Package: profanity-protector
profanity-protectorNoticeThis is a fork ofvzhou842excellent work, since the original package did not work on my system.
I switched the tokenizer from Bag-of-words to a transformer based embedding(MiniLM-L6-v2) and extended the training data with the following data-sourcesKaggle hate speech and offensive language datasetSurgeAI's Profanity listMIT's english words listFeel free to check out my changes and let me know if you have any feedback.A fast, robust Python library to check for profanity or offensive language in strings. Read more about how and whyprofanity-checkwas built inthis blog post. You can also test outprofanity-checkin your browser.How It Worksprofanity-checkuses a linear SVM model trained on 200k human-labeled samples of clean and profane text strings. Its model is simple but surprisingly effective, meaningprofanity-checkis both robust and extremely performant.Why Use profanity-check?No Explicit BlacklistMany profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example,profanityusesthis wordlist, and evenbetter-profanitystill usesa 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 whichprofanity-checkis better is the phrase"You cocksucker"-profanitythinks this is clean because it doesn't have"cocksucker"in its wordlist.PerformanceOther libraries likeprofanity-filteruse 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) usinga Kaggle dataset of Wikipedia commentsyielded roughly the following results:Package1 Prediction (ms)10 Predictions (ms)100 Predictions (ms)profanity-check0.20.53.5profanity-filter60120013000profanity0.31.224profanity-checkis anywhere from300 - 4000 times fasterthanprofanity-filterin this benchmark!AccuracyThis table speaks for itself:PackageTest AccuracyBalanced Test AccuracyPrecisionRecallF1 Scoreprofanity-check95.0%93.0%86.1%89.6%0.88profanity-filter91.8%83.6%85.4%70.2%0.77profanity85.6%65.1%91.7%30.8%0.46See the How section below for more details on the dataset used for these results.Installation$ pip install profanity-checkUsagefromprofanity_checkimportpredict,predict_probpredict(['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 bothpredict()andpredict_probreturnnumpyarrays.More on How/Why It WorksHowSpecial thanks to the authors of the datasets used in this project.profanity-checkwas trained on a combined dataset from 2 sources:t-davidson/hate-speech-and-offensive-language, used in their paperAutomated Hate Speech Detection and the Problem of Offensive LanguagetheToxic Comment Classification Challengeon Kaggle.profanity-checkrelies heavily on the excellentscikit-learnlibrary. It's mostly powered byscikit-learnclassesCountVectorizer,LinearSVC, andCalibratedClassifierCV. It uses aBag-of-words modelto vectorize input strings before feeding them to a linear classifier.WhyOne simplified way you could think about whyprofanity-checkworks 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!CaveatsThis 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.