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---
language:
- en
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
base_model: microsoft/codebert-base
model-index:
- name: codebert-base-Malicious_URLs
results: []
---
# codebert-base-Malicious_URLs
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base).
It achieves the following results on the evaluation set:
- Loss: 0.8225
- Accuracy: 0.7279
- Weighted f1: 0.6508
- Micro f1: 0.7279
- Macro f1: 0.4611
- Weighted recall: 0.7279
- Micro recall: 0.7279
- Macro recall: 0.4422
- Weighted precision: 0.6256
- Micro precision: 0.7279
- Macro precision: 0.5436
## 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/Malicious%20URLs%20-%20CodeBERT.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
_Input Word Length:_
_Input Word Length By Class:_
_Class Distribution:_
/Images/Class%20Distribution.png)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8273 | 1.0 | 6450 | 0.8225 | 0.7279 | 0.6508 | 0.7279 | 0.4611 | 0.7279 | 0.7279 | 0.4422 | 0.6256 | 0.7279 | 0.5436 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |