---
license: apache-2.0
base_model: jackaduma/SecBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: aptner_secbert
  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. -->

# aptner_secbert

This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3230
- Precision: 0.5124
- Recall: 0.5356
- F1: 0.5237
- Accuracy: 0.9142

## 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6662        | 0.59  | 500  | 0.3587          | 0.4744    | 0.4743 | 0.4744 | 0.9113   |
| 0.3128        | 1.19  | 1000 | 0.3230          | 0.5124    | 0.5356 | 0.5237 | 0.9142   |
| 0.2374        | 1.78  | 1500 | 0.3429          | 0.4750    | 0.5714 | 0.5188 | 0.9083   |
| 0.1904        | 2.37  | 2000 | 0.3650          | 0.4945    | 0.5598 | 0.5251 | 0.9090   |
| 0.1521        | 2.97  | 2500 | 0.3765          | 0.4713    | 0.5783 | 0.5193 | 0.9055   |
| 0.1101        | 3.56  | 3000 | 0.4023          | 0.4727    | 0.5744 | 0.5186 | 0.9067   |
| 0.1019        | 4.15  | 3500 | 0.4322          | 0.4726    | 0.5571 | 0.5114 | 0.9056   |
| 0.0764        | 4.74  | 4000 | 0.4595          | 0.4592    | 0.5897 | 0.5163 | 0.9039   |
| 0.0619        | 5.34  | 4500 | 0.4755          | 0.4740    | 0.5783 | 0.5210 | 0.9062   |
| 0.059         | 5.93  | 5000 | 0.4514          | 0.5055    | 0.5649 | 0.5335 | 0.9126   |
| 0.0429        | 6.52  | 5500 | 0.5036          | 0.474     | 0.5666 | 0.5162 | 0.9065   |
| 0.0425        | 7.12  | 6000 | 0.5249          | 0.4767    | 0.5726 | 0.5203 | 0.9064   |
| 0.0349        | 7.71  | 6500 | 0.5537          | 0.4634    | 0.5744 | 0.5129 | 0.9038   |
| 0.0338        | 8.3   | 7000 | 0.5301          | 0.4839    | 0.5672 | 0.5223 | 0.9089   |
| 0.0255        | 8.9   | 7500 | 0.5545          | 0.4731    | 0.5735 | 0.5185 | 0.9059   |
| 0.0253        | 9.49  | 8000 | 0.5526          | 0.4789    | 0.5702 | 0.5206 | 0.9074   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1