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	6	7.6292	0.0	7.6280	2.7619
No log	2.0	12	3.5104	0.0114	3.5098	1.8735
No log	3.0	18	2.3832	0.0665	2.3826	1.5436
No log	4.0	24	1.6967	0.0488	1.6963	1.3024
No log	5.0	30	1.1371	0.0675	1.1370	1.0663
No log	6.0	36	1.0510	0.0393	1.0507	1.0251
No log	7.0	42	1.2150	0.0791	1.2148	1.1022
No log	8.0	48	0.9106	0.1733	0.9104	0.9541
No log	9.0	54	0.7960	0.3858	0.7960	0.8922
No log	10.0	60	0.6775	0.4741	0.6776	0.8232
No log	11.0	66	0.6474	0.4537	0.6475	0.8046
No log	12.0	72	0.7419	0.4172	0.7422	0.8615
No log	13.0	78	0.6746	0.4733	0.6749	0.8215
No log	14.0	84	0.6134	0.4260	0.6138	0.7834
No log	15.0	90	0.6750	0.4645	0.6755	0.8219
No log	16.0	96	0.5910	0.4923	0.5915	0.7691
No log	17.0	102	0.5533	0.4978	0.5538	0.7442
No log	18.0	108	0.5954	0.5072	0.5959	0.7719
No log	19.0	114	0.6144	0.4880	0.6148	0.7841
No log	20.0	120	1.0183	0.3559	1.0187	1.0093
No log	21.0	126	0.6872	0.4079	0.6873	0.8290
No log	22.0	132	0.6035	0.5438	0.6040	0.7772
No log	23.0	138	0.8283	0.3576	0.8287	0.9103
No log	24.0	144	0.5599	0.5267	0.5603	0.7485
No log	25.0	150	0.5548	0.5623	0.5552	0.7451
No log	26.0	156	0.7848	0.4482	0.7850	0.8860
No log	27.0	162	0.5976	0.5891	0.5981	0.7733
No log	28.0	168	0.5688	0.5527	0.5692	0.7545
No log	29.0	174	0.5832	0.5758	0.5836	0.7639
No log	30.0	180	0.7234	0.5767	0.7238	0.8507
No log	31.0	186	0.6892	0.5789	0.6895	0.8303
No log	32.0	192	0.6361	0.5890	0.6364	0.7977
No log	33.0	198	0.5723	0.5728	0.5727	0.7568
No log	34.0	204	0.6428	0.6050	0.6431	0.8019
No log	35.0	210	0.6512	0.6089	0.6516	0.8072
No log	36.0	216	0.8697	0.5425	0.8699	0.9327
No log	37.0	222	0.6942	0.6051	0.6945	0.8334
No log	38.0	228	0.6724	0.6039	0.6727	0.8202
No log	39.0	234	0.6256	0.6029	0.6260	0.7912
No log	40.0	240	0.7465	0.5976	0.7468	0.8642
No log	41.0	246	0.6313	0.5808	0.6316	0.7947
No log	42.0	252	0.7396	0.5935	0.7399	0.8602
No log	43.0	258	1.0144	0.5392	1.0146	1.0073
No log	44.0	264	0.6527	0.5915	0.6531	0.8082
No log	45.0	270	0.7690	0.5824	0.7693	0.8771
No log	46.0	276	0.7816	0.6008	0.7818	0.8842
No log	47.0	282	0.8175	0.5791	0.8179	0.9044
No log	48.0	288	0.9840	0.5193	0.9841	0.9920
No log	49.0	294	0.9464	0.5239	0.9464	0.9728
No log	50.0	300	0.8121	0.5626	0.8123	0.9013
No log	51.0	306	1.0027	0.5350	1.0028	1.0014
No log	52.0	312	0.8565	0.5631	0.8566	0.9255
No log	53.0	318	0.6556	0.5986	0.6558	0.8098
No log	54.0	324	1.0118	0.5167	1.0120	1.0060
No log	55.0	330	1.1905	0.4920	1.1907	1.0912

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_fold3

ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV15_k7_task1_organization_k7_k7_fold3