metadata

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. 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:

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