UIT-xlnet-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
0.6047	1.0	139	0.5968	0.1435	0.5	0.1300
0.551	2.0	278	0.5818	0.1393	0.4981	0.1318
0.549	3.0	417	0.5342	0.3274	0.5764	0.1931
0.4438	4.0	556	0.5083	0.4820	0.6362	0.3105
0.3647	5.0	695	0.4219	0.6477	0.7325	0.4043
0.3083	6.0	834	0.4281	0.6663	0.7489	0.4079
0.2307	7.0	973	0.4171	0.6962	0.7756	0.4404
0.1962	8.0	1112	0.4786	0.6985	0.7706	0.4242
0.1404	9.0	1251	0.5594	0.6960	0.7769	0.4152
0.0739	10.0	1390	0.5989	0.7033	0.7768	0.4567
0.0604	11.0	1529	0.6251	0.7028	0.7758	0.4603
0.0357	12.0	1668	0.6687	0.7077	0.7822	0.4531
0.0198	13.0	1807	0.7097	0.6973	0.7701	0.4422
0.0339	14.0	1946	0.7104	0.6992	0.7732	0.4531
0.0228	15.0	2085	0.7339	0.7150	0.7842	0.4765
0.0147	16.0	2224	0.7418	0.6941	0.7734	0.4711
0.0078	17.0	2363	0.7514	0.7130	0.7833	0.4765
0.0069	18.0	2502	0.7544	0.7191	0.7866	0.4765
0.0067	19.0	2641	0.7570	0.7146	0.7845	0.4693
0.005	20.0	2780	0.7579	0.7134	0.7834	0.4729