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
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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
| 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](https://huggingface.co/distilbert/distilbert-base-uncased) on an [kmack/Phishing_urls](https://huggingface.co/datasets/kmack/Phishing_urls) dataset. |