xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT-MICRO

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: 0.5171
• F1: 0.8326
• Roc Auc: 0.8759
• Accuracy: 0.6052

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.4028	1.0	318	0.3883	0.7185	0.7928	0.4082
0.3124	2.0	636	0.3370	0.7842	0.8459	0.5051
0.2128	3.0	954	0.3211	0.7902	0.8422	0.5493
0.1418	4.0	1272	0.3509	0.8048	0.8562	0.5437
0.0971	5.0	1590	0.3839	0.8036	0.8524	0.5642
0.0538	6.0	1908	0.4280	0.8232	0.8744	0.5800
0.038	7.0	2226	0.4600	0.8251	0.8766	0.5831
0.0251	8.0	2544	0.4612	0.8184	0.8670	0.5808
0.0251	9.0	2862	0.5169	0.8228	0.8746	0.5729
0.016	10.0	3180	0.5178	0.8258	0.8731	0.5902
0.0119	11.0	3498	0.5177	0.8305	0.8757	0.5997
0.0063	12.0	3816	0.5171	0.8326	0.8759	0.6052
0.0021	13.0	4134	0.5385	0.8325	0.8792	0.5934
0.0012	14.0	4452	0.5370	0.8284	0.8736	0.5950
0.0039	15.0	4770	0.5462	0.8324	0.8786	0.5997
0.0027	16.0	5088	0.5458	0.8320	0.8767	0.6044

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0