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	6	6.5563	0.0	6.5563	2.5605
No log	2.0	12	3.9051	0.0039	3.9051	1.9761
No log	3.0	18	1.8662	0.0382	1.8662	1.3661
No log	4.0	24	0.9736	0.0212	0.9736	0.9867
No log	5.0	30	0.7278	0.1927	0.7278	0.8531
No log	6.0	36	0.6705	0.4970	0.6705	0.8189
No log	7.0	42	0.7303	0.4070	0.7303	0.8546
No log	8.0	48	1.1980	0.2384	1.1980	1.0945
No log	9.0	54	0.5640	0.5026	0.5640	0.7510
No log	10.0	60	0.4581	0.4671	0.4581	0.6769
No log	11.0	66	0.5033	0.5819	0.5033	0.7094
No log	12.0	72	0.8753	0.4258	0.8753	0.9356
No log	13.0	78	0.4728	0.6184	0.4728	0.6876
No log	14.0	84	0.5235	0.5956	0.5235	0.7235
No log	15.0	90	0.6701	0.4898	0.6701	0.8186
No log	16.0	96	0.5112	0.6310	0.5112	0.7150
No log	17.0	102	0.4906	0.6035	0.4906	0.7004
No log	18.0	108	0.6174	0.5477	0.6174	0.7858
No log	19.0	114	0.4880	0.5938	0.4880	0.6986
No log	20.0	120	0.4865	0.6249	0.4865	0.6975
No log	21.0	126	0.5025	0.5870	0.5025	0.7088
No log	22.0	132	0.5645	0.5668	0.5645	0.7513
No log	23.0	138	0.6845	0.4830	0.6845	0.8273
No log	24.0	144	0.7521	0.4952	0.7521	0.8672
No log	25.0	150	0.7564	0.4142	0.7564	0.8697
No log	26.0	156	0.5715	0.5620	0.5715	0.7560
No log	27.0	162	0.6221	0.5591	0.6221	0.7888
No log	28.0	168	0.4796	0.5832	0.4796	0.6925
No log	29.0	174	0.4854	0.6203	0.4854	0.6967
No log	30.0	180	0.5167	0.6024	0.5167	0.7188
No log	31.0	186	0.5900	0.5721	0.5900	0.7681
No log	32.0	192	0.5038	0.6380	0.5038	0.7098
No log	33.0	198	0.4838	0.6466	0.4838	0.6956
No log	34.0	204	0.4965	0.6370	0.4965	0.7046
No log	35.0	210	0.5192	0.6371	0.5192	0.7206
No log	36.0	216	0.4884	0.5984	0.4884	0.6989
No log	37.0	222	0.5435	0.518	0.5435	0.7372
No log	38.0	228	0.5122	0.6043	0.5122	0.7157
No log	39.0	234	0.5003	0.5756	0.5003	0.7073
No log	40.0	240	0.4678	0.6171	0.4678	0.6840
No log	41.0	246	0.4896	0.6237	0.4896	0.6997
No log	42.0	252	0.4849	0.6079	0.4849	0.6963
No log	43.0	258	0.6121	0.5536	0.6121	0.7824
No log	44.0	264	0.5120	0.5840	0.5120	0.7156
No log	45.0	270	0.5059	0.6474	0.5059	0.7113
No log	46.0	276	0.4605	0.6290	0.4605	0.6786
No log	47.0	282	0.4716	0.6473	0.4716	0.6868
No log	48.0	288	0.4771	0.6143	0.4771	0.6907
No log	49.0	294	0.4948	0.5951	0.4948	0.7034
No log	50.0	300	0.4924	0.6550	0.4924	0.7017
No log	51.0	306	0.4766	0.6164	0.4766	0.6904
No log	52.0	312	0.4820	0.5989	0.4820	0.6943
No log	53.0	318	0.4668	0.6230	0.4668	0.6832
No log	54.0	324	0.4770	0.6074	0.4770	0.6907
No log	55.0	330	0.4714	0.5734	0.4714	0.6866
No log	56.0	336	0.5214	0.5895	0.5214	0.7221
No log	57.0	342	0.4771	0.6077	0.4771	0.6907
No log	58.0	348	0.4923	0.5880	0.4923	0.7016
No log	59.0	354	0.5410	0.5963	0.5410	0.7355
No log	60.0	360	0.4948	0.5826	0.4948	0.7034
No log	61.0	366	0.5185	0.5965	0.5185	0.7201
No log	62.0	372	0.4734	0.6423	0.4734	0.6880
No log	63.0	378	0.4780	0.6409	0.4780	0.6914
No log	64.0	384	0.4754	0.6386	0.4754	0.6895
No log	65.0	390	0.4852	0.6376	0.4852	0.6965
No log	66.0	396	0.4900	0.6134	0.4900	0.7000
No log	67.0	402	0.5272	0.6016	0.5272	0.7261
No log	68.0	408	0.5336	0.5974	0.5336	0.7305
No log	69.0	414	0.5084	0.6103	0.5084	0.7130
No log	70.0	420	0.4852	0.6352	0.4852	0.6966
No log	71.0	426	0.5143	0.6239	0.5143	0.7171
No log	72.0	432	0.5014	0.6382	0.5014	0.7081
No log	73.0	438	0.4812	0.5987	0.4812	0.6937
No log	74.0	444	0.4923	0.6551	0.4923	0.7016
No log	75.0	450	0.4972	0.6635	0.4972	0.7051
No log	76.0	456	0.4923	0.6557	0.4923	0.7016
No log	77.0	462	0.4933	0.6213	0.4933	0.7024
No log	78.0	468	0.5009	0.6393	0.5009	0.7077
No log	79.0	474	0.4973	0.6292	0.4973	0.7052
No log	80.0	480	0.4911	0.6665	0.4911	0.7008
No log	81.0	486	0.4781	0.6324	0.4781	0.6914
No log	82.0	492	0.4839	0.6527	0.4839	0.6957
No log	83.0	498	0.4936	0.6245	0.4936	0.7026
0.3281	84.0	504	0.5274	0.5810	0.5274	0.7262
0.3281	85.0	510	0.4870	0.6338	0.4870	0.6979
0.3281	86.0	516	0.5096	0.5888	0.5096	0.7139
0.3281	87.0	522	0.4733	0.6420	0.4733	0.6880
0.3281	88.0	528	0.4879	0.6497	0.4879	0.6985
0.3281	89.0	534	0.5721	0.5755	0.5721	0.7564
0.3281	90.0	540	0.4713	0.6290	0.4713	0.6865
0.3281	91.0	546	0.5039	0.6415	0.5039	0.7099
0.3281	92.0	552	0.4977	0.6335	0.4977	0.7055
0.3281	93.0	558	0.5033	0.6429	0.5033	0.7094
0.3281	94.0	564	0.4891	0.6460	0.4891	0.6994
0.3281	95.0	570	0.4752	0.6236	0.4752	0.6893
0.3281	96.0	576	0.5130	0.5832	0.5130	0.7162
0.3281	97.0	582	0.4815	0.6233	0.4815	0.6939
0.3281	98.0	588	0.4763	0.6367	0.4763	0.6902
0.3281	99.0	594	0.4983	0.6058	0.4983	0.7059
0.3281	100.0	600	0.4759	0.6327	0.4759	0.6899
0.3281	101.0	606	0.4949	0.6395	0.4949	0.7035
0.3281	102.0	612	0.4997	0.6471	0.4997	0.7069
0.3281	103.0	618	0.4908	0.6357	0.4908	0.7006
0.3281	104.0	624	0.4886	0.6335	0.4886	0.6990
0.3281	105.0	630	0.4893	0.6490	0.4893	0.6995
0.3281	106.0	636	0.4812	0.6511	0.4812	0.6937
0.3281	107.0	642	0.5045	0.6329	0.5045	0.7103
0.3281	108.0	648	0.4868	0.6562	0.4868	0.6977
0.3281	109.0	654	0.4790	0.6525	0.4790	0.6921
0.3281	110.0	660	0.4725	0.6221	0.4725	0.6874

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold0

Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold0