Instructions to use pogzyb/markuplm-phish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pogzyb/markuplm-phish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pogzyb/markuplm-phish")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("pogzyb/markuplm-phish") model = AutoModelForSequenceClassification.from_pretrained("pogzyb/markuplm-phish") - Notebooks
- Google Colab
- Kaggle
MarkupLM-Phish
The MarkupLM base model fine-tuned for phish classification!
Example Pipeline
import requests
from transformers import AutoProcessor, pipeline
phish_processor = AutoProcessor.from_pretrained(
"pogzyb/markuplm-phish", trust_remote_code=True
)
phish_pipeline = pipeline(
"text-classification",
model="pogzyb/markuplm-phish",
tokenizer=phish_processor,
max_length=512,
padding="max_length",
truncation=True,
)
html_string = requests.get("https://huggingface.co").text
phish_pipeline(html_string)
[{'label': 'negative', 'score': 0.9991624355316162}]
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