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	7.2517	0.0	7.2517	2.6929
No log	2.0	6	4.0673	0.0115	4.0673	2.0167
No log	3.0	9	2.9048	0.0115	2.9048	1.7044
No log	4.0	12	1.8590	0.0782	1.8590	1.3635
No log	5.0	15	1.5184	0.0418	1.5184	1.2322
No log	6.0	18	1.1701	0.0316	1.1701	1.0817
No log	7.0	21	1.1924	0.0520	1.1924	1.0920
No log	8.0	24	1.5002	0.0929	1.5002	1.2248
No log	9.0	27	1.0829	0.0520	1.0829	1.0406
No log	10.0	30	1.1073	0.1118	1.1073	1.0523
No log	11.0	33	0.9007	0.2230	0.9007	0.9490
No log	12.0	36	0.7667	0.3335	0.7667	0.8756
No log	13.0	39	0.7445	0.3541	0.7445	0.8628
No log	14.0	42	0.7404	0.2896	0.7404	0.8605
No log	15.0	45	0.7200	0.3143	0.7200	0.8485
No log	16.0	48	0.6846	0.3479	0.6846	0.8274
No log	17.0	51	0.7217	0.3507	0.7217	0.8495
No log	18.0	54	0.7857	0.4124	0.7857	0.8864
No log	19.0	57	0.8151	0.3715	0.8151	0.9028
No log	20.0	60	0.7914	0.4276	0.7914	0.8896
No log	21.0	63	0.8179	0.4045	0.8179	0.9044
No log	22.0	66	0.7261	0.4506	0.7261	0.8521
No log	23.0	69	0.8308	0.4482	0.8308	0.9115
No log	24.0	72	0.8951	0.3917	0.8951	0.9461
No log	25.0	75	0.7156	0.4686	0.7156	0.8459
No log	26.0	78	0.7182	0.4518	0.7182	0.8475
No log	27.0	81	0.7648	0.4409	0.7648	0.8746
No log	28.0	84	0.7396	0.4444	0.7396	0.8600
No log	29.0	87	0.6992	0.4574	0.6992	0.8362
No log	30.0	90	0.6966	0.4817	0.6966	0.8346
No log	31.0	93	0.7361	0.5141	0.7361	0.8579
No log	32.0	96	0.7894	0.4885	0.7894	0.8885
No log	33.0	99	0.8097	0.4900	0.8097	0.8999
No log	34.0	102	0.8850	0.4522	0.8850	0.9407
No log	35.0	105	0.8950	0.4566	0.8950	0.9460
No log	36.0	108	0.7276	0.4854	0.7276	0.8530
No log	37.0	111	0.7939	0.4976	0.7939	0.8910
No log	38.0	114	0.7388	0.4697	0.7388	0.8595
No log	39.0	117	0.7480	0.5004	0.7480	0.8649
No log	40.0	120	0.6804	0.4975	0.6804	0.8249
No log	41.0	123	0.7551	0.4815	0.7551	0.8690
No log	42.0	126	0.7562	0.4681	0.7562	0.8696
No log	43.0	129	0.7454	0.5093	0.7454	0.8634
No log	44.0	132	0.6760	0.5248	0.6760	0.8222
No log	45.0	135	0.6910	0.5055	0.6910	0.8312
No log	46.0	138	0.7428	0.4978	0.7428	0.8619
No log	47.0	141	0.7397	0.4619	0.7397	0.8601
No log	48.0	144	0.6983	0.4732	0.6983	0.8356
No log	49.0	147	0.6731	0.4824	0.6731	0.8204
No log	50.0	150	0.7534	0.4804	0.7534	0.8680
No log	51.0	153	0.7410	0.5035	0.7410	0.8608
No log	52.0	156	0.6707	0.5029	0.6707	0.8190
No log	53.0	159	0.6920	0.5120	0.6920	0.8319
No log	54.0	162	0.6931	0.4627	0.6931	0.8325
No log	55.0	165	0.6988	0.4532	0.6988	0.8360
No log	56.0	168	0.7499	0.4943	0.7499	0.8660
No log	57.0	171	0.7445	0.4607	0.7445	0.8628
No log	58.0	174	0.6953	0.4821	0.6953	0.8338
No log	59.0	177	0.7053	0.4693	0.7053	0.8398
No log	60.0	180	0.6957	0.4695	0.6957	0.8341
No log	61.0	183	0.7035	0.5133	0.7035	0.8387
No log	62.0	186	0.7451	0.4940	0.7451	0.8632
No log	63.0	189	0.6631	0.5066	0.6631	0.8143
No log	64.0	192	0.6526	0.5088	0.6526	0.8079

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV11_k3_task1_organization_k3_k3_fold0

ASAP_FineTuningBERT_AugV11_k3_task1_organization_k3_k3_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV11_k3_task1_organization_k3_k3_fold0