---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: opp_115_data_security
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# opp_115_data_security

This model is a fine-tuned version of [mukund/privbert](https://huggingface.co/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

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 164  | 0.1156          | 0.9687   |
| No log        | 2.0   | 328  | 0.0891          | 0.9733   |


### 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},
	}