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|
%global _empty_manifest_terminate_build 0
Name: python-profanity-check
Version: 1.0.3
Release: 1
Summary: A fast, robust library to check for offensive language in strings.
License: MIT License
URL: https://github.com/vzhou842/profanity-check
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/35/4b/6a9f3b7a24e9e7a5393e3522e571cb8289fbaa8e69449dba821714b16677/profanity-check-1.0.3.tar.gz
BuildArch: noarch
Requires: python3-scikit-learn
%description
# profanity-check
[](https://travis-ci.com/vzhou842/profanity-check)
[](https://pypi.org/project/profanity-check/)
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/). You can also test out `profanity-check` [in your browser](https://repl.it/@vzhou842/profanity-check-playground).
## 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 profanity-check
```
## Usage
```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` was 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.
%package -n python3-profanity-check
Summary: A fast, robust library to check for offensive language in strings.
Provides: python-profanity-check
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-profanity-check
# profanity-check
[](https://travis-ci.com/vzhou842/profanity-check)
[](https://pypi.org/project/profanity-check/)
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/). You can also test out `profanity-check` [in your browser](https://repl.it/@vzhou842/profanity-check-playground).
## 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 profanity-check
```
## Usage
```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` was 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.
%package help
Summary: Development documents and examples for profanity-check
Provides: python3-profanity-check-doc
%description help
# profanity-check
[](https://travis-ci.com/vzhou842/profanity-check)
[](https://pypi.org/project/profanity-check/)
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/). You can also test out `profanity-check` [in your browser](https://repl.it/@vzhou842/profanity-check-playground).
## 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 profanity-check
```
## Usage
```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` was 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.
%prep
%autosetup -n profanity-check-1.0.3
%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-profanity-check -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1
- Package Spec generated
|
