opp_115_data_security

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opp_115_data_security

This model is a fine-tuned version of mukund/privbert on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0891
• Accuracy: 0.9733

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3

Cite

If you use this model in research, please cite the below paper.

@article{jakarai2024,
		author  = {Md Jakaria and
		           Danny Yuxing Huang and
                   Anupam Das},
		title   = {Connecting the Dots: Tracing Data Endpoints in IoT Devices},
		journal = {Proceedings on Privacy Enhancing Technologies (PoPETs)},
		year    = {2024},
		volume  = {2024},
		number  = {3},
	}