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	9.9618	0.0	9.9598	3.1559
No log	2.0	6	7.9127	0.0	7.9113	2.8127
No log	3.0	9	6.0768	0.0090	6.0753	2.4648
No log	4.0	12	4.7138	0.0038	4.7125	2.1708
No log	5.0	15	3.7050	0.0038	3.7039	1.9246
No log	6.0	18	2.8320	0.0	2.8310	1.6826
No log	7.0	21	2.0927	0.0365	2.0918	1.4463
No log	8.0	24	1.6266	0.0102	1.6259	1.2751
No log	9.0	27	1.2606	0.0102	1.2601	1.1225
No log	10.0	30	1.0306	0.0102	1.0301	1.0149
No log	11.0	33	1.0612	0.0365	1.0607	1.0299
No log	12.0	36	0.7919	0.3965	0.7916	0.8897
No log	13.0	39	0.6820	0.4258	0.6817	0.8257
No log	14.0	42	0.9179	0.3467	0.9177	0.9580
No log	15.0	45	0.7518	0.4587	0.7516	0.8670
No log	16.0	48	0.8738	0.4452	0.8738	0.9348
No log	17.0	51	0.6374	0.5489	0.6374	0.7984
No log	18.0	54	0.5971	0.5690	0.5972	0.7728
No log	19.0	57	0.5587	0.5510	0.5589	0.7476
No log	20.0	60	0.8840	0.4580	0.8844	0.9404
No log	21.0	63	0.5836	0.5152	0.5839	0.7641
No log	22.0	66	0.8312	0.4444	0.8315	0.9119
No log	23.0	69	0.7651	0.4659	0.7655	0.8749
No log	24.0	72	0.5602	0.5743	0.5606	0.7487
No log	25.0	75	0.8715	0.4801	0.8718	0.9337
No log	26.0	78	0.5867	0.6141	0.5870	0.7662
No log	27.0	81	0.8045	0.5243	0.8049	0.8972
No log	28.0	84	0.6187	0.5996	0.6191	0.7868
No log	29.0	87	0.9381	0.4715	0.9385	0.9687
No log	30.0	90	0.5823	0.6063	0.5826	0.7633
No log	31.0	93	0.5817	0.6172	0.5820	0.7629
No log	32.0	96	0.9763	0.4392	0.9766	0.9882
No log	33.0	99	0.5834	0.5989	0.5837	0.7640
No log	34.0	102	0.8831	0.4459	0.8835	0.9399
No log	35.0	105	0.7946	0.4867	0.7950	0.8916
No log	36.0	108	0.5746	0.6251	0.5748	0.7582
No log	37.0	111	0.8920	0.4431	0.8923	0.9446
No log	38.0	114	0.5743	0.6121	0.5745	0.7580
No log	39.0	117	0.5868	0.6025	0.5870	0.7662
No log	40.0	120	0.7822	0.4888	0.7825	0.8846
No log	41.0	123	0.5784	0.5968	0.5786	0.7607
No log	42.0	126	0.6506	0.5498	0.6509	0.8068
No log	43.0	129	0.5770	0.6064	0.5772	0.7597
No log	44.0	132	0.6334	0.5651	0.6336	0.7960
No log	45.0	135	0.5848	0.5953	0.5850	0.7648
No log	46.0	138	0.6670	0.5209	0.6672	0.8168
No log	47.0	141	0.6006	0.6031	0.6008	0.7751
No log	48.0	144	0.7070	0.5307	0.7072	0.8410
No log	49.0	147	0.6078	0.6131	0.6079	0.7797
No log	50.0	150	0.6227	0.5944	0.6229	0.7893
No log	51.0	153	0.5854	0.6007	0.5857	0.7653
No log	52.0	156	0.6661	0.5330	0.6664	0.8164
No log	53.0	159	0.5778	0.6108	0.5781	0.7603
No log	54.0	162	0.5701	0.6084	0.5704	0.7553
No log	55.0	165	0.7613	0.4797	0.7616	0.8727
No log	56.0	168	0.6098	0.5909	0.6101	0.7811

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_nosemantic_FineTuningBERT_AugV12_k5_task1_organization_k5_k5_fold3