Instructions to use JeswinMS4/URL_DETECTION with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeswinMS4/URL_DETECTION with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeswinMS4/URL_DETECTION")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeswinMS4/URL_DETECTION") model = AutoModelForSequenceClassification.from_pretrained("JeswinMS4/URL_DETECTION") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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With this model, Classifies url addresses as malware and benign.
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Type the domain name of the url address in the text field for classification in API: Like this:
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"huggingface.com"
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To test the model, visit [SITE](https://www.usom.gov.tr/adres). Harmful links used
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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With this model, Classifies url addresses as malware and benign.
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Type the domain name of the url address in the text field for classification in API: Like this:
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"huggingface.com"
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To test the model, visit [SITE](https://www.usom.gov.tr/adres). Harmful links used are listed on this site.
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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