UIT-NO-PRExlnet-large-cased-finetuned

This model is a fine-tuned version of xlnet/xlnet-large-cased on the None dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
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: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.6008	1.0	139	0.5907	0.0994	0.5061	0.1227
0.5594	2.0	278	0.5834	0.1435	0.5	0.1300
0.4458	3.0	417	0.3967	0.6474	0.7336	0.4007
0.3153	4.0	556	0.3647	0.7128	0.7775	0.4495
0.2474	5.0	695	0.3392	0.7382	0.7952	0.4693
0.1915	6.0	834	0.3702	0.7346	0.7980	0.5054
0.1194	7.0	973	0.4083	0.7340	0.7994	0.4982
0.0953	8.0	1112	0.4656	0.7507	0.8101	0.4910
0.0482	9.0	1251	0.5682	0.7438	0.7934	0.4838
0.0504	10.0	1390	0.5374	0.7419	0.8069	0.4729
0.0265	11.0	1529	0.6019	0.7408	0.8011	0.4838
0.0082	12.0	1668	0.6136	0.7429	0.8015	0.4874
0.0077	13.0	1807	0.6212	0.7461	0.8020	0.4982
0.0117	14.0	1946	0.6089	0.7519	0.8086	0.4928
0.0044	15.0	2085	0.6246	0.7508	0.8050	0.5
0.0041	16.0	2224	0.6382	0.7460	0.8005	0.4946
0.0024	17.0	2363	0.6333	0.7467	0.8011	0.5
0.0053	18.0	2502	0.6311	0.7534	0.8047	0.5018
0.0027	19.0	2641	0.6311	0.7508	0.8032	0.5036
0.0029	20.0	2780	0.6318	0.7520	0.8037	0.5054