viperDEE/phishing-links-detection-using-transformers

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distilbert-zim-phishing

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• —Loss: 0.3919
• —Accuracy: 0.9032
• —Precision: 0.8667
• —Recall: 0.9286
• —F1: 0.8966

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
• —trainbatchsize: 4
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 20
• —num_epochs: 10
• —mixedprecisiontraining: Native AMP
• —labelsmoothingfactor: 0.1

Training results

Framework versions

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