Instructions to use phishbot/ScamLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phishbot/ScamLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phishbot/ScamLLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("phishbot/ScamLLM") model = AutoModelForSequenceClassification.from_pretrained("phishbot/ScamLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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### Results
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Achieved an accuracy of 94% with an F1-score of 0.94, on test sets.
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<!--## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.
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If you find Isitphish to be useful, please cite it with:
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```
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@misc{roy2023chatbots,
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title={From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude},
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author={Sayak Saha Roy and Poojitha Thota and Krishna Vamsi Naragam and Shirin Nilizadeh},
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year={2023},
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eprint={2310.19181},
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archivePrefix={arXiv},
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primaryClass={cs.CR}
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}
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```-->
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### Results
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Achieved an accuracy of 94% with an F1-score of 0.94, on test sets.
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