Navyasri17/phishing_emails-data
收藏Hugging Face2026-04-07 更新2026-04-12 收录
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https://hf-mirror.com/datasets/Navyasri17/phishing_emails-data
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# 🛡️ 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](https://www.kaggle.com/datasets/subhajournal/phishingemails) 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
```python
from phishing_items import Item
item = Item(data_row)
if item.include:
print(item.prompt)
```
## 📚 Source
- Original dataset: [Kaggle - Phishing Emails](https://www.kaggle.com/datasets/subhajournal/phishingemails)
- Transformed by: [your GitHub or Hugging Face handle]
提供机构：
Navyasri17
