majorprojectmalware/malware-detection-model-v3

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malware-detection-model-v3

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

• —eval_loss: 0.6934
• —evalmodelpreparation_time: 0.0016
• —eval_accuracy: 0.2308
• —eval_f1: 0.3750
• —evalrecallmalicious: 1.0
• —eval_runtime: 2135.6727
• —evalsamplesper_second: 2.169
• —evalstepsper_second: 0.068
• —step: 0

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: 3e-05
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 5
• —mixedprecisiontraining: Native AMP

Framework versions

• —Transformers 4.50.3
• —Pytorch 2.6.0+cu124
• —Datasets 3.5.0
• —Tokenizers 0.21.1