---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
- prompt injection
- security
- jailbreak
- prompt security
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: prompt-tackler
  results: []
datasets:
- reshabhs/SPML_Chatbot_Prompt_Injection
- VMware/open-instruct
- jackhhao/jailbreak-classification
- cgoosen/prompt_injection_combined
language: 
  - en
  - afr
  - fr
#thumbnail: "url to a thumbnail used in social sharing"
library_name: transformers
---

<!-- 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. -->

# prompt-tackler

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0101
- Accuracy: 0.9984
- Precision: 0.9984
- Recall: 0.9984
- F1: 0.9984

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0242        | 1.0   | 3058  | 0.0167          | 0.9967   | 0.9968    | 0.9967 | 0.9967 |
| 0.0146        | 2.0   | 6116  | 0.0163          | 0.9977   | 0.9977    | 0.9977 | 0.9977 |
| 0.009         | 3.0   | 9174  | 0.0112          | 0.9984   | 0.9984    | 0.9984 | 0.9984 |
| 0.0029        | 4.0   | 12232 | 0.0101          | 0.9984   | 0.9984    | 0.9984 | 0.9984 |
| 0.0029        | 5.0   | 15290 | 0.0179          | 0.9980   | 0.9981    | 0.9980 | 0.9980 |
| 0.0012        | 6.0   | 18348 | 0.0160          | 0.9985   | 0.9985    | 0.9985 | 0.9985 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.5.0+cu124
- Datasets 2.18.0
- Tokenizers 0.19.1