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malware-detection-model-v3 – AI Model by majorprojectmalware | AlphaNeural AI
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malware-detection-model-v3
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transformers
safetensors
distilbert
text-classification
generated_from_trainer
distilbert/distilbert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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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
eval_model_preparation_time: 0.0016
eval_accuracy: 0.2308
eval_f1: 0.3750
eval_recall_malicious: 1.0
eval_runtime: 2135.6727
eval_samples_per_second: 2.169
eval_steps_per_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
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH 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
Framework versions
Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1
