---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-trustairlab-jailbreak-augmented
  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. -->

# bert-base-uncased-trustairlab-jailbreak-augmented

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1346
- Accuracy: 0.9598

## 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 adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5493        | 1.0   | 4240  | 0.2043          | 0.9302   |
| 0.2353        | 2.0   | 8480  | 0.1689          | 0.9442   |
| 0.0884        | 3.0   | 12720 | 0.1558          | 0.9525   |
| 0.1884        | 4.0   | 16960 | 0.1752          | 0.9546   |
| 0.1707        | 5.0   | 21200 | 0.1340          | 0.9597   |
| 0.1012        | 6.0   | 25440 | 0.1505          | 0.9616   |
| 0.0637        | 7.0   | 29680 | 0.1591          | 0.9614   |
| 0.0655        | 8.0   | 33920 | 0.1762          | 0.9630   |


### Framework versions

- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2