%global _empty_manifest_terminate_build 0 Name: python-alt-profanity-check Version: 1.2.2 Release: 1 Summary: A fast, robust library to check for offensive language in strings. Dropdown replacement of "profanity-check". License: MIT License URL: https://github.com/dimitrismistriotis/alt-profanity-check Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c8/99/8876bacf238fd98909083252463a06317710490f7b3e389101f26dfd9bc0/alt-profanity-check-1.2.2.tar.gz BuildArch: noarch %description # Alt-profanity-check Alt profanity check is a drop-in replacement of the `profanity-check` library for the not so well maintained : > 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](https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/). 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 and **maybe** introduce some features or datasets. ## Changelog See [CHANGELOG.md](https://github.com/dimitrismistriotis/alt-profanity-check/blob/master/CHANGELOG.md) ## 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? ### No Explicit Blacklist Many profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, [profanity](https://pypi.org/project/profanity/) uses [this wordlist](https://github.com/ben174/profanity/blob/master/profanity/data/wordlist.txt), and even [better-profanity](https://pypi.org/project/better-profanity/) still uses [a wordlist]( https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt). 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. ### Performance Other libraries like [profanity-filter](https://github.com/rominf/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]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) 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! ### Accuracy 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. ## Installation ``` $ pip install alt-profanity-check ``` ### For older Python versions #### Python 3.7 From Scikit-learn's [Github page](https://github.com/scikit-learn/scikit-learn): > 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 to **1.0.2.1**. #### Python 3.6 Following Scikit-learn, **Python3.6** is not supported after its 1.0 version if you are using 3.6 pin alt-profanity-check to **0.24.2**. ## Usage You can test from the command line: ```shell profanity_check "Check something" "Check something else" ``` ```python 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`](https://pypi.org/project/numpy/) arrays. ## More on How/Why It Works ### How Special thanks to the authors of the datasets used in this project. `profanity-check` hence also `alt-profanity-check` is trained on a combined dataset from 2 sources: - [t-davidson/hate-speech-and-offensive-language]( https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master/data), used in their paper *Automated Hate Speech Detection and the Problem of Offensive Language* - the [Toxic Comment Classification Challenge]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) on Kaggle. `profanity-check` relies heavily on the excellent [`scikit-learn`](https://scikit-learn.org/) library. It's mostly powered by `scikit-learn` classes [`CountVectorizer`]( https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html), [`LinearSVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html), and [`CalibratedClassifierCV`]( https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html). It uses a [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model) to vectorize input strings before feeding them to a linear classifier. ### Why 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! ## Caveats 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. ## Developer Notes - Create a virtual environment from the project - `pip install -r development_requirements.txt` ### Retraining data With the above in place: ```shell cd profanity_check/data python train_model.py ``` ### Uploading to PyPi Currently trying to automate it using Github Actions; see: `.github/workflows/package_release_dry_run.yml`. Setup: - Set up your "~/.pypirc" with the appropriate token - `pip install -r requirements_for_uploading.txt` which installs twine New Version: With `x.y.z` as the version to be uploaded: First tag: ```shell git tag -a vx.y.z -m "Version x.y.z" git push --tags ``` Then upload: ```shell python setup.py sdist twine upload dist/alt-profanity-check-x.y.z.tar.gz ``` %package -n python3-alt-profanity-check Summary: A fast, robust library to check for offensive language in strings. Dropdown replacement of "profanity-check". Provides: python-alt-profanity-check BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-alt-profanity-check # Alt-profanity-check Alt profanity check is a drop-in replacement of the `profanity-check` library for the not so well maintained : > 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](https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/). 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 and **maybe** introduce some features or datasets. ## Changelog See [CHANGELOG.md](https://github.com/dimitrismistriotis/alt-profanity-check/blob/master/CHANGELOG.md) ## 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? ### No Explicit Blacklist Many profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, [profanity](https://pypi.org/project/profanity/) uses [this wordlist](https://github.com/ben174/profanity/blob/master/profanity/data/wordlist.txt), and even [better-profanity](https://pypi.org/project/better-profanity/) still uses [a wordlist]( https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt). 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. ### Performance Other libraries like [profanity-filter](https://github.com/rominf/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]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) 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! ### Accuracy 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. ## Installation ``` $ pip install alt-profanity-check ``` ### For older Python versions #### Python 3.7 From Scikit-learn's [Github page](https://github.com/scikit-learn/scikit-learn): > 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 to **1.0.2.1**. #### Python 3.6 Following Scikit-learn, **Python3.6** is not supported after its 1.0 version if you are using 3.6 pin alt-profanity-check to **0.24.2**. ## Usage You can test from the command line: ```shell profanity_check "Check something" "Check something else" ``` ```python 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`](https://pypi.org/project/numpy/) arrays. ## More on How/Why It Works ### How Special thanks to the authors of the datasets used in this project. `profanity-check` hence also `alt-profanity-check` is trained on a combined dataset from 2 sources: - [t-davidson/hate-speech-and-offensive-language]( https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master/data), used in their paper *Automated Hate Speech Detection and the Problem of Offensive Language* - the [Toxic Comment Classification Challenge]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) on Kaggle. `profanity-check` relies heavily on the excellent [`scikit-learn`](https://scikit-learn.org/) library. It's mostly powered by `scikit-learn` classes [`CountVectorizer`]( https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html), [`LinearSVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html), and [`CalibratedClassifierCV`]( https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html). It uses a [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model) to vectorize input strings before feeding them to a linear classifier. ### Why 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! ## Caveats 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. ## Developer Notes - Create a virtual environment from the project - `pip install -r development_requirements.txt` ### Retraining data With the above in place: ```shell cd profanity_check/data python train_model.py ``` ### Uploading to PyPi Currently trying to automate it using Github Actions; see: `.github/workflows/package_release_dry_run.yml`. Setup: - Set up your "~/.pypirc" with the appropriate token - `pip install -r requirements_for_uploading.txt` which installs twine New Version: With `x.y.z` as the version to be uploaded: First tag: ```shell git tag -a vx.y.z -m "Version x.y.z" git push --tags ``` Then upload: ```shell python setup.py sdist twine upload dist/alt-profanity-check-x.y.z.tar.gz ``` %package help Summary: Development documents and examples for alt-profanity-check Provides: python3-alt-profanity-check-doc %description help # Alt-profanity-check Alt profanity check is a drop-in replacement of the `profanity-check` library for the not so well maintained : > 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](https://victorzhou.com/blog/better-profanity-detection-with-scikit-learn/). 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 and **maybe** introduce some features or datasets. ## Changelog See [CHANGELOG.md](https://github.com/dimitrismistriotis/alt-profanity-check/blob/master/CHANGELOG.md) ## 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? ### No Explicit Blacklist Many profanity detection libraries use a hard-coded list of bad words to detect and filter profanity. For example, [profanity](https://pypi.org/project/profanity/) uses [this wordlist](https://github.com/ben174/profanity/blob/master/profanity/data/wordlist.txt), and even [better-profanity](https://pypi.org/project/better-profanity/) still uses [a wordlist]( https://github.com/snguyenthanh/better_profanity/blob/master/better_profanity/profanity_wordlist.txt). 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. ### Performance Other libraries like [profanity-filter](https://github.com/rominf/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]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) 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! ### Accuracy 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. ## Installation ``` $ pip install alt-profanity-check ``` ### For older Python versions #### Python 3.7 From Scikit-learn's [Github page](https://github.com/scikit-learn/scikit-learn): > 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 to **1.0.2.1**. #### Python 3.6 Following Scikit-learn, **Python3.6** is not supported after its 1.0 version if you are using 3.6 pin alt-profanity-check to **0.24.2**. ## Usage You can test from the command line: ```shell profanity_check "Check something" "Check something else" ``` ```python 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`](https://pypi.org/project/numpy/) arrays. ## More on How/Why It Works ### How Special thanks to the authors of the datasets used in this project. `profanity-check` hence also `alt-profanity-check` is trained on a combined dataset from 2 sources: - [t-davidson/hate-speech-and-offensive-language]( https://github.com/t-davidson/hate-speech-and-offensive-language/tree/master/data), used in their paper *Automated Hate Speech Detection and the Problem of Offensive Language* - the [Toxic Comment Classification Challenge]( https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data) on Kaggle. `profanity-check` relies heavily on the excellent [`scikit-learn`](https://scikit-learn.org/) library. It's mostly powered by `scikit-learn` classes [`CountVectorizer`]( https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html), [`LinearSVC`](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html), and [`CalibratedClassifierCV`]( https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html). It uses a [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model) to vectorize input strings before feeding them to a linear classifier. ### Why 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! ## Caveats 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. ## Developer Notes - Create a virtual environment from the project - `pip install -r development_requirements.txt` ### Retraining data With the above in place: ```shell cd profanity_check/data python train_model.py ``` ### Uploading to PyPi Currently trying to automate it using Github Actions; see: `.github/workflows/package_release_dry_run.yml`. Setup: - Set up your "~/.pypirc" with the appropriate token - `pip install -r requirements_for_uploading.txt` which installs twine New Version: With `x.y.z` as the version to be uploaded: First tag: ```shell git tag -a vx.y.z -m "Version x.y.z" git push --tags ``` Then upload: ```shell python setup.py sdist twine upload dist/alt-profanity-check-x.y.z.tar.gz ``` %prep %autosetup -n alt-profanity-check-1.2.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-alt-profanity-check -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.2.2-1 - Package Spec generated