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	5	7.1649	0.0	7.1649	2.6767
No log	2.0	10	4.9153	0.0115	4.9153	2.2170
No log	3.0	15	2.8578	0.0	2.8578	1.6905
No log	4.0	20	1.5052	0.0316	1.5052	1.2269
No log	5.0	25	0.8798	0.0593	0.8798	0.9380
No log	6.0	30	1.1677	0.0550	1.1677	1.0806
No log	7.0	35	0.5813	0.4295	0.5813	0.7624
No log	8.0	40	0.5617	0.4963	0.5617	0.7495
No log	9.0	45	0.5556	0.5102	0.5556	0.7454
No log	10.0	50	0.4910	0.4827	0.4910	0.7007
No log	11.0	55	0.5095	0.4924	0.5095	0.7138
No log	12.0	60	0.7338	0.4560	0.7338	0.8566
No log	13.0	65	0.7125	0.4679	0.7125	0.8441
No log	14.0	70	0.7361	0.4223	0.7361	0.8580
No log	15.0	75	0.7204	0.4784	0.7204	0.8487
No log	16.0	80	0.9263	0.3747	0.9263	0.9624
No log	17.0	85	0.6661	0.5579	0.6661	0.8162
No log	18.0	90	0.8838	0.4001	0.8838	0.9401
No log	19.0	95	0.6098	0.5244	0.6098	0.7809
No log	20.0	100	0.6141	0.4736	0.6141	0.7836
No log	21.0	105	0.6364	0.5024	0.6364	0.7977
No log	22.0	110	0.7080	0.4661	0.7080	0.8414
No log	23.0	115	0.5386	0.5193	0.5386	0.7339
No log	24.0	120	0.5406	0.5639	0.5406	0.7353
No log	25.0	125	0.7369	0.4707	0.7369	0.8584
No log	26.0	130	0.5202	0.5611	0.5202	0.7213
No log	27.0	135	0.6807	0.4637	0.6807	0.8251
No log	28.0	140	0.5567	0.5301	0.5567	0.7461
No log	29.0	145	0.5625	0.5506	0.5625	0.7500
No log	30.0	150	0.5514	0.5582	0.5514	0.7426
No log	31.0	155	0.5444	0.5593	0.5444	0.7379
No log	32.0	160	0.6079	0.5428	0.6079	0.7797
No log	33.0	165	0.6732	0.5242	0.6732	0.8205
No log	34.0	170	0.5789	0.5475	0.5789	0.7608
No log	35.0	175	0.5238	0.5670	0.5238	0.7237
No log	36.0	180	0.5515	0.5580	0.5515	0.7426
No log	37.0	185	0.6717	0.5367	0.6717	0.8196
No log	38.0	190	0.5904	0.5389	0.5904	0.7684
No log	39.0	195	0.5896	0.5454	0.5896	0.7678
No log	40.0	200	0.5585	0.5534	0.5585	0.7473
No log	41.0	205	0.5217	0.5740	0.5217	0.7223
No log	42.0	210	0.5035	0.5807	0.5035	0.7096
No log	43.0	215	0.5384	0.5613	0.5384	0.7338
No log	44.0	220	0.5232	0.5814	0.5232	0.7233
No log	45.0	225	0.5346	0.5574	0.5346	0.7312
No log	46.0	230	0.5165	0.5675	0.5165	0.7187
No log	47.0	235	0.4994	0.5831	0.4994	0.7067
No log	48.0	240	0.5235	0.5555	0.5235	0.7236
No log	49.0	245	0.5544	0.5573	0.5544	0.7446
No log	50.0	250	0.5426	0.5487	0.5426	0.7366
No log	51.0	255	0.4931	0.5760	0.4931	0.7022
No log	52.0	260	0.4791	0.6001	0.4791	0.6922
No log	53.0	265	0.4983	0.5804	0.4983	0.7059
No log	54.0	270	0.5149	0.5642	0.5149	0.7176
No log	55.0	275	0.4933	0.5730	0.4933	0.7024
No log	56.0	280	0.5258	0.5618	0.5258	0.7251
No log	57.0	285	0.4824	0.5781	0.4824	0.6945
No log	58.0	290	0.5077	0.5529	0.5077	0.7125
No log	59.0	295	0.5211	0.5645	0.5211	0.7219
No log	60.0	300	0.4890	0.5747	0.4890	0.6993
No log	61.0	305	0.4836	0.5939	0.4836	0.6954
No log	62.0	310	0.5590	0.5738	0.5590	0.7477
No log	63.0	315	0.4909	0.5850	0.4909	0.7007
No log	64.0	320	0.4811	0.5788	0.4811	0.6936
No log	65.0	325	0.4886	0.5896	0.4886	0.6990
No log	66.0	330	0.4981	0.5800	0.4981	0.7057
No log	67.0	335	0.4941	0.5769	0.4941	0.7029
No log	68.0	340	0.4964	0.5736	0.4964	0.7046
No log	69.0	345	0.5017	0.5740	0.5017	0.7083
No log	70.0	350	0.4919	0.5806	0.4919	0.7014
No log	71.0	355	0.4799	0.6031	0.4799	0.6928
No log	72.0	360	0.5113	0.5697	0.5113	0.7150
No log	73.0	365	0.4838	0.5922	0.4838	0.6956
No log	74.0	370	0.5105	0.5726	0.5105	0.7145
No log	75.0	375	0.4890	0.5957	0.4890	0.6993
No log	76.0	380	0.4881	0.5881	0.4881	0.6987
No log	77.0	385	0.4965	0.5614	0.4965	0.7047
No log	78.0	390	0.5061	0.5649	0.5061	0.7114
No log	79.0	395	0.5068	0.5670	0.5068	0.7119
No log	80.0	400	0.4913	0.5557	0.4913	0.7009
No log	81.0	405	0.4959	0.5561	0.4959	0.7042
No log	82.0	410	0.4956	0.5569	0.4956	0.7040
No log	83.0	415	0.5069	0.5686	0.5069	0.7120
No log	84.0	420	0.4878	0.5738	0.4878	0.6984
No log	85.0	425	0.4865	0.5743	0.4865	0.6975
No log	86.0	430	0.4825	0.5756	0.4825	0.6946
No log	87.0	435	0.4925	0.5676	0.4925	0.7018
No log	88.0	440	0.4842	0.5798	0.4842	0.6958
No log	89.0	445	0.4891	0.5771	0.4891	0.6993
No log	90.0	450	0.4898	0.5792	0.4898	0.6998
No log	91.0	455	0.4929	0.5803	0.4929	0.7021

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.3.1
Tokenizers 0.21.0

genki10/ASAP_FineTuningBERT_AugV16_k7_task1_organization_k7_k7_fold0

ASAP_FineTuningBERT_AugV16_k7_task1_organization_k7_k7_fold0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

genki10/
ASAP_FineTuningBERT_AugV16_k7_task1_organization_k7_k7_fold0