🛡️ Phishing Email Classification Dataset
This dataset is curated for fine-tuning LLMs on the task of phishing email detection. It originates from this Kaggle dataset and has been transformed to better suit LLM-based classification tasks.
📦 Dataset Features
- Each row is a labeled email, with either:
safe email(label = 0)phishing email(label = 1)
- The dataset includes metadata (sender, receiver, date, subject) and cleaned email body.
- Two main columns:
Email Text: Complete formatted text including metadata and message content.label: Binary label indicating if the email is phishing.
🧠 LLM Fine-Tuning Ready
Processed using a phishing_items.py parser:
Truncates or filters emails based on token limits for LLM input (between 30 and 250 tokens).
Builds classification prompts in the format:
Is the following email safe or phishing?? [email content] Email type is: [safe email/phishing email]Optimized for models such as
meta-llama/Meta-Llama-3.1-8B.
🧼 Preprocessing Highlights
- Removes non-informative characters (e.g.,
=,>,\) and extra whitespace. - Tokenized with Hugging Face's
AutoTokenizer. - Discards overly short emails (under 120 characters or under 30 tokens).
🗂️ Example Usage
from phishing_items import Item
item = Item(data_row)
if item.include:
print(item.prompt)
📚 Source
- Original dataset: Kaggle - Phishing Emails
- Transformed by: [your GitHub or Hugging Face handle]