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:

• Loss: 0.7544
• F1: 0.7191
• Roc Auc: 0.7866
• Accuracy: 0.4765

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

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 results

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

Framework versions

• Transformers 4.48.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.21.0