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	4	7.9416	0.0	7.9416	2.8181
No log	2.0	8	6.6137	-0.0119	6.6137	2.5717
No log	3.0	12	4.8839	0.0039	4.8839	2.2100
No log	4.0	16	3.7261	0.0	3.7261	1.9303
No log	5.0	20	2.5229	0.0381	2.5229	1.5884
No log	6.0	24	1.7079	0.0316	1.7079	1.3069
No log	7.0	28	1.3492	0.0316	1.3492	1.1616
No log	8.0	32	1.4552	0.0991	1.4552	1.2063
No log	9.0	36	1.0581	0.0921	1.0581	1.0287
No log	10.0	40	1.3939	0.1664	1.3939	1.1806
No log	11.0	44	0.8159	0.4223	0.8159	0.9033
No log	12.0	48	1.0109	0.3021	1.0109	1.0054
No log	13.0	52	0.6610	0.3696	0.6610	0.8130
No log	14.0	56	1.0982	0.3004	1.0982	1.0479
No log	15.0	60	0.6533	0.4040	0.6533	0.8083
No log	16.0	64	0.9106	0.3441	0.9106	0.9543
No log	17.0	68	0.7207	0.4700	0.7207	0.8489
No log	18.0	72	0.7863	0.4632	0.7863	0.8867
No log	19.0	76	0.9040	0.4242	0.9040	0.9508
No log	20.0	80	0.7363	0.4538	0.7363	0.8581
No log	21.0	84	0.9092	0.4282	0.9092	0.9535
No log	22.0	88	0.9381	0.4725	0.9381	0.9686
No log	23.0	92	1.0386	0.3880	1.0386	1.0191
No log	24.0	96	0.7578	0.5223	0.7578	0.8705
No log	25.0	100	0.8034	0.5053	0.8034	0.8963
No log	26.0	104	0.8392	0.4602	0.8392	0.9161
No log	27.0	108	0.8135	0.4803	0.8135	0.9019
No log	28.0	112	1.1489	0.3681	1.1489	1.0719
No log	29.0	116	0.7838	0.4956	0.7838	0.8853
No log	30.0	120	1.1093	0.4473	1.1093	1.0532
No log	31.0	124	0.9459	0.4111	0.9459	0.9726
No log	32.0	128	0.7000	0.5043	0.7000	0.8367
No log	33.0	132	0.7449	0.5299	0.7449	0.8631
No log	34.0	136	0.7895	0.4881	0.7895	0.8885
No log	35.0	140	0.6994	0.5047	0.6994	0.8363
No log	36.0	144	0.6939	0.5107	0.6939	0.8330
No log	37.0	148	0.7109	0.5173	0.7109	0.8431
No log	38.0	152	0.7776	0.5141	0.7776	0.8818
No log	39.0	156	0.7361	0.5372	0.7361	0.8580
No log	40.0	160	0.7652	0.5231	0.7652	0.8747
No log	41.0	164	0.7427	0.5505	0.7427	0.8618
No log	42.0	168	0.7212	0.5176	0.7212	0.8493
No log	43.0	172	0.7246	0.5242	0.7246	0.8512
No log	44.0	176	0.7329	0.5345	0.7329	0.8561
No log	45.0	180	0.7062	0.5399	0.7062	0.8404
No log	46.0	184	0.7231	0.5547	0.7231	0.8503
No log	47.0	188	0.6918	0.5293	0.6918	0.8318
No log	48.0	192	0.7177	0.5278	0.7177	0.8472
No log	49.0	196	0.7484	0.5085	0.7484	0.8651
No log	50.0	200	0.6808	0.4932	0.6808	0.8251
No log	51.0	204	0.6785	0.5129	0.6785	0.8237
No log	52.0	208	0.6434	0.5222	0.6434	0.8021
No log	53.0	212	0.6684	0.5264	0.6684	0.8176
No log	54.0	216	0.6514	0.5245	0.6514	0.8071
No log	55.0	220	0.6401	0.5411	0.6401	0.8001
No log	56.0	224	0.6446	0.5482	0.6446	0.8029
No log	57.0	228	0.6305	0.5279	0.6305	0.7941
No log	58.0	232	0.6267	0.5495	0.6267	0.7916
No log	59.0	236	0.6904	0.5297	0.6904	0.8309
No log	60.0	240	0.6315	0.5492	0.6315	0.7947
No log	61.0	244	0.6435	0.5187	0.6435	0.8022
No log	62.0	248	0.6913	0.5340	0.6913	0.8315
No log	63.0	252	0.6642	0.5225	0.6642	0.8150
No log	64.0	256	0.6373	0.5433	0.6373	0.7983
No log	65.0	260	0.6469	0.5362	0.6469	0.8043
No log	66.0	264	0.6714	0.5164	0.6714	0.8194

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_nosemanticV2_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0

ASAP_nosemanticV2_FineTuningBERT_AugV12_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_nosemanticV2_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold0