profanity-sanitizer 0.1.4

A profanity sanitizer with fuzzy matching, semantic similarity, and AI-based toxicity detection

Profanity Sanitizer

A simple sanitizer for offensive words with options to leverage machine learning for toxicity control Caveat it that it's currently very slow

Features

• Exact word matching against a list of banned words
• Fuzzy matching to catch misspellings and intentional obfuscation
• Semantic similarity checking to identify contextually similar offensive content and prevent false positives
• AI-based toxicity detection using pre-trained models
• Customizable word lists (banned and allowed words)
• Character replacement handling to catch common letter-to-number substitutions
• Adjustable thresholds for fuzzy matching, semantic similarity, and toxicity detection

Installation

1. Install using pip:

pip install profanity-sanitizer

2. Install the required spaCy model:

python -m spacy download en_core_web_lg

Usage

Here's a basic example of how to use the ProfanityFilter:

from profanity_sanitizer import ProfanitySanitizer

Initialize the filter

ps = ProfanitySanitizer()

Check some text

result = ps.check_text("This is a test sentence.")
print(result.is_clean) # True or False
print(result.reason) # Reason for the result
print(result.problematic_score) # 0-100 score indicating how problematic the text is

Configuration

You can customize the ProfanityFilter by adjusting its parameters:

pf = ProfanitySanitizer(
    banned_words_file="path/to/custom_banned_words.json",
    allowed_words_file="path/to/custom_allowed_words.json",
    replacements_file="path/to/custom_replacements.json",
    use_fuzzy=True,
    fuzzy_threshold=80,
    use_ai=True,
    model_name="unitary/toxic-bert",
    use_semantic=True,
    similarity_threshold=0.8,
    toxicity_threshold=0.5,
    min_problematic_score_threshold=65
)

Configuration Options

• banned_words_file (str): Path to a JSON file containing custom banned words.

• allowed_words_file (str): Path to a JSON file containing custom allowed words.

• replacements_file (str): Path to a JSON file containing custom character replacements.

• use_fuzzy (bool): Enable fuzzy matching for misspellings and obfuscation.

  • while flagging some intentionally misspelled words, it will also flag false positives, to prevent this you can tunr on use_semantic
  • for example 'fxck' will get flagged but 'duck' will also get flagged is use_semantic is off

• fuzzy_threshold (int): Similarity threshold for fuzzy matching (0-100).

• use_ai (bool): Enable AI-based toxicity detection.

  • This detects hateful strings that might be too annoying to put down to the banned_words
  • Strings like 'I want to hurt people' or 'I kill dogs' will get flagged

• model_name (str): Name of the pre-trained model for toxicity detection.

  • Only change this if you know what you're doing

• use_semantic (bool): Enable semantic similarity checking.

  • This uses spaCy semantic checking to prevent false positives, mostly used with fuzzy on,

• similarity_threshold (float): Threshold for semantic similarity (0.0-1.0).

• toxicity_threshold (float): Threshold for AI-based toxicity detection (0.0-1.0).

• min_problematic_score_threshold (int): Minimum score to consider text problematic (0-100).