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A fast, robust library to check for offensive language in strings.

Project description

profanity-check

Build Status

A fast, robust Python library to check for profanity or offensive language in strings.

How It Works

profanity-check uses a linear SVM model trained on 200k human-labeled samples of clean and profane text strings. Its model is simple but surprisingly effective, meaning profanity-check is both robust and extremely performant.

Why Use profanity-check?

Many profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, profanity uses this wordlist, and even better-profanity still uses a wordlist. There are obviously glaring issues with this approach, and, while they might be performant, these libraries are not accurate at all.

Other libraries like profanity-filter use more sophisticated methods that are much more accurate but at the cost of performance. A benchmark (performed December 2018 on a new 2018 Macbook Pro) using a Kaggle dataset of Wikipedia comments yielded roughly the following results:

Package 1 Prediction (ms) 10 Predictions (ms) 100 Predictions (ms)
profanity-check 0.2 0.5 3.5
profanity-filter 60 1200 13000
profanity 0.3 1.2 24

profanity-check is anywhere from 300 - 4000 times faster than profanity-filter in this benchmark!

Installation

$ pip install profanity-check

Usage

from profanity_check import predict, predict_prob

predict(['predict() takes an array and returns a 1 for each string if it's offensive, else 0.'])
# [0]

predict(['fuck you'])
# [1]

predict_prob(['predict_prob() takes an array and returns the probability each string is offensive'])
# [0.08686173]

predict_prob(['go to hell, you scum'])
# [0.7618861]

Note that both predict() and predict_prob return numpy arrays.

More on How It Works

Special thanks to the authors of the datasets used in this project. profanity-check was trained on a combined dataset from 2 sources:

profanity-check relies heavily on the excellent scikit-learn library. It's mostly powered by scikit-learn classes CountVectorizer, LinearSVC, and CalibratedClassifierCV.

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