license: mit
library_name: transformers
pipeline_tag: text-classification
tags:
- URL
- BinaryCalssification
- URL Classification
- BERT URL calssifier
widget:
- text: torgi.kz/help/id2.txt
- text: vvps.ws/44/mothersdarlingcross.php?
- text: >-
torrentreactor.net/torrents/897644/ANGELCORPSE-4-(All)-Studio-Albums%5EWaPo
🔒🤖 Binary URL Classifier with BERT
Welcome to our Binary URL Classifier! 🎉
What it Does?
This classifier categorizes URLs into two categories: Good (Non-Malicious) and Bad (Malicious). It uses BERT, a powerful language model, to understand the content and context of URLs for accurate predictions.
How it Works?
Data: Trained on a curated dataset of good and bad URLs from this repo https://github.com/faizann24/Using-machine-learning-to-detect-malicious-URLs/tree/master?tab=readme-ov-file.
Training: Fine-tuned a pre-trained BERT model using Hugging Face's Transformers library.
Evaluation: Validation(15%) and Test(15%) set kept Aside.
Inference: Provides probability scores for new URLs, indicating their likelihood of being good or bad.
Usage 🚀
Input: Provide a URL to classify.
Output: Receive a probability score for its safety.
Why Trust Us? 🌟
Robust Training: Trained on diverse and representative datasets.
Evaluation: Rigorously tested for accuracy and reliability.
Simple & Effective: Empowers users to make informed decisions about URL safety.
Let's Make the Web Safer! 🌐
Our Binary URL Classifier with BERT is here to enhance online safety. Whether you're a security professional, developer, or concerned user, it helps identify malicious URLs for proactive protection.
Try it now and experience the power of BERT in URL classification! We're open to feedback and suggestions for continuous improvement. Stay safe online! 🔒🛡️