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	12.7935	0.0	12.7936	3.5768
No log	2.0	4	10.7832	0.0130	10.7836	3.2838
No log	3.0	6	8.9114	0.0	8.9116	2.9852
No log	4.0	8	7.5067	0.0	7.5069	2.7399
No log	5.0	10	5.9199	0.0369	5.9202	2.4331
No log	6.0	12	4.6453	0.0151	4.6456	2.1554
No log	7.0	14	3.5313	0.0	3.5317	1.8793
No log	8.0	16	2.5986	0.0	2.5990	1.6121
No log	9.0	18	2.0174	0.0678	2.0179	1.4205
No log	10.0	20	1.4335	0.0213	1.4339	1.1975
No log	11.0	22	1.1156	0.0107	1.1160	1.0564
No log	12.0	24	0.9744	0.0107	0.9749	0.9874
No log	13.0	26	0.8261	0.3175	0.8264	0.9091
No log	14.0	28	0.8689	0.1971	0.8692	0.9323
No log	15.0	30	0.6913	0.4286	0.6915	0.8316
No log	16.0	32	0.5908	0.4857	0.5909	0.7687
No log	17.0	34	0.9194	0.4229	0.9196	0.9589
No log	18.0	36	0.8235	0.4524	0.8237	0.9076
No log	19.0	38	0.6492	0.4990	0.6494	0.8059
No log	20.0	40	0.6743	0.4926	0.6745	0.8213
No log	21.0	42	0.6790	0.4693	0.6792	0.8241
No log	22.0	44	0.6715	0.4418	0.6717	0.8196
No log	23.0	46	0.6499	0.4840	0.6501	0.8063
No log	24.0	48	0.6933	0.5175	0.6935	0.8327
No log	25.0	50	0.6473	0.5617	0.6473	0.8045
No log	26.0	52	0.7068	0.5549	0.7067	0.8407
No log	27.0	54	0.8955	0.4741	0.8954	0.9462
No log	28.0	56	0.8814	0.5197	0.8812	0.9387
No log	29.0	58	0.9340	0.5164	0.9337	0.9663
No log	30.0	60	1.0501	0.4540	1.0501	1.0247
No log	31.0	62	0.9826	0.4789	0.9826	0.9913
No log	32.0	64	0.9377	0.4941	0.9378	0.9684
No log	33.0	66	1.0666	0.4714	1.0667	1.0328
No log	34.0	68	0.9073	0.5140	0.9074	0.9526
No log	35.0	70	1.0126	0.4810	1.0128	1.0064
No log	36.0	72	0.7411	0.5628	0.7414	0.8610
No log	37.0	74	0.8488	0.5275	0.8490	0.9214
No log	38.0	76	0.8389	0.5436	0.8390	0.9160
No log	39.0	78	0.9352	0.5116	0.9353	0.9671
No log	40.0	80	1.0607	0.4933	1.0608	1.0300
No log	41.0	82	0.9387	0.5046	0.9388	0.9689
No log	42.0	84	0.7241	0.5346	0.7243	0.8511
No log	43.0	86	0.9724	0.4837	0.9724	0.9861
No log	44.0	88	0.9945	0.4750	0.9945	0.9973
No log	45.0	90	0.7135	0.5452	0.7137	0.8448
No log	46.0	92	0.7826	0.5394	0.7827	0.8847
No log	47.0	94	1.0487	0.4774	1.0487	1.0241
No log	48.0	96	0.8973	0.5093	0.8973	0.9473
No log	49.0	98	0.8762	0.5147	0.8762	0.9361
No log	50.0	100	0.8349	0.5275	0.8350	0.9138
No log	51.0	102	0.7634	0.5580	0.7636	0.8739
No log	52.0	104	0.7785	0.5431	0.7787	0.8824
No log	53.0	106	0.8581	0.5050	0.8583	0.9265
No log	54.0	108	0.7630	0.5295	0.7632	0.8736
No log	55.0	110	0.9643	0.4983	0.9644	0.9820
No log	56.0	112	0.9369	0.4951	0.9369	0.9679
No log	57.0	114	0.9136	0.4973	0.9136	0.9558
No log	58.0	116	0.8950	0.5010	0.8950	0.9461
No log	59.0	118	0.8190	0.5327	0.8190	0.9050
No log	60.0	120	0.8960	0.5041	0.8960	0.9466
No log	61.0	122	0.7564	0.5441	0.7565	0.8698
No log	62.0	124	0.7890	0.5342	0.7891	0.8883
No log	63.0	126	0.9707	0.4934	0.9707	0.9852
No log	64.0	128	0.8671	0.4976	0.8671	0.9312
No log	65.0	130	0.7276	0.5532	0.7277	0.8531
No log	66.0	132	0.8073	0.5223	0.8073	0.8985

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold2

ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV21_k1_task1_organization_k1_k1_fold2