---
library_name: transformers
license: apache-2.0
base_model: protectai/deberta-v3-small-prompt-injection-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: prompt-tackler
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/christogoosen/prompt-tackler/runs/w2bjzmse)
# prompt-tackler

This model is a fine-tuned version of [protectai/deberta-v3-small-prompt-injection-v2](https://huggingface.co/protectai/deberta-v3-small-prompt-injection-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0186
- Accuracy: 0.9959
- Precision: 0.9959
- Recall: 0.9959
- F1: 0.9959

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0177        | 1.0   | 20686  | 0.0222          | 0.9943   | 0.9943    | 0.9943 | 0.9943 |
| 0.012         | 2.0   | 41372  | 0.0186          | 0.9959   | 0.9959    | 0.9959 | 0.9959 |
| 0.0084        | 3.0   | 62058  | 0.0278          | 0.9955   | 0.9955    | 0.9955 | 0.9955 |
| 0.0216        | 4.0   | 82744  | 0.0256          | 0.9959   | 0.9959    | 0.9959 | 0.9959 |
| 0.0038        | 5.0   | 103430 | 0.0327          | 0.9963   | 0.9963    | 0.9963 | 0.9963 |
| 0.0           | 6.0   | 124116 | 0.0383          | 0.9963   | 0.9963    | 0.9963 | 0.9963 |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.9.1+cu128
- Datasets 2.21.0
- Tokenizers 0.21.4