Instructions to use kmack/malicious-url-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmack/malicious-url-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kmack/malicious-url-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kmack/malicious-url-detection") model = AutoModelForSequenceClassification.from_pretrained("kmack/malicious-url-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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Download README.md from kmack/malicious-url-detection: direct link, hf CLI and curl.
- Browser
- Download file 690 Bytes
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https://huggingface.co/kmack/malicious-url-detection/resolve/main/README.md
- Command line
-
hf download hf://kmack/malicious-url-detection/README.md
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curl -L -o README.md https://huggingface.co/kmack/malicious-url-detection/resolve/main/README.md
690 Bytes
metadata
library_name: transformers
tags:
- distilbert
- bert
license: apache-2.0
datasets:
- kmack/Phishing_urls
language:
- en
pipeline_tag: text-classification
Malicious-Url-Detection
Using this model, you can detects harmful links created to harm people such as phishing, malware urls. this model Classifies if the urls addresses are malware and benign. Type the domain name of the url address in the text field for classification in API: Like this: "huggingface.com"
This model is a fine-tuned version of distilbert-base-uncased on an kmack/Phishing_urls dataset.