Email Spam Dataset: 6,485 Labeled Text Messages

6,485 labeled text messages for binary spam classification. The dataset was created by SchoolP and was last updated on June 8, 2026. It contains two columns for message content and a label indicating 'ham' or 'spam'.

Use Cases

• Train binary spam classifiers based on labeled message content.
• Benchmark NLP models for text classification tasks.
• Analyze linguistic patterns distinguishing 'ham' from 'spam' messages.

Strengths

• Contains 6,485 total samples for model training.
• Provides clear binary labels ('ham' or 'spam') for supervised learning.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

huggingface, author SchoolP

Freshness

Last updated 2026-06-08 12:15:50; freshness should be verified.