---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: deberta-v3-base-prompt-injection-v1
  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. -->

# deberta-v3-base-prompt-injection-v1

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 0.9999
- Recall: 0.9997
- Precision: 0.9998
- F1: 0.9998

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | Recall | Precision | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0038        | 1.0   | 36130  | 0.0026          | 0.9998   | 0.9994 | 0.9992    | 0.9993 |
| 0.0001        | 2.0   | 72260  | 0.0021          | 0.9998   | 0.9997 | 0.9989    | 0.9993 |
| 0.0           | 3.0   | 108390 | 0.0015          | 0.9999   | 0.9997 | 0.9995    | 0.9996 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0