hebrew-toxicity-detector

Hebrew Toxicity Detector

A fine-tuned BERT-based model for detecting toxic and offensive text, with a focus on Hebrew social media comments.

Project Context

This model was developed as part of my final Computer Science project.
The goal is to improve automatic detection of harmful, offensive, and toxic comments, especially in Hebrew, where existing toxicity models often perform less accurately.

Model Description

The model is based on a Transformer text-classification architecture and was fine-tuned on a custom dataset of toxic and non-toxic examples.

It is designed to classify short user-generated text such as:

• YouTube comments
• Social media posts
• Informal Hebrew text
• Offensive slang and insults

Intended Use

This model is used as part of a browser extension that helps reduce direct exposure to harmful comments by blurring content detected as toxic.

Files

• model.safetensors — fine-tuned model weights
• config.json — model configuration
• tokenizer.json — tokenizer file
• tokenizer_config.json — tokenizer configuration

Usage

from transformers import pipeline

model_path = "maayan3330/hebrew-toxicity-detector"

classifier = pipeline(
    "text-classification",
    model=model_path,
    tokenizer=model_path
)

result = classifier("אתה פשוט מגעיל")
print(result)