Instructions to use dima806/phishing-email-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/phishing-email-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/phishing-email-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/phishing-email-detection") model = AutoModelForSequenceClassification.from_pretrained("dima806/phishing-email-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a66a6a45c1f12c6425cb255d3b6bcb8263a8fd424d2a0d56bc03e5b0858f251
- Size of remote file:
- 263 MB
- SHA256:
- 45bb5e5608370c51e63ef9120627515093b1ef4d7cd6693e54301a894a2d910c
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