---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
- URL
- Security
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: albert-base-v2-Malicious_URLs
  results: []
pipeline_tag: text-classification
---

# albert-base-v2-Malicious_URLs

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co./albert-base-v2).

It achieves the following results on the evaluation set:
- Loss: 0.8368
- Accuracy: 0.7267
- F1:
  - Weighted: 0.6482
  - Micro: 0.7267
  - Macro: 0.4521
- Recall
  - Weighted: 0.7267
  - Micro: 0.7267
  - Macro: 0.4294
- Precision
  - Weighted: 0.6262
  - Micro: 0.7267
  - Macro: 0.5508

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multiclass%20Classification/Malicious%20URLs%20-%20ALBERT-Base_v2/Malicious%20URLs%20ALBERT-Base%20v2.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset

## 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: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.7839        | 1.0   | 51087 | 0.8368          | 0.7267   | 0.6482      | 0.7267   | 0.4521   | 0.7267          | 0.7267       | 0.4294       | 0.6262             | 0.7267          | 0.5508          |

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3