---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: MALWARE-URL-DETECTION
  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. -->

# URL-DETECTION
With this model, Classifies url addresses as malware and benign.
Type the domain name of the url address in the text field for classification in API: Like this: 
"huggingface.com"  
To test the model, visit [SITE](https://www.usom.gov.tr/adres). Harmful links used are listed on this site.

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2122
- Accuracy: 0.945
- Precision: 0.9611
- Recall: 0.9287
- F1: 0.9446

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 63   | 0.2153          | 0.921    | 0.9953    | 0.8475 | 0.9155 |
| No log        | 2.0   | 126  | 0.1927          | 0.946    | 0.9669    | 0.9248 | 0.9453 |
| No log        | 3.0   | 189  | 0.2122          | 0.945    | 0.9611    | 0.9287 | 0.9446 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3