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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	2	11.4364	0.0111	11.4342	3.3814
No log	2.0	4	10.2559	0.0	10.2538	3.2022
No log	3.0	6	8.5685	0.0	8.5668	2.9269
No log	4.0	8	6.7098	0.0	6.7084	2.5901
No log	5.0	10	4.6561	0.0227	4.6550	2.1575
No log	6.0	12	3.4481	0.0038	3.4469	1.8566
No log	7.0	14	2.4119	0.1247	2.4112	1.5528
No log	8.0	16	1.9670	0.1009	1.9664	1.4023
No log	9.0	18	1.2446	0.0365	1.2441	1.1154
No log	10.0	20	1.0547	0.0365	1.0542	1.0268
No log	11.0	22	0.8682	0.2674	0.8680	0.9316
No log	12.0	24	0.8306	0.3271	0.8305	0.9113
No log	13.0	26	0.7557	0.4016	0.7557	0.8693
No log	14.0	28	0.6190	0.4619	0.6190	0.7868
No log	15.0	30	0.6399	0.5154	0.6401	0.8001
No log	16.0	32	1.0329	0.3237	1.0332	1.0165
No log	17.0	34	0.7372	0.4558	0.7375	0.8588
No log	18.0	36	0.6971	0.4792	0.6975	0.8352
No log	19.0	38	0.9070	0.4000	0.9076	0.9527
No log	20.0	40	0.8033	0.4495	0.8040	0.8967
No log	21.0	42	0.5771	0.5728	0.5778	0.7601
No log	22.0	44	0.8935	0.4541	0.8944	0.9457
No log	23.0	46	0.8029	0.4980	0.8039	0.8966
No log	24.0	48	0.9154	0.4615	0.9164	0.9573
No log	25.0	50	0.9065	0.4611	0.9076	0.9527
No log	26.0	52	0.9981	0.4246	0.9992	0.9996
No log	27.0	54	1.4498	0.3364	1.4510	1.2046
No log	28.0	56	0.8700	0.5031	0.8711	0.9333
No log	29.0	58	0.9759	0.4554	0.9771	0.9885
No log	30.0	60	1.3913	0.3361	1.3925	1.1801
No log	31.0	62	0.8301	0.4870	0.8313	0.9118
No log	32.0	64	0.6153	0.5754	0.6163	0.7850
No log	33.0	66	0.7778	0.4971	0.7789	0.8826
No log	34.0	68	1.0342	0.4076	1.0354	1.0176
No log	35.0	70	0.6708	0.5369	0.6719	0.8197
No log	36.0	72	0.6765	0.5329	0.6776	0.8232
No log	37.0	74	0.9024	0.4322	0.9036	0.9506
No log	38.0	76	0.6662	0.5459	0.6673	0.8169
No log	39.0	78	0.7022	0.5332	0.7033	0.8386
No log	40.0	80	0.8924	0.4613	0.8936	0.9453
No log	41.0	82	0.6731	0.5485	0.6742	0.8211
No log	42.0	84	0.6859	0.5555	0.6868	0.8288
No log	43.0	86	0.8955	0.4796	0.8966	0.9469
No log	44.0	88	0.7649	0.5184	0.7659	0.8752
No log	45.0	90	0.8957	0.4791	0.8968	0.9470
No log	46.0	92	0.7932	0.5181	0.7943	0.8912
No log	47.0	94	0.6668	0.6129	0.6677	0.8171
No log	48.0	96	0.7689	0.5112	0.7699	0.8775
No log	49.0	98	1.1397	0.4099	1.1408	1.0681
No log	50.0	100	0.9710	0.4301	0.9721	0.9860
No log	51.0	102	0.6383	0.6153	0.6391	0.7994
No log	52.0	104	0.6474	0.6029	0.6481	0.8050
No log	53.0	106	0.6312	0.6021	0.6321	0.7950
No log	54.0	108	0.8651	0.4515	0.8661	0.9307
No log	55.0	110	0.8007	0.4845	0.8016	0.8953
No log	56.0	112	0.6133	0.6200	0.6141	0.7837
No log	57.0	114	0.6142	0.5947	0.6150	0.7842
No log	58.0	116	0.7456	0.5245	0.7465	0.8640
No log	59.0	118	0.8565	0.4670	0.8575	0.9260
No log	60.0	120	0.6868	0.5303	0.6877	0.8293
No log	61.0	122	0.7016	0.5274	0.7025	0.8382
No log	62.0	124	0.9015	0.4624	0.9026	0.9500
No log	63.0	126	0.8409	0.4958	0.8420	0.9176
No log	64.0	128	0.6834	0.5756	0.6844	0.8273
No log	65.0	130	0.7287	0.5269	0.7298	0.8543
No log	66.0	132	0.7592	0.5254	0.7603	0.8719
No log	67.0	134	0.6600	0.5821	0.6609	0.8130
No log	68.0	136	0.7104	0.5427	0.7113	0.8434
No log	69.0	138	0.7274	0.5253	0.7283	0.8534
No log	70.0	140	0.6830	0.5537	0.6838	0.8270
No log	71.0	142	0.7903	0.5101	0.7913	0.8896
No log	72.0	144	0.7517	0.5146	0.7527	0.8676
No log	73.0	146	0.7884	0.5110	0.7894	0.8885
No log	74.0	148	0.7918	0.5098	0.7928	0.8904
No log	75.0	150	0.6654	0.5457	0.6662	0.8162
No log	76.0	152	0.6960	0.5339	0.6968	0.8348
No log	77.0	154	0.7676	0.5131	0.7684	0.8766
No log	78.0	156	0.7083	0.5426	0.7091	0.8421
No log	79.0	158	0.8075	0.4989	0.8084	0.8991
No log	80.0	160	0.8297	0.4966	0.8307	0.9114
No log	81.0	162	0.7005	0.5482	0.7014	0.8375
No log	82.0	164	0.6833	0.5636	0.6842	0.8272
No log	83.0	166	0.6876	0.5652	0.6885	0.8298
No log	84.0	168	0.6562	0.5779	0.6571	0.8106
No log	85.0	170	0.6447	0.5964	0.6456	0.8035
No log	86.0	172	0.7726	0.5170	0.7736	0.8795

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold3

Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k3_task1_organization_k3_k3_fold3