---
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: anonymouspd/SecureBERT-CyNER
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# anonymouspd/SecureBERT-CyNER

This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on the [CyNER](https://github.com/aiforsec/CyNER) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0685
- Precision: 0.7334
- Recall: 0.8046
- F1: 0.7674
- Accuracy: 0.9836

It achieves the following results on the prediction set:
- Loss: 0.0993
- Precision: 0.7181
- Recall: 0.7564
- F1: 0.7367
- Accuracy: 0.9761

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1929        | 1.42  | 500  | 0.0685          | 0.7334    | 0.8046 | 0.7674 | 0.9836   |
| 0.048         | 2.84  | 1000 | 0.0745          | 0.8054    | 0.7931 | 0.7992 | 0.9837   |
| 0.0299        | 4.26  | 1500 | 0.0720          | 0.7936    | 0.8493 | 0.8205 | 0.9857   |
| 0.0199        | 5.68  | 2000 | 0.0846          | 0.8049    | 0.8327 | 0.8186 | 0.9848   |
| 0.014         | 7.1   | 2500 | 0.0878          | 0.7909    | 0.8455 | 0.8173 | 0.9847   |
| 0.0098        | 8.52  | 3000 | 0.0907          | 0.7830    | 0.8250 | 0.8035 | 0.9845   |
| 0.0073        | 9.94  | 3500 | 0.0917          | 0.7946    | 0.8301 | 0.8120 | 0.9852   |


### Framework versions

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