opp_115_policy_change – AI Model by jakariamd | AlphaNeural AI

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jakariamd/
opp_115_policy_change

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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opp_115_policy_change

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

Loss: 0.0403
Accuracy: 0.9920

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

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	171	0.0609	0.9890
No log	2.0	342	0.0403	0.9920

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},
	}