UIT-deberta-v3-large-finetuned

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

Model description

More information needed

More information needed

More information needed

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
0.5182	1.0	139	0.4623	0.4534	0.6457	0.3087
0.3803	2.0	278	0.3662	0.7072	0.7767	0.4061
0.2764	3.0	417	0.3630	0.7128	0.7816	0.4729
0.1508	4.0	556	0.3983	0.7345	0.7944	0.4585
0.124	5.0	695	0.4009	0.7591	0.8210	0.5
0.0748	6.0	834	0.4494	0.7575	0.8153	0.4964
0.0458	7.0	973	0.5271	0.7462	0.8013	0.5018
0.0356	8.0	1112	0.5749	0.7515	0.8060	0.5018
0.0158	9.0	1251	0.6486	0.7290	0.7894	0.4765
0.013	10.0	1390	0.6190	0.7552	0.8133	0.5054
0.0052	11.0	1529	0.6594	0.7571	0.8121	0.5162
0.0024	12.0	1668	0.6776	0.7533	0.8128	0.5018
0.0016	13.0	1807	0.6816	0.7590	0.8154	0.5108
0.0014	14.0	1946	0.6997	0.7597	0.8152	0.5144
0.0017	15.0	2085	0.7081	0.7572	0.8121	0.5162
0.0013	16.0	2224	0.7061	0.7607	0.8161	0.5126
0.0056	17.0	2363	0.7092	0.7611	0.8161	0.5162
0.0013	18.0	2502	0.7142	0.7609	0.8155	0.5162
0.0013	19.0	2641	0.7157	0.7609	0.8155	0.5162
0.0012	20.0	2780	0.7158	0.7609	0.8155	0.5162