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	3	8.5220	0.0	8.5195	2.9188
No log	2.0	6	6.1738	0.0	6.1716	2.4843
No log	3.0	9	4.4415	0.0	4.4394	2.1070
No log	4.0	12	3.0575	0.0	3.0555	1.7480
No log	5.0	15	2.1205	0.1600	2.1187	1.4556
No log	6.0	18	1.6987	0.0530	1.6970	1.3027
No log	7.0	21	1.2661	0.0	1.2646	1.1246
No log	8.0	24	1.1796	0.0	1.1782	1.0854
No log	9.0	27	1.4090	0.0443	1.4075	1.1864
No log	10.0	30	1.1029	0.0379	1.1015	1.0495
No log	11.0	33	0.9255	0.2291	0.9241	0.9613
No log	12.0	36	0.8036	0.4077	0.8023	0.8957
No log	13.0	39	0.6945	0.4927	0.6932	0.8326
No log	14.0	42	0.6595	0.4755	0.6582	0.8113
No log	15.0	45	0.5995	0.5028	0.5984	0.7735
No log	16.0	48	0.6048	0.4811	0.6036	0.7769
No log	17.0	51	0.6837	0.4742	0.6826	0.8262
No log	18.0	54	0.6534	0.5085	0.6522	0.8076
No log	19.0	57	0.7160	0.4884	0.7148	0.8455
No log	20.0	60	0.7477	0.5584	0.7466	0.8641
No log	21.0	63	1.0079	0.4895	1.0070	1.0035
No log	22.0	66	0.7321	0.5736	0.7312	0.8551
No log	23.0	69	0.6552	0.5661	0.6542	0.8088
No log	24.0	72	0.6989	0.5598	0.6979	0.8354
No log	25.0	75	0.7116	0.5606	0.7107	0.8431
No log	26.0	78	0.6957	0.5189	0.6949	0.8336
No log	27.0	81	0.6426	0.5948	0.6419	0.8012
No log	28.0	84	0.7521	0.5277	0.7513	0.8668
No log	29.0	87	0.6494	0.5839	0.6486	0.8054
No log	30.0	90	0.7839	0.4819	0.7831	0.8849
No log	31.0	93	0.6588	0.5845	0.6580	0.8112
No log	32.0	96	0.7496	0.5329	0.7488	0.8654
No log	33.0	99	0.6991	0.5652	0.6984	0.8357
No log	34.0	102	0.7044	0.5411	0.7036	0.8388
No log	35.0	105	0.6654	0.6119	0.6647	0.8153
No log	36.0	108	0.7847	0.5129	0.7840	0.8855
No log	37.0	111	0.6490	0.5939	0.6483	0.8052
No log	38.0	114	0.7202	0.5482	0.7195	0.8482
No log	39.0	117	0.7577	0.5518	0.7571	0.8701
No log	40.0	120	0.6528	0.5603	0.6522	0.8076
No log	41.0	123	0.5861	0.6040	0.5854	0.7651
No log	42.0	126	0.6936	0.5598	0.6931	0.8325
No log	43.0	129	0.7405	0.4944	0.7398	0.8601
No log	44.0	132	0.5981	0.5933	0.5975	0.7730
No log	45.0	135	0.9596	0.4838	0.9591	0.9794
No log	46.0	138	0.6732	0.5586	0.6726	0.8201
No log	47.0	141	0.6972	0.5058	0.6965	0.8346
No log	48.0	144	0.7157	0.5367	0.7152	0.8457
No log	49.0	147	0.6310	0.5933	0.6304	0.7940
No log	50.0	150	0.6930	0.5543	0.6923	0.8321
No log	51.0	153	0.6676	0.5861	0.6670	0.8167
No log	52.0	156	0.5684	0.6136	0.5678	0.7535
No log	53.0	159	0.6101	0.5970	0.6095	0.7807
No log	54.0	162	0.6434	0.6049	0.6428	0.8018
No log	55.0	165	0.6245	0.5701	0.6239	0.7899
No log	56.0	168	0.6326	0.6087	0.6321	0.7950
No log	57.0	171	0.6728	0.5501	0.6723	0.8199
No log	58.0	174	0.6336	0.6112	0.6331	0.7957
No log	59.0	177	0.6095	0.6142	0.6090	0.7804
No log	60.0	180	0.6466	0.5488	0.6461	0.8038
No log	61.0	183	0.6396	0.6051	0.6391	0.7994
No log	62.0	186	0.5989	0.5848	0.5983	0.7735
No log	63.0	189	0.6126	0.6021	0.6120	0.7823
No log	64.0	192	0.5830	0.5966	0.5825	0.7632
No log	65.0	195	0.6122	0.6015	0.6116	0.7821
No log	66.0	198	0.6669	0.6030	0.6665	0.8164
No log	67.0	201	0.6061	0.5956	0.6055	0.7782
No log	68.0	204	0.6071	0.6148	0.6066	0.7788
No log	69.0	207	0.6146	0.5968	0.6141	0.7836
No log	70.0	210	0.6074	0.5915	0.6068	0.7790
No log	71.0	213	0.5972	0.5957	0.5966	0.7724
No log	72.0	216	0.6031	0.5891	0.6025	0.7762
No log	73.0	219	0.6121	0.5941	0.6115	0.7820
No log	74.0	222	0.6116	0.5899	0.6110	0.7817
No log	75.0	225	0.6332	0.5827	0.6327	0.7954
No log	76.0	228	0.6238	0.6021	0.6233	0.7895
No log	77.0	231	0.6324	0.5578	0.6319	0.7949
No log	78.0	234	0.6005	0.5955	0.6000	0.7746
No log	79.0	237	0.6282	0.6084	0.6277	0.7923
No log	80.0	240	0.6371	0.5753	0.6366	0.7979
No log	81.0	243	0.6445	0.5652	0.6440	0.8025
No log	82.0	246	0.6311	0.6096	0.6306	0.7941
No log	83.0	249	0.5962	0.5950	0.5957	0.7718
No log	84.0	252	0.6121	0.5786	0.6116	0.7820
No log	85.0	255	0.6178	0.5933	0.6173	0.7857
No log	86.0	258	0.6179	0.5956	0.6174	0.7857
No log	87.0	261	0.6047	0.5986	0.6042	0.7773
No log	88.0	264	0.6002	0.5976	0.5997	0.7744

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold1

Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold1