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	4	10.5556	0.0	10.5554	3.2489
No log	2.0	8	7.1817	0.0	7.1819	2.6799
No log	3.0	12	4.3248	0.0102	4.3253	2.0797
No log	4.0	16	2.7815	0.0	2.7820	1.6679
No log	5.0	20	1.7297	0.0463	1.7301	1.3153
No log	6.0	24	1.0396	0.0107	1.0400	1.0198
No log	7.0	28	0.7705	0.3036	0.7709	0.8780
No log	8.0	32	0.8645	0.2309	0.8647	0.9299
No log	9.0	36	0.9254	0.2965	0.9257	0.9621
No log	10.0	40	0.5822	0.4862	0.5823	0.7631
No log	11.0	44	1.3973	0.2842	1.3974	1.1821
No log	12.0	48	0.4921	0.4807	0.4921	0.7015
No log	13.0	52	1.4495	0.2923	1.4497	1.2040
No log	14.0	56	0.4821	0.5394	0.4821	0.6944
No log	15.0	60	1.0249	0.3393	1.0250	1.0124
No log	16.0	64	0.8229	0.4166	0.8230	0.9072
No log	17.0	68	0.4967	0.5478	0.4967	0.7048
No log	18.0	72	0.6882	0.4786	0.6882	0.8296
No log	19.0	76	0.6941	0.4824	0.6941	0.8331
No log	20.0	80	0.5752	0.5951	0.5751	0.7584
No log	21.0	84	0.7998	0.4691	0.7996	0.8942
No log	22.0	88	0.5241	0.6056	0.5240	0.7239
No log	23.0	92	0.5261	0.5746	0.5259	0.7252
No log	24.0	96	0.8061	0.4471	0.8060	0.8978
No log	25.0	100	0.5850	0.5575	0.5850	0.7648
No log	26.0	104	0.5463	0.6054	0.5463	0.7391
No log	27.0	108	0.7724	0.4225	0.7724	0.8789
No log	28.0	112	0.5676	0.5740	0.5676	0.7534
No log	29.0	116	0.5369	0.5970	0.5368	0.7327
No log	30.0	120	0.7114	0.5028	0.7114	0.8434
No log	31.0	124	0.5178	0.6381	0.5177	0.7195
No log	32.0	128	0.6584	0.5472	0.6583	0.8113
No log	33.0	132	0.5205	0.6077	0.5204	0.7214
No log	34.0	136	0.6123	0.5494	0.6122	0.7824
No log	35.0	140	0.5514	0.5948	0.5513	0.7425
No log	36.0	144	0.6166	0.5636	0.6164	0.7851
No log	37.0	148	0.5240	0.6115	0.5239	0.7238
No log	38.0	152	0.6098	0.5235	0.6098	0.7809
No log	39.0	156	0.5490	0.5761	0.5489	0.7409
No log	40.0	160	0.5541	0.5755	0.5540	0.7443
No log	41.0	164	0.6844	0.5140	0.6843	0.8272
No log	42.0	168	0.5058	0.6274	0.5056	0.7111
No log	43.0	172	0.6863	0.4791	0.6863	0.8284
No log	44.0	176	0.5203	0.6083	0.5202	0.7213
No log	45.0	180	0.6688	0.5202	0.6687	0.8177
No log	46.0	184	0.5131	0.6029	0.5129	0.7162
No log	47.0	188	0.6586	0.5328	0.6585	0.8115
No log	48.0	192	0.5445	0.6025	0.5443	0.7378
No log	49.0	196	0.5316	0.5918	0.5315	0.7290
No log	50.0	200	0.6417	0.5432	0.6417	0.8010
No log	51.0	204	0.5593	0.5846	0.5591	0.7477
No log	52.0	208	0.6425	0.5535	0.6424	0.8015
No log	53.0	212	0.5603	0.5874	0.5602	0.7485
No log	54.0	216	0.5370	0.5867	0.5369	0.7327
No log	55.0	220	0.6449	0.5545	0.6448	0.8030
No log	56.0	224	0.5348	0.5844	0.5348	0.7313
No log	57.0	228	0.5100	0.5924	0.5100	0.7142
No log	58.0	232	0.5307	0.5978	0.5307	0.7285
No log	59.0	236	0.5204	0.6063	0.5204	0.7214
No log	60.0	240	0.5437	0.5961	0.5436	0.7373
No log	61.0	244	0.5144	0.5894	0.5144	0.7172

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold2

Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version27NewTestASAP_FineTuningBERT_AugV27_k7_task1_organization_k7_k7_fold2