9271	0.0	6.9271	2.6319
No log	7.0	7	6.4681	0.0	6.4681	2.5433
No log	8.0	8	6.0374	0.0097	6.0374	2.4571
No log	9.0	9	5.6609	0.0109	5.6609	2.3793
No log	10.0	10	5.2667	0.0115	5.2667	2.2949
No log	11.0	11	4.8540	0.0039	4.8540	2.2032
No log	12.0	12	4.4693	0.0039	4.4693	2.1141
No log	13.0	13	4.1322	0.0039	4.1322	2.0328
No log	14.0	14	3.7849	0.0	3.7849	1.9455
No log	15.0	15	3.4115	0.0	3.4115	1.8470
No log	16.0	16	3.0660	0.0	3.0660	1.7510
No log	17.0	17	2.7511	0.0	2.7511	1.6586
No log	18.0	18	2.4342	0.0847	2.4342	1.5602
No log	19.0	19	2.1591	0.0764	2.1591	1.4694
No log	20.0	20	1.9252	0.0511	1.9252	1.3875
No log	21.0	21	1.6879	0.0316	1.6879	1.2992
No log	22.0	22	1.5069	0.0316	1.5069	1.2276
No log	23.0	23	1.3674	0.0316	1.3674	1.1693
No log	24.0	24	1.2385	0.0316	1.2385	1.1129
No log	25.0	25	1.1351	0.0316	1.1351	1.0654
No log	26.0	26	1.0323	0.0316	1.0323	1.0160
No log	27.0	27	0.9396	0.0316	0.9396	0.9693
No log	28.0	28	0.8657	0.1010	0.8657	0.9304
No log	29.0	29	0.8098	0.4008	0.8098	0.8999
No log	30.0	30	0.7761	0.4157	0.7761	0.8810
No log	31.0	31	0.7317	0.3655	0.7317	0.8554
No log	32.0	32	0.6950	0.3039	0.6950	0.8336
No log	33.0	33	0.6641	0.2839	0.6641	0.8149
No log	34.0	34	0.6285	0.2919	0.6285	0.7928
No log	35.0	35	0.5988	0.3119	0.5988	0.7738
No log	36.0	36	0.5957	0.3069	0.5957	0.7718
No log	37.0	37	0.5613	0.3426	0.5613	0.7492
No log	38.0	38	0.5296	0.3882	0.5296	0.7277
No log	39.0	39	0.5257	0.3732	0.5257	0.7251
No log	40.0	40	0.5261	0.3695	0.5261	0.7253
No log	41.0	41	0.4870	0.4291	0.4870	0.6979
No log	42.0	42	0.4828	0.4403	0.4828	0.6948
No log	43.0	43	0.4737	0.4548	0.4737	0.6882
No log	44.0	44	0.4865	0.5098	0.4865	0.6975
No log	45.0	45	0.4820	0.5855	0.4820	0.6943
No log	46.0	46	0.4843	0.5765	0.4843	0.6959
No log	47.0	47	0.4748	0.6035	0.4748	0.6891
No log	48.0	48	0.5102	0.5999	0.5102	0.7143
No log	49.0	49	0.5217	0.6043	0.5217	0.7223
No log	50.0	50	0.4762	0.6586	0.4762	0.6901
No log	51.0	51	0.4801	0.6458	0.4801	0.6929
No log	52.0	52	0.4793	0.6556	0.4793	0.6923
No log	53.0	53	0.5311	0.6375	0.5311	0.7287
No log	54.0	54	0.5397	0.6565	0.5397	0.7346
No log	55.0	55	0.5068	0.6634	0.5068	0.7119
No log	56.0	56	0.5134	0.6550	0.5134	0.7165
No log	57.0	57	0.5173	0.6594	0.5173	0.7192
No log	58.0	58	0.5741	0.6642	0.5741	0.7577
No log	59.0	59	0.5782	0.6566	0.5782	0.7604
No log	60.0	60	0.5237	0.6725	0.5237	0.7237
No log	61.0	61	0.5308	0.6728	0.5308	0.7286
No log	62.0	62	0.5533	0.6662	0.5533	0.7439
No log	63.0	63	0.5741	0.6668	0.5741	0.7577
No log	64.0	64	0.5360	0.6779	0.5360	0.7321
No log	65.0	65	0.4848	0.6779	0.4848	0.6963
No log	66.0	66	0.4805	0.6695	0.4805	0.6932
No log	67.0	67	0.5092	0.6764	0.5092	0.7136
No log	68.0	68	0.5147	0.6791	0.5147	0.7174
No log	69.0	69	0.4790	0.6829	0.4790	0.6921
No log	70.0	70	0.4769	0.6772	0.4769	0.6906
No log	71.0	71	0.4990	0.6681	0.4990	0.7064
No log	72.0	72	0.5018	0.6632	0.5018	0.7084
No log	73.0	73	0.4740	0.6708	0.4740	0.6885
No log	74.0	74	0.4822	0.6701	0.4822	0.6944
No log	75.0	75	0.4891	0.6712	0.4891	0.6994
No log	76.0	76	0.5076	0.6560	0.5076	0.7125
No log	77.0	77	0.5539	0.6425	0.5539	0.7443
No log	78.0	78	0.5286	0.6474	0.5286	0.7270
No log	79.0	79	0.4759	0.6736	0.4759	0.6899
No log	80.0	80	0.4692	0.6640	0.4692	0.6850
No log	81.0	81	0.4816	0.6707	0.4816	0.6940
No log	82.0	82	0.5162	0.6555	0.5162	0.7185
No log	83.0	83	0.5368	0.6461	0.5368	0.7327
No log	84.0	84	0.5425	0.6414	0.5425	0.7366
No log	85.0	85	0.5123	0.6703	0.5123	0.7158
No log	86.0	86	0.4940	0.6816	0.4940	0.7029
No log	87.0	87	0.4943	0.6816	0.4943	0.7031
No log	88.0	88	0.5082	0.6746	0.5082	0.7129
No log	89.0	89	0.5189	0.6718	0.5189	0.7204

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

Version12AGAINNNASAP_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/
Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0