Text Classification
Transformers
ONNX
Safetensors
English
bert
cybersecurity
phishing-detection
nlp
social-engineering
text-embeddings-inference
Instructions to use xynaxdev/ph5-text-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xynaxdev/ph5-text-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xynaxdev/ph5-text-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xynaxdev/ph5-text-bert") model = AutoModelForSequenceClassification.from_pretrained("xynaxdev/ph5-text-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -130,7 +130,7 @@ print(f"Phishing Probability: {p_phish:.4f}")
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```bibtex
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@article{devi2026ph5,
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title={Ph5: A Multimodal Framework for Cross-Modality Evasion-Resilient Phishing Detection in Multi-Vector Environments},
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author={
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journal={Journal of Computer & Mathematical Sciences / JCMM},
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year={2026}
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}
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```bibtex
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@article{devi2026ph5,
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title={Ph5: A Multimodal Framework for Cross-Modality Evasion-Resilient Phishing Detection in Multi-Vector Environments},
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author={Ritu Devi, Deepti Singh and Akash Sah},
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journal={Journal of Computer & Mathematical Sciences / JCMM},
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year={2026}
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}
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