phishGuard-AI

This model is a fine-tuned version of UBC-NLP/MARBERT on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1009
• Accuracy: 0.9821
• F1: 0.9850
• Precision: 0.9911
• Recall: 0.9821

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	4	0.3616	0.9911	0.9896	0.9912	0.9911
No log	2.0	8	0.2057	0.9940	0.9940	0.9940	0.9940
No log	3.0	12	0.1473	0.9792	0.9829	0.9904	0.9792
No log	4.0	16	0.1119	0.9821	0.9850	0.9911	0.9821
No log	5.0	20	0.1009	0.9821	0.9850	0.9911	0.9821

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2