Instructions to use aisuko/phishing-binary-classification_student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aisuko/phishing-binary-classification_student with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aisuko/phishing-binary-classification_student")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aisuko/phishing-binary-classification_student") model = AutoModelForSequenceClassification.from_pretrained("aisuko/phishing-binary-classification_student") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0fad6afdaa007f8d15bcb2ca6614fcf323040c420115e599a3db3d812f1eb1f
- Size of remote file:
- 211 MB
- SHA256:
- 71fe1420ab955693732f6cd14c4a5babb936efb195906c41f37ae4e0c2e6746c
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