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	2	10.0907	0.0224	10.0907	3.1766
No log	2.0	4	6.6233	0.0	6.6233	2.5736
No log	3.0	6	6.0057	0.0	6.0057	2.4506
No log	4.0	8	5.3478	0.0516	5.3478	2.3125
No log	5.0	10	4.7480	0.0	4.7480	2.1790
No log	6.0	12	4.0047	0.0	4.0047	2.0012
No log	7.0	14	3.3800	0.0	3.3800	1.8385
No log	8.0	16	3.0890	0.0	3.0890	1.7576
No log	9.0	18	2.3559	0.0	2.3559	1.5349
No log	10.0	20	1.8937	0.0391	1.8937	1.3761
No log	11.0	22	1.5648	0.0472	1.5648	1.2509
No log	12.0	24	1.3804	0.0472	1.3804	1.1749
No log	13.0	26	1.1324	0.0	1.1324	1.0641
No log	14.0	28	1.0423	0.0472	1.0423	1.0209
No log	15.0	30	0.9232	0.0472	0.9232	0.9608
No log	16.0	32	1.3238	0.1787	1.3238	1.1506
No log	17.0	34	1.2635	0.2238	1.2635	1.1241
No log	18.0	36	0.8735	0.3770	0.8735	0.9346
No log	19.0	38	0.7297	0.4774	0.7297	0.8542
No log	20.0	40	0.8823	0.3800	0.8823	0.9393
No log	21.0	42	0.8476	0.3792	0.8476	0.9206
No log	22.0	44	0.7269	0.4424	0.7269	0.8526
No log	23.0	46	0.7883	0.4463	0.7883	0.8879
No log	24.0	48	1.5279	0.3322	1.5279	1.2361
No log	25.0	50	1.0036	0.4720	1.0036	1.0018
No log	26.0	52	0.7266	0.4658	0.7266	0.8524
No log	27.0	54	0.7659	0.5441	0.7659	0.8751
No log	28.0	56	0.8321	0.4798	0.8321	0.9122
No log	29.0	58	0.6427	0.4887	0.6427	0.8017
No log	30.0	60	0.9061	0.4678	0.9061	0.9519
No log	31.0	62	1.1744	0.4048	1.1744	1.0837
No log	32.0	64	0.6993	0.5161	0.6993	0.8362
No log	33.0	66	0.6762	0.4234	0.6762	0.8223
No log	34.0	68	0.6853	0.4855	0.6853	0.8278
No log	35.0	70	0.8684	0.5070	0.8684	0.9319
No log	36.0	72	0.6160	0.5770	0.6160	0.7848
No log	37.0	74	0.5843	0.5753	0.5843	0.7644
No log	38.0	76	0.4845	0.5964	0.4845	0.6961
No log	39.0	78	0.8911	0.5056	0.8911	0.9440
No log	40.0	80	0.9409	0.5342	0.9409	0.9700
No log	41.0	82	0.5841	0.6509	0.5841	0.7643
No log	42.0	84	0.6607	0.5626	0.6607	0.8128
No log	43.0	86	0.5120	0.6667	0.5120	0.7155
No log	44.0	88	0.6118	0.5382	0.6118	0.7822
No log	45.0	90	0.4983	0.6686	0.4983	0.7059
No log	46.0	92	0.5279	0.6429	0.5279	0.7266
No log	47.0	94	0.5891	0.6820	0.5891	0.7675
No log	48.0	96	0.5978	0.6246	0.5978	0.7732
No log	49.0	98	0.4812	0.625	0.4812	0.6937
No log	50.0	100	0.4837	0.6154	0.4837	0.6955
No log	51.0	102	0.6943	0.6224	0.6943	0.8332
No log	52.0	104	0.5419	0.6759	0.5419	0.7361
No log	53.0	106	0.5696	0.6477	0.5696	0.7547
No log	54.0	108	0.5037	0.6014	0.5037	0.7097
No log	55.0	110	0.6165	0.6121	0.6165	0.7851
No log	56.0	112	0.5385	0.6246	0.5385	0.7338
No log	57.0	114	0.6843	0.6019	0.6843	0.8272
No log	58.0	116	0.7317	0.5394	0.7317	0.8554
No log	59.0	118	0.5286	0.5852	0.5286	0.7270
No log	60.0	120	0.6176	0.544	0.6176	0.7859
No log	61.0	122	0.5719	0.5483	0.5719	0.7562
No log	62.0	124	0.5626	0.6188	0.5626	0.7501
No log	63.0	126	0.6653	0.6003	0.6653	0.8157
No log	64.0	128	0.5447	0.6201	0.5447	0.7381
No log	65.0	130	0.5744	0.5917	0.5744	0.7579
No log	66.0	132	0.5846	0.5958	0.5846	0.7646
No log	67.0	134	0.5693	0.6188	0.5693	0.7545

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version12NewTestASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Version12NewTestASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version12NewTestASAP_FineTuningBERT_AugV12_k1_task1_organization_k1_k1_fold1