0xRafu/distillbert-sms

distilbert-results

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: 0.0278

Model description

More information needed

Intended uses & limitations

Binary classification

Training and evaluation data

Dataset from Investigating Evasive Techniques in SMS Spam Filtering: A Comparative Analysis of Machine Learning Models

Citation for the dataset authors

@article{salman2024investigating,
  title={Investigating Evasive Techniques in SMS Spam Filtering: A Comparative Analysis of Machine Learning Models},
  author={Salman, Muhammad and Ikram, Muhammad and Kaafar, Mohamed Ali},
  journal={IEEE Access},
  year={2024},
  publisher={IEEE}
}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-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
• lr_scheduler_warmup_steps: 200
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.0114	1.0	1676	0.0293
0.0058	2.0	3352	0.0278

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 2.14.4
• Tokenizers 0.21.1