---
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: our_data
  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. -->

# our_data

This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4668
- Precision: 0.4762
- Recall: 0.5291
- F1: 0.5013
- Accuracy: 0.7376

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.9177        | 0.4   | 500   | 1.6839          | 0.06      | 0.0278 | 0.0380 | 0.6004   |
| 1.4976        | 0.81  | 1000  | 1.4936          | 0.2281    | 0.2659 | 0.2456 | 0.6313   |
| 1.2309        | 1.21  | 1500  | 1.2915          | 0.2650    | 0.3148 | 0.2878 | 0.6657   |
| 1.0546        | 1.61  | 2000  | 1.2454          | 0.2950    | 0.3796 | 0.3320 | 0.6804   |
| 0.9405        | 2.01  | 2500  | 1.2377          | 0.3613    | 0.3532 | 0.3572 | 0.6916   |
| 0.7501        | 2.42  | 3000  | 1.1723          | 0.3607    | 0.4180 | 0.3873 | 0.7171   |
| 0.7133        | 2.82  | 3500  | 1.1584          | 0.3632    | 0.4444 | 0.3998 | 0.7160   |
| 0.5896        | 3.22  | 4000  | 1.2288          | 0.4103    | 0.4444 | 0.4267 | 0.7306   |
| 0.5353        | 3.63  | 4500  | 1.2319          | 0.3978    | 0.4815 | 0.4357 | 0.7254   |
| 0.5432        | 4.03  | 5000  | 1.2173          | 0.4269    | 0.4868 | 0.4549 | 0.7306   |
| 0.4062        | 4.43  | 5500  | 1.2832          | 0.4398    | 0.5026 | 0.4691 | 0.7272   |
| 0.4485        | 4.83  | 6000  | 1.2196          | 0.4212    | 0.5093 | 0.4611 | 0.7412   |
| 0.3614        | 5.24  | 6500  | 1.3155          | 0.4325    | 0.4960 | 0.4621 | 0.7325   |
| 0.3308        | 5.64  | 7000  | 1.3501          | 0.4184    | 0.5119 | 0.4604 | 0.7354   |
| 0.3645        | 6.04  | 7500  | 1.3391          | 0.4359    | 0.5172 | 0.4731 | 0.7366   |
| 0.2982        | 6.45  | 8000  | 1.3889          | 0.4093    | 0.5225 | 0.4590 | 0.7315   |
| 0.2845        | 6.85  | 8500  | 1.4109          | 0.4452    | 0.5159 | 0.4779 | 0.7377   |
| 0.2482        | 7.25  | 9000  | 1.4668          | 0.4762    | 0.5291 | 0.5013 | 0.7376   |
| 0.2636        | 7.66  | 9500  | 1.4925          | 0.4540    | 0.5357 | 0.4915 | 0.7341   |
| 0.2605        | 8.06  | 10000 | 1.4916          | 0.4586    | 0.5344 | 0.4936 | 0.7405   |
| 0.1989        | 8.46  | 10500 | 1.5096          | 0.4661    | 0.5370 | 0.4991 | 0.7387   |
| 0.2415        | 8.86  | 11000 | 1.4698          | 0.4603    | 0.5450 | 0.4991 | 0.7443   |
| 0.2488        | 9.27  | 11500 | 1.4736          | 0.4578    | 0.5304 | 0.4914 | 0.7455   |
| 0.2129        | 9.67  | 12000 | 1.5067          | 0.4640    | 0.5450 | 0.5012 | 0.7439   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0