UIT-NO-PREUIT-xlnet-large-cased-finetuned-finetuned

This model is a fine-tuned version of sercetexam9/UIT-xlnet-large-cased-finetuned on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0463
• F1: 0.7122
• Roc Auc: 0.7806
• Accuracy: 0.4711

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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.0528	1.0	139	0.8260	0.6956	0.7780	0.4260
0.0697	2.0	278	0.7774	0.6760	0.7605	0.4097
0.0662	3.0	417	0.7810	0.6889	0.7644	0.4043
0.0539	4.0	556	0.8407	0.7021	0.7861	0.4224
0.0355	5.0	695	0.8692	0.6723	0.7466	0.3845
0.0633	6.0	834	0.8634	0.6629	0.7517	0.4007
0.0493	7.0	973	0.8450	0.6750	0.7581	0.4350
0.0397	8.0	1112	0.9122	0.6912	0.7759	0.4224
0.0321	9.0	1251	1.0101	0.6669	0.7618	0.4061
0.0208	10.0	1390	0.9714	0.6922	0.7682	0.4296
0.0131	11.0	1529	0.9408	0.6907	0.7711	0.4097
0.0121	12.0	1668	0.9753	0.6966	0.7695	0.4332
0.0057	13.0	1807	0.9916	0.6816	0.7642	0.4188
0.0055	14.0	1946	0.9780	0.6983	0.7734	0.4495
0.0121	15.0	2085	0.9855	0.7038	0.7826	0.4440
0.0097	16.0	2224	1.0266	0.6979	0.7733	0.4386
0.0073	17.0	2363	1.0192	0.6991	0.7749	0.4422
0.0021	18.0	2502	1.0567	0.6971	0.7740	0.4332
0.0053	19.0	2641	1.0234	0.7062	0.7778	0.4549
0.0007	20.0	2780	1.0558	0.6921	0.7672	0.4368
0.0006	21.0	2919	1.0601	0.7005	0.7759	0.4422
0.0021	22.0	3058	1.0578	0.7076	0.7817	0.4422
0.0005	23.0	3197	1.0613	0.7031	0.7749	0.4495
0.0013	24.0	3336	1.0467	0.7111	0.7812	0.4657
0.0006	25.0	3475	1.0463	0.7122	0.7806	0.4711
0.0006	26.0	3614	1.0443	0.7076	0.7791	0.4639
0.0005	27.0	3753	1.0476	0.7077	0.7792	0.4657
0.0005	28.0	3892	1.0490	0.7080	0.7791	0.4639
0.0005	29.0	4031	1.0487	0.7073	0.7784	0.4621
0.0005	30.0	4170	1.0485	0.7070	0.7781	0.4621

Framework versions

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