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	2	11.7492	-0.0268	11.7469	3.4274
No log	2.0	4	9.6935	0.0	9.6917	3.1132
No log	3.0	6	7.9519	0.0	7.9502	2.8196
No log	4.0	8	6.5707	0.0	6.5691	2.5630
No log	5.0	10	5.5896	0.0120	5.5884	2.3640
No log	6.0	12	4.6260	0.0	4.6248	2.1505
No log	7.0	14	3.7776	0.0	3.7765	1.9433
No log	8.0	16	3.1320	0.0	3.1309	1.7694
No log	9.0	18	2.4571	0.1676	2.4561	1.5672
No log	10.0	20	1.9869	0.0202	1.9860	1.4092
No log	11.0	22	1.5746	0.0202	1.5738	1.2545
No log	12.0	24	1.3001	0.0202	1.2995	1.1399
No log	13.0	26	1.1255	0.0202	1.1249	1.0606
No log	14.0	28	1.0141	0.0256	1.0137	1.0068
No log	15.0	30	1.2738	0.1250	1.2737	1.1286
No log	16.0	32	1.4882	0.1840	1.4882	1.2199
No log	17.0	34	1.0866	0.2302	1.0867	1.0424
No log	18.0	36	0.8553	0.4398	0.8555	0.9249
No log	19.0	38	0.8367	0.4537	0.8370	0.9149
No log	20.0	40	1.2684	0.2784	1.2688	1.1264
No log	21.0	42	1.1418	0.3290	1.1423	1.0688
No log	22.0	44	0.8582	0.4751	0.8588	0.9267
No log	23.0	46	0.7913	0.4823	0.7920	0.8899
No log	24.0	48	0.8700	0.4670	0.8707	0.9331
No log	25.0	50	0.9195	0.4050	0.9202	0.9592
No log	26.0	52	0.6982	0.4838	0.6989	0.8360
No log	27.0	54	0.6815	0.4753	0.6822	0.8260
No log	28.0	56	0.9113	0.4136	0.9122	0.9551
No log	29.0	58	0.9812	0.3891	0.9821	0.9910
No log	30.0	60	0.7401	0.4590	0.7409	0.8608
No log	31.0	62	0.7773	0.4906	0.7781	0.8821
No log	32.0	64	0.7177	0.4983	0.7183	0.8475
No log	33.0	66	0.7233	0.5114	0.7240	0.8509
No log	34.0	68	0.8388	0.4796	0.8395	0.9162
No log	35.0	70	1.0062	0.4175	1.0068	1.0034
No log	36.0	72	0.8087	0.4821	0.8096	0.8998
No log	37.0	74	0.8375	0.4768	0.8385	0.9157
No log	38.0	76	1.1425	0.3960	1.1432	1.0692
No log	39.0	78	0.9238	0.4287	0.9244	0.9614
No log	40.0	80	0.7913	0.4941	0.7918	0.8898
No log	41.0	82	0.7857	0.4668	0.7860	0.8866
No log	42.0	84	0.8040	0.4531	0.8045	0.8969
No log	43.0	86	0.7570	0.4928	0.7576	0.8704
No log	44.0	88	0.7741	0.5002	0.7749	0.8803
No log	45.0	90	0.8262	0.4914	0.8271	0.9094
No log	46.0	92	0.8410	0.4985	0.8419	0.9175
No log	47.0	94	0.8255	0.5117	0.8264	0.9091
No log	48.0	96	0.8504	0.5071	0.8512	0.9226
No log	49.0	98	0.7635	0.5119	0.7642	0.8742
No log	50.0	100	0.8663	0.4606	0.8670	0.9311
No log	51.0	102	0.7359	0.5099	0.7366	0.8583
No log	52.0	104	0.9331	0.4843	0.9337	0.9663
No log	53.0	106	1.0682	0.4742	1.0689	1.0339
No log	54.0	108	0.7990	0.5173	0.7996	0.8942
No log	55.0	110	0.7890	0.4974	0.7896	0.8886
No log	56.0	112	0.8058	0.4992	0.8065	0.8980
No log	57.0	114	0.8201	0.5177	0.8209	0.9060
No log	58.0	116	0.8536	0.5177	0.8544	0.9244
No log	59.0	118	0.8061	0.5004	0.8069	0.8983
No log	60.0	120	0.7675	0.5066	0.7682	0.8765
No log	61.0	122	0.7343	0.5243	0.7348	0.8572
No log	62.0	124	0.7792	0.4994	0.7797	0.8830
No log	63.0	126	0.7404	0.5108	0.7407	0.8607
No log	64.0	128	0.7458	0.5169	0.7461	0.8638
No log	65.0	130	0.7798	0.5115	0.7802	0.8833
No log	66.0	132	0.7619	0.5148	0.7624	0.8731
No log	67.0	134	0.7890	0.5310	0.7895	0.8886
No log	68.0	136	0.7826	0.5133	0.7832	0.8850
No log	69.0	138	0.7791	0.5068	0.7799	0.8831
No log	70.0	140	0.7804	0.4878	0.7812	0.8839
No log	71.0	142	0.7849	0.4847	0.7856	0.8864
No log	72.0	144	0.7869	0.4879	0.7877	0.8875
No log	73.0	146	0.7814	0.5015	0.7820	0.8843
No log	74.0	148	0.7630	0.5019	0.7637	0.8739
No log	75.0	150	0.7634	0.4785	0.7641	0.8741
No log	76.0	152	0.7741	0.4909	0.7748	0.8802
No log	77.0	154	0.7738	0.5120	0.7744	0.8800
No log	78.0	156	0.8553	0.5062	0.8559	0.9251
No log	79.0	158	0.8526	0.5082	0.8531	0.9236
No log	80.0	160	0.7777	0.5215	0.7783	0.8822
No log	81.0	162	0.7604	0.5065	0.7610	0.8723
No log	82.0	164	0.7627	0.5085	0.7633	0.8737
No log	83.0	166	0.7565	0.5053	0.7571	0.8701
No log	84.0	168	0.7565	0.5198	0.7571	0.8701
No log	85.0	170	0.7615	0.5289	0.7621	0.8730
No log	86.0	172	0.7745	0.5068	0.7751	0.8804
No log	87.0	174	0.7692	0.5125	0.7698	0.8774

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version2ASAP_FineTuningBERT_AugV5_k1_task1_organization_k1_k1_fold3

Version2ASAP_FineTuningBERT_AugV5_k1_task1_organization_k1_k1_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version2ASAP_FineTuningBERT_AugV5_k1_task1_organization_k1_k1_fold3