nathanReitinger/mlcb

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nathanReitinger/mlcb

This model is a fine-tuned version of dbernsohn/roberta-javascript on the mlcb dataset. It achieves the following results on the evaluation set:

• —Train Loss: 0.0463
• —Validation Loss: 0.0930
• —Train Accuracy: 0.9708
• —Epoch: 4

Intended uses & limitations

The model can be used to identify whether a JavaScript program is engaging in canvas fingerprinting.

Training and evaluation data

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': False, 'islegacyoptimizer': False, 'learningrate': {'classname': 'PolynomialDecay', 'config': {'initiallearningrate': 2e-05, 'decaysteps': 910, 'endlearningrate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta1': 0.9, 'beta2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• —training_precision: float32

Training results

Framework versions

• —Transformers 4.30.2
• —TensorFlow 2.11.0
• —Datasets 2.13.2
• —Tokenizers 0.13.3

Citation

@inproceedings{reitinger2021ml,
  title={ML-CB: Machine Learning Canvas Block.},
  author={Nathan Reitinger and Michelle L Mazurek},
  journal={Proc.\ PETS},
  volume={2021},
  number={3},
  pages={453--473},
  year={2021}
}

• —OSF
• —GitHub
• —Data