pip install bleepy-profanity-check
profanity-check version used for Bleepy
A fast, robust Python library to check for profanity or offensive language in strings. Read more about how and why profanity-check was built in this blog post. You can also test out profanity-check in your browser.
Notice: We created a library for this project, see: https://pypi.org/project/alt-profanity-check/ and https://gitlab.com/dimitrios/alt-profanity-check/
Profanity-check is originally created by Victor Zhou, and updated by other contributors. This version of profanity-check is used by Bleepy to detect and bleep tagalog and english profanity from videos. Therefore this version is called bleepy-profanity-check.
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.
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.
A simple example for which profanity-check is better is the phrase "You cocksucker" - profanity thinks this is clean because it doesn't have "cocksucker" in its wordlist.
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!
This table speaks for itself:
| Package | Test Accuracy | Balanced Test Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|
| profanity-check | 95.0% | 93.0% | 86.1% | 89.6% | 0.88 |
| profanity-filter | 91.8% | 83.6% | 85.4% | 70.2% | 0.77 |
| profanity | 85.6% | 65.1% | 91.7% | 30.8% | 0.46 |
See the How section below for more details on the dataset used for these results.
$ pip install profanity-check
from profanity_check import predict, predict_prob
predict(['predict() takes an array and returns a 1 for each string if it is 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.
Special thanks to the authors of the datasets used in this project. profanity-check was trained on a combined dataset from 2 sources:
- t-davidson/hate-speech-and-offensive-language, used in their paper Automated Hate Speech Detection and the Problem of Offensive Language
- the Toxic Comment Classification Challenge on Kaggle.
profanity-check relies heavily on the excellent scikit-learn library. It's mostly powered by scikit-learn classes CountVectorizer, LinearSVC, and CalibratedClassifierCV. It uses a Bag-of-words model to vectorize input strings before feeding them to a linear classifier.
One simplified way you could think about why profanity-check works is this: during the training process, the model learns which words are "bad" and how "bad" they are because those words will appear more often in offensive texts. Thus, it's as if the training process is picking out the "bad" words out of all possible words and using those to make future predictions. This is better than just relying on arbitrary word blacklists chosen by humans!
This library is far from perfect. For example, it has a hard time picking up on less common variants of swear words like "f4ck you" or "you b1tch" because they don't appear often enough in the training corpus. Never treat any prediction from this library as unquestionable truth, because it does and will make mistakes. Instead, use this library as a heuristic.