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: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	1.0	6	6.0271	0.0511	6.0254	2.4547
No log	2.0	12	2.5188	0.1127	2.5180	1.5868
No log	3.0	18	1.1954	0.0365	1.1949	1.0931
No log	4.0	24	0.7974	0.3119	0.7972	0.8928
No log	5.0	30	0.8130	0.3099	0.8128	0.9015
No log	6.0	36	0.8756	0.3438	0.8756	0.9357
No log	7.0	42	0.8315	0.4014	0.8318	0.9120
No log	8.0	48	0.5663	0.5554	0.5668	0.7529
No log	9.0	54	0.6631	0.5093	0.6637	0.8147
No log	10.0	60	0.6914	0.5008	0.6920	0.8319
No log	11.0	66	0.8372	0.4428	0.8378	0.9153
No log	12.0	72	0.7470	0.4674	0.7476	0.8646
No log	13.0	78	0.8330	0.4267	0.8335	0.9130
No log	14.0	84	0.9803	0.3501	0.9809	0.9904
No log	15.0	90	0.8409	0.4262	0.8415	0.9173
No log	16.0	96	0.8067	0.4736	0.8074	0.8985
No log	17.0	102	0.7463	0.4889	0.7469	0.8642
No log	18.0	108	0.6788	0.5196	0.6795	0.8243
No log	19.0	114	0.5709	0.5564	0.5715	0.7560
No log	20.0	120	0.6201	0.5352	0.6207	0.7879
No log	21.0	126	0.5689	0.5245	0.5695	0.7546
No log	22.0	132	0.5721	0.5306	0.5726	0.7567
No log	23.0	138	0.8698	0.4164	0.8704	0.9329
No log	24.0	144	0.7839	0.4875	0.7845	0.8857
No log	25.0	150	0.5441	0.5751	0.5447	0.7380
No log	26.0	156	0.5524	0.5654	0.5529	0.7436
No log	27.0	162	0.8688	0.4370	0.8694	0.9324
No log	28.0	168	0.5354	0.5982	0.5359	0.7321
No log	29.0	174	0.6647	0.5470	0.6653	0.8156
No log	30.0	180	0.8868	0.4164	0.8874	0.9420
No log	31.0	186	0.5569	0.5602	0.5574	0.7466
No log	32.0	192	0.8903	0.4410	0.8910	0.9439
No log	33.0	198	0.7094	0.5081	0.7099	0.8426
No log	34.0	204	0.6260	0.5406	0.6266	0.7916
No log	35.0	210	0.5771	0.5143	0.5776	0.7600
No log	36.0	216	0.6855	0.5259	0.6860	0.8283
No log	37.0	222	0.6771	0.5366	0.6778	0.8233
No log	38.0	228	0.7467	0.4952	0.7474	0.8645
No log	39.0	234	0.5744	0.6211	0.5750	0.7583
No log	40.0	240	0.5590	0.6078	0.5595	0.7480
No log	41.0	246	0.6932	0.4944	0.6939	0.8330
No log	42.0	252	0.6063	0.5166	0.6069	0.7790
No log	43.0	258	0.5561	0.5647	0.5565	0.7460
No log	44.0	264	0.9409	0.3960	0.9415	0.9703
No log	45.0	270	0.5530	0.5743	0.5535	0.7440
No log	46.0	276	0.6144	0.5385	0.6150	0.7842
No log	47.0	282	0.5759	0.5648	0.5764	0.7592
No log	48.0	288	0.6095	0.5570	0.6100	0.7810
No log	49.0	294	0.5823	0.5753	0.5828	0.7634
No log	50.0	300	0.5525	0.5722	0.5530	0.7436
No log	51.0	306	0.6529	0.5387	0.6534	0.8083
No log	52.0	312	0.5509	0.5752	0.5513	0.7425
No log	53.0	318	0.5576	0.5975	0.5580	0.7470
No log	54.0	324	0.6162	0.5510	0.6168	0.7853
No log	55.0	330	0.5888	0.5501	0.5893	0.7676
No log	56.0	336	0.5449	0.5764	0.5453	0.7384
No log	57.0	342	0.5395	0.5832	0.5400	0.7348
No log	58.0	348	0.6961	0.5151	0.6966	0.8347
No log	59.0	354	0.5311	0.5894	0.5315	0.7290
No log	60.0	360	0.5816	0.5541	0.5821	0.7630
No log	61.0	366	0.6256	0.5579	0.6261	0.7913
No log	62.0	372	0.5558	0.5767	0.5563	0.7459
No log	63.0	378	0.5449	0.5866	0.5454	0.7385
No log	64.0	384	0.5950	0.5870	0.5955	0.7717
No log	65.0	390	0.6925	0.5217	0.6931	0.8325
No log	66.0	396	0.5390	0.6026	0.5395	0.7345
No log	67.0	402	0.6276	0.5398	0.6282	0.7926
No log	68.0	408	0.5735	0.5613	0.5740	0.7576
No log	69.0	414	0.5417	0.5961	0.5422	0.7363

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold3

Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k10_task1_organization_k10_k10_fold3