Package: alt-profanity-check
Alt-profanity-checkAlt profanity check is a drop-in replacement of theprofanity-checklibrary for the not so well
maintainedhttps://github.com/vzhou842/profanity-check: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.Our aim is to follow scikit-learn's (main dependency) versions and post models trained with the
same version number, example alt-profanity-check version 1.2.3.4 should be trained with the
1.2.3.4 version of the scikit-learn library.For joblib which is the next major dependency we will be using the latest one which was available
when we trained the models.Last but not least we aim to clean up the codebase a bit andmaybeintroduce some features or
datasets.Learn Python from the Maintainer of alt-profanity-check 🎓🧑‍💻️⌨️I am teaching Python through Mentorcruise, aiming both to beginners and seasoned developers who want to get to the next level in their learning journey:https://mentorcruise.com/mentor/dimitriosmistriotis/. Please mention that you found me through this repository.ChangelogSeeCHANGELOG.mdHow 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 alt-profanity-checkFor older Python versionsPython 3.7From Scikit-learn'sGithub page:scikit-learn 1.0 and later require Python 3.7 or newer.
scikit-learn 1.1 and later require Python 3.8 or newer.Which means that from 1.1.2 and later, Python 3.7 is not supported, hence:
If you are using 3.6 pin alt-profanity-check to1.0.2.1.Python 3.6Following Scikit-learn,Python3.6is not supported after its 1.0 version if you are using 3.6 pin
alt-profanity-check to0.24.2.UsageYou can test from the command line:profanity_check"Check something""Check something else"fromprofanity_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-checkhence alsoalt-profanity-checkis 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.Developer NotesCreate a virtual environment from the projectpip install -r development_requirements.txtRetraining dataWith the above in place:cdprofanity_check/data
pythontrain_model.pyUploading to PyPiCurrently trying to automate it using Github Actions; see:.github/workflows/package_release_dry_run.yml.Setup:Set up your "~/.pypirc" with the appropriate tokenpip install -r requirements_for_uploading.txtwhich installs twineNew Version:Withx.y.zas the version to be uploaded:First tag:gittag-avx.y.z-m"Version x.y.z"gitpush--tagsThen upload:pythonsetup.pysdist
twineuploaddist/alt-profanity-check-x.y.z.tar.gz