PRE-xlnet-large-cased-finetuned-augmentation-2

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

• Loss: 0.3383
• F1: 0.7385
• Roc Auc: 0.8538
• Accuracy: 0.7857

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
0.324	1.0	389	0.2935	0.1551	0.5725	0.5663
0.2498	2.0	778	0.1995	0.5340	0.7415	0.7072
0.2112	3.0	1167	0.1986	0.5552	0.7607	0.6950
0.2189	4.0	1556	0.1733	0.6698	0.8068	0.7426
0.1263	5.0	1945	0.1857	0.6895	0.8174	0.7516
0.1076	6.0	2334	0.1919	0.6977	0.8059	0.7671
0.0683	7.0	2723	0.2189	0.7003	0.8168	0.7625
0.0405	8.0	3112	0.2597	0.7169	0.8385	0.7671
0.0212	9.0	3501	0.2865	0.7172	0.8446	0.7606
0.0349	10.0	3890	0.3011	0.6998	0.8268	0.7638
0.0198	11.0	4279	0.3188	0.7204	0.8393	0.7754
0.0222	12.0	4668	0.3385	0.7347	0.8589	0.7780
0.0037	13.0	5057	0.3355	0.7330	0.8467	0.7851
0.0071	14.0	5446	0.3383	0.7385	0.8538	0.7857
0.0011	15.0	5835	0.3536	0.7301	0.8430	0.7831
0.0045	16.0	6224	0.3494	0.7358	0.8453	0.7857
0.0013	17.0	6613	0.3555	0.7339	0.8473	0.7844

Framework versions

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