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---
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},
} |