Small (0.1B params) Spam Detection model optimized for Italian text

Reddit r/LocalLLaMA / 4/2/2026

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Key Points

  • A small (~0.1B parameters) spam-detection model has been fine-tuned specifically for classifying Italian text as spam or not spam.
  • The model targets common spam categories including unsolicited ads, fraud/pyramid schemes, phishing, misleading content, malware/harmful links, and adult/explicit material.
  • It also flags attention-grabbing patterns such as excessive capitalization or punctuation.
  • Users can run the model via the Artifex library by specifying language="italian" and passing input text for a spam/not-spam score.
  • The intended use is as a first-layer filter for email, messaging, or other text communication systems, with a warning against high-stakes deployments without human review.

https://huggingface.co/tanaos/tanaos-spam-detection-italian

A small Spam Detection model specifically fine-tuned to recognize spam content from text in Italian. The following types of content are considered spam:

  1. Unsolicited commercial advertisement or non-commercial proselytizing.
  2. Fraudulent schemes. including get-rich-quick and pyramid schemes.
  3. Phishing attempts. unrealistic offers or announcements.
  4. Content with deceptive or misleading information.
  5. Malware or harmful links.
  6. Adult content or explicit material.
  7. Excessive use of capitalization or punctuation to grab attention.

How to use

Use this model through the Artifex library:

install Artifex with

pip install artifex 

use the model with

from artifex import Artifex spam_detection = Artifex().spam_detection(language="italian") print(spam_detection("Hai vinto un iPhone 16! Clicca qui per ottenere il tuo premio.")) # >>> [{'label': 'spam', 'score': 0.9989}] 

Intended Uses

This model is intended to:

  • Serve as a first-layer spam filter for email systems, messaging applications, or any other text-based communication platform, if the text is in Italian.
  • Help reduce unwanted or harmful messages by classifying text as spam or not spam.

Not intended for:

  • Use in high-stakes scenarios where misclassification could lead to significant consequences without further human review.
submitted by /u/Ok_Hold_5385
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