Reza-Barati/distilbert-base-uncased-finetuned-for-phishing-detection

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Reza-Barati/distilbert-base-uncased-finetuned-for-phishing-detection

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• —Train Loss: 0.0173
• —Validation Loss: 0.1326
• —Train Accuracy: 0.9669
• —Train Precision: 0.9690
• —Train Recall: 0.9518
• —Train F1: 0.9603
• —Epoch: 2

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:

• —optimizer: {'name': 'Adam', 'weightdecay': None, 'clipnorm': None, 'globalclipnorm': None, 'clipvalue': None, 'useema': False, 'emamomentum': 0.99, 'emaoverwritefrequency': None, 'jitcompile': True, 'islegacyoptimizer': False, 'learningrate': {'module': 'keras.optimizers.schedules', 'classname': 'PolynomialDecay', 'config': {'initiallearningrate': 2e-05, 'decaysteps': 24270, 'endlearningrate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registeredname': None}, 'beta1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• —training_precision: float32

Training results

Framework versions

• —Transformers 4.38.2
• —TensorFlow 2.15.0
• —Datasets 2.18.0
• —Tokenizers 0.15.2