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	5	7.1142	0.0	7.1142	2.6672
No log	2.0	10	4.9725	0.0115	4.9725	2.2299
No log	3.0	15	2.9204	0.0	2.9204	1.7089
No log	4.0	20	1.6793	0.0316	1.6793	1.2959
No log	5.0	25	1.7896	0.1182	1.7896	1.3377
No log	6.0	30	0.9917	0.0484	0.9917	0.9958
No log	7.0	35	1.7279	0.1714	1.7279	1.3145
No log	8.0	40	1.3018	0.1657	1.3018	1.1409
No log	9.0	45	0.7721	0.2499	0.7721	0.8787
No log	10.0	50	0.8548	0.3513	0.8548	0.9245
No log	11.0	55	0.7335	0.3747	0.7335	0.8564
No log	12.0	60	0.7439	0.3926	0.7439	0.8625
No log	13.0	65	0.7607	0.4234	0.7607	0.8722
No log	14.0	70	0.9583	0.3540	0.9583	0.9789
No log	15.0	75	0.7890	0.4289	0.7890	0.8883
No log	16.0	80	0.8129	0.4296	0.8129	0.9016
No log	17.0	85	0.8082	0.4167	0.8082	0.8990
No log	18.0	90	0.8783	0.4161	0.8783	0.9372
No log	19.0	95	0.8000	0.4382	0.8000	0.8944
No log	20.0	100	0.8053	0.4628	0.8053	0.8974
No log	21.0	105	0.8031	0.4310	0.8031	0.8962
No log	22.0	110	0.9670	0.3856	0.9670	0.9834
No log	23.0	115	0.8690	0.3953	0.8690	0.9322
No log	24.0	120	0.8009	0.4436	0.8009	0.8949
No log	25.0	125	0.8180	0.4278	0.8180	0.9044
No log	26.0	130	0.7492	0.4840	0.7492	0.8656
No log	27.0	135	0.9219	0.3863	0.9219	0.9601
No log	28.0	140	0.8230	0.4461	0.8230	0.9072
No log	29.0	145	0.7457	0.4612	0.7457	0.8635
No log	30.0	150	0.7181	0.4520	0.7181	0.8474
No log	31.0	155	0.7436	0.4649	0.7436	0.8623
No log	32.0	160	0.7571	0.4639	0.7571	0.8701
No log	33.0	165	0.8437	0.4447	0.8437	0.9185
No log	34.0	170	0.7299	0.4379	0.7299	0.8543
No log	35.0	175	0.7141	0.4730	0.7141	0.8450
No log	36.0	180	0.7683	0.4446	0.7683	0.8765
No log	37.0	185	0.7068	0.4837	0.7068	0.8407
No log	38.0	190	0.7639	0.4306	0.7639	0.8740
No log	39.0	195	0.6522	0.4866	0.6522	0.8076
No log	40.0	200	0.7018	0.5030	0.7018	0.8377
No log	41.0	205	0.6950	0.5240	0.6950	0.8336
No log	42.0	210	0.7124	0.5049	0.7124	0.8441
No log	43.0	215	0.7282	0.4857	0.7282	0.8534
No log	44.0	220	0.7067	0.4910	0.7067	0.8407
No log	45.0	225	0.7118	0.5115	0.7118	0.8437
No log	46.0	230	0.6898	0.5155	0.6898	0.8306
No log	47.0	235	0.7250	0.4958	0.7250	0.8515
No log	48.0	240	0.7568	0.4556	0.7568	0.8699
No log	49.0	245	0.7108	0.4968	0.7108	0.8431
No log	50.0	250	0.7255	0.5030	0.7255	0.8517
No log	51.0	255	0.8559	0.4435	0.8559	0.9252
No log	52.0	260	0.6833	0.4875	0.6833	0.8266
No log	53.0	265	0.6799	0.5180	0.6799	0.8245
No log	54.0	270	0.6888	0.5016	0.6888	0.8300
No log	55.0	275	0.6923	0.5079	0.6923	0.8320
No log	56.0	280	0.7228	0.5146	0.7228	0.8502
No log	57.0	285	0.6923	0.5245	0.6923	0.8320
No log	58.0	290	0.6734	0.5279	0.6734	0.8206
No log	59.0	295	0.6627	0.5221	0.6627	0.8141
No log	60.0	300	0.6883	0.5236	0.6883	0.8296
No log	61.0	305	0.7678	0.4969	0.7678	0.8763
No log	62.0	310	0.6950	0.5159	0.6950	0.8337
No log	63.0	315	0.7120	0.4947	0.7120	0.8438
No log	64.0	320	0.6892	0.5300	0.6892	0.8302
No log	65.0	325	0.6960	0.5159	0.6960	0.8343
No log	66.0	330	0.7221	0.4891	0.7221	0.8498
No log	67.0	335	0.7101	0.4803	0.7101	0.8427
No log	68.0	340	0.6975	0.4853	0.6975	0.8352
No log	69.0	345	0.6829	0.5091	0.6829	0.8264
No log	70.0	350	0.6841	0.4824	0.6841	0.8271
No log	71.0	355	0.6866	0.5002	0.6866	0.8286
No log	72.0	360	0.6915	0.4984	0.6915	0.8316
No log	73.0	365	0.6853	0.4928	0.6853	0.8278
No log	74.0	370	0.7075	0.5006	0.7075	0.8411
No log	75.0	375	0.6748	0.5055	0.6748	0.8215
No log	76.0	380	0.6858	0.5080	0.6858	0.8281
No log	77.0	385	0.6890	0.5063	0.6890	0.8301
No log	78.0	390	0.6733	0.5073	0.6733	0.8205
No log	79.0	395	0.6885	0.5142	0.6885	0.8298
No log	80.0	400	0.7032	0.4786	0.7032	0.8386
No log	81.0	405	0.6701	0.5062	0.6701	0.8186
No log	82.0	410	0.6728	0.5089	0.6728	0.8202
No log	83.0	415	0.6604	0.5260	0.6604	0.8126
No log	84.0	420	0.6634	0.5312	0.6634	0.8145
No log	85.0	425	0.6754	0.5221	0.6754	0.8218
No log	86.0	430	0.6858	0.5125	0.6858	0.8281
No log	87.0	435	0.6960	0.4941	0.6960	0.8343
No log	88.0	440	0.6655	0.5135	0.6655	0.8158
No log	89.0	445	0.6501	0.5240	0.6501	0.8063
No log	90.0	450	0.6490	0.5268	0.6490	0.8056
No log	91.0	455	0.6535	0.5226	0.6535	0.8084
No log	92.0	460	0.6769	0.5103	0.6769	0.8228
No log	93.0	465	0.6716	0.5263	0.6716	0.8195
No log	94.0	470	0.6681	0.5240	0.6681	0.8174
No log	95.0	475	0.6741	0.5165	0.6741	0.8210
No log	96.0	480	0.6721	0.5169	0.6721	0.8198
No log	97.0	485	0.6717	0.5169	0.6717	0.8196
No log	98.0	490	0.6700	0.5236	0.6700	0.8185
No log	99.0	495	0.6681	0.5200	0.6681	0.8174
0.341	100.0	500	0.6676	0.5195	0.6676	0.8171

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemanticV2_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold0