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	8.5205	0.0	8.5187	2.9187
No log	2.0	6	6.8047	0.0	6.8032	2.6083
No log	3.0	9	4.7635	0.0114	4.7622	2.1822
No log	4.0	12	3.3313	0.0	3.3302	1.8249
No log	5.0	15	2.3458	0.1238	2.3448	1.5313
No log	6.0	18	1.5896	0.0610	1.5889	1.2605
No log	7.0	21	1.1391	0.0202	1.1385	1.0670
No log	8.0	24	1.0197	0.0162	1.0190	1.0095
No log	9.0	27	0.7967	0.2953	0.7962	0.8923
No log	10.0	30	1.1190	0.0999	1.1185	1.0576
No log	11.0	33	0.6436	0.4734	0.6434	0.8021
No log	12.0	36	0.7139	0.4592	0.7137	0.8448
No log	13.0	39	1.1617	0.4428	1.1618	1.0779
No log	14.0	42	1.0150	0.4684	1.0150	1.0075
No log	15.0	45	0.6019	0.5281	0.6019	0.7758
No log	16.0	48	0.6503	0.4936	0.6502	0.8063
No log	17.0	51	0.6401	0.4928	0.6401	0.8001
No log	18.0	54	1.0385	0.3636	1.0380	1.0188
No log	19.0	57	0.6332	0.4891	0.6332	0.7957
No log	20.0	60	0.6036	0.5038	0.6033	0.7768
No log	21.0	63	0.6695	0.3512	0.6696	0.8183
No log	22.0	66	0.5737	0.5490	0.5735	0.7573
No log	23.0	69	0.5934	0.5260	0.5935	0.7704
No log	24.0	72	0.6717	0.5298	0.6716	0.8195
No log	25.0	75	0.6664	0.5517	0.6664	0.8164
No log	26.0	78	0.6594	0.5644	0.6590	0.8118
No log	27.0	81	0.6581	0.5466	0.6578	0.8110
No log	28.0	84	0.6511	0.5547	0.6510	0.8068
No log	29.0	87	0.6269	0.6117	0.6266	0.7916
No log	30.0	90	0.7314	0.5724	0.7313	0.8551
No log	31.0	93	0.8326	0.5292	0.8325	0.9124
No log	32.0	96	0.6546	0.5890	0.6543	0.8089
No log	33.0	99	0.6308	0.5951	0.6306	0.7941
No log	34.0	102	0.7837	0.5778	0.7833	0.8850
No log	35.0	105	1.0119	0.4868	1.0116	1.0058
No log	36.0	108	0.7549	0.5742	0.7546	0.8687
No log	37.0	111	0.7003	0.5864	0.7001	0.8367
No log	38.0	114	0.6802	0.5795	0.6799	0.8246
No log	39.0	117	0.8841	0.5149	0.8836	0.9400
No log	40.0	120	0.6872	0.5841	0.6869	0.8288
No log	41.0	123	0.6942	0.5845	0.6940	0.8330
No log	42.0	126	0.7437	0.6030	0.7434	0.8622
No log	43.0	129	0.8370	0.5753	0.8366	0.9146
No log	44.0	132	0.8736	0.5310	0.8733	0.9345
No log	45.0	135	0.7149	0.6212	0.7146	0.8453
No log	46.0	138	0.6613	0.6269	0.6611	0.8131
No log	47.0	141	0.7577	0.5834	0.7574	0.8703
No log	48.0	144	0.8274	0.5727	0.8269	0.9093
No log	49.0	147	0.8034	0.5758	0.8029	0.8960
No log	50.0	150	0.6782	0.5909	0.6778	0.8233
No log	51.0	153	0.6557	0.6003	0.6553	0.8095
No log	52.0	156	0.8355	0.5188	0.8350	0.9138
No log	53.0	159	0.8521	0.5197	0.8517	0.9229
No log	54.0	162	0.6402	0.6030	0.6399	0.7999
No log	55.0	165	0.7440	0.5726	0.7437	0.8624
No log	56.0	168	0.7892	0.5938	0.7888	0.8881
No log	57.0	171	0.7479	0.5802	0.7475	0.8646
No log	58.0	174	0.7012	0.5911	0.7009	0.8372
No log	59.0	177	0.7778	0.5634	0.7774	0.8817
No log	60.0	180	0.8783	0.5319	0.8778	0.9369
No log	61.0	183	0.6725	0.5785	0.6723	0.8199
No log	62.0	186	0.6111	0.6225	0.6109	0.7816
No log	63.0	189	0.6752	0.6004	0.6748	0.8215
No log	64.0	192	0.7702	0.5698	0.7699	0.8774
No log	65.0	195	0.7298	0.5912	0.7294	0.8541
No log	66.0	198	0.6699	0.6150	0.6696	0.8183

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV15_k3_task1_organization_k3_k3_fold3

ASAP_FineTuningBERT_AugV15_k3_task1_organization_k3_k3_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV15_k3_task1_organization_k3_k3_fold3