Phishing Email Classification Dataset for LLM Fine-Tuning

Comprising labeled emails for phishing detection, with each row classified as a safe email (label=0) or a phishing email (label=1). It includes metadata such as sender, receiver, date, and subject, along with a cleaned email body. The dataset is curated for fine-tuning large language models on this classification task.

Use Cases

• Fine-tune an LLM on the cleaned email body text to classify emails as phishing (label=1) or safe (label=0).
• Analyze patterns in the sender and subject metadata to identify common phishing campaign characteristics.
• Use the date and receiver metadata to study temporal or recipient-based trends in phishing attempts.

Strengths

• Dataset is specifically curated for LLM-based phishing email classification tasks.
• Includes both email body text and metadata features like sender, receiver, date, and subject for each labeled email.

Limitations

• The dataset's size, row count, and column count are unknown, limiting assessment of its scale and feature richness.
• The original source is a Kaggle dataset, which may introduce unknown biases or limitations from its initial collection.

Provenance

Source

Originates from a Kaggle dataset, transformed by the author.

Collection Method

Curated and transformed from an existing Kaggle dataset to suit LLM-based classification.

Freshness

Last updated on 2025-06-22.