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	1	11.8684	0.0	11.8657	3.4447
No log	2.0	2	10.4801	-0.0002	10.4775	3.2369
No log	3.0	3	9.3592	0.0	9.3567	3.0589
No log	4.0	4	8.7532	0.0	8.7507	2.9582
No log	5.0	5	8.1723	0.0	8.1697	2.8583
No log	6.0	6	7.3499	0.0	7.3473	2.7106
No log	7.0	7	6.3854	-0.0007	6.3830	2.5265
No log	8.0	8	5.9485	0.0159	5.9466	2.4386
No log	9.0	9	5.1373	0.0170	5.1353	2.2661
No log	10.0	10	4.6293	0.0143	4.6269	2.1510
No log	11.0	11	4.0670	0.0025	4.0649	2.0161
No log	12.0	12	3.7793	0.0	3.7774	1.9435
No log	13.0	13	3.2050	0.0	3.2033	1.7898
No log	14.0	14	2.5634	0.0211	2.5616	1.6005
No log	15.0	15	2.2802	0.1408	2.2784	1.5094
No log	16.0	16	2.5146	0.0319	2.5130	1.5852
No log	17.0	17	2.2232	0.0466	2.2216	1.4905
No log	18.0	18	1.6180	0.0669	1.6165	1.2714
No log	19.0	19	1.3879	0.0379	1.3863	1.1774
No log	20.0	20	1.2722	0.0315	1.2706	1.1272
No log	21.0	21	1.1053	0.0211	1.1039	1.0507
No log	22.0	22	1.1010	0.0106	1.0996	1.0486
No log	23.0	23	1.0672	0.0	1.0659	1.0324
No log	24.0	24	0.9476	-0.0027	0.9463	0.9728
No log	25.0	25	0.8548	0.3169	0.8535	0.9239
No log	26.0	26	0.8137	0.3422	0.8126	0.9014
No log	27.0	27	0.7847	0.3279	0.7835	0.8852
No log	28.0	28	0.7552	0.3139	0.7541	0.8684
No log	29.0	29	0.7424	0.2667	0.7414	0.8611
No log	30.0	30	0.7482	0.2227	0.7473	0.8644
No log	31.0	31	0.7225	0.2497	0.7216	0.8495
No log	32.0	32	0.6832	0.3034	0.6823	0.8260
No log	33.0	33	0.6544	0.3279	0.6535	0.8084
No log	34.0	34	0.6627	0.3332	0.6619	0.8136
No log	35.0	35	0.7033	0.3300	0.7025	0.8382
No log	36.0	36	0.6349	0.3743	0.6341	0.7963
No log	37.0	37	0.5650	0.4356	0.5643	0.7512
No log	38.0	38	0.6031	0.4964	0.6025	0.7762
No log	39.0	39	0.5416	0.5616	0.5410	0.7355
No log	40.0	40	0.5245	0.6198	0.5239	0.7238
No log	41.0	41	0.5742	0.6173	0.5737	0.7575
No log	42.0	42	0.5294	0.6400	0.5289	0.7273
No log	43.0	43	0.4638	0.6657	0.4632	0.6806
No log	44.0	44	0.5105	0.6613	0.5101	0.7142
No log	45.0	45	0.7418	0.5789	0.7415	0.8611
No log	46.0	46	0.7726	0.5633	0.7723	0.8788
No log	47.0	47	0.6070	0.6515	0.6068	0.7790
No log	48.0	48	0.5824	0.6644	0.5821	0.7630
No log	49.0	49	0.6986	0.6230	0.6985	0.8357
No log	50.0	50	0.7164	0.6217	0.7163	0.8464
No log	51.0	51	0.6139	0.6591	0.6138	0.7834
No log	52.0	52	0.6298	0.6483	0.6297	0.7935
No log	53.0	53	0.8179	0.6114	0.8179	0.9044
No log	54.0	54	0.8223	0.6158	0.8224	0.9068
No log	55.0	55	0.6566	0.6553	0.6565	0.8103
No log	56.0	56	0.6668	0.6568	0.6668	0.8166
No log	57.0	57	0.8566	0.6183	0.8567	0.9256
No log	58.0	58	0.9661	0.5876	0.9662	0.9830
No log	59.0	59	0.9719	0.5847	0.9721	0.9859
No log	60.0	60	0.7870	0.6396	0.7871	0.8872
No log	61.0	61	0.7964	0.6411	0.7965	0.8925
No log	62.0	62	0.9657	0.5790	0.9658	0.9828
No log	63.0	63	0.9934	0.5704	0.9936	0.9968

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold1

Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
Version19ASAP_FineTuningBERT_AugV19_k1_task1_organization_k1_k1_fold1