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	10.6559	0.0091	10.6531	3.2639
No log	2.0	8	6.9989	-0.0005	6.9962	2.6450
No log	3.0	12	4.5505	-0.0030	4.5492	2.1329
No log	4.0	16	2.5435	0.0351	2.5420	1.5944
No log	5.0	20	1.7581	0.0722	1.7564	1.3253
No log	6.0	24	2.0334	0.0142	2.0318	1.4254
No log	7.0	28	0.9516	0.0	0.9503	0.9748
No log	8.0	32	1.6347	0.0482	1.6331	1.2779
No log	9.0	36	1.3248	0.0172	1.3233	1.1503
No log	10.0	40	0.8546	0.1625	0.8533	0.9237
No log	11.0	44	0.8694	0.2626	0.8682	0.9318
No log	12.0	48	0.9080	0.1192	0.9067	0.9522
No log	13.0	52	0.5671	0.4569	0.5661	0.7524
No log	14.0	56	0.5295	0.5796	0.5287	0.7271
No log	15.0	60	0.4894	0.5874	0.4886	0.6990
No log	16.0	64	0.9547	0.2710	0.9539	0.9767
No log	17.0	68	0.6103	0.5537	0.6094	0.7806
No log	18.0	72	0.4498	0.6191	0.4491	0.6702
No log	19.0	76	0.5304	0.5779	0.5297	0.7278
No log	20.0	80	0.9723	0.4644	0.9720	0.9859
No log	21.0	84	0.8866	0.4678	0.8861	0.9413
No log	22.0	88	0.7391	0.5514	0.7386	0.8594
No log	23.0	92	0.5374	0.6076	0.5367	0.7326
No log	24.0	96	0.9260	0.5249	0.9256	0.9621
No log	25.0	100	0.6281	0.5198	0.6273	0.7920
No log	26.0	104	1.0580	0.4779	1.0576	1.0284
No log	27.0	108	0.7761	0.5454	0.7756	0.8807
No log	28.0	112	0.5562	0.6440	0.5555	0.7453
No log	29.0	116	0.7885	0.5676	0.7881	0.8877
No log	30.0	120	0.8455	0.5433	0.8451	0.9193
No log	31.0	124	0.5614	0.5921	0.5606	0.7487
No log	32.0	128	0.9671	0.5271	0.9668	0.9832
No log	33.0	132	0.6421	0.5870	0.6414	0.8009
No log	34.0	136	0.8068	0.5768	0.8064	0.8980
No log	35.0	140	0.5391	0.6469	0.5384	0.7337
No log	36.0	144	0.8932	0.5558	0.8928	0.9449
No log	37.0	148	0.7975	0.5725	0.7971	0.8928
No log	38.0	152	0.6418	0.5960	0.6411	0.8007
No log	39.0	156	0.7667	0.5666	0.7662	0.8753
No log	40.0	160	0.7137	0.5925	0.7132	0.8445
No log	41.0	164	0.7369	0.5965	0.7364	0.8581
No log	42.0	168	0.7807	0.5725	0.7802	0.8833
No log	43.0	172	0.7528	0.5671	0.7521	0.8672
No log	44.0	176	0.7008	0.5924	0.7002	0.8368
No log	45.0	180	0.6720	0.5861	0.6713	0.8193
No log	46.0	184	0.6933	0.5980	0.6926	0.8323
No log	47.0	188	0.6798	0.5916	0.6791	0.8240
No log	48.0	192	0.9526	0.5123	0.9521	0.9757
No log	49.0	196	0.6456	0.6153	0.6449	0.8030
No log	50.0	200	0.6138	0.6278	0.6131	0.7830
No log	51.0	204	0.8510	0.5421	0.8505	0.9222
No log	52.0	208	0.7905	0.5576	0.7897	0.8887
No log	53.0	212	0.7912	0.5764	0.7906	0.8892
No log	54.0	216	0.5493	0.6403	0.5483	0.7405
No log	55.0	220	0.7677	0.5833	0.7671	0.8759

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_fold1

ASAP_nosemanticV2_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1

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_fold1