---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llm_firewall_distilbert-base-uncased
  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. -->

# llm_firewall_distilbert-base-uncased

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1218
- Accuracy: 0.9451

# Latest finetune 5 Dec 2023
{'eval_loss': 0.12179878354072571,
 'eval_accuracy': 0.9450980392156862,
 'eval_runtime': 5.8053,
 'eval_samples_per_second': 43.925,
 'eval_steps_per_second': 2.756,
 'epoch': 20.0}

## Model description

Finetuned distilbert-uncased on prompts that are either malicious or benign.

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3191        | 1.0   | 64   | 0.5996          | 0.7255   |
| 0.5065        | 2.0   | 128  | 0.4536          | 0.8      |
| 0.4134        | 3.0   | 192  | 0.3856          | 0.8275   |
| 0.3294        | 4.0   | 256  | 0.2654          | 0.8824   |
| 0.2536        | 5.0   | 320  | 0.1977          | 0.9216   |
| 0.2001        | 6.0   | 384  | 0.1671          | 0.9412   |
| 0.2144        | 7.0   | 448  | 0.1670          | 0.9373   |
| 0.2017        | 8.0   | 512  | 0.1575          | 0.9333   |
| 0.1819        | 9.0   | 576  | 0.1866          | 0.9294   |
| 0.143         | 10.0  | 640  | 0.1834          | 0.9373   |
| 0.153         | 11.0  | 704  | 0.1589          | 0.9412   |
| 0.1469        | 12.0  | 768  | 0.1347          | 0.9451   |
| 0.1568        | 13.0  | 832  | 0.1425          | 0.9451   |
| 0.139         | 14.0  | 896  | 0.1438          | 0.9451   |
| 0.1889        | 15.0  | 960  | 0.1330          | 0.9451   |
| 0.1185        | 16.0  | 1024 | 0.1323          | 0.9451   |
| 0.1166        | 17.0  | 1088 | 0.1280          | 0.9451   |
| 0.1475        | 18.0  | 1152 | 0.1233          | 0.9451   |
| 0.1145        | 19.0  | 1216 | 0.1225          | 0.9451   |
| 0.1121        | 20.0  | 1280 | 0.1218          | 0.9451   |


### Framework versions

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